From f3e20c7e962521edd988b003c9e32ce27e570775 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sat, 15 Mar 2025 10:34:57 -0400 Subject: [PATCH 01/85] Split physical data and GT data functionality up --- pointblank/datascan.py | 47 ++++++++++++++++++++++++++++++------------ 1 file changed, 34 insertions(+), 13 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index e237a95e5..759afd3a7 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -4,7 +4,7 @@ from dataclasses import dataclass, field from importlib.metadata import version from math import floor, log10 -from typing import Any +from typing import TYPE_CHECKING, Any import narwhals as nw from great_tables import GT, google_font, html, loc, style @@ -15,6 +15,10 @@ from pointblank._utils import _get_tbl_type, _select_df_lib from pointblank._utils_html import _create_table_dims_html, _create_table_type_html +if TYPE_CHECKING: + from narwhals.typing import CompliantDataFrame + + __all__ = ["DataScan", "col_summary_tbl"] @@ -619,17 +623,17 @@ def _get_column_data(self, column: str) -> dict | None: # If the column is not found, return None return None - def get_tabular_report(self) -> GT: - column_data = self.profile["columns"] + @property + def summary_data(self) -> CompliantDataFrame: + return self._summary_data - tbl_name = self.tbl_name + def _gen_stats_list(self) -> None: + """Set the `stats_list` attribute.""" + column_data = self.profile["columns"] stats_list = [] datetime_row_list = [] - n_rows = self.profile["dimensions"]["rows"] - n_columns = self.profile["dimensions"]["columns"] - # Iterate over each column's data and obtain a dictionary of statistics for each column for idx, col in enumerate(column_data): if "statistics" in col and ( @@ -646,21 +650,38 @@ def get_tabular_report(self) -> GT: stats_list.append(col_dict) + self._stats_list = stats_list + + def _summary_data_from_list(self) -> None: + """Set the `_summary_data` attribute from the raw stats list.""" + # Determine which DataFrame library is available and construct the DataFrame # based on the available library df_lib = _select_df_lib(preference="polars") df_lib_str = str(df_lib) + # TODO: this can probably be simplified if "polars" in df_lib_str: import polars as pl - stats_df = pl.DataFrame(stats_list) + stats_df: CompliantDataFrame = pl.DataFrame(self._stats_list) else: import pandas as pd - stats_df = pd.DataFrame(stats_list) + stats_df: CompliantDataFrame = pd.DataFrame(self._stats_list) - stats_df = pl.DataFrame(stats_list) + self._summary_data: CompliantDataFrame = pl.DataFrame(stats_df) + + def get_tabular_report(self) -> GT: + try: + summary_data = self.summary_data + except AttributeError: + self._gen_stats_list() # TODO: Unify/clarify these in more elegant way + self._summary_data_from_list() + summary_data = self.summary_data + + n_rows = self.profile["dimensions"]["rows"] + n_columns = self.profile["dimensions"]["columns"] stat_columns = [ "missing_vals", @@ -679,7 +700,7 @@ def get_tabular_report(self) -> GT: # Create the label, table type, and thresholds HTML fragments table_type_html = _create_table_type_html( - tbl_type=self.tbl_type, tbl_name=tbl_name, font_size="10px" + tbl_type=self.tbl_type, tbl_name=self.tbl_name, font_size="10px" ) tbl_dims_html = _create_table_dims_html(columns=n_columns, rows=n_rows, font_size="10px") @@ -697,7 +718,7 @@ def get_tabular_report(self) -> GT: # TODO: Ensure width is 905px in total gt_tbl = ( - GT(stats_df) + GT(self._summary_data) .tab_header(title=html(combined_title)) .cols_align(align="right", columns=stat_columns) .opt_table_font(font=google_font("IBM Plex Sans")) @@ -736,7 +757,7 @@ def get_tabular_report(self) -> GT: style=style.borders(sides="left", style="none"), locations=loc.body( columns=["p05", "q_1", "med", "q_3", "p95", "max"], - rows=datetime_row_list, + rows=self._stats_list, ), ) .tab_style( From 91f63f422b44aea6b4fc3022d4ccb8fefc17c26f Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sat, 15 Mar 2025 10:39:25 -0400 Subject: [PATCH 02/85] Unify datascan summary data access in property --- pointblank/datascan.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 759afd3a7..d52404022 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -625,7 +625,12 @@ def _get_column_data(self, column: str) -> dict | None: @property def summary_data(self) -> CompliantDataFrame: - return self._summary_data + try: + return self._summary_data + except AttributeError: + self._gen_stats_list() + self._summary_data_from_list() + return self._summary_data def _gen_stats_list(self) -> None: """Set the `stats_list` attribute.""" @@ -673,12 +678,7 @@ def _summary_data_from_list(self) -> None: self._summary_data: CompliantDataFrame = pl.DataFrame(stats_df) def get_tabular_report(self) -> GT: - try: - summary_data = self.summary_data - except AttributeError: - self._gen_stats_list() # TODO: Unify/clarify these in more elegant way - self._summary_data_from_list() - summary_data = self.summary_data + summary_data = self.summary_data n_rows = self.profile["dimensions"]["rows"] n_columns = self.profile["dimensions"]["columns"] From f392e647e5977ee8281bc99ff297b8892ef6e016 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 16 Mar 2025 10:35:00 -0400 Subject: [PATCH 03/85] Begin refactor of datascan to expose internal data --- pointblank/datascan.py | 908 +++++++++++++++++++++-------------------- 1 file changed, 470 insertions(+), 438 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index d52404022..9263a85fb 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -1,10 +1,11 @@ from __future__ import annotations +import contextlib import json -from dataclasses import dataclass, field +from dataclasses import asdict, dataclass, field from importlib.metadata import version from math import floor, log10 -from typing import TYPE_CHECKING, Any +from typing import TYPE_CHECKING, Any, NamedTuple import narwhals as nw from great_tables import GT, google_font, html, loc, style @@ -16,12 +17,136 @@ from pointblank._utils_html import _create_table_dims_html, _create_table_type_html if TYPE_CHECKING: - from narwhals.typing import CompliantDataFrame + from narwhals.series import Series + from narwhals.typing import CompliantDataFrame, Frame __all__ = ["DataScan", "col_summary_tbl"] +@dataclass +class _ColumnProfile: + colname: str + colnumber: int + coltype: str + sample_data: Any | None = None + n_unique_vals: int = -1 + f_unique_vals: float = -1 + f_missing_vals: float = -1 + _n_missing_vals: int = field(default=0, init=False) + + @property + def n_missing_vals(self) -> int: + return self._n_missing_vals + + @n_missing_vals.setter + def n_missing_vals(self, value: int) -> None: + self._n_missing_vals = value + if value > 0 and self.n_unique_vals > 0: + self.n_unique_vals -= 1 + + @property + def is_numeric(self) -> bool: + inds = ("int", "float") + return any(ind in str(self.coltype).lower() for ind in inds) + + @property + def is_string(self) -> bool: + inds = ("string", "categorical") + return any(ind in str(self.coltype).lower() for ind in inds) + + @property + def is_date(self) -> bool: + inds = ("date",) + return any(ind in str(self.coltype).lower() for ind in inds) + + @property + def is_bool(self) -> bool: + inds = ("bool",) + return any(ind in str(self.coltype).lower() for ind in inds) + + def _fetch_public_attrs(self) -> dict: + return {k: v for k, v in asdict(self).items() if not k.startswith("_")} + + def spawn_profile(self, _type: type | str) -> _ColumnProfile: + attrs = self._fetch_public_attrs() + if _type is str: + return _StringProfile(**attrs) + if _type is float or _type is int: + return _NumericProfile(**attrs) + if _type is bool: + return _BoolProfile(**attrs) + if _type == "date": + return _DateProfile(**attrs) + + raise NotImplementedError + + def _fetch_statistics(self, col_data: Series): + self.mean = float(col_data.mean()) + self.std = float(col_data.std()) + self.min = float(col_data.min()) + self.max = float(col_data.max()) + self.p05 = float(col_data.quantile(0.005, interpolation="linear")) + self.q_1 = float(col_data.quantile(0.25, interpolation="linear")) + self.med = float(col_data.median()) + self.q_3 = float(col_data.quantile(0.75, interpolation="linear")) + self.p95 = float(col_data.quantile(0.95, interpolation="linear")) + self.iqr = self.q_3 - self.q_1 + + +@dataclass +class _DateProfile(_ColumnProfile): + min: str | None = None + max: str | None = None + + +@dataclass +class _BoolProfile(_ColumnProfile): + ntrue: int = -1 + nfalse: int = -1 + + +@dataclass +class _StringProfile(_ColumnProfile): + mean: float = -1 + std: float = -1 + min: float = -1 + max: float = -1 + p05: float = -1 + q_1: float = -1 + med: float = -1 + q_3: float = -1 + p95: float = -1 + iqr: float = -1 + + +@dataclass +class _NumericProfile(_ColumnProfile): + n_negative_vals: int = 0 + f_negative_vals: float = 0.0 + n_zero_vals: int = 0 + f_zero_vals: float = 0.0 + n_positive_vals: int = 0 + f_positive_vals: float = 0.0 + mean: float = -1 + std: float = -1 + min: float = -1 + max: float = -1 + p05: float = -1 + q_1: float = -1 + med: float = -1 + q_3: float = -1 + p95: float = -1 + iqr: float = -1 + + +class _DataProfile(NamedTuple): + table_name: str | None + row_count: int + columns: list[str] = [] + column_profiles: list[_ColumnProfile] = [] + + @dataclass class DataScan: """ @@ -117,12 +242,11 @@ class DataScan: A DataScan object. """ - data: FrameT | Any + data: FrameT | Frame | Any tbl_name: str | None = None data_alt: Any | None = field(init=False) tbl_category: str = field(init=False) tbl_type: str = field(init=False) - profile: dict = field(init=False) def __post_init__(self): # Determine if the data is a DataFrame that could be handled by Narwhals, @@ -145,473 +269,376 @@ def __post_init__(self): self.data_alt = None # Generate the profile based on the `tbl_category` value - if self.tbl_category == "dataframe": - self.profile = self._generate_profile_df() + self.profile: _DataProfile = self._generate_profile_df() - if self.tbl_category == "ibis": - self.profile = self._generate_profile_ibis() + def _generate_profile_df(self) -> _DataProfile: + tbl_name: str | None = self.tbl_name or None + alt: Frame = self.data + try: + row_count = len(alt) + except Exception as e: + is_ibis: bool = "ibis.common.exceptions.Expression" in str(e) + row_count = alt.count() - def _generate_profile_df(self) -> dict: - profile = {} + columns = alt.columns - if self.tbl_name: - profile["tbl_name"] = self.tbl_name + profile = _DataProfile(table_name=tbl_name, row_count=row_count, columns=columns) - row_count = self.data_alt.shape[0] - column_count = self.data_alt.shape[1] + for idx, column in enumerate(columns): + col_data: Series = self.data[column] + native_dtype = str(col_data.dtype) - profile.update( - { - "tbl_type": self.tbl_type, - "dimensions": {"rows": row_count, "columns": column_count}, - "columns": [], - } - ) - - for idx, column in enumerate(self.data_alt.columns): - col_data = self.data_alt[column] - native_dtype = str(self.data[column].dtype) + col_profile = _ColumnProfile(colname=column, coltype=native_dtype, colnumber=idx) # # Collection of sample data # - if "date" in str(col_data.dtype).lower(): - sample_data = col_data.drop_nulls().head(5).cast(nw.String).to_list() - sample_data = [str(x) for x in sample_data] - else: - sample_data = col_data.drop_nulls().head(5).to_list() + raw_sample_data = col_data + with contextlib.suppress(Exception): + raw_sample_data = raw_sample_data.drop_nulls() + raw_sample_data = raw_sample_data.head(5).to_list() + sample_data: list[str] = [str(x) for x in raw_sample_data] + + col_profile.sample_data = sample_data + col_profile.n_missing_vals = int(col_data.is_null().sum()) + col_profile.n_unique_vals = int(col_data.n_unique()) + + # TODO: These should probably live on the class + col_profile.f_missing_vals = _round_to_sig_figs( + col_profile.n_missing_vals / row_count, 3 + ) + col_profile.f_unique_vals = _round_to_sig_figs(col_profile.n_unique_vals / row_count, 3) - n_missing_vals = int(col_data.is_null().sum()) - n_unique_vals = int(col_data.n_unique()) + # + # Numerical columns + # + prof: _ColumnProfile # help mypy + if col_profile.is_numeric: + numeric_profile: _NumericProfile = col_profile.spawn_profile(int) - # If there are missing values, subtract 1 from the number of unique values - # to account for the missing value which shouldn't be included in the count - if (n_missing_vals > 0) and (n_unique_vals > 0): - n_unique_vals = n_unique_vals - 1 + numeric_profile.n_negative_vals = int(col_data.is_between(-1e26, -1e-26).sum()) + numeric_profile.f_negative_vals = _round_to_sig_figs( + numeric_profile.n_negative_vals / row_count, 3 + ) - f_missing_vals = _round_to_sig_figs(n_missing_vals / row_count, 3) - f_unique_vals = _round_to_sig_figs(n_unique_vals / row_count, 3) + numeric_profile.n_zero_vals = int(col_data.is_between(0, 0).sum()) + numeric_profile.f_zero_vals = _round_to_sig_figs( + numeric_profile.n_zero_vals / row_count, 3 + ) + + numeric_profile.n_positive_vals = ( + row_count + - numeric_profile.n_missing_vals + - numeric_profile.n_negative_vals + - numeric_profile.n_zero_vals + ) + numeric_profile.f_positive_vals = _round_to_sig_figs( + numeric_profile.n_positive_vals / row_count, 3 + ) - col_profile = { - "column_name": column, - "column_type": native_dtype, - "column_number": idx + 1, - "n_missing_values": n_missing_vals, - "f_missing_values": f_missing_vals, - "n_unique_values": n_unique_vals, - "f_unique_values": f_unique_vals, - } + numeric_profile._fetch_statistics(col_data) - # - # Numerical columns - # - if "int" in str(col_data.dtype).lower() or "float" in str(col_data.dtype).lower(): - n_negative_vals = int(col_data.is_between(-1e26, -1e-26).sum()) - f_negative_vals = _round_to_sig_figs(n_negative_vals / row_count, 3) - - n_zero_vals = int(col_data.is_between(0, 0).sum()) - f_zero_vals = _round_to_sig_figs(n_zero_vals / row_count, 3) - - n_positive_vals = row_count - n_missing_vals - n_negative_vals - n_zero_vals - f_positive_vals = _round_to_sig_figs(n_positive_vals / row_count, 3) - - col_profile_additional = { - "n_negative_values": n_negative_vals, - "f_negative_values": f_negative_vals, - "n_zero_values": n_zero_vals, - "f_zero_values": f_zero_vals, - "n_positive_values": n_positive_vals, - "f_positive_values": f_positive_vals, - "sample_data": sample_data, - } - col_profile.update(col_profile_additional) - - col_profile_stats = { - "statistics": { - "numerical": { - "descriptive": { - "mean": round(float(col_data.mean()), 2), - "std_dev": round(float(col_data.std()), 4), - }, - "quantiles": { - "min": float(col_data.min()), - "p05": round( - float(col_data.quantile(0.05, interpolation="linear")), 2 - ), - "q_1": round( - float(col_data.quantile(0.25, interpolation="linear")), 2 - ), - "med": float(col_data.median()), - "q_3": round( - float(col_data.quantile(0.75, interpolation="linear")), 2 - ), - "p95": round( - float(col_data.quantile(0.95, interpolation="linear")), 2 - ), - "max": float(col_data.max()), - "iqr": round( - float(col_data.quantile(0.75, interpolation="linear")) - - float(col_data.quantile(0.25, interpolation="linear")), - 2, - ), - }, - } - } - } - col_profile.update(col_profile_stats) + prof = numeric_profile # # String columns # - elif ( - "string" in str(col_data.dtype).lower() - or "categorical" in str(col_data.dtype).lower() - ): - col_profile_additional = { - "sample_data": sample_data, - } - col_profile.update(col_profile_additional) - + elif col_profile.is_string: # Transform `col_data` to a column of string lengths col_str_len_data = col_data.str.len_chars() - - col_profile_stats = { - "statistics": { - "string_lengths": { - "descriptive": { - "mean": round(float(col_str_len_data.mean()), 2), - "std_dev": round(float(col_str_len_data.std()), 4), - }, - "quantiles": { - "min": int(col_str_len_data.min()), - "p05": int(col_str_len_data.quantile(0.05, interpolation="linear")), - "q_1": int(col_str_len_data.quantile(0.25, interpolation="linear")), - "med": int(col_str_len_data.median()), - "q_3": int(col_str_len_data.quantile(0.75, interpolation="linear")), - "p95": int(col_str_len_data.quantile(0.95, interpolation="linear")), - "max": int(col_str_len_data.max()), - "iqr": int(col_str_len_data.quantile(0.75, interpolation="linear")) - - int(col_str_len_data.quantile(0.25, interpolation="linear")), - }, - } - } - } - col_profile.update(col_profile_stats) + str_prof = col_profile.spawn_profile(str) + str_prof._fetch_statistics(col_str_len_data) + prof = str_prof # # Date and datetime columns # - elif "date" in str(col_data.dtype).lower(): - col_profile_additional = { - "sample_data": sample_data, - } - col_profile.update(col_profile_additional) - - min_date = str(col_data.min()) - max_date = str(col_data.max()) - - col_profile_stats = { - "statistics": { - "datetime": { - "min": min_date, - "max": max_date, - } - } - } - col_profile.update(col_profile_stats) - - # - # Boolean columns - # - elif "bool" in str(col_data.dtype).lower(): - col_profile_additional = { - "sample_data": sample_data, - } - col_profile.update(col_profile_additional) - - n_true_values = int(col_data.sum()) - f_true_values = _round_to_sig_figs(n_true_values / row_count, 3) - - n_false_values = row_count - n_missing_vals - n_true_values - f_false_values = _round_to_sig_figs(n_false_values / row_count, 3) - - col_profile_stats = { - "statistics": { - "boolean": { - "n_true_values": n_true_values, - "f_true_values": f_true_values, - "n_false_values": n_false_values, - "f_false_values": f_false_values, - } - } - } - col_profile.update(col_profile_stats) - - profile["columns"].append(col_profile) - - return profile - - def _generate_profile_ibis(self) -> dict: - profile = {} - - if self.tbl_name: - profile["tbl_name"] = self.tbl_name - - from pointblank.validate import get_row_count + elif col_profile.is_date: + date_prof = col_profile.spawn_profile("date") - row_count = get_row_count(data=self.data) - column_count = len(self.data.columns) + date_prof.min = str(col_data.min()) + date_prof.max = str(col_data.max()) - profile.update( - { - "tbl_type": self.tbl_type, - "dimensions": {"rows": row_count, "columns": column_count}, - "columns": [], - } - ) - - # Determine which DataFrame library is available - df_lib = _select_df_lib(preference="polars") - df_lib_str = str(df_lib) - - if "polars" in df_lib_str: - df_lib_use = "polars" - else: - df_lib_use = "pandas" - - column_dtypes = list(self.data.schema().items()) - - for idx, column in enumerate(self.data.columns): - dtype_str = str(column_dtypes[idx][1]) - - col_data = self.data[column] - col_data_no_null = self.data.drop_null().head(5)[column] + prof = date_prof # - # Collection of sample data + # Boolean columns # - if "date" in dtype_str.lower() or "timestamp" in dtype_str.lower(): - if df_lib_use == "polars": - import polars as pl + elif col_profile.is_bool: + bool_prof: _BoolProfile = col_profile.spawn_profile(bool) + ntrue: int = int(col_data.sum()) + bool_prof.ntrue = ntrue + # TODO: document trivalence + bool_prof.nfalse = row_count - ntrue - sample_data = col_data_no_null.to_polars().cast(pl.String).to_list() - else: - sample_data = col_data_no_null.to_pandas().astype(str).to_list() else: - if df_lib_use == "polars": - sample_data = col_data_no_null.to_polars().to_list() - else: - sample_data = col_data_no_null.to_pandas().to_list() - - n_missing_vals = int(_to_df_lib(col_data.isnull().sum(), df_lib=df_lib_use)) - n_unique_vals = int(_to_df_lib(col_data.nunique(), df_lib=df_lib_use)) - - # If there are missing values, subtract 1 from the number of unique values - # to account for the missing value which shouldn't be included in the count - if (n_missing_vals > 0) and (n_unique_vals > 0): - n_unique_vals = n_unique_vals - 1 - - f_missing_vals = _round_to_sig_figs(n_missing_vals / row_count, 3) - f_unique_vals = _round_to_sig_figs(n_unique_vals / row_count, 3) - - col_profile = { - "column_name": column, - "column_type": dtype_str, - "column_number": idx + 1, - "n_missing_values": n_missing_vals, - "f_missing_values": f_missing_vals, - "n_unique_values": n_unique_vals, - "f_unique_values": f_unique_vals, - } - - # - # Numerical columns - # - if "int" in dtype_str.lower() or "float" in dtype_str.lower(): - n_negative_vals = int( - _to_df_lib(col_data.between(-1e26, -1e-26).sum(), df_lib=df_lib_use) - ) - f_negative_vals = _round_to_sig_figs(n_negative_vals / row_count, 3) - - n_zero_vals = int(_to_df_lib(col_data.between(0, 0).sum(), df_lib=df_lib_use)) - f_zero_vals = _round_to_sig_figs(n_zero_vals / row_count, 3) - - n_positive_vals = row_count - n_missing_vals - n_negative_vals - n_zero_vals - f_positive_vals = _round_to_sig_figs(n_positive_vals / row_count, 3) - - col_profile_additional = { - "n_negative_values": n_negative_vals, - "f_negative_values": f_negative_vals, - "n_zero_values": n_zero_vals, - "f_zero_values": f_zero_vals, - "n_positive_values": n_positive_vals, - "f_positive_values": f_positive_vals, - "sample_data": sample_data, - } - col_profile.update(col_profile_additional) - - col_profile_stats = { - "statistics": { - "numerical": { - "descriptive": { - "mean": round(_to_df_lib(col_data.mean(), df_lib=df_lib_use), 2), - "std_dev": round(_to_df_lib(col_data.std(), df_lib=df_lib_use), 4), - }, - "quantiles": { - "min": _to_df_lib(col_data.min(), df_lib=df_lib_use), - "p05": round( - _to_df_lib(col_data.approx_quantile(0.05), df_lib=df_lib_use), - 2, - ), - "q_1": round( - _to_df_lib(col_data.approx_quantile(0.25), df_lib=df_lib_use), - 2, - ), - "med": _to_df_lib(col_data.median(), df_lib=df_lib_use), - "q_3": round( - _to_df_lib(col_data.approx_quantile(0.75), df_lib=df_lib_use), - 2, - ), - "p95": round( - _to_df_lib(col_data.approx_quantile(0.95), df_lib=df_lib_use), - 2, - ), - "max": _to_df_lib(col_data.max(), df_lib=df_lib_use), - "iqr": round( - _to_df_lib(col_data.quantile(0.75), df_lib=df_lib_use) - - _to_df_lib(col_data.quantile(0.25), df_lib=df_lib_use), - 2, - ), - }, - } - } - } - col_profile.update(col_profile_stats) + raise NotImplementedError # pragma: no-cover - # - # String columns - # - elif "string" in dtype_str.lower() or "char" in dtype_str.lower(): - col_profile_additional = { - "sample_data": sample_data, - } - col_profile.update(col_profile_additional) - - # Transform `col_data` to a column of string lengths - col_str_len_data = col_data.length() - - col_profile_stats = { - "statistics": { - "string_lengths": { - "descriptive": { - "mean": round( - float(_to_df_lib(col_str_len_data.mean(), df_lib=df_lib_use)), 2 - ), - "std_dev": round( - float(_to_df_lib(col_str_len_data.std(), df_lib=df_lib_use)), 4 - ), - }, - "quantiles": { - "min": int(_to_df_lib(col_str_len_data.min(), df_lib=df_lib_use)), - "p05": int( - _to_df_lib( - col_str_len_data.approx_quantile(0.05), - df_lib=df_lib_use, - ) - ), - "q_1": int( - _to_df_lib( - col_str_len_data.approx_quantile(0.25), - df_lib=df_lib_use, - ) - ), - "med": int( - _to_df_lib(col_str_len_data.median(), df_lib=df_lib_use) - ), - "q_3": int( - _to_df_lib( - col_str_len_data.approx_quantile(0.75), - df_lib=df_lib_use, - ) - ), - "p95": int( - _to_df_lib( - col_str_len_data.approx_quantile(0.95), - df_lib=df_lib_use, - ) - ), - "max": int(_to_df_lib(col_str_len_data.max(), df_lib=df_lib_use)), - "iqr": int( - _to_df_lib( - col_str_len_data.approx_quantile(0.75), - df_lib=df_lib_use, - ) - ) - - int( - _to_df_lib( - col_str_len_data.approx_quantile(0.25), - df_lib=df_lib_use, - ) - ), - }, - } - } - } - col_profile.update(col_profile_stats) - - # - # Date and datetime columns - # - elif "date" in dtype_str.lower() or "timestamp" in dtype_str.lower(): - col_profile_additional = { - "sample_data": sample_data, - } - col_profile.update(col_profile_additional) - - min_date = _to_df_lib(col_data.min(), df_lib=df_lib_use) - max_date = _to_df_lib(col_data.max(), df_lib=df_lib_use) - - col_profile_stats = { - "statistics": { - "datetime": { - "min": str(min_date), - "max": str(max_date), - } - } - } - col_profile.update(col_profile_stats) - - # - # Boolean columns - # - elif "bool" in dtype_str.lower(): - col_profile_additional = { - "sample_data": sample_data, - } - col_profile.update(col_profile_additional) - - n_true_values = _to_df_lib(col_data.cast(int).sum(), df_lib=df_lib) - f_true_values = _round_to_sig_figs(n_true_values / row_count, 3) - - n_false_values = row_count - n_missing_vals - n_true_values - f_false_values = _round_to_sig_figs(n_false_values / row_count, 3) - - col_profile_stats = { - "statistics": { - "boolean": { - "n_true_values": n_true_values, - "f_true_values": f_true_values, - "n_false_values": n_false_values, - "f_false_values": f_false_values, - } - } - } - col_profile.update(col_profile_stats) - - profile["columns"].append(col_profile) + profile.column_profiles.append(prof) return profile + # def _generate_profile_ibis(self) -> dict: + # profile = {} + + # if self.tbl_name: + # profile["tbl_name"] = self.tbl_name + + # from pointblank.validate import get_row_count + + # row_count = get_row_count(data=self.data) + # column_count = len(self.data.columns) + + # profile.update( + # { + # "tbl_type": self.tbl_type, + # "dimensions": {"rows": row_count, "columns": column_count}, + # "columns": [], + # } + # ) + + # # Determine which DataFrame library is available + # df_lib = _select_df_lib(preference="polars") + # df_lib_str = str(df_lib) + + # if "polars" in df_lib_str: + # df_lib_use = "polars" + # else: + # df_lib_use = "pandas" + + # column_dtypes = list(self.data.schema().items()) + + # for idx, column in enumerate(self.data.columns): + # dtype_str = str(column_dtypes[idx][1]) + + # col_data = self.data[column] + # col_data_no_null = self.data.drop_null().head(5)[column] + + # # + # # Collection of sample data + # # + # if "date" in dtype_str.lower() or "timestamp" in dtype_str.lower(): + # if df_lib_use == "polars": + # import polars as pl + + # sample_data = col_data_no_null.to_polars().cast(pl.String).to_list() + # else: + # sample_data = col_data_no_null.to_pandas().astype(str).to_list() + # else: + # if df_lib_use == "polars": + # sample_data = col_data_no_null.to_polars().to_list() + # else: + # sample_data = col_data_no_null.to_pandas().to_list() + + # n_missing_vals = int(_to_df_lib(col_data.isnull().sum(), df_lib=df_lib_use)) + # n_unique_vals = int(_to_df_lib(col_data.nunique(), df_lib=df_lib_use)) + + # # If there are missing values, subtract 1 from the number of unique values + # # to account for the missing value which shouldn't be included in the count + # if (n_missing_vals > 0) and (n_unique_vals > 0): + # n_unique_vals = n_unique_vals - 1 + + # f_missing_vals = _round_to_sig_figs(n_missing_vals / row_count, 3) + # f_unique_vals = _round_to_sig_figs(n_unique_vals / row_count, 3) + + # col_profile = { + # "column_name": column, + # "column_type": dtype_str, + # "column_number": idx + 1, + # "n_missing_values": n_missing_vals, + # "f_missing_values": f_missing_vals, + # "n_unique_values": n_unique_vals, + # "f_unique_values": f_unique_vals, + # } + + # # + # # Numerical columns + # # + # if "int" in dtype_str.lower() or "float" in dtype_str.lower(): + # n_negative_vals = int( + # _to_df_lib(col_data.between(-1e26, -1e-26).sum(), df_lib=df_lib_use) + # ) + # f_negative_vals = _round_to_sig_figs(n_negative_vals / row_count, 3) + + # n_zero_vals = int(_to_df_lib(col_data.between(0, 0).sum(), df_lib=df_lib_use)) + # f_zero_vals = _round_to_sig_figs(n_zero_vals / row_count, 3) + + # n_positive_vals = row_count - n_missing_vals - n_negative_vals - n_zero_vals + # f_positive_vals = _round_to_sig_figs(n_positive_vals / row_count, 3) + + # col_profile_additional = { + # "n_negative_values": n_negative_vals, + # "f_negative_values": f_negative_vals, + # "n_zero_values": n_zero_vals, + # "f_zero_values": f_zero_vals, + # "n_positive_values": n_positive_vals, + # "f_positive_values": f_positive_vals, + # "sample_data": sample_data, + # } + # col_profile.update(col_profile_additional) + + # col_profile_stats = { + # "statistics": { + # "numerical": { + # "descriptive": { + # "mean": round(_to_df_lib(col_data.mean(), df_lib=df_lib_use), 2), + # "std_dev": round(_to_df_lib(col_data.std(), df_lib=df_lib_use), 4), + # }, + # "quantiles": { + # "min": _to_df_lib(col_data.min(), df_lib=df_lib_use), + # "p05": round( + # _to_df_lib(col_data.approx_quantile(0.05), df_lib=df_lib_use), + # 2, + # ), + # "q_1": round( + # _to_df_lib(col_data.approx_quantile(0.25), df_lib=df_lib_use), + # 2, + # ), + # "med": _to_df_lib(col_data.median(), df_lib=df_lib_use), + # "q_3": round( + # _to_df_lib(col_data.approx_quantile(0.75), df_lib=df_lib_use), + # 2, + # ), + # "p95": round( + # _to_df_lib(col_data.approx_quantile(0.95), df_lib=df_lib_use), + # 2, + # ), + # "max": _to_df_lib(col_data.max(), df_lib=df_lib_use), + # "iqr": round( + # _to_df_lib(col_data.quantile(0.75), df_lib=df_lib_use) + # - _to_df_lib(col_data.quantile(0.25), df_lib=df_lib_use), + # 2, + # ), + # }, + # } + # } + # } + # col_profile.update(col_profile_stats) + + # # + # # String columns + # # + # elif "string" in dtype_str.lower() or "char" in dtype_str.lower(): + # col_profile_additional = { + # "sample_data": sample_data, + # } + # col_profile.update(col_profile_additional) + + # # Transform `col_data` to a column of string lengths + # col_str_len_data = col_data.length() + + # col_profile_stats = { + # "statistics": { + # "string_lengths": { + # "descriptive": { + # "mean": round( + # float(_to_df_lib(col_str_len_data.mean(), df_lib=df_lib_use)), 2 + # ), + # "std_dev": round( + # float(_to_df_lib(col_str_len_data.std(), df_lib=df_lib_use)), 4 + # ), + # }, + # "quantiles": { + # "min": int(_to_df_lib(col_str_len_data.min(), df_lib=df_lib_use)), + # "p05": int( + # _to_df_lib( + # col_str_len_data.approx_quantile(0.05), + # df_lib=df_lib_use, + # ) + # ), + # "q_1": int( + # _to_df_lib( + # col_str_len_data.approx_quantile(0.25), + # df_lib=df_lib_use, + # ) + # ), + # "med": int( + # _to_df_lib(col_str_len_data.median(), df_lib=df_lib_use) + # ), + # "q_3": int( + # _to_df_lib( + # col_str_len_data.approx_quantile(0.75), + # df_lib=df_lib_use, + # ) + # ), + # "p95": int( + # _to_df_lib( + # col_str_len_data.approx_quantile(0.95), + # df_lib=df_lib_use, + # ) + # ), + # "max": int(_to_df_lib(col_str_len_data.max(), df_lib=df_lib_use)), + # "iqr": int( + # _to_df_lib( + # col_str_len_data.approx_quantile(0.75), + # df_lib=df_lib_use, + # ) + # ) + # - int( + # _to_df_lib( + # col_str_len_data.approx_quantile(0.25), + # df_lib=df_lib_use, + # ) + # ), + # }, + # } + # } + # } + # col_profile.update(col_profile_stats) + + # # + # # Date and datetime columns + # # + # elif "date" in dtype_str.lower() or "timestamp" in dtype_str.lower(): + # col_profile_additional = { + # "sample_data": sample_data, + # } + # col_profile.update(col_profile_additional) + + # min_date = _to_df_lib(col_data.min(), df_lib=df_lib_use) + # max_date = _to_df_lib(col_data.max(), df_lib=df_lib_use) + + # col_profile_stats = { + # "statistics": { + # "datetime": { + # "min": str(min_date), + # "max": str(max_date), + # } + # } + # } + # col_profile.update(col_profile_stats) + + # # + # # Boolean columns + # # + # elif "bool" in dtype_str.lower(): + # col_profile_additional = { + # "sample_data": sample_data, + # } + # col_profile.update(col_profile_additional) + + # n_true_values = _to_df_lib(col_data.cast(int).sum(), df_lib=df_lib) + # f_true_values = _round_to_sig_figs(n_true_values / row_count, 3) + + # n_false_values = row_count - n_missing_vals - n_true_values + # f_false_values = _round_to_sig_figs(n_false_values / row_count, 3) + + # col_profile_stats = { + # "statistics": { + # "boolean": { + # "n_true_values": n_true_values, + # "f_true_values": f_true_values, + # "n_false_values": n_false_values, + # "f_false_values": f_false_values, + # } + # } + # } + # col_profile.update(col_profile_stats) + + # profile["columns"].append(col_profile) + + # return profile + def _get_column_data(self, column: str) -> dict | None: column_data = self.profile["columns"] @@ -810,7 +837,12 @@ def to_dict(self) -> dict: return self.profile def to_json(self) -> str: - return json.dumps(self.profile, indent=4) + profiles: list[dict] = [] + for profile in self.profile.column_profiles: + attrs = profile._fetch_public_attrs() + profiles.append(attrs) + + return json.dumps(profiles, indent=4) def save_to_json(self, output_file: str): with open(output_file, "w") as f: From e1a20b3eefa5e94cb558b2f22168be10dc17e443 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 16 Mar 2025 12:14:53 -0400 Subject: [PATCH 04/85] add dedicated profile file --- pointblank/scan_profile.py | 175 +++++++++++++++++++++++++++++++++++++ 1 file changed, 175 insertions(+) create mode 100644 pointblank/scan_profile.py diff --git a/pointblank/scan_profile.py b/pointblank/scan_profile.py new file mode 100644 index 000000000..52ddd4388 --- /dev/null +++ b/pointblank/scan_profile.py @@ -0,0 +1,175 @@ +from __future__ import annotations + +from abc import ABC, abstractmethod +from dataclasses import asdict, dataclass +from enum import Enum +from typing import TYPE_CHECKING, Any, NamedTuple + +import narwhals as nw + +if TYPE_CHECKING: + from narwhals.dataframe import DataFrame + from narwhals.series import Series + + +class _TypeMap(Enum): + NUMERIC = ("int", "float") + STRING = ("string", "categorical") + DATE = ("date",) + BOOL = ("bool",) + + @classmethod + def fetch_prof_map(cls) -> dict[_TypeMap, type[ColumnProfile]]: + return { + cls.BOOL: _BoolProfile, + cls.NUMERIC: _NumericProfile, + cls.STRING: _StringProfile, + cls.DATE: _DateProfile, + } + + @classmethod + def fetch_profile(cls, dtype: Any) -> type[ColumnProfile]: + stringified: str = str(dtype).lower() + for _type in cls: + inds: tuple[str, ...] = _type.value + is_match: bool = any(ind for ind in inds if ind in stringified) + if is_match: + return cls.fetch_prof_map()[_type] + raise NotImplementedError # pragma: no-cover + + +class _ColumnProfileABC(ABC): + @abstractmethod + def calc_stats(self, data: DataFrame) -> None: ... + + +@dataclass +class ColumnProfile(_ColumnProfileABC): + colname: str + colnumber: int + coltype: str + sample_data: Any | None = None + f_unique_vals: float = -1 + f_missing_vals: float = -1 + + @property + def n_missing_vals(self) -> int: + return self._n_missing_vals + + @n_missing_vals.setter + def n_missing_vals(self, value: Any) -> None: + intified: int = int(value) + self._n_missing_vals = intified + if intified > 0 and self.n_unique_vals > 0: + self.n_unique_vals -= 1 + + @property + def n_unique_vals(self) -> int: + return self._n_unique_vals + + @n_unique_vals.setter + def n_unique_vals(self, value: Any) -> None: + self._n_unique_vals = int(value) + + def _fetch_public_attrs(self) -> dict: + return {k: v for k, v in asdict(self).items() if not k.startswith("_")} + + def spawn_profile(self, _subprofile: type[ColumnProfile]) -> ColumnProfile: + attrs = self._fetch_public_attrs() + return _subprofile(**attrs) + + def _calc_general_sats(self, col_data: DataFrame): + res: dict[str, Series[Any]] = col_data.select( + _mean=nw.all().mean(), + _std=nw.all().std(), + _min=nw.all().min(), + _max=nw.all().max(), + _p05=nw.all().quantile(0.005, interpolation="linear"), + _q_1=nw.all().quantile(0.25, interpolation="linear"), + _med=nw.all().median(), + _q_3=nw.all().quantile(0.75, interpolation="linear"), + _p95=nw.all().quantile(0.95, interpolation="linear"), + ).to_dict() + + ## Assign stats directly from the data: + for attr, series in res.items(): + public_name = attr[1:] + val: float = float(series.item()) + setattr(self, public_name, val) + + ## Post-hoc calculations: + ## This is a space to make calculation off of the calculations + self.iqr = self.q_3 - self.q_1 + + def calc_stats(self, data: DataFrame) -> None: # pragma: no-cover + msg = "Statistics should be calculated from subclasses of `ColumnProfile`." + raise NotImplementedError(msg) + + +class _DateProfile(ColumnProfile): + min: str | None = None + max: str | None = None + + def calc_stats(self, data: DataFrame): + res = data.select(_min=nw.all().min(), _max=nw.all().max()).to_dict() + + ## Pull out elements: + self.min = str(res["_min"].item()) + self.max = str(res["_max"].item()) + + +class _BoolProfile(ColumnProfile): + ntrue: int = -1 + nfalse: int = -1 + + def calc_stats(self, data: DataFrame): + self.ntrue: int = int(data.select(nw.all().sum()).item()) + # TODO: document trivalence + row_count = len(data) + self.nfalse = row_count - self.ntrue + + +class _StringProfile(ColumnProfile): + mean: float = -1 + std: float = -1 + min: float = -1 + max: float = -1 + p05: float = -1 + q_1: float = -1 + med: float = -1 + q_3: float = -1 + p95: float = -1 + iqr: float = -1 + + def calc_stats(self, data: DataFrame): + col_str_len_data = data.select(nw.all().str.len_chars()) + self._calc_general_sats(col_str_len_data) + + +class _NumericProfile(ColumnProfile): + n_negative_vals: int = 0 + f_negative_vals: float = 0.0 + n_zero_vals: int = 0 + f_zero_vals: float = 0.0 + n_positive_vals: int = 0 + f_positive_vals: float = 0.0 + mean: float = -1 + std: float = -1 + min: float = -1 + max: float = -1 + p05: float = -1 + q_1: float = -1 + med: float = -1 + q_3: float = -1 + p95: float = -1 + iqr: float = -1 + + def calc_stats(self, data: DataFrame): + self._calc_general_sats(data) + + +class _DataProfile(NamedTuple): # TODO: feels redundant + table_name: str | None + row_count: int + columns: list[str] = [] + column_profiles: list[ColumnProfile] = [] From 9cba8b205048b9ef7139d967881221d82c5de02c Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 16 Mar 2025 14:33:43 -0400 Subject: [PATCH 05/85] Fully implement statistics calculation --- pointblank/datascan.py | 602 +++++-------------------------------- pointblank/scan_profile.py | 77 ++++- 2 files changed, 149 insertions(+), 530 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 9263a85fb..4c94bfc1d 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -2,10 +2,10 @@ import contextlib import json -from dataclasses import asdict, dataclass, field +import warnings from importlib.metadata import version from math import floor, log10 -from typing import TYPE_CHECKING, Any, NamedTuple +from typing import TYPE_CHECKING, Any import narwhals as nw from great_tables import GT, google_font, html, loc, style @@ -13,141 +13,20 @@ from narwhals.typing import FrameT from pointblank._constants import SVG_ICONS_FOR_DATA_TYPES -from pointblank._utils import _get_tbl_type, _select_df_lib +from pointblank._utils import _select_df_lib from pointblank._utils_html import _create_table_dims_html, _create_table_type_html +from pointblank.scan_profile import ColumnProfile, _DataProfile, _TypeMap if TYPE_CHECKING: - from narwhals.series import Series - from narwhals.typing import CompliantDataFrame, Frame + from collections.abc import Mapping + from narwhals.dataframe import DataFrame + from narwhals.typing import CompliantDataFrame, IntoDataFrame -__all__ = ["DataScan", "col_summary_tbl"] - - -@dataclass -class _ColumnProfile: - colname: str - colnumber: int - coltype: str - sample_data: Any | None = None - n_unique_vals: int = -1 - f_unique_vals: float = -1 - f_missing_vals: float = -1 - _n_missing_vals: int = field(default=0, init=False) - - @property - def n_missing_vals(self) -> int: - return self._n_missing_vals - - @n_missing_vals.setter - def n_missing_vals(self, value: int) -> None: - self._n_missing_vals = value - if value > 0 and self.n_unique_vals > 0: - self.n_unique_vals -= 1 - - @property - def is_numeric(self) -> bool: - inds = ("int", "float") - return any(ind in str(self.coltype).lower() for ind in inds) - @property - def is_string(self) -> bool: - inds = ("string", "categorical") - return any(ind in str(self.coltype).lower() for ind in inds) - - @property - def is_date(self) -> bool: - inds = ("date",) - return any(ind in str(self.coltype).lower() for ind in inds) - - @property - def is_bool(self) -> bool: - inds = ("bool",) - return any(ind in str(self.coltype).lower() for ind in inds) - - def _fetch_public_attrs(self) -> dict: - return {k: v for k, v in asdict(self).items() if not k.startswith("_")} - - def spawn_profile(self, _type: type | str) -> _ColumnProfile: - attrs = self._fetch_public_attrs() - if _type is str: - return _StringProfile(**attrs) - if _type is float or _type is int: - return _NumericProfile(**attrs) - if _type is bool: - return _BoolProfile(**attrs) - if _type == "date": - return _DateProfile(**attrs) +__all__ = ["DataScan", "col_summary_tbl"] - raise NotImplementedError - def _fetch_statistics(self, col_data: Series): - self.mean = float(col_data.mean()) - self.std = float(col_data.std()) - self.min = float(col_data.min()) - self.max = float(col_data.max()) - self.p05 = float(col_data.quantile(0.005, interpolation="linear")) - self.q_1 = float(col_data.quantile(0.25, interpolation="linear")) - self.med = float(col_data.median()) - self.q_3 = float(col_data.quantile(0.75, interpolation="linear")) - self.p95 = float(col_data.quantile(0.95, interpolation="linear")) - self.iqr = self.q_3 - self.q_1 - - -@dataclass -class _DateProfile(_ColumnProfile): - min: str | None = None - max: str | None = None - - -@dataclass -class _BoolProfile(_ColumnProfile): - ntrue: int = -1 - nfalse: int = -1 - - -@dataclass -class _StringProfile(_ColumnProfile): - mean: float = -1 - std: float = -1 - min: float = -1 - max: float = -1 - p05: float = -1 - q_1: float = -1 - med: float = -1 - q_3: float = -1 - p95: float = -1 - iqr: float = -1 - - -@dataclass -class _NumericProfile(_ColumnProfile): - n_negative_vals: int = 0 - f_negative_vals: float = 0.0 - n_zero_vals: int = 0 - f_zero_vals: float = 0.0 - n_positive_vals: int = 0 - f_positive_vals: float = 0.0 - mean: float = -1 - std: float = -1 - min: float = -1 - max: float = -1 - p05: float = -1 - q_1: float = -1 - med: float = -1 - q_3: float = -1 - p95: float = -1 - iqr: float = -1 - - -class _DataProfile(NamedTuple): - table_name: str | None - row_count: int - columns: list[str] = [] - column_profiles: list[_ColumnProfile] = [] - - -@dataclass class DataScan: """ Get a summary of a dataset. @@ -242,403 +121,75 @@ class DataScan: A DataScan object. """ - data: FrameT | Frame | Any - tbl_name: str | None = None - data_alt: Any | None = field(init=False) - tbl_category: str = field(init=False) - tbl_type: str = field(init=False) - - def __post_init__(self): - # Determine if the data is a DataFrame that could be handled by Narwhals, - # or an Ibis Table - self.tbl_type = _get_tbl_type(data=self.data) - ibis_tbl = "ibis.expr.types.relations.Table" in str(type(self.data)) - pl_pd_tbl = "polars" in self.tbl_type or "pandas" in self.tbl_type - - # Set the table category based on the type of table (this will be used to determine - # how to handle the data) - if ibis_tbl: - self.tbl_category = "ibis" - else: - self.tbl_category = "dataframe" - - # If the data is DataFrame, convert it to a Narwhals DataFrame - if pl_pd_tbl: - self.data_alt = nw.from_native(self.data) - else: - self.data_alt = None + def __init__( + self, data: IntoDataFrame, tbl_name: str | None = None, *, force_collect: bool = True + ) -> None: + self.nw_data: DataFrame = nw.from_native(data) + + # # TODO: this must be wrong, investigate a more idiomatic way + is_lazy = self.nw_data._level == "lazy" + if is_lazy and not force_collect: # ? this should be allowed (eventually?) + msg = ( + "`DataScan` requires a dataframe to avoid unexpected computations " + "of the caller's execution graph. Please collect the data into a " + "narwhals compliant dataframe first or turn on `force_collect`." + ) + raise TypeError(msg) + if is_lazy and force_collect: + self.nw_data = self.nw_data.collect() - # Generate the profile based on the `tbl_category` value + self.tbl_name: str | None = tbl_name self.profile: _DataProfile = self._generate_profile_df() def _generate_profile_df(self) -> _DataProfile: - tbl_name: str | None = self.tbl_name or None - alt: Frame = self.data - try: - row_count = len(alt) - except Exception as e: - is_ibis: bool = "ibis.common.exceptions.Expression" in str(e) - row_count = alt.count() + row_count = len(self.nw_data) - columns = alt.columns + columns: list[str] = self.nw_data.columns - profile = _DataProfile(table_name=tbl_name, row_count=row_count, columns=columns) + profile = _DataProfile(table_name=self.tbl_name, row_count=row_count, columns=columns) - for idx, column in enumerate(columns): - col_data: Series = self.data[column] - native_dtype = str(col_data.dtype) + schema: Mapping[str, Any] = self.nw_data.schema + for column in columns: + col_data: DataFrame = self.nw_data.select(column) - col_profile = _ColumnProfile(colname=column, coltype=native_dtype, colnumber=idx) + ## Handle dtyping: + native_dtype = schema[column] + try: + prof: type[ColumnProfile] = _TypeMap.fetch_profile(native_dtype) + except NotImplementedError: + continue - # - # Collection of sample data - # - raw_sample_data = col_data - with contextlib.suppress(Exception): - raw_sample_data = raw_sample_data.drop_nulls() - raw_sample_data = raw_sample_data.head(5).to_list() - sample_data: list[str] = [str(x) for x in raw_sample_data] - - col_profile.sample_data = sample_data - col_profile.n_missing_vals = int(col_data.is_null().sum()) - col_profile.n_unique_vals = int(col_data.n_unique()) - - # TODO: These should probably live on the class - col_profile.f_missing_vals = _round_to_sig_figs( - col_profile.n_missing_vals / row_count, 3 - ) - col_profile.f_unique_vals = _round_to_sig_figs(col_profile.n_unique_vals / row_count, 3) - - # - # Numerical columns - # - prof: _ColumnProfile # help mypy - if col_profile.is_numeric: - numeric_profile: _NumericProfile = col_profile.spawn_profile(int) - - numeric_profile.n_negative_vals = int(col_data.is_between(-1e26, -1e-26).sum()) - numeric_profile.f_negative_vals = _round_to_sig_figs( - numeric_profile.n_negative_vals / row_count, 3 - ) + col_profile = ColumnProfile(colname=column, coltype=native_dtype) - numeric_profile.n_zero_vals = int(col_data.is_between(0, 0).sum()) - numeric_profile.f_zero_vals = _round_to_sig_figs( - numeric_profile.n_zero_vals / row_count, 3 - ) + ## Collect Sample Data: + ## This is the most consistent way (i think) to get the samples out of the data. + ## We can avoid writing our own logic to determine operations and rely on narwhals. + raw_vals: list[Any] = col_data.drop_nulls().head(5).to_dict()[column].to_list() + col_profile.sample_data = [str(x) for x in raw_vals] - numeric_profile.n_positive_vals = ( - row_count - - numeric_profile.n_missing_vals - - numeric_profile.n_negative_vals - - numeric_profile.n_zero_vals + try: + col_profile.n_unique_vals = col_data.is_unique().sum() # set this before missing + except Exception as e: # tendancy to introduce internal panics + msg = f"Could not calculate uniqueness and missing values: {e!s}" + warnings.warn(msg) + else: + col_profile.n_missing_vals = col_data.null_count().item() + # TODO: These should probably live on the class + col_profile.f_missing_vals = _round_to_sig_figs( + col_profile.n_missing_vals / row_count, 3 ) - numeric_profile.f_positive_vals = _round_to_sig_figs( - numeric_profile.n_positive_vals / row_count, 3 + col_profile.f_unique_vals = _round_to_sig_figs( + col_profile.n_unique_vals / row_count, 3 ) - numeric_profile._fetch_statistics(col_data) - - prof = numeric_profile - - # - # String columns - # - elif col_profile.is_string: - # Transform `col_data` to a column of string lengths - col_str_len_data = col_data.str.len_chars() - str_prof = col_profile.spawn_profile(str) - str_prof._fetch_statistics(col_str_len_data) - prof = str_prof - - # - # Date and datetime columns - # - elif col_profile.is_date: - date_prof = col_profile.spawn_profile("date") - - date_prof.min = str(col_data.min()) - date_prof.max = str(col_data.max()) - - prof = date_prof - - # - # Boolean columns - # - elif col_profile.is_bool: - bool_prof: _BoolProfile = col_profile.spawn_profile(bool) - ntrue: int = int(col_data.sum()) - bool_prof.ntrue = ntrue - # TODO: document trivalence - bool_prof.nfalse = row_count - ntrue - - else: - raise NotImplementedError # pragma: no-cover + sub_profile: ColumnProfile = col_profile.spawn_profile(prof) + with contextlib.suppress(NotImplementedError): + sub_profile.calc_stats(col_data) - profile.column_profiles.append(prof) + profile.column_profiles.append(sub_profile) return profile - # def _generate_profile_ibis(self) -> dict: - # profile = {} - - # if self.tbl_name: - # profile["tbl_name"] = self.tbl_name - - # from pointblank.validate import get_row_count - - # row_count = get_row_count(data=self.data) - # column_count = len(self.data.columns) - - # profile.update( - # { - # "tbl_type": self.tbl_type, - # "dimensions": {"rows": row_count, "columns": column_count}, - # "columns": [], - # } - # ) - - # # Determine which DataFrame library is available - # df_lib = _select_df_lib(preference="polars") - # df_lib_str = str(df_lib) - - # if "polars" in df_lib_str: - # df_lib_use = "polars" - # else: - # df_lib_use = "pandas" - - # column_dtypes = list(self.data.schema().items()) - - # for idx, column in enumerate(self.data.columns): - # dtype_str = str(column_dtypes[idx][1]) - - # col_data = self.data[column] - # col_data_no_null = self.data.drop_null().head(5)[column] - - # # - # # Collection of sample data - # # - # if "date" in dtype_str.lower() or "timestamp" in dtype_str.lower(): - # if df_lib_use == "polars": - # import polars as pl - - # sample_data = col_data_no_null.to_polars().cast(pl.String).to_list() - # else: - # sample_data = col_data_no_null.to_pandas().astype(str).to_list() - # else: - # if df_lib_use == "polars": - # sample_data = col_data_no_null.to_polars().to_list() - # else: - # sample_data = col_data_no_null.to_pandas().to_list() - - # n_missing_vals = int(_to_df_lib(col_data.isnull().sum(), df_lib=df_lib_use)) - # n_unique_vals = int(_to_df_lib(col_data.nunique(), df_lib=df_lib_use)) - - # # If there are missing values, subtract 1 from the number of unique values - # # to account for the missing value which shouldn't be included in the count - # if (n_missing_vals > 0) and (n_unique_vals > 0): - # n_unique_vals = n_unique_vals - 1 - - # f_missing_vals = _round_to_sig_figs(n_missing_vals / row_count, 3) - # f_unique_vals = _round_to_sig_figs(n_unique_vals / row_count, 3) - - # col_profile = { - # "column_name": column, - # "column_type": dtype_str, - # "column_number": idx + 1, - # "n_missing_values": n_missing_vals, - # "f_missing_values": f_missing_vals, - # "n_unique_values": n_unique_vals, - # "f_unique_values": f_unique_vals, - # } - - # # - # # Numerical columns - # # - # if "int" in dtype_str.lower() or "float" in dtype_str.lower(): - # n_negative_vals = int( - # _to_df_lib(col_data.between(-1e26, -1e-26).sum(), df_lib=df_lib_use) - # ) - # f_negative_vals = _round_to_sig_figs(n_negative_vals / row_count, 3) - - # n_zero_vals = int(_to_df_lib(col_data.between(0, 0).sum(), df_lib=df_lib_use)) - # f_zero_vals = _round_to_sig_figs(n_zero_vals / row_count, 3) - - # n_positive_vals = row_count - n_missing_vals - n_negative_vals - n_zero_vals - # f_positive_vals = _round_to_sig_figs(n_positive_vals / row_count, 3) - - # col_profile_additional = { - # "n_negative_values": n_negative_vals, - # "f_negative_values": f_negative_vals, - # "n_zero_values": n_zero_vals, - # "f_zero_values": f_zero_vals, - # "n_positive_values": n_positive_vals, - # "f_positive_values": f_positive_vals, - # "sample_data": sample_data, - # } - # col_profile.update(col_profile_additional) - - # col_profile_stats = { - # "statistics": { - # "numerical": { - # "descriptive": { - # "mean": round(_to_df_lib(col_data.mean(), df_lib=df_lib_use), 2), - # "std_dev": round(_to_df_lib(col_data.std(), df_lib=df_lib_use), 4), - # }, - # "quantiles": { - # "min": _to_df_lib(col_data.min(), df_lib=df_lib_use), - # "p05": round( - # _to_df_lib(col_data.approx_quantile(0.05), df_lib=df_lib_use), - # 2, - # ), - # "q_1": round( - # _to_df_lib(col_data.approx_quantile(0.25), df_lib=df_lib_use), - # 2, - # ), - # "med": _to_df_lib(col_data.median(), df_lib=df_lib_use), - # "q_3": round( - # _to_df_lib(col_data.approx_quantile(0.75), df_lib=df_lib_use), - # 2, - # ), - # "p95": round( - # _to_df_lib(col_data.approx_quantile(0.95), df_lib=df_lib_use), - # 2, - # ), - # "max": _to_df_lib(col_data.max(), df_lib=df_lib_use), - # "iqr": round( - # _to_df_lib(col_data.quantile(0.75), df_lib=df_lib_use) - # - _to_df_lib(col_data.quantile(0.25), df_lib=df_lib_use), - # 2, - # ), - # }, - # } - # } - # } - # col_profile.update(col_profile_stats) - - # # - # # String columns - # # - # elif "string" in dtype_str.lower() or "char" in dtype_str.lower(): - # col_profile_additional = { - # "sample_data": sample_data, - # } - # col_profile.update(col_profile_additional) - - # # Transform `col_data` to a column of string lengths - # col_str_len_data = col_data.length() - - # col_profile_stats = { - # "statistics": { - # "string_lengths": { - # "descriptive": { - # "mean": round( - # float(_to_df_lib(col_str_len_data.mean(), df_lib=df_lib_use)), 2 - # ), - # "std_dev": round( - # float(_to_df_lib(col_str_len_data.std(), df_lib=df_lib_use)), 4 - # ), - # }, - # "quantiles": { - # "min": int(_to_df_lib(col_str_len_data.min(), df_lib=df_lib_use)), - # "p05": int( - # _to_df_lib( - # col_str_len_data.approx_quantile(0.05), - # df_lib=df_lib_use, - # ) - # ), - # "q_1": int( - # _to_df_lib( - # col_str_len_data.approx_quantile(0.25), - # df_lib=df_lib_use, - # ) - # ), - # "med": int( - # _to_df_lib(col_str_len_data.median(), df_lib=df_lib_use) - # ), - # "q_3": int( - # _to_df_lib( - # col_str_len_data.approx_quantile(0.75), - # df_lib=df_lib_use, - # ) - # ), - # "p95": int( - # _to_df_lib( - # col_str_len_data.approx_quantile(0.95), - # df_lib=df_lib_use, - # ) - # ), - # "max": int(_to_df_lib(col_str_len_data.max(), df_lib=df_lib_use)), - # "iqr": int( - # _to_df_lib( - # col_str_len_data.approx_quantile(0.75), - # df_lib=df_lib_use, - # ) - # ) - # - int( - # _to_df_lib( - # col_str_len_data.approx_quantile(0.25), - # df_lib=df_lib_use, - # ) - # ), - # }, - # } - # } - # } - # col_profile.update(col_profile_stats) - - # # - # # Date and datetime columns - # # - # elif "date" in dtype_str.lower() or "timestamp" in dtype_str.lower(): - # col_profile_additional = { - # "sample_data": sample_data, - # } - # col_profile.update(col_profile_additional) - - # min_date = _to_df_lib(col_data.min(), df_lib=df_lib_use) - # max_date = _to_df_lib(col_data.max(), df_lib=df_lib_use) - - # col_profile_stats = { - # "statistics": { - # "datetime": { - # "min": str(min_date), - # "max": str(max_date), - # } - # } - # } - # col_profile.update(col_profile_stats) - - # # - # # Boolean columns - # # - # elif "bool" in dtype_str.lower(): - # col_profile_additional = { - # "sample_data": sample_data, - # } - # col_profile.update(col_profile_additional) - - # n_true_values = _to_df_lib(col_data.cast(int).sum(), df_lib=df_lib) - # f_true_values = _round_to_sig_figs(n_true_values / row_count, 3) - - # n_false_values = row_count - n_missing_vals - n_true_values - # f_false_values = _round_to_sig_figs(n_false_values / row_count, 3) - - # col_profile_stats = { - # "statistics": { - # "boolean": { - # "n_true_values": n_true_values, - # "f_true_values": f_true_values, - # "n_false_values": n_false_values, - # "f_false_values": f_false_values, - # } - # } - # } - # col_profile.update(col_profile_stats) - - # profile["columns"].append(col_profile) - - # return profile - def _get_column_data(self, column: str) -> dict | None: column_data = self.profile["columns"] @@ -661,24 +212,26 @@ def summary_data(self) -> CompliantDataFrame: def _gen_stats_list(self) -> None: """Set the `stats_list` attribute.""" - column_data = self.profile["columns"] + column_profiles = self.profile.column_profiles stats_list = [] datetime_row_list = [] # Iterate over each column's data and obtain a dictionary of statistics for each column - for idx, col in enumerate(column_data): - if "statistics" in col and ( - "numerical" in col["statistics"] or "string_lengths" in col["statistics"] - ): - col_dict = _process_numerical_string_column_data(col) - elif "statistics" in col and "datetime" in col["statistics"]: - col_dict = _process_datetime_column_data(col) - datetime_row_list.append(idx) - elif "statistics" in col and "boolean" in col["statistics"]: - col_dict = _process_boolean_column_data(col) - else: - col_dict = _process_other_column_data(col) + for idx, col in enumerate(column_profiles): + col_dict = col._proc_as_html() + + # if "statistics" in col and ( + # "numerical" in col["statistics"] or "string_lengths" in col["statistics"] + # ): + # col_dict = _process_numerical_string_column_data(col) + # elif "statistics" in col and "datetime" in col["statistics"]: + # col_dict = _process_datetime_column_data(col) + # datetime_row_list.append(idx) + # elif "statistics" in col and "boolean" in col["statistics"]: + # col_dict = _process_boolean_column_data(col) + # else: + # col_dict = _process_other_column_data(col) stats_list.append(col_dict) @@ -686,6 +239,7 @@ def _gen_stats_list(self) -> None: def _summary_data_from_list(self) -> None: """Set the `_summary_data` attribute from the raw stats list.""" + raise NotImplementedError # Determine which DataFrame library is available and construct the DataFrame # based on the available library @@ -842,11 +396,12 @@ def to_json(self) -> str: attrs = profile._fetch_public_attrs() profiles.append(attrs) - return json.dumps(profiles, indent=4) + return json.dumps(profiles, indent=4, default=str) def save_to_json(self, output_file: str): + json_string: str = self.to_json() with open(output_file, "w") as f: - json.dump(self.profile, f, indent=4) + json.dump(json_string, f, indent=4) def col_summary_tbl(data: FrameT | Any, tbl_name: str | None = None) -> GT: @@ -1169,6 +724,7 @@ def _process_boolean_column_data(column_data: dict) -> dict: def _process_other_column_data(column_data: dict) -> dict: + raise column_number = column_data["column_number"] column_name = column_data["column_name"] column_type = column_data["column_type"] diff --git a/pointblank/scan_profile.py b/pointblank/scan_profile.py index 52ddd4388..064bc64ff 100644 --- a/pointblank/scan_profile.py +++ b/pointblank/scan_profile.py @@ -1,6 +1,8 @@ from __future__ import annotations +import contextlib from abc import ABC, abstractmethod +from collections import defaultdict from dataclasses import asdict, dataclass from enum import Enum from typing import TYPE_CHECKING, Any, NamedTuple @@ -12,7 +14,8 @@ from narwhals.series import Series -class _TypeMap(Enum): +class _TypeMap(Enum): # ! ordered + STRUCT = ("struct",) NUMERIC = ("int", "float") STRING = ("string", "categorical") DATE = ("date",) @@ -20,12 +23,14 @@ class _TypeMap(Enum): @classmethod def fetch_prof_map(cls) -> dict[_TypeMap, type[ColumnProfile]]: - return { + default = defaultdict(lambda: ColumnProfile) + implemented_dict: dict[_TypeMap, type[ColumnProfile]] = { cls.BOOL: _BoolProfile, cls.NUMERIC: _NumericProfile, cls.STRING: _StringProfile, cls.DATE: _DateProfile, } + return default | implemented_dict @classmethod def fetch_profile(cls, dtype: Any) -> type[ColumnProfile]: @@ -42,11 +47,13 @@ class _ColumnProfileABC(ABC): @abstractmethod def calc_stats(self, data: DataFrame) -> None: ... + @abstractmethod + def _proc_as_html(self) -> dict: ... # TODO: type hint this + @dataclass class ColumnProfile(_ColumnProfileABC): colname: str - colnumber: int coltype: str sample_data: Any | None = None f_unique_vals: float = -1 @@ -75,6 +82,8 @@ def _fetch_public_attrs(self) -> dict: return {k: v for k, v in asdict(self).items() if not k.startswith("_")} def spawn_profile(self, _subprofile: type[ColumnProfile]) -> ColumnProfile: + if type[_subprofile] == type[ColumnProfile]: + return self # spawn self if no subprofile attrs = self._fetch_public_attrs() return _subprofile(**attrs) @@ -94,16 +103,58 @@ def _calc_general_sats(self, col_data: DataFrame): ## Assign stats directly from the data: for attr, series in res.items(): public_name = attr[1:] - val: float = float(series.item()) + val: float | None = None + with contextlib.suppress(TypeError, ValueError): + val: float = float(series.item()) setattr(self, public_name, val) ## Post-hoc calculations: ## This is a space to make calculation off of the calculations - self.iqr = self.q_3 - self.q_1 + try: + self.iqr = self.q_3 - self.q_1 + except TypeError: # if quartiles could not be calculated + self.iqr = None def calc_stats(self, data: DataFrame) -> None: # pragma: no-cover - msg = "Statistics should be calculated from subclasses of `ColumnProfile`." - raise NotImplementedError(msg) + raise NotImplementedError + + def _proc_as_html(self): # pragma: no-cover + column_number = column_data["column_number"] + column_name = column_data["column_name"] + column_type = column_data["column_type"] + + column_name_and_type = ( + f"
{column_name}
" + f"
{column_type}
" + ) + + # Get the Missing and Unique value counts and fractions + missing_vals = column_data["n_missing_values"] + unique_vals = column_data["n_unique_values"] + missing_vals_frac = _compact_decimal_fmt(column_data["f_missing_values"]) + unique_vals_frac = _compact_decimal_fmt(column_data["f_unique_values"]) + + missing_vals_str = f"{missing_vals}
{missing_vals_frac}" + unique_vals_str = f"{unique_vals}
{unique_vals_frac}" + + # Create a single dictionary with the statistics for the column + return { + "column_number": column_number, + "icon": SVG_ICONS_FOR_DATA_TYPES["object"], + "column_name": column_name_and_type, + "missing_vals": missing_vals_str, + "unique_vals": unique_vals_str, + "mean": "—", + "std_dev": "—", + "min": "—", + "p05": "—", + "q_1": "—", + "med": "—", + "q_3": "—", + "p95": "—", + "max": "—", + "iqr": "—", + } class _DateProfile(ColumnProfile): @@ -117,6 +168,9 @@ def calc_stats(self, data: DataFrame): self.min = str(res["_min"].item()) self.max = str(res["_max"].item()) + def _proc_as_html(self): + raise NotImplementedError + class _BoolProfile(ColumnProfile): ntrue: int = -1 @@ -128,6 +182,9 @@ def calc_stats(self, data: DataFrame): row_count = len(data) self.nfalse = row_count - self.ntrue + def _proc_as_html(self): + raise NotImplementedError + class _StringProfile(ColumnProfile): mean: float = -1 @@ -145,6 +202,9 @@ def calc_stats(self, data: DataFrame): col_str_len_data = data.select(nw.all().str.len_chars()) self._calc_general_sats(col_str_len_data) + def _proc_as_html(self): + raise NotImplementedError + class _NumericProfile(ColumnProfile): n_negative_vals: int = 0 @@ -167,6 +227,9 @@ class _NumericProfile(ColumnProfile): def calc_stats(self, data: DataFrame): self._calc_general_sats(data) + def _proc_as_html(self): + raise NotImplementedError + class _DataProfile(NamedTuple): # TODO: feels redundant table_name: str | None From fc183d86d88fee0d693d6016789a58e677a6a216 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Mon, 17 Mar 2025 16:41:28 -0400 Subject: [PATCH 06/85] Adding everything for check in --- Makefile | 4 +- pointblank/_datascan_utils.py | 67 + pointblank/compare.py | 27 + pointblank/datascan.py | 290 +-- pointblank/scan_profile.py | 205 ++- pyproject.toml | 3 + tests/test_compare.py | 23 + tests/test_datascan.py | 154 +- uv.lock | 3142 +++++++++++++++++++++++++++++++++ 9 files changed, 3482 insertions(+), 433 deletions(-) create mode 100644 pointblank/_datascan_utils.py create mode 100644 pointblank/compare.py create mode 100644 tests/test_compare.py create mode 100644 uv.lock diff --git a/Makefile b/Makefile index 70fe21084..742fee4c4 100644 --- a/Makefile +++ b/Makefile @@ -1,7 +1,9 @@ .PHONY: check test: - pytest --cov=pointblank --cov-report=xml + uv run pytest --cov=pointblank --cov-report=xml \ + --randomly-seed=12301998 + test-update: pytest --snapshot-update diff --git a/pointblank/_datascan_utils.py b/pointblank/_datascan_utils.py new file mode 100644 index 000000000..080c42952 --- /dev/null +++ b/pointblank/_datascan_utils.py @@ -0,0 +1,67 @@ +from __future__ import annotations + +from math import floor, log10 +from typing import TYPE_CHECKING + +from great_tables.vals import fmt_integer, fmt_number, fmt_scientific + +if TYPE_CHECKING: + pass + + +def _to_df_lib(expr: any, df_lib: str) -> any: + if df_lib == "polars": + return expr.to_polars() + else: + return expr.to_pandas() + + +def _round_to_sig_figs(value: float, sig_figs: int) -> float: + if value == 0: + return 0 + return round(value, sig_figs - int(floor(log10(abs(value)))) - 1) + + +def _compact_integer_fmt(value: float | int) -> str: + if value == 0: + formatted = "0" + elif abs(value) >= 1 and abs(value) < 10_000: + formatted = fmt_integer(value, use_seps=False)[0] + else: + formatted = fmt_scientific(value, decimals=1, exp_style="E1")[0] + + return formatted + + +def _compact_decimal_fmt(value: float | int) -> str: + if value == 0: + formatted = "0.00" + elif abs(value) < 1 and abs(value) >= 0.01: + formatted = fmt_number(value, decimals=2)[0] + elif abs(value) < 0.01: + formatted = fmt_scientific(value, decimals=1, exp_style="E1")[0] + elif abs(value) >= 1 and abs(value) < 1000: + formatted = fmt_number(value, n_sigfig=3)[0] + elif abs(value) >= 1000 and abs(value) < 10_000: + formatted = fmt_number(value, decimals=0, use_seps=False)[0] + else: + formatted = fmt_scientific(value, decimals=1, exp_style="E1")[0] + + return formatted + + +def _compact_0_1_fmt(value: float | int) -> str: + if value == 0: + formatted = " 0.00" + elif value == 1: + formatted = " 1.00" + elif abs(value) < 1 and abs(value) >= 0.01: + formatted = " " + fmt_number(value, decimals=2)[0] + elif abs(value) < 0.01: + formatted = "<0.01" + elif abs(value) > 0.99: + formatted = ">0.99" + else: + formatted = fmt_number(value, n_sigfig=3)[0] + + return formatted diff --git a/pointblank/compare.py b/pointblank/compare.py new file mode 100644 index 000000000..04dd6ca95 --- /dev/null +++ b/pointblank/compare.py @@ -0,0 +1,27 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +from pointblank import DataScan + +if TYPE_CHECKING: + from narwhals.typing import IntoFrame + + +class Compare: + def __init__(self, a: IntoFrame, b: IntoFrame) -> None: + self.a: IntoFrame = a + self.b: IntoFrame = b + + def compare(self) -> None: + ## Scan both frames + self._scana = DataScan(self.a) + self._scanb = DataScan(self.b) + + ## Get summary outs + summarya = self._scana.summary_data + summaryb = self._scana.summary_data + + summarya.columns + + self._scana.profile diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 4c94bfc1d..0ab8ffdbe 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -4,16 +4,14 @@ import json import warnings from importlib.metadata import version -from math import floor, log10 from typing import TYPE_CHECKING, Any import narwhals as nw from great_tables import GT, google_font, html, loc, style -from great_tables.vals import fmt_integer, fmt_number, fmt_scientific from narwhals.typing import FrameT from pointblank._constants import SVG_ICONS_FOR_DATA_TYPES -from pointblank._utils import _select_df_lib +from pointblank._datascan_utils import _compact_0_1_fmt, _compact_decimal_fmt, _round_to_sig_figs from pointblank._utils_html import _create_table_dims_html, _create_table_type_html from pointblank.scan_profile import ColumnProfile, _DataProfile, _TypeMap @@ -21,7 +19,7 @@ from collections.abc import Mapping from narwhals.dataframe import DataFrame - from narwhals.typing import CompliantDataFrame, IntoDataFrame + from narwhals.typing import IntoDataFrame __all__ = ["DataScan", "col_summary_tbl"] @@ -146,7 +144,12 @@ def _generate_profile_df(self) -> _DataProfile: columns: list[str] = self.nw_data.columns - profile = _DataProfile(table_name=self.tbl_name, row_count=row_count, columns=columns) + profile = _DataProfile( + table_name=self.tbl_name, + row_count=row_count, + columns=columns, + implementation=self.nw_data.implementation, + ) schema: Mapping[str, Any] = self.nw_data.schema for column in columns: @@ -202,89 +205,18 @@ def _get_column_data(self, column: str) -> dict | None: return None @property - def summary_data(self) -> CompliantDataFrame: - try: - return self._summary_data - except AttributeError: - self._gen_stats_list() - self._summary_data_from_list() - return self._summary_data - - def _gen_stats_list(self) -> None: - """Set the `stats_list` attribute.""" - column_profiles = self.profile.column_profiles - - stats_list = [] - datetime_row_list = [] - - # Iterate over each column's data and obtain a dictionary of statistics for each column - for idx, col in enumerate(column_profiles): - col_dict = col._proc_as_html() - - # if "statistics" in col and ( - # "numerical" in col["statistics"] or "string_lengths" in col["statistics"] - # ): - # col_dict = _process_numerical_string_column_data(col) - # elif "statistics" in col and "datetime" in col["statistics"]: - # col_dict = _process_datetime_column_data(col) - # datetime_row_list.append(idx) - # elif "statistics" in col and "boolean" in col["statistics"]: - # col_dict = _process_boolean_column_data(col) - # else: - # col_dict = _process_other_column_data(col) - - stats_list.append(col_dict) - - self._stats_list = stats_list - - def _summary_data_from_list(self) -> None: - """Set the `_summary_data` attribute from the raw stats list.""" - raise NotImplementedError - - # Determine which DataFrame library is available and construct the DataFrame - # based on the available library - df_lib = _select_df_lib(preference="polars") - df_lib_str = str(df_lib) - - # TODO: this can probably be simplified - if "polars" in df_lib_str: - import polars as pl - - stats_df: CompliantDataFrame = pl.DataFrame(self._stats_list) - else: - import pandas as pd - - stats_df: CompliantDataFrame = pd.DataFrame(self._stats_list) - - self._summary_data: CompliantDataFrame = pl.DataFrame(stats_df) + def summary_data(self) -> DataFrame: # TODO: Think this type hint is wrong + return self.profile.as_dataframe() def get_tabular_report(self) -> GT: - summary_data = self.summary_data - - n_rows = self.profile["dimensions"]["rows"] - n_columns = self.profile["dimensions"]["columns"] - - stat_columns = [ - "missing_vals", - "unique_vals", - "mean", - "std_dev", - "min", - "p05", - "q_1", - "med", - "q_3", - "p95", - "max", - "iqr", - ] - # Create the label, table type, and thresholds HTML fragments table_type_html = _create_table_type_html( - tbl_type=self.tbl_type, tbl_name=self.tbl_name, font_size="10px" + tbl_type=str(self.profile.implementation), tbl_name=self.tbl_name, font_size="10px" ) - tbl_dims_html = _create_table_dims_html(columns=n_columns, rows=n_rows, font_size="10px") + tbl_dims_html = _create_table_dims_html( + columns=len(self.profile.columns), rows=self.profile.row_count, font_size="10px" + ) # Compose the subtitle HTML fragment combined_title = ( @@ -296,10 +228,15 @@ def get_tabular_report(self) -> GT: "" ) + ## Construct HTML Rows: + stats_list: list[dict] = [col._proc_as_html() for col in self.profile.column_profiles] + + raise NotImplementedError("construct using implementation") + # TODO: Ensure width is 905px in total gt_tbl = ( - GT(self._summary_data) + GT() .tab_header(title=html(combined_title)) .cols_align(align="right", columns=stat_columns) .opt_table_font(font=google_font("IBM Plex Sans")) @@ -487,193 +424,6 @@ def col_summary_tbl(data: FrameT | Any, tbl_name: str | None = None) -> GT: return scanner.get_tabular_report() -def _to_df_lib(expr: any, df_lib: str) -> any: - if df_lib == "polars": - return expr.to_polars() - else: - return expr.to_pandas() - - -def _round_to_sig_figs(value: float, sig_figs: int) -> float: - if value == 0: - return 0 - return round(value, sig_figs - int(floor(log10(abs(value)))) - 1) - - -def _compact_integer_fmt(value: float | int) -> str: - if value == 0: - formatted = "0" - elif abs(value) >= 1 and abs(value) < 10_000: - formatted = fmt_integer(value, use_seps=False)[0] - else: - formatted = fmt_scientific(value, decimals=1, exp_style="E1")[0] - - return formatted - - -def _compact_decimal_fmt(value: float | int) -> str: - if value == 0: - formatted = "0.00" - elif abs(value) < 1 and abs(value) >= 0.01: - formatted = fmt_number(value, decimals=2)[0] - elif abs(value) < 0.01: - formatted = fmt_scientific(value, decimals=1, exp_style="E1")[0] - elif abs(value) >= 1 and abs(value) < 1000: - formatted = fmt_number(value, n_sigfig=3)[0] - elif abs(value) >= 1000 and abs(value) < 10_000: - formatted = fmt_number(value, decimals=0, use_seps=False)[0] - else: - formatted = fmt_scientific(value, decimals=1, exp_style="E1")[0] - - return formatted - - -def _compact_0_1_fmt(value: float | int) -> str: - if value == 0: - formatted = " 0.00" - elif value == 1: - formatted = " 1.00" - elif abs(value) < 1 and abs(value) >= 0.01: - formatted = " " + fmt_number(value, decimals=2)[0] - elif abs(value) < 0.01: - formatted = "<0.01" - elif abs(value) > 0.99: - formatted = ">0.99" - else: - formatted = fmt_number(value, n_sigfig=3)[0] - - return formatted - - -def _process_numerical_string_column_data(column_data: dict) -> dict: - column_number = column_data["column_number"] - column_name = column_data["column_name"] - column_type = column_data["column_type"] - - column_name_and_type = ( - f"
{column_name}
" - f"
{column_type}
" - ) - - # Determine if the column is a numerical or string column - if "numerical" in column_data["statistics"]: - key = "numerical" - icon = "numeric" - elif "string_lengths" in column_data["statistics"]: - key = "string_lengths" - icon = "string" - - # Get the Missing and Unique value counts and fractions - missing_vals = column_data["n_missing_values"] - unique_vals = column_data["n_unique_values"] - missing_vals_frac = _compact_decimal_fmt(column_data["f_missing_values"]) - unique_vals_frac = _compact_decimal_fmt(column_data["f_unique_values"]) - - missing_vals_str = f"{missing_vals}
{missing_vals_frac}" - unique_vals_str = f"{unique_vals}
{unique_vals_frac}" - - # Get the descriptive and quantile statistics - descriptive_stats = column_data["statistics"][key]["descriptive"] - quantile_stats = column_data["statistics"][key]["quantiles"] - - # Get all values from the descriptive and quantile stats into a single list - quantile_stats_vals = [v[1] for v in quantile_stats.items()] - - # Determine if the quantile stats are all integerlike - integerlike = [] - - # Determine if the quantile stats are integerlike - for val in quantile_stats_vals: - # Check if a quantile value is a number and then if it is intergerlike - if not isinstance(val, (int, float)): - continue - else: - integerlike.append(val % 1 == 0) - quantile_vals_integerlike = all(integerlike) - - # Determine the formatter to use for the quantile values - if quantile_vals_integerlike: - q_formatter = _compact_integer_fmt - else: - q_formatter = _compact_decimal_fmt - - # Format the descriptive statistics (mean and standard deviation) - for key, value in descriptive_stats.items(): - descriptive_stats[key] = _compact_decimal_fmt(value=value) - - # Format the quantile statistics - for key, value in quantile_stats.items(): - quantile_stats[key] = q_formatter(value=value) - - # Create a single dictionary with the statistics for the column - stats_dict = { - "column_number": column_number, - "icon": SVG_ICONS_FOR_DATA_TYPES[icon], - "column_name": column_name_and_type, - "missing_vals": missing_vals_str, - "unique_vals": unique_vals_str, - **descriptive_stats, - **quantile_stats, - } - - return stats_dict - - -def _process_datetime_column_data(column_data: dict) -> dict: - column_number = column_data["column_number"] - column_name = column_data["column_name"] - column_type = column_data["column_type"] - - long_column_type = len(column_type) > 22 - - if long_column_type: - column_type_style = "font-size: 7.5px; color: gray; margin-top: 3px; margin-bottom: 2px;" - else: - column_type_style = "font-size: 11px; color: gray;" - - column_name_and_type = ( - f"
{column_name}
" - f"
{column_type}
" - ) - - # Get the Missing and Unique value counts and fractions - missing_vals = column_data["n_missing_values"] - unique_vals = column_data["n_unique_values"] - missing_vals_frac = _compact_decimal_fmt(column_data["f_missing_values"]) - unique_vals_frac = _compact_decimal_fmt(column_data["f_unique_values"]) - - missing_vals_str = f"{missing_vals}
{missing_vals_frac}" - unique_vals_str = f"{unique_vals}
{unique_vals_frac}" - - # Get the min and max date - min_date = column_data["statistics"]["datetime"]["min"] - max_date = column_data["statistics"]["datetime"]["max"] - - # Format the dates so that they don't break across lines - min_max_date_str = f" {min_date} – {max_date}" - - # Create a single dictionary with the statistics for the column - stats_dict = { - "column_number": column_number, - "icon": SVG_ICONS_FOR_DATA_TYPES["date"], - "column_name": column_name_and_type, - "missing_vals": missing_vals_str, - "unique_vals": unique_vals_str, - "mean": "—", - "std_dev": "—", - "min": min_max_date_str, - "p05": "", - "q_1": "", - "med": "", - "q_3": "", - "p95": "", - "max": "", - "iqr": "—", - } - - return stats_dict - - def _process_boolean_column_data(column_data: dict) -> dict: column_number = column_data["column_number"] column_name = column_data["column_name"] diff --git a/pointblank/scan_profile.py b/pointblank/scan_profile.py index 064bc64ff..5ffdd4d9c 100644 --- a/pointblank/scan_profile.py +++ b/pointblank/scan_profile.py @@ -3,12 +3,15 @@ import contextlib from abc import ABC, abstractmethod from collections import defaultdict -from dataclasses import asdict, dataclass +from dataclasses import dataclass from enum import Enum -from typing import TYPE_CHECKING, Any, NamedTuple +from typing import TYPE_CHECKING, Any import narwhals as nw +from pointblank._constants import SVG_ICONS_FOR_DATA_TYPES +from pointblank._datascan_utils import _compact_decimal_fmt, _compact_integer_fmt + if TYPE_CHECKING: from narwhals.dataframe import DataFrame from narwhals.series import Series @@ -56,8 +59,6 @@ class ColumnProfile(_ColumnProfileABC): colname: str coltype: str sample_data: Any | None = None - f_unique_vals: float = -1 - f_missing_vals: float = -1 @property def n_missing_vals(self) -> int: @@ -78,8 +79,15 @@ def n_unique_vals(self) -> int: def n_unique_vals(self, value: Any) -> None: self._n_unique_vals = int(value) - def _fetch_public_attrs(self) -> dict: - return {k: v for k, v in asdict(self).items() if not k.startswith("_")} + def _fetch_public_attrs(self) -> dict: # TODO: generic type hint + # Get all attributes, including properties + attrs = {} + for k, v in vars(self.__class__).items(): + if isinstance(v, property) and not k.startswith("_"): + attrs[k] = getattr(self, k) + # Add non-private instance attributes + attrs.update({k: v for k, v in vars(self).items() if not k.startswith("_")}) + return attrs def spawn_profile(self, _subprofile: type[ColumnProfile]) -> ColumnProfile: if type[_subprofile] == type[ColumnProfile]: @@ -118,30 +126,15 @@ def _calc_general_sats(self, col_data: DataFrame): def calc_stats(self, data: DataFrame) -> None: # pragma: no-cover raise NotImplementedError - def _proc_as_html(self): # pragma: no-cover - column_number = column_data["column_number"] - column_name = column_data["column_name"] - column_type = column_data["column_type"] - - column_name_and_type = ( - f"
{column_name}
" - f"
{column_type}
" - ) - - # Get the Missing and Unique value counts and fractions - missing_vals = column_data["n_missing_values"] - unique_vals = column_data["n_unique_values"] - missing_vals_frac = _compact_decimal_fmt(column_data["f_missing_values"]) - unique_vals_frac = _compact_decimal_fmt(column_data["f_unique_values"]) - - missing_vals_str = f"{missing_vals}
{missing_vals_frac}" - unique_vals_str = f"{unique_vals}
{unique_vals_frac}" + def _proc_as_html(self): + missing_vals_str = f"{self.n_missing_vals}
{self.f_missing_vals}" + unique_vals_str = f"{self.n_unique_vals}
{self.f_unique_vals}" # Create a single dictionary with the statistics for the column + # TODO: This should be declaritively typed return { - "column_number": column_number, "icon": SVG_ICONS_FOR_DATA_TYPES["object"], - "column_name": column_name_and_type, + "column_name": _fmt_col_header(self.colname, self.coltype), "missing_vals": missing_vals_str, "unique_vals": unique_vals_str, "mean": "—", @@ -169,7 +162,59 @@ def calc_stats(self, data: DataFrame): self.max = str(res["_max"].item()) def _proc_as_html(self): - raise NotImplementedError + column_number = column_data["column_number"] + + column_name = column_data["column_name"] + column_type = column_data["column_type"] + + long_column_type = len(column_type) > 22 + + if long_column_type: + column_type_style = ( + "font-size: 7.5px; color: gray; margin-top: 3px; margin-bottom: 2px;" + ) + else: + column_type_style = "font-size: 11px; color: gray;" + + column_name_and_type = ( + f"
{column_name}
" + f"
{column_type}
" + ) + + # Get the Missing and Unique value counts and fractions + missing_vals = column_data["n_missing_values"] + unique_vals = column_data["n_unique_values"] + missing_vals_frac = _compact_decimal_fmt(column_data["f_missing_values"]) + unique_vals_frac = _compact_decimal_fmt(column_data["f_unique_values"]) + + missing_vals_str = f"{missing_vals}
{missing_vals_frac}" + unique_vals_str = f"{unique_vals}
{unique_vals_frac}" + + # Get the min and max date + min_date = column_data["statistics"]["datetime"]["min"] + max_date = column_data["statistics"]["datetime"]["max"] + + # Format the dates so that they don't break across lines + min_max_date_str = f" {min_date} – {max_date}" + + # Create a single dictionary with the statistics for the column + return { + "column_number": column_number, + "icon": SVG_ICONS_FOR_DATA_TYPES["date"], + "column_name": column_name_and_type, + "missing_vals": missing_vals_str, + "unique_vals": unique_vals_str, + "mean": "—", + "std_dev": "—", + "min": min_max_date_str, + "p05": "", + "q_1": "", + "med": "", + "q_3": "", + "p95": "", + "max": "", + "iqr": "—", + } class _BoolProfile(ColumnProfile): @@ -199,7 +244,8 @@ class _StringProfile(ColumnProfile): iqr: float = -1 def calc_stats(self, data: DataFrame): - col_str_len_data = data.select(nw.all().str.len_chars()) + # cast as string to avoid ambiguity in cat/enum/etc. + col_str_len_data = data.select(nw.all().cast(nw.String).str.len_chars()) self._calc_general_sats(col_str_len_data) def _proc_as_html(self): @@ -228,11 +274,102 @@ def calc_stats(self, data: DataFrame): self._calc_general_sats(data) def _proc_as_html(self): - raise NotImplementedError + icon = "numeric" # TODO: should this be here + + missing_vals_frac = _compact_decimal_fmt(self.f_missing_vals) + unique_vals_frac = _compact_decimal_fmt(self.f_unique_vals) + + missing_vals_str = f"{self.n_missing_vals}
{missing_vals_frac}" + unique_vals_str = f"{self.n_unique_vals}
{unique_vals_frac}" + + # Get the descriptive and quantile statistics + descriptive_stats = column_data["statistics"][key]["descriptive"] + quantile_stats = column_data["statistics"][key]["quantiles"] + + # Get all values from the descriptive and quantile stats into a single list + quantile_stats_vals = [v[1] for v in quantile_stats.items()] + + # Determine if the quantile stats are all integerlike + integerlike = [] + + # Determine if the quantile stats are integerlike + for val in quantile_stats_vals: + # Check if a quantile value is a number and then if it is intergerlike + if not isinstance(val, (int, float)): + continue + else: + integerlike.append(val % 1 == 0) + quantile_vals_integerlike = all(integerlike) + + # Determine the formatter to use for the quantile values + if quantile_vals_integerlike: + q_formatter = _compact_integer_fmt + else: + q_formatter = _compact_decimal_fmt + + # Format the descriptive statistics (mean and standard deviation) + for key, value in descriptive_stats.items(): + descriptive_stats[key] = _compact_decimal_fmt(value=value) + + # Format the quantile statistics + for key, value in quantile_stats.items(): + quantile_stats[key] = q_formatter(value=value) + + # Create a single dictionary with the statistics for the column + return { + "icon": SVG_ICONS_FOR_DATA_TYPES[icon], + "column_name": column_name_and_type, + "missing_vals": missing_vals_str, + "unique_vals": unique_vals_str, + **descriptive_stats, + **quantile_stats, + } -class _DataProfile(NamedTuple): # TODO: feels redundant - table_name: str | None - row_count: int - columns: list[str] = [] - column_profiles: list[ColumnProfile] = [] +class _DataProfile: # TODO: feels redundant and weird + def __init__( + self, + table_name: str | None, + row_count: int, + columns: list[str], + implementation: nw.Implementation, + ): + self.table_name: str | None = table_name + self.row_count: int = row_count + self.columns: list[str] = columns + self.implementation = implementation + self.column_profiles: list[ColumnProfile] = [] + + def as_dataframe(self) -> DataFrame: + ## Convert list[dict] to dict[list]: + ## We remove nested types like `sample_data`. + ## We stringify all non-primitive types. + from collections import defaultdict + + col_dict = {col.colname: col._fetch_public_attrs() for col in self.column_profiles} + nested_types = ("sample_data",) + prim_types = (int, str, float) + for col in col_dict: + for nested_type in nested_types: + col_dict[col].pop(nested_type, None) + for key in list(col_dict[col].keys()): + if not isinstance(col_dict[col][key], prim_types): + col_dict[col][key] = str(col_dict[col][key]) + + dict_of_lists = defaultdict(list) + for col_data in col_dict.values(): + for key, value in col_data.items(): + dict_of_lists[key].append(value) + dict_of_lists = dict(dict_of_lists) + + return nw.from_dict(dict_of_lists, backend=self.implementation) + + def __repr__(self) -> str: # pragma: no cover + return f"<_DataProfile(table_name={self.table_name}, row_count={self.row_count}, columns={self.columns})>" + + +def _fmt_col_header(name: str, _type: str) -> str: + return ( + f"
{name!s}
" + f"
{_type!s}
" + ) diff --git a/pyproject.toml b/pyproject.toml index d22cbdbd0..0762f5452 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -65,6 +65,7 @@ dev = [ "black", "duckdb>=1.1.3", "griffe==0.38.1", + "hypothesis>=6.129.2", "ibis-framework[duckdb,mysql,postgres,sqlite]>=9.5.0", "jupyter", "nbclient>=0.10.0", @@ -76,7 +77,9 @@ dev = [ "pyright>=1.1.244", "pytest>=3", "pytest-cov", + "pytest-randomly>=3.16.0", "pytest-snapshot", + "pytest-xdist>=3.6.1", "quartodoc>=0.8.1; python_version >= '3.9'", "ruff>=0.9.9", ] diff --git a/tests/test_compare.py b/tests/test_compare.py new file mode 100644 index 000000000..e4368f3e0 --- /dev/null +++ b/tests/test_compare.py @@ -0,0 +1,23 @@ +from __future__ import annotations +import pytest + +from pointblank.compare import Compare +import polars.testing.parametric as pt +from hypothesis import given + + +@pytest.mark.skip +@given( + dfa=pt.dataframes(min_size=100, max_size=1_000, allow_null=False), + dfb=pt.dataframes(min_size=100, max_size=1_000, allow_null=False), +) +def test_compare_basic(dfa, dfb) -> None: + comp = Compare(dfa, dfb) + + comp.compare() + + raise NotImplementedError + + +if __name__ == "__main__": + pytest.main([__file__]) diff --git a/tests/test_datascan.py b/tests/test_datascan.py index 28b3c9ba8..2df5d18bb 100644 --- a/tests/test_datascan.py +++ b/tests/test_datascan.py @@ -1,143 +1,44 @@ import pytest -import sys - -from unittest.mock import patch +import narwhals as nw +from hypothesis import given +import polars.testing.parametric as pt from great_tables import GT -from pointblank.validate import load_dataset -from pointblank.datascan import ( - DataScan, - col_summary_tbl, - _compact_0_1_fmt, - _compact_decimal_fmt, - _compact_integer_fmt, -) - - -@pytest.mark.parametrize("tbl_type", ["pandas", "polars", "duckdb"]) -def test_datascan_class(tbl_type): - dataset = load_dataset(dataset="small_table", tbl_type=tbl_type) - scanner = DataScan(data=dataset) - - assert scanner.data.equals(dataset) - assert scanner.tbl_name is None - assert scanner.profile is not None - assert isinstance(scanner.profile, dict) - - if tbl_type == "duckdb": - assert scanner.tbl_type == "duckdb" - assert scanner.tbl_category == "ibis" - assert scanner.data_alt is None - - if tbl_type == "polars": - assert scanner.tbl_type == "polars" - assert scanner.tbl_category == "dataframe" - assert scanner.data_alt is not None - - if tbl_type == "pandas": - assert scanner.tbl_type == "pandas" - assert scanner.tbl_category == "dataframe" - assert scanner.data_alt is not None - - -@pytest.mark.parametrize("tbl_type", ["pandas", "polars", "duckdb"]) -def test_datascan_class_use_tbl_name(tbl_type): - dataset = load_dataset(dataset="small_table", tbl_type=tbl_type) - scanner = DataScan(data=dataset, tbl_name="my_small_table") - - assert scanner.tbl_name == "my_small_table" +from pointblank.datascan import DataScan, col_summary_tbl +from pointblank._datascan_utils import _compact_0_1_fmt, _compact_decimal_fmt, _compact_integer_fmt -@pytest.mark.parametrize("tbl_type", ["pandas", "polars", "duckdb"]) -def test_datascan_no_fail(tbl_type): - small_table = load_dataset(dataset="small_table", tbl_type=tbl_type) - DataScan(data=small_table) +@given(df=pt.dataframes(min_size=5)) +def test_datascan_class_parametric(df) -> None: + scanner = DataScan(data=df) - game_revenue = load_dataset(dataset="game_revenue", tbl_type=tbl_type) - DataScan(data=game_revenue) + df_nw = nw.from_native(df) - nycflights = load_dataset(dataset="nycflights", tbl_type=tbl_type) - DataScan(data=nycflights) + summary_res = scanner.summary_data + ## Go through checks: + cols = summary_res.select("colname").to_polars().to_series().to_list() -@pytest.mark.parametrize("tbl_type", ["pandas", "polars", "duckdb"]) -def test_datascan_dict_output(tbl_type): - dataset = load_dataset(dataset="small_table", tbl_type=tbl_type) - scanner = DataScan(data=dataset) + msg = "cols are not the same" + df_cols = df_nw.columns + assert set(cols) == set(df_cols), msg - assert isinstance(scanner.to_dict(), dict) - scan_dict = scanner.to_dict() - - assert isinstance(scan_dict, dict) - - assert scanner.to_dict() == scan_dict - - -@pytest.mark.parametrize("tbl_type", ["pandas", "polars", "duckdb"]) -def test_datascan_json_output(tbl_type): - dataset = load_dataset(dataset="small_table", tbl_type=tbl_type) - scanner = DataScan(data=dataset) +@given(df=pt.dataframes(min_size=5)) +def test_datascan_json_output(df): + scanner = DataScan(data=df) profile_json = scanner.to_json() assert isinstance(profile_json, str) -def test_datascan_json_file_output(tmp_path): - dataset = load_dataset(dataset="small_table") - scanner = DataScan(data=dataset) - - profile_json = scanner.to_json() - - file_path = tmp_path / "profile.json" - scanner.save_to_json(output_file=file_path) - - assert file_path.exists() - assert file_path.is_file() - - with open(file_path, "r") as f: - file_content = f.read() +@given(df=pt.dataframes(min_size=5)) +def test_col_summary_tbl(df): + col_summary = col_summary_tbl(df) - assert profile_json == file_content - - -@pytest.mark.parametrize("tbl_type", ["pandas", "polars", "duckdb"]) -def test_datascan_tabular_output_small_table(tbl_type): - dataset = load_dataset(dataset="small_table", tbl_type=tbl_type) - scanner = DataScan(data=dataset) - - tabular_output = scanner.get_tabular_report() - - assert isinstance(tabular_output, GT) - - -@pytest.mark.parametrize("tbl_type", ["pandas", "polars", "duckdb"]) -def test_datascan_tabular_output_game_revenue(tbl_type): - dataset = load_dataset(dataset="game_revenue", tbl_type=tbl_type) - scanner = DataScan(data=dataset) - - tabular_output = scanner.get_tabular_report() - - assert isinstance(tabular_output, GT) - - -@pytest.mark.parametrize("tbl_type", ["pandas", "polars", "duckdb"]) -def test_datascan_tabular_output_nycflights(tbl_type): - dataset = load_dataset(dataset="nycflights", tbl_type=tbl_type) - scanner = DataScan(data=dataset) - - tabular_output = scanner.get_tabular_report() - - assert isinstance(tabular_output, GT) - - -def test_col_summary_tbl(): - dataset = load_dataset(dataset="small_table") - col_summary = col_summary_tbl(dataset) - - assert isinstance(col_summary, GT) + assert isinstance(df, GT) def test_datascan_class_raises(): @@ -151,13 +52,6 @@ def test_datascan_class_raises(): DataScan(data=[1, 2, 3]) -def test_datascan_ibis_table_no_polars(): - # Mock the absence of the Polars library - with patch.dict(sys.modules, {"polars": None}): - small_table = load_dataset(dataset="small_table", tbl_type="duckdb") - DataScan(data=small_table) - - def test_compact_integer_fmt(): _compact_integer_fmt(value=0) == "0" _compact_integer_fmt(value=0.4) == "0" @@ -200,3 +94,7 @@ def test_compact_0_1_fmt(): _compact_0_1_fmt(value=0.99) == "0.99" _compact_0_1_fmt(value=0.991) == ">0.99" _compact_0_1_fmt(value=226.1) == "226" + + +if __name__ == "__main__": + pytest.main([__file__]) diff --git a/uv.lock b/uv.lock new 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"https://files.pythonhosted.org/packages/b7/1a/7e4798e9339adc931158c9d69ecc34f5e6791489d469f5e50ec15e35f458/zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931", size = 9630 }, -] From b22dacb6fa1ae115b39beb2361b2eb8c91276b90 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Wed, 19 Mar 2025 07:19:35 -0400 Subject: [PATCH 08/85] account for panic exceptions specially --- pointblank/datascan.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 0ab8ffdbe..e5510eca3 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -151,6 +151,13 @@ def _generate_profile_df(self) -> _DataProfile: implementation=self.nw_data.implementation, ) + if self.nw_data.implementation == "POLARS": + import polars as pl + + catcher = pl.exceptions.PanicException # does not inheret from `Exception` + else: + catcher = Exception + schema: Mapping[str, Any] = self.nw_data.schema for column in columns: col_data: DataFrame = self.nw_data.select(column) @@ -172,7 +179,7 @@ def _generate_profile_df(self) -> _DataProfile: try: col_profile.n_unique_vals = col_data.is_unique().sum() # set this before missing - except Exception as e: # tendancy to introduce internal panics + except catcher as e: # tendancy to introduce internal panics msg = f"Could not calculate uniqueness and missing values: {e!s}" warnings.warn(msg) else: From 06b5b3b25fe8f97c501113268bc3ddd1a4a26576 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Wed, 19 Mar 2025 18:10:08 -0400 Subject: [PATCH 09/85] add conftest quality of life --- tests/conftest.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) create mode 100644 tests/conftest.py diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 000000000..187ccec47 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,12 @@ +import os +import pytest + +if os.getenv("_PYTEST_RAISE", "0") != "0": + + @pytest.hookimpl(tryfirst=True) + def pytest_exception_interact(call): + raise call.excinfo.value + + @pytest.hookimpl(tryfirst=True) + def pytest_internalerror(excinfo): + raise excinfo.value From 906cbf37f4fceeb2fe11931036d2f6e2e57730ce Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Wed, 19 Mar 2025 21:55:55 -0400 Subject: [PATCH 10/85] Add chatlas to dev dependency --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 0762f5452..b455458d3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -63,6 +63,7 @@ docs = [ [dependency-groups] dev = [ "black", + "chatlas>=0.4.0", "duckdb>=1.1.3", "griffe==0.38.1", "hypothesis>=6.129.2", From 8cc3fe3775d70a7ea9017fe39ad6022a50cd19e2 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Wed, 19 Mar 2025 21:56:19 -0400 Subject: [PATCH 11/85] add dict pivot function --- pointblank/_utils.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/pointblank/_utils.py b/pointblank/_utils.py index 888742b5d..9107fcae7 100644 --- a/pointblank/_utils.py +++ b/pointblank/_utils.py @@ -746,3 +746,14 @@ def _format_to_float_value( formatted_vals = _get_column_of_values(gt, column_name="x", context="html") return formatted_vals[0] + + +def _pivot_to_dict(col_dict): + result_dict = {} + for col, sub_dict in col_dict.items(): + for key, value in sub_dict.items(): + # add columns fields not present + if key not in result_dict: + result_dict[key] = [None] * len(col_dict) + result_dict[key][list(col_dict.keys()).index(col)] = value + return result_dict From eb44d1744c06e07aa1e8c07f2cde5cbfcc78c209 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Wed, 19 Mar 2025 21:56:46 -0400 Subject: [PATCH 12/85] complete data exposure and partially complete html creation --- pointblank/datascan.py | 262 ++++++++++------------------------------- 1 file changed, 59 insertions(+), 203 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index e5510eca3..cf4239b58 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -1,8 +1,7 @@ from __future__ import annotations -import contextlib +import itertools import json -import warnings from importlib.metadata import version from typing import TYPE_CHECKING, Any @@ -10,10 +9,8 @@ from great_tables import GT, google_font, html, loc, style from narwhals.typing import FrameT -from pointblank._constants import SVG_ICONS_FOR_DATA_TYPES -from pointblank._datascan_utils import _compact_0_1_fmt, _compact_decimal_fmt, _round_to_sig_figs from pointblank._utils_html import _create_table_dims_html, _create_table_type_html -from pointblank.scan_profile import ColumnProfile, _DataProfile, _TypeMap +from pointblank.scan_profile import ColumnProfile, _DataProfile, _Metadata, _TypeMap if TYPE_CHECKING: from collections.abc import Mapping @@ -150,20 +147,14 @@ def _generate_profile_df(self) -> _DataProfile: columns=columns, implementation=self.nw_data.implementation, ) - - if self.nw_data.implementation == "POLARS": - import polars as pl - - catcher = pl.exceptions.PanicException # does not inheret from `Exception` - else: - catcher = Exception - schema: Mapping[str, Any] = self.nw_data.schema for column in columns: col_data: DataFrame = self.nw_data.select(column) ## Handle dtyping: native_dtype = schema[column] + if _TypeMap.is_illegal(native_dtype): + continue try: prof: type[ColumnProfile] = _TypeMap.fetch_profile(native_dtype) except NotImplementedError: @@ -177,43 +168,20 @@ def _generate_profile_df(self) -> _DataProfile: raw_vals: list[Any] = col_data.drop_nulls().head(5).to_dict()[column].to_list() col_profile.sample_data = [str(x) for x in raw_vals] - try: - col_profile.n_unique_vals = col_data.is_unique().sum() # set this before missing - except catcher as e: # tendancy to introduce internal panics - msg = f"Could not calculate uniqueness and missing values: {e!s}" - warnings.warn(msg) - else: - col_profile.n_missing_vals = col_data.null_count().item() - # TODO: These should probably live on the class - col_profile.f_missing_vals = _round_to_sig_figs( - col_profile.n_missing_vals / row_count, 3 - ) - col_profile.f_unique_vals = _round_to_sig_figs( - col_profile.n_unique_vals / row_count, 3 - ) + # TODO: put this on the ColumnProfile + col_profile.n_unique_vals = col_data.is_unique().sum() # set this before missing + col_profile.n_missing_vals = col_data.null_count().item() sub_profile: ColumnProfile = col_profile.spawn_profile(prof) - with contextlib.suppress(NotImplementedError): - sub_profile.calc_stats(col_data) + sub_profile.calc_stats(col_data) profile.column_profiles.append(sub_profile) return profile - def _get_column_data(self, column: str) -> dict | None: - column_data = self.profile["columns"] - - # Find the column in the column data and return the - for col in column_data: - if col["column_name"] == column: - return col - - # If the column is not found, return None - return None - @property def summary_data(self) -> DataFrame: # TODO: Think this type hint is wrong - return self.profile.as_dataframe() + return self.profile.as_dataframe().to_native() def get_tabular_report(self) -> GT: # Create the label, table type, and thresholds HTML fragments @@ -235,93 +203,71 @@ def get_tabular_report(self) -> GT: "" ) - ## Construct HTML Rows: - stats_list: list[dict] = [col._proc_as_html() for col in self.profile.column_profiles] + metadata = _Metadata( + row_count=self.profile.row_count, implementation=self.profile.implementation + ) + + dataframes = [] + for col in self.profile.column_profiles: + col_df = col.to_df_row(metadata=metadata) + dataframes.append(col_df) - raise NotImplementedError("construct using implementation") + concatted = nw.concat(dataframes, how="diagonal").to_native() # TODO: Ensure width is 905px in total + all_stat_cols = set( + itertools.chain.from_iterable( + profile.fetch_stat_cols() for profile in self.profile.column_profiles + ) + ) + gt_tbl = ( - GT() + GT(concatted) .tab_header(title=html(combined_title)) - .cols_align(align="right", columns=stat_columns) + # .cols_align(align="right", columns=stat_columns) .opt_table_font(font=google_font("IBM Plex Sans")) .opt_align_table_header(align="left") .tab_style( style=style.text(font=google_font("IBM Plex Mono")), locations=loc.body(), ) - .tab_style( - style=style.text(size="10px"), - locations=loc.body(columns=stat_columns), - ) - .tab_style( - style=style.text(size="14px"), - locations=loc.body(columns="column_number"), - ) + .cols_move_to_start(["colname", "coltype", "icon"]) + # .tab_style( + # style=style.text(size="10px"), + # locations=loc.body(columns=stat_columns), + # ) .tab_style( style=style.text(size="12px"), - locations=loc.body(columns="column_name"), - ) - .tab_style( - style=style.css("white-space: pre; overflow-x: visible;"), - locations=loc.body(columns="min"), - ) - .tab_style( - style=style.borders(sides="left", color="#D3D3D3", style="solid"), - locations=loc.body(columns=["missing_vals", "mean", "iqr"]), - ) - .tab_style( - style=style.borders(sides="left", color="#E5E5E5", style="dashed"), - locations=loc.body( - columns=["std_dev", "min", "p05", "q_1", "med", "q_3", "p95", "max"] - ), - ) - .tab_style( - style=style.borders(sides="left", style="none"), - locations=loc.body( - columns=["p05", "q_1", "med", "q_3", "p95", "max"], - rows=self._stats_list, - ), - ) - .tab_style( - style=style.fill(color="#FCFCFC"), - locations=loc.body(columns=["missing_vals", "unique_vals", "iqr"]), - ) - .cols_label( - column_number="", - icon="", - column_name="Column", - missing_vals="NAs", - unique_vals="Uniq.", - mean="Mean", - std_dev="S.D.", - min="Min", - p05="P05", - q_1="Q1", - med="Med", - q_3="Q3", - p95="P95", - max="Max", - iqr="IQR", + locations=loc.body(columns="colname"), ) + # .tab_style( + # style=style.css("white-space: pre; overflow-x: visible;"), + # locations=loc.body(columns="min"), + # ) + # .tab_style( + # style=style.borders(sides="left", color="#D3D3D3", style="solid"), + # locations=loc.body(columns=["n_missing_vals", "mean", "iqr"]), + # ) + # .tab_style( + # style=style.borders(sides="left", color="#E5E5E5", style="dashed"), + # locations=loc.body( + # columns=["std", "min", "p05", "q_1", "med", "q_3", "p95", "max"] + # ), + # ) + # .tab_style( + # style=style.borders(sides="left", style="none"), + # locations=loc.body( + # columns=["p05", "q_1", "med", "q_3", "p95", "max"], + # rows=self._stats_list, + # ), + # ) + # .tab_style( + # style=style.fill(color="#FCFCFC"), + # locations=loc.body(columns=["n_missing_vals", "n_unique_vals", "iqr"]), + # ) .cols_width( - column_number="40px", - icon="35px", - column_name="200px", - missing_vals="50px", - unique_vals="50px", - mean="50px", - std_dev="50px", - min="50px", - p05="50px", - q_1="50px", - med="50px", - q_3="50px", - p95="50px", - max="50px", - iqr="50px", # 875 px total + icon="35px", colname="200px", **{stat_col: "50px" for stat_col in all_stat_cols} ) ) @@ -332,6 +278,7 @@ def get_tabular_report(self) -> GT: return gt_tbl def to_dict(self) -> dict: + raise NotImplementedError return self.profile def to_json(self) -> str: @@ -429,94 +376,3 @@ def col_summary_tbl(data: FrameT | Any, tbl_name: str | None = None) -> GT: scanner = DataScan(data=data, tbl_name=tbl_name) return scanner.get_tabular_report() - - -def _process_boolean_column_data(column_data: dict) -> dict: - column_number = column_data["column_number"] - column_name = column_data["column_name"] - column_type = column_data["column_type"] - - column_name_and_type = ( - f"
{column_name}
" - f"
{column_type}
" - ) - - # Get the Missing and Unique value counts and fractions - missing_vals = column_data["n_missing_values"] - missing_vals_frac = _compact_decimal_fmt(column_data["f_missing_values"]) - missing_vals_str = f"{missing_vals}
{missing_vals_frac}" - - # Get the fractions of True and False values - f_true_values = column_data["statistics"]["boolean"]["f_true_values"] - f_false_values = column_data["statistics"]["boolean"]["f_false_values"] - - true_vals_frac_fmt = _compact_0_1_fmt(f_true_values) - false_vals_frac_fmt = _compact_0_1_fmt(f_false_values) - - # Create an HTML string that combines fractions for the True and False values - true_false_vals_str = f"T{true_vals_frac_fmt}
F{false_vals_frac_fmt}" - - # unique_vals_str = f"{unique_vals}
{unique_vals_frac}" - - # Create a single dictionary with the statistics for the column - stats_dict = { - "column_number": column_number, - "icon": SVG_ICONS_FOR_DATA_TYPES["boolean"], - "column_name": column_name_and_type, - "missing_vals": missing_vals_str, - "unique_vals": true_false_vals_str, - "mean": "—", - "std_dev": "—", - "min": "—", - "p05": "—", - "q_1": "—", - "med": "—", - "q_3": "—", - "p95": "—", - "max": "—", - "iqr": "—", - } - - return stats_dict - - -def _process_other_column_data(column_data: dict) -> dict: - raise - column_number = column_data["column_number"] - column_name = column_data["column_name"] - column_type = column_data["column_type"] - - column_name_and_type = ( - f"
{column_name}
" - f"
{column_type}
" - ) - - # Get the Missing and Unique value counts and fractions - missing_vals = column_data["n_missing_values"] - unique_vals = column_data["n_unique_values"] - missing_vals_frac = _compact_decimal_fmt(column_data["f_missing_values"]) - unique_vals_frac = _compact_decimal_fmt(column_data["f_unique_values"]) - - missing_vals_str = f"{missing_vals}
{missing_vals_frac}" - unique_vals_str = f"{unique_vals}
{unique_vals_frac}" - - # Create a single dictionary with the statistics for the column - stats_dict = { - "column_number": column_number, - "icon": SVG_ICONS_FOR_DATA_TYPES["object"], - "column_name": column_name_and_type, - "missing_vals": missing_vals_str, - "unique_vals": unique_vals_str, - "mean": "—", - "std_dev": "—", - "min": "—", - "p05": "—", - "q_1": "—", - "med": "—", - "q_3": "—", - "p95": "—", - "max": "—", - "iqr": "—", - } - - return stats_dict From 5f9db0eb8972a685339b14bf16d1ed39a0852867 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Wed, 19 Mar 2025 21:57:00 -0400 Subject: [PATCH 13/85] ensure debugger stops in conftest --- tests/conftest.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/tests/conftest.py b/tests/conftest.py index 187ccec47..6776c1228 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,7 +1,14 @@ -import os +# conftest.py +import sys import pytest -if os.getenv("_PYTEST_RAISE", "0") != "0": + +def is_debugging(): + return "debugpy" in sys.modules + + +# enable_stop_on_exceptions if the debugger is running during a test +if is_debugging(): @pytest.hookimpl(tryfirst=True) def pytest_exception_interact(call): From f42de7b6771ff99c57660c83b9f602f2a29171eb Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Wed, 19 Mar 2025 21:57:17 -0400 Subject: [PATCH 14/85] add more parametric testing --- tests/test_datascan.py | 32 ++++++++++++++++++++++++++------ 1 file changed, 26 insertions(+), 6 deletions(-) diff --git a/tests/test_datascan.py b/tests/test_datascan.py index 2df5d18bb..9f301fccd 100644 --- a/tests/test_datascan.py +++ b/tests/test_datascan.py @@ -1,15 +1,23 @@ import pytest import narwhals as nw -from hypothesis import given +from hypothesis import given, settings import polars.testing.parametric as pt from great_tables import GT +import polars as pl from pointblank.datascan import DataScan, col_summary_tbl from pointblank._datascan_utils import _compact_0_1_fmt, _compact_decimal_fmt, _compact_integer_fmt -@given(df=pt.dataframes(min_size=5)) +## Setup Strategies: +happy_path_df = pt.dataframes( + min_size=5, allowed_dtypes=[pl.Int64, pl.Float64, pl.String, pl.Categorical] +) + + +@given(df=happy_path_df) +@settings(max_examples=5) def test_datascan_class_parametric(df) -> None: scanner = DataScan(data=df) @@ -18,12 +26,23 @@ def test_datascan_class_parametric(df) -> None: summary_res = scanner.summary_data ## Go through checks: - cols = summary_res.select("colname").to_polars().to_series().to_list() + cols = summary_res.select("colname").to_series().to_list() - msg = "cols are not the same" + msg = "cols must be the same" df_cols = df_nw.columns assert set(cols) == set(df_cols), msg + msg = "return type must be same as input" + assert isinstance(summary_res, type(df)), msg + + msg = "did not return correct amount of summary rows" + assert len(summary_res) == len(cols) # only for happy path + + msg = "contains sample data" + assert "sample_data" in summary_res.columns + + # TODO: Should contain many more cases + @given(df=pt.dataframes(min_size=5)) def test_datascan_json_output(df): @@ -34,11 +53,12 @@ def test_datascan_json_output(df): assert isinstance(profile_json, str) -@given(df=pt.dataframes(min_size=5)) +@given(df=happy_path_df) +@settings(max_examples=5) def test_col_summary_tbl(df): col_summary = col_summary_tbl(df) - assert isinstance(df, GT) + assert isinstance(col_summary, GT) def test_datascan_class_raises(): From 94e40b1c509828e999b5651cd8a2d8c00c9e12ea Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Wed, 19 Mar 2025 21:57:41 -0400 Subject: [PATCH 15/85] consolidate html creation --- pointblank/scan_profile.py | 303 +++++++++++++++++-------------------- 1 file changed, 143 insertions(+), 160 deletions(-) diff --git a/pointblank/scan_profile.py b/pointblank/scan_profile.py index 5ffdd4d9c..79013381c 100644 --- a/pointblank/scan_profile.py +++ b/pointblank/scan_profile.py @@ -5,25 +5,37 @@ from collections import defaultdict from dataclasses import dataclass from enum import Enum -from typing import TYPE_CHECKING, Any +from typing import TYPE_CHECKING, Any, TypedDict import narwhals as nw from pointblank._constants import SVG_ICONS_FOR_DATA_TYPES -from pointblank._datascan_utils import _compact_decimal_fmt, _compact_integer_fmt +from pointblank._utils import _pivot_to_dict if TYPE_CHECKING: from narwhals.dataframe import DataFrame from narwhals.series import Series +## Columns that pose risks for exceptions or don't have handlers +ILLEGAL_TYPES = ("struct",) + + +class _Metadata(TypedDict): + row_count: int + implementation: nw.Implementation + + class _TypeMap(Enum): # ! ordered - STRUCT = ("struct",) NUMERIC = ("int", "float") STRING = ("string", "categorical") DATE = ("date",) BOOL = ("bool",) + @classmethod + def is_illegal(cls, dtype: Any) -> bool: + return any(ind for ind in ILLEGAL_TYPES if ind in str(dtype).lower()) + @classmethod def fetch_prof_map(cls) -> dict[_TypeMap, type[ColumnProfile]]: default = defaultdict(lambda: ColumnProfile) @@ -45,13 +57,27 @@ def fetch_profile(cls, dtype: Any) -> type[ColumnProfile]: return cls.fetch_prof_map()[_type] raise NotImplementedError # pragma: no-cover + @classmethod + def fetch_icon(cls, _type: _TypeMap) -> str: + icon_map = { + cls.NUMERIC: "numeric", + cls.STRING: "string", + cls.DATE: "date", + cls.BOOL: "boolean", + } + try: + icon_key = icon_map[_type] + except KeyError: + icon_key = "object" + return SVG_ICONS_FOR_DATA_TYPES[icon_key] + class _ColumnProfileABC(ABC): - @abstractmethod - def calc_stats(self, data: DataFrame) -> None: ... + @abstractmethod # TODO: This should be a class variable + def fetch_stat_cols(self) -> tuple[str, ...]: ... @abstractmethod - def _proc_as_html(self) -> dict: ... # TODO: type hint this + def calc_stats(self, data: DataFrame) -> None: ... @dataclass @@ -59,6 +85,67 @@ class ColumnProfile(_ColumnProfileABC): colname: str coltype: str sample_data: Any | None = None + # TODO: Solve the -1 problem generally + # must exist in the initializer + _n_missing_vals: int = -1 + _n_unique_vals: int = -1 + # _type: _TypeMap | None = None # TODO: This should be a class variable + + def fetch_stat_cols(self) -> tuple[str, ...]: + return ("n_missing_vals", "n_unique_vals") + + def to_df_row(self, metadata: _Metadata, *, format_html: bool = False): + all_stats = self._fetch_public_attrs() + + # add properties + # TODO: this won't hold long term + props = { + "n_missing_vals": self.n_missing_vals, + "n_unique_vals": self.n_unique_vals, + } + all_stats.update(props) + + # add icon + icon: str = _TypeMap.fetch_icon(self._type) + + flat_stats = {k: v for k, v in all_stats.items() if k != "sample_data"} | {"icon": icon} + + sample_data: str = ", ".join(all_stats["sample_data"]) + + # cast as primitive types: + primitive_types = (int, float, str, bool) + valid_stats = {} + for col, val in flat_stats.items(): + if val is None: + continue # None values have variable defaults which confuse the concat + is_primitive: bool = any(ind for ind in primitive_types if isinstance(val, ind)) + if is_primitive: + valid_stats[col] = val + continue + + # we can stringify these only. + # designed to avoid NaN, nan, inf, etc. which may be added later. + # these values will create inadvertent mixed types + if isinstance(val, nw.dtypes.DType): + valid_stats[col] = str(val) + continue + + valid_stats[col] = val + + df = nw.from_dict(data=flat_stats, backend=metadata["implementation"]).with_columns( + sample_data=nw.lit(sample_data), + ) + + if not format_html: + return df + + raise + + def calc_frac_missing_vals(self, row_count: int) -> float: + return self.n_missing_vals / row_count + + def calc_frac_unique_vals(self, row_count: int) -> float: + return self.n_unique_vals / row_count @property def n_missing_vals(self) -> int: @@ -83,7 +170,13 @@ def _fetch_public_attrs(self) -> dict: # TODO: generic type hint # Get all attributes, including properties attrs = {} for k, v in vars(self.__class__).items(): - if isinstance(v, property) and not k.startswith("_"): + if callable(v): + continue + if isinstance(v, property): + kprivate: str = "_" + k + attrs[kprivate] = getattr(self, k) + continue + if not k.startswith("_"): attrs[k] = getattr(self, k) # Add non-private instance attributes attrs.update({k: v for k, v in vars(self).items() if not k.startswith("_")}) @@ -126,33 +219,15 @@ def _calc_general_sats(self, col_data: DataFrame): def calc_stats(self, data: DataFrame) -> None: # pragma: no-cover raise NotImplementedError - def _proc_as_html(self): - missing_vals_str = f"{self.n_missing_vals}
{self.f_missing_vals}" - unique_vals_str = f"{self.n_unique_vals}
{self.f_unique_vals}" - - # Create a single dictionary with the statistics for the column - # TODO: This should be declaritively typed - return { - "icon": SVG_ICONS_FOR_DATA_TYPES["object"], - "column_name": _fmt_col_header(self.colname, self.coltype), - "missing_vals": missing_vals_str, - "unique_vals": unique_vals_str, - "mean": "—", - "std_dev": "—", - "min": "—", - "p05": "—", - "q_1": "—", - "med": "—", - "q_3": "—", - "p95": "—", - "max": "—", - "iqr": "—", - } - class _DateProfile(ColumnProfile): min: str | None = None max: str | None = None + _type: _TypeMap = _TypeMap.DATE + + def fetch_stat_cols(self) -> tuple[str, ...]: + date_stats = ("min", "max") + return date_stats + super().fetch_stat_cols() def calc_stats(self, data: DataFrame): res = data.select(_min=nw.all().min(), _max=nw.all().max()).to_dict() @@ -161,65 +236,15 @@ def calc_stats(self, data: DataFrame): self.min = str(res["_min"].item()) self.max = str(res["_max"].item()) - def _proc_as_html(self): - column_number = column_data["column_number"] - - column_name = column_data["column_name"] - column_type = column_data["column_type"] - - long_column_type = len(column_type) > 22 - - if long_column_type: - column_type_style = ( - "font-size: 7.5px; color: gray; margin-top: 3px; margin-bottom: 2px;" - ) - else: - column_type_style = "font-size: 11px; color: gray;" - - column_name_and_type = ( - f"
{column_name}
" - f"
{column_type}
" - ) - - # Get the Missing and Unique value counts and fractions - missing_vals = column_data["n_missing_values"] - unique_vals = column_data["n_unique_values"] - missing_vals_frac = _compact_decimal_fmt(column_data["f_missing_values"]) - unique_vals_frac = _compact_decimal_fmt(column_data["f_unique_values"]) - - missing_vals_str = f"{missing_vals}
{missing_vals_frac}" - unique_vals_str = f"{unique_vals}
{unique_vals_frac}" - - # Get the min and max date - min_date = column_data["statistics"]["datetime"]["min"] - max_date = column_data["statistics"]["datetime"]["max"] - - # Format the dates so that they don't break across lines - min_max_date_str = f" {min_date} – {max_date}" - - # Create a single dictionary with the statistics for the column - return { - "column_number": column_number, - "icon": SVG_ICONS_FOR_DATA_TYPES["date"], - "column_name": column_name_and_type, - "missing_vals": missing_vals_str, - "unique_vals": unique_vals_str, - "mean": "—", - "std_dev": "—", - "min": min_max_date_str, - "p05": "", - "q_1": "", - "med": "", - "q_3": "", - "p95": "", - "max": "", - "iqr": "—", - } - class _BoolProfile(ColumnProfile): ntrue: int = -1 nfalse: int = -1 + _type: _TypeMap = _TypeMap.BOOL + + def fetch_stat_cols(self) -> tuple[str, ...]: + bool_stat_cols = ("ntrue", "nfalse") + return bool_stat_cols + super().fetch_stat_cols() def calc_stats(self, data: DataFrame): self.ntrue: int = int(data.select(nw.all().sum()).item()) @@ -227,9 +252,6 @@ def calc_stats(self, data: DataFrame): row_count = len(data) self.nfalse = row_count - self.ntrue - def _proc_as_html(self): - raise NotImplementedError - class _StringProfile(ColumnProfile): mean: float = -1 @@ -242,15 +264,17 @@ class _StringProfile(ColumnProfile): q_3: float = -1 p95: float = -1 iqr: float = -1 + _type: _TypeMap = _TypeMap.STRING + + def fetch_stat_cols(self) -> tuple[str, ...]: + str_stat_cols = ("mean", "std", "min", "max", "p05", "q_1", "med", "q_3", "p95", "iqr") + return super().fetch_stat_cols() + str_stat_cols def calc_stats(self, data: DataFrame): # cast as string to avoid ambiguity in cat/enum/etc. col_str_len_data = data.select(nw.all().cast(nw.String).str.len_chars()) self._calc_general_sats(col_str_len_data) - def _proc_as_html(self): - raise NotImplementedError - class _NumericProfile(ColumnProfile): n_negative_vals: int = 0 @@ -269,62 +293,32 @@ class _NumericProfile(ColumnProfile): q_3: float = -1 p95: float = -1 iqr: float = -1 + _type: _TypeMap = _TypeMap.NUMERIC + + def fetch_stat_cols(self): + numeric_stat_cols = ( + "n_negative_vals", + "f_negative_vals", + "n_zero_vals", + "f_zero_vals", + "n_positive_vals", + "f_positive_vals", + "mean", + "std", + "min", + "max", + "p05", + "q_1", + "med", + "q_3", + "p95", + "iqr", + ) + return super().fetch_stat_cols() + numeric_stat_cols def calc_stats(self, data: DataFrame): self._calc_general_sats(data) - def _proc_as_html(self): - icon = "numeric" # TODO: should this be here - - missing_vals_frac = _compact_decimal_fmt(self.f_missing_vals) - unique_vals_frac = _compact_decimal_fmt(self.f_unique_vals) - - missing_vals_str = f"{self.n_missing_vals}
{missing_vals_frac}" - unique_vals_str = f"{self.n_unique_vals}
{unique_vals_frac}" - - # Get the descriptive and quantile statistics - descriptive_stats = column_data["statistics"][key]["descriptive"] - quantile_stats = column_data["statistics"][key]["quantiles"] - - # Get all values from the descriptive and quantile stats into a single list - quantile_stats_vals = [v[1] for v in quantile_stats.items()] - - # Determine if the quantile stats are all integerlike - integerlike = [] - - # Determine if the quantile stats are integerlike - for val in quantile_stats_vals: - # Check if a quantile value is a number and then if it is intergerlike - if not isinstance(val, (int, float)): - continue - else: - integerlike.append(val % 1 == 0) - quantile_vals_integerlike = all(integerlike) - - # Determine the formatter to use for the quantile values - if quantile_vals_integerlike: - q_formatter = _compact_integer_fmt - else: - q_formatter = _compact_decimal_fmt - - # Format the descriptive statistics (mean and standard deviation) - for key, value in descriptive_stats.items(): - descriptive_stats[key] = _compact_decimal_fmt(value=value) - - # Format the quantile statistics - for key, value in quantile_stats.items(): - quantile_stats[key] = q_formatter(value=value) - - # Create a single dictionary with the statistics for the column - return { - "icon": SVG_ICONS_FOR_DATA_TYPES[icon], - "column_name": column_name_and_type, - "missing_vals": missing_vals_str, - "unique_vals": unique_vals_str, - **descriptive_stats, - **quantile_stats, - } - class _DataProfile: # TODO: feels redundant and weird def __init__( @@ -344,25 +338,14 @@ def as_dataframe(self) -> DataFrame: ## Convert list[dict] to dict[list]: ## We remove nested types like `sample_data`. ## We stringify all non-primitive types. - from collections import defaultdict + + # TODO: I think this is overly complex col_dict = {col.colname: col._fetch_public_attrs() for col in self.column_profiles} - nested_types = ("sample_data",) - prim_types = (int, str, float) - for col in col_dict: - for nested_type in nested_types: - col_dict[col].pop(nested_type, None) - for key in list(col_dict[col].keys()): - if not isinstance(col_dict[col][key], prim_types): - col_dict[col][key] = str(col_dict[col][key]) - - dict_of_lists = defaultdict(list) - for col_data in col_dict.values(): - for key, value in col_data.items(): - dict_of_lists[key].append(value) - dict_of_lists = dict(dict_of_lists) - - return nw.from_dict(dict_of_lists, backend=self.implementation) + + result_dict = _pivot_to_dict(col_dict) + + return nw.from_dict(result_dict, backend=self.implementation) def __repr__(self) -> str: # pragma: no cover return f"<_DataProfile(table_name={self.table_name}, row_count={self.row_count}, columns={self.columns})>" From 8589f4d2c2ebcbd85bc048ce851089eae92cebac Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Thu, 20 Mar 2025 21:47:34 -0400 Subject: [PATCH 16/85] make table column generation dynamic with analysis --- pointblank/datascan.py | 6 +++++- pointblank/scan_profile.py | 2 +- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index cf4239b58..963b2636a 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -212,6 +212,7 @@ def get_tabular_report(self) -> GT: col_df = col.to_df_row(metadata=metadata) dataframes.append(col_df) + # TODO : Generic type this concatted = nw.concat(dataframes, how="diagonal").to_native() # TODO: Ensure width is 905px in total @@ -222,6 +223,9 @@ def get_tabular_report(self) -> GT: ) ) + # find what stat cols were used in the analysis + present_stat_cols: set[str] = set(concatted.columns) & set(all_stat_cols) + gt_tbl = ( GT(concatted) .tab_header(title=html(combined_title)) @@ -267,7 +271,7 @@ def get_tabular_report(self) -> GT: # locations=loc.body(columns=["n_missing_vals", "n_unique_vals", "iqr"]), # ) .cols_width( - icon="35px", colname="200px", **{stat_col: "50px" for stat_col in all_stat_cols} + icon="35px", colname="200px", **{stat_col: "50px" for stat_col in present_stat_cols} ) ) diff --git a/pointblank/scan_profile.py b/pointblank/scan_profile.py index 79013381c..775d05a78 100644 --- a/pointblank/scan_profile.py +++ b/pointblank/scan_profile.py @@ -132,7 +132,7 @@ def to_df_row(self, metadata: _Metadata, *, format_html: bool = False): valid_stats[col] = val - df = nw.from_dict(data=flat_stats, backend=metadata["implementation"]).with_columns( + df = nw.from_dict(data=valid_stats, backend=metadata["implementation"]).with_columns( sample_data=nw.lit(sample_data), ) From ff41c601052f131642e05d9379de2161097f8587 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 23 Mar 2025 16:03:57 -0400 Subject: [PATCH 17/85] type hint mapping instead of dict on utils --- pointblank/_utils.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/pointblank/_utils.py b/pointblank/_utils.py index 9107fcae7..f7d52f321 100644 --- a/pointblank/_utils.py +++ b/pointblank/_utils.py @@ -12,6 +12,8 @@ from pointblank._constants import ASSERTION_TYPE_METHOD_MAP, GENERAL_COLUMN_TYPES if TYPE_CHECKING: + from collections.abc import Mapping + from pointblank._typing import AbsoluteBounds, Tolerance @@ -748,7 +750,7 @@ def _format_to_float_value( return formatted_vals[0] -def _pivot_to_dict(col_dict): +def _pivot_to_dict(col_dict: Mapping[str, Any]): # TODO : Type hint and unit test result_dict = {} for col, sub_dict in col_dict.items(): for key, value in sub_dict.items(): From a95b42a33351fe7d91e7a68b47685758c5c60d48 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 23 Mar 2025 16:04:29 -0400 Subject: [PATCH 18/85] consolidate dataframe creation and html --- pointblank/datascan.py | 116 ++++++++++++++++++++--------------------- 1 file changed, 56 insertions(+), 60 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 963b2636a..8b1fab32e 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -1,6 +1,5 @@ from __future__ import annotations -import itertools import json from importlib.metadata import version from typing import TYPE_CHECKING, Any @@ -10,7 +9,8 @@ from narwhals.typing import FrameT from pointblank._utils_html import _create_table_dims_html, _create_table_type_html -from pointblank.scan_profile import ColumnProfile, _DataProfile, _Metadata, _TypeMap +from pointblank.scan_profile import ColumnProfile, _DataProfile, _TypeMap +from pointblank.scan_profile_stats import COLUMN_ORDER_REGISTRY if TYPE_CHECKING: from collections.abc import Mapping @@ -18,6 +18,8 @@ from narwhals.dataframe import DataFrame from narwhals.typing import IntoDataFrame + from pointblank.scan_profile_stats import StatGroup + __all__ = ["DataScan", "col_summary_tbl"] @@ -168,9 +170,7 @@ def _generate_profile_df(self) -> _DataProfile: raw_vals: list[Any] = col_data.drop_nulls().head(5).to_dict()[column].to_list() col_profile.sample_data = [str(x) for x in raw_vals] - # TODO: put this on the ColumnProfile - col_profile.n_unique_vals = col_data.is_unique().sum() # set this before missing - col_profile.n_missing_vals = col_data.null_count().item() + col_profile.calc_stats(col_data) sub_profile: ColumnProfile = col_profile.spawn_profile(prof) sub_profile.calc_stats(col_data) @@ -181,7 +181,7 @@ def _generate_profile_df(self) -> _DataProfile: @property def summary_data(self) -> DataFrame: # TODO: Think this type hint is wrong - return self.profile.as_dataframe().to_native() + return self.profile.as_dataframe() def get_tabular_report(self) -> GT: # Create the label, table type, and thresholds HTML fragments @@ -203,73 +203,72 @@ def get_tabular_report(self) -> GT: "" ) - metadata = _Metadata( - row_count=self.profile.row_count, implementation=self.profile.implementation - ) + # TODO: Ensure width is 905px in total - dataframes = [] - for col in self.profile.column_profiles: - col_df = col.to_df_row(metadata=metadata) - dataframes.append(col_df) + data = self.profile.as_dataframe(format_html=True) + # TODO: Remove all null columns - # TODO : Generic type this - concatted = nw.concat(dataframes, how="diagonal").to_native() + # find what stat cols were used in the analysis + non_stat_cols = ("icon", "colname", "coltype") # TODO: need a better place for this + present_stat_cols: set[str] = set(data.columns) - set(non_stat_cols) - # TODO: Ensure width is 905px in total + target_order: list[str] = list(non_stat_cols) + right_border_cols: list[str] = [non_stat_cols[-1]] + last_group: StatGroup = COLUMN_ORDER_REGISTRY[0].group + for col in COLUMN_ORDER_REGISTRY: + if col.name in present_stat_cols: + cur_group: StatGroup = col.group + target_order.append(col.name) - all_stat_cols = set( - itertools.chain.from_iterable( - profile.fetch_stat_cols() for profile in self.profile.column_profiles - ) - ) + start_new_group: bool = last_group != cur_group + if start_new_group: + last_col_added = target_order[-1] + right_border_cols.append(last_col_added) - # find what stat cols were used in the analysis - present_stat_cols: set[str] = set(concatted.columns) & set(all_stat_cols) + right_border_cols.append(target_order[-1]) # add border to last stat col + + target_order.append("sample_data") + + assert set(data.columns) == set(target_order), "Internal: fields calculated have no order." gt_tbl = ( - GT(concatted) + GT(data) .tab_header(title=html(combined_title)) - # .cols_align(align="right", columns=stat_columns) + .cols_align(align="right", columns=list(present_stat_cols)) .opt_table_font(font=google_font("IBM Plex Sans")) .opt_align_table_header(align="left") .tab_style( style=style.text(font=google_font("IBM Plex Mono")), locations=loc.body(), ) - .cols_move_to_start(["colname", "coltype", "icon"]) - # .tab_style( - # style=style.text(size="10px"), - # locations=loc.body(columns=stat_columns), - # ) + ## Order + .cols_move_to_start(target_order) + ## Generic Styling + .tab_style( + style=style.text(size="10px"), + locations=loc.body(columns=list(present_stat_cols)), + ) .tab_style( style=style.text(size="12px"), locations=loc.body(columns="colname"), ) - # .tab_style( - # style=style.css("white-space: pre; overflow-x: visible;"), - # locations=loc.body(columns="min"), - # ) - # .tab_style( - # style=style.borders(sides="left", color="#D3D3D3", style="solid"), - # locations=loc.body(columns=["n_missing_vals", "mean", "iqr"]), - # ) - # .tab_style( - # style=style.borders(sides="left", color="#E5E5E5", style="dashed"), - # locations=loc.body( - # columns=["std", "min", "p05", "q_1", "med", "q_3", "p95", "max"] - # ), - # ) - # .tab_style( - # style=style.borders(sides="left", style="none"), - # locations=loc.body( - # columns=["p05", "q_1", "med", "q_3", "p95", "max"], - # rows=self._stats_list, - # ), - # ) - # .tab_style( - # style=style.fill(color="#FCFCFC"), - # locations=loc.body(columns=["n_missing_vals", "n_unique_vals", "iqr"]), - # ) + ## Borders + .tab_style( + style=style.borders(sides="right", color="#D3D3D3", style="solid"), + locations=loc.body(columns=right_border_cols), + ) + .tab_style( + style=style.borders(sides="left", color="#E5E5E5", style="dashed"), + locations=loc.body(columns=list(present_stat_cols)), + ) + .tab_style( + style=style.borders(sides="left", style="none"), + locations=loc.body( + columns=["p05", "q_1", "median", "q_3", "p95", "max"], + rows=list(present_stat_cols), + ), + ) + ## Formatting .cols_width( icon="35px", colname="200px", **{stat_col: "50px" for stat_col in present_stat_cols} ) @@ -286,12 +285,9 @@ def to_dict(self) -> dict: return self.profile def to_json(self) -> str: - profiles: list[dict] = [] - for profile in self.profile.column_profiles: - attrs = profile._fetch_public_attrs() - profiles.append(attrs) + prof_dict = self.profile.as_dataframe().to_dict(as_series=False) - return json.dumps(profiles, indent=4, default=str) + return json.dumps(prof_dict, indent=4, default=str) def save_to_json(self, output_file: str): json_string: str = self.to_json() From 3043c570dea12b571c6f48eccff187df0c9f1134 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 23 Mar 2025 16:04:45 -0400 Subject: [PATCH 19/85] add dedicated statistics profile --- pointblank/scan_profile_stats.py | 160 +++++++++++++++++++++++++++++++ 1 file changed, 160 insertions(+) create mode 100644 pointblank/scan_profile_stats.py diff --git a/pointblank/scan_profile_stats.py b/pointblank/scan_profile_stats.py new file mode 100644 index 000000000..215d3e478 --- /dev/null +++ b/pointblank/scan_profile_stats.py @@ -0,0 +1,160 @@ +from __future__ import annotations + +from abc import ABC +from dataclasses import dataclass +from enum import Enum, auto +from typing import TYPE_CHECKING, ClassVar + +import narwhals as nw + +if TYPE_CHECKING: + from typing import Any + + +class StatGroup(Enum): + DESCR = auto() + STRUCTURE = auto() + LOGIC = auto() + + +class Stat(ABC): + val: Any + name: ClassVar[str] + group: ClassVar[StatGroup] + expr: ClassVar[nw.Expr] + py_type: ClassVar[type] = float + + +@dataclass(frozen=True) +class MeanStat(Stat): + val: str + name: ClassVar[str] = "mean" + group = StatGroup.DESCR + expr: ClassVar[nw.Expr] = nw.all().mean().cast(nw.Float64) + py_type: ClassVar[type] = float + + +@dataclass(frozen=True) +class StdStat(Stat): + val: str + name: ClassVar[str] = "std" + group = StatGroup.DESCR + expr: ClassVar[nw.Expr] = nw.all().std().cast(nw.Float64) + py_type: ClassVar[type] = float + + +@dataclass(frozen=True) +class MinStat(Stat): + val: str + name: ClassVar[str] = "min" + group = StatGroup.DESCR + expr: ClassVar[nw.Expr] = nw.all().min().cast(nw.Float64) + py_type: ClassVar[type] = float + + +@dataclass(frozen=True) +class MaxStat(Stat): + val: str + name: ClassVar[str] = "max" + group = StatGroup.DESCR + expr: ClassVar[nw.Expr] = nw.all().max().cast(nw.Float64) + py_type: ClassVar[type] = float + + +@dataclass(frozen=True) +class P05Stat(Stat): + val: str + name: ClassVar[str] = "p05" + group = StatGroup.DESCR + expr: ClassVar[nw.Expr] = nw.all().quantile(0.005, interpolation="linear").cast(nw.Float64) + py_type: ClassVar[type] = float + + +@dataclass(frozen=True) +class Q1Stat(Stat): + val: str + name: ClassVar[str] = "q_1" + group = StatGroup.DESCR + expr: ClassVar[nw.Expr] = nw.all().quantile(0.25, interpolation="linear").cast(nw.Float64) + py_type: ClassVar[type] = float + + +@dataclass(frozen=True) +class MedianStat(Stat): + val: str + name: ClassVar[str] = "median" + group = StatGroup.DESCR + expr: ClassVar[nw.Expr] = nw.all().median().cast(nw.Float64) + py_type: ClassVar[type] = float + + +@dataclass(frozen=True) +class Q3Stat(Stat): + val: str + name: ClassVar[str] = "q_3" + group = StatGroup.DESCR + expr: ClassVar[nw.Expr] = nw.all().quantile(0.75, interpolation="linear").cast(nw.Float64) + py_type: ClassVar[type] = float + + +@dataclass(frozen=True) +class P95Stat(Stat): + val: str + name: ClassVar[str] = "p95" + group = StatGroup.DESCR + expr: ClassVar[nw.Expr] = nw.all().quantile(0.95, interpolation="linear").cast(nw.Float64) + py_type: ClassVar[type] = float + + +@dataclass(frozen=True) +class NTrue(Stat): + val: int + name: ClassVar[str] = "n_true" + group = StatGroup.LOGIC + expr: ClassVar[nw.Expr] = nw.all().sum().cast(nw.Boolean) + py_type: ClassVar[type] = int + + +@dataclass(frozen=True) +class NFalse(Stat): + val: int + name: ClassVar[str] = "n_false" + group = StatGroup.LOGIC + # TODO: I don't think this is valid + expr: ClassVar[nw.Expr] = ~nw.all().sum().cast(nw.Boolean) + py_type: ClassVar[type] = int + + +@dataclass(frozen=True) +class NMissing(Stat): + val: int + name: ClassVar[str] = "n_missing" + group = StatGroup.STRUCTURE + expr: ClassVar[nw.Expr] = nw.all().null_count() + py_type: ClassVar[type] = int + + +@dataclass(frozen=True) +class NUnique(Stat): + val: int + name: ClassVar[str] = "n_unique" + group = StatGroup.STRUCTURE + expr: ClassVar[nw.Expr] = nw.all().n_unique() + py_type: ClassVar[type] = int + + +COLUMN_ORDER_REGISTRY: tuple[type[Stat], ...] = ( + NUnique, + NMissing, + MinStat, + MaxStat, + MeanStat, + StdStat, + P05Stat, + Q1Stat, + MedianStat, + Q3Stat, + P95Stat, + NTrue, + NFalse, +) From e94a6a38670d321c626f1105799a2cc56368396f Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 23 Mar 2025 16:05:00 -0400 Subject: [PATCH 20/85] implement stat profile classes --- pointblank/scan_profile.py | 356 ++++++++++++++----------------------- 1 file changed, 135 insertions(+), 221 deletions(-) diff --git a/pointblank/scan_profile.py b/pointblank/scan_profile.py index 775d05a78..49781fec7 100644 --- a/pointblank/scan_profile.py +++ b/pointblank/scan_profile.py @@ -1,32 +1,49 @@ from __future__ import annotations -import contextlib from abc import ABC, abstractmethod from collections import defaultdict -from dataclasses import dataclass +from collections.abc import Sequence +from dataclasses import dataclass, field from enum import Enum from typing import TYPE_CHECKING, Any, TypedDict import narwhals as nw from pointblank._constants import SVG_ICONS_FOR_DATA_TYPES -from pointblank._utils import _pivot_to_dict +from pointblank.scan_profile_stats import ( + MaxStat, + MeanStat, + MedianStat, + MinStat, + NFalse, + NMissing, + NTrue, + NUnique, + P05Stat, + P95Stat, + Q1Stat, + Q3Stat, + Stat, + StdStat, +) if TYPE_CHECKING: + from collections.abc import MutableSequence + from narwhals.dataframe import DataFrame - from narwhals.series import Series ## Columns that pose risks for exceptions or don't have handlers ILLEGAL_TYPES = ("struct",) -class _Metadata(TypedDict): +class _Metadata(TypedDict): # TODO: Need a better name row_count: int implementation: nw.Implementation -class _TypeMap(Enum): # ! ordered +class _TypeMap(Enum): # ! ordered; + # TODO: consolidate w/other stats? NUMERIC = ("int", "float") STRING = ("string", "categorical") DATE = ("date",) @@ -73,9 +90,6 @@ def fetch_icon(cls, _type: _TypeMap) -> str: class _ColumnProfileABC(ABC): - @abstractmethod # TODO: This should be a class variable - def fetch_stat_cols(self) -> tuple[str, ...]: ... - @abstractmethod def calc_stats(self, data: DataFrame) -> None: ... @@ -84,240 +98,125 @@ def calc_stats(self, data: DataFrame) -> None: ... class ColumnProfile(_ColumnProfileABC): colname: str coltype: str - sample_data: Any | None = None - # TODO: Solve the -1 problem generally - # must exist in the initializer - _n_missing_vals: int = -1 - _n_unique_vals: int = -1 - # _type: _TypeMap | None = None # TODO: This should be a class variable - - def fetch_stat_cols(self) -> tuple[str, ...]: - return ("n_missing_vals", "n_unique_vals") - - def to_df_row(self, metadata: _Metadata, *, format_html: bool = False): - all_stats = self._fetch_public_attrs() - - # add properties - # TODO: this won't hold long term - props = { - "n_missing_vals": self.n_missing_vals, - "n_unique_vals": self.n_unique_vals, - } - all_stats.update(props) - - # add icon - icon: str = _TypeMap.fetch_icon(self._type) - - flat_stats = {k: v for k, v in all_stats.items() if k != "sample_data"} | {"icon": icon} - - sample_data: str = ", ".join(all_stats["sample_data"]) - - # cast as primitive types: - primitive_types = (int, float, str, bool) - valid_stats = {} - for col, val in flat_stats.items(): - if val is None: - continue # None values have variable defaults which confuse the concat - is_primitive: bool = any(ind for ind in primitive_types if isinstance(val, ind)) - if is_primitive: - valid_stats[col] = val - continue - - # we can stringify these only. - # designed to avoid NaN, nan, inf, etc. which may be added later. - # these values will create inadvertent mixed types - if isinstance(val, nw.dtypes.DType): - valid_stats[col] = str(val) - continue - - valid_stats[col] = val - - df = nw.from_dict(data=valid_stats, backend=metadata["implementation"]).with_columns( - sample_data=nw.lit(sample_data), - ) - - if not format_html: - return df - - raise - - def calc_frac_missing_vals(self, row_count: int) -> float: - return self.n_missing_vals / row_count - - def calc_frac_unique_vals(self, row_count: int) -> float: - return self.n_unique_vals / row_count + statistics: MutableSequence[Stat] = field(default_factory=lambda: []) @property - def n_missing_vals(self) -> int: - return self._n_missing_vals - - @n_missing_vals.setter - def n_missing_vals(self, value: Any) -> None: - intified: int = int(value) - self._n_missing_vals = intified - if intified > 0 and self.n_unique_vals > 0: - self.n_unique_vals -= 1 - - @property - def n_unique_vals(self) -> int: - return self._n_unique_vals - - @n_unique_vals.setter - def n_unique_vals(self, value: Any) -> None: - self._n_unique_vals = int(value) - - def _fetch_public_attrs(self) -> dict: # TODO: generic type hint - # Get all attributes, including properties - attrs = {} - for k, v in vars(self.__class__).items(): - if callable(v): - continue - if isinstance(v, property): - kprivate: str = "_" + k - attrs[kprivate] = getattr(self, k) - continue - if not k.startswith("_"): - attrs[k] = getattr(self, k) - # Add non-private instance attributes - attrs.update({k: v for k, v in vars(self).items() if not k.startswith("_")}) - return attrs + def sample_data(self) -> Sequence[Any]: + return self._sample_data + + @sample_data.setter + def sample_data(self, value: object) -> None: + # TODO: type guard this + if value is None: + self._sample_data = [] + return + if isinstance(value, Sequence): + self._sample_data = value + return + raise NotImplementedError def spawn_profile(self, _subprofile: type[ColumnProfile]) -> ColumnProfile: + # TODO: There might be an easier way to do this built in if type[_subprofile] == type[ColumnProfile]: return self # spawn self if no subprofile - attrs = self._fetch_public_attrs() - return _subprofile(**attrs) - - def _calc_general_sats(self, col_data: DataFrame): - res: dict[str, Series[Any]] = col_data.select( - _mean=nw.all().mean(), - _std=nw.all().std(), - _min=nw.all().min(), - _max=nw.all().max(), - _p05=nw.all().quantile(0.005, interpolation="linear"), - _q_1=nw.all().quantile(0.25, interpolation="linear"), - _med=nw.all().median(), - _q_3=nw.all().quantile(0.75, interpolation="linear"), - _p95=nw.all().quantile(0.95, interpolation="linear"), - ).to_dict() - - ## Assign stats directly from the data: - for attr, series in res.items(): - public_name = attr[1:] - val: float | None = None - with contextlib.suppress(TypeError, ValueError): - val: float = float(series.item()) - setattr(self, public_name, val) - - ## Post-hoc calculations: - ## This is a space to make calculation off of the calculations - try: - self.iqr = self.q_3 - self.q_1 - except TypeError: # if quartiles could not be calculated - self.iqr = None + inst = _subprofile(coltype=self.coltype, colname=self.colname, statistics=self.statistics) + # instantiate non-initializing properties + inst.sample_data = self.sample_data + return inst - def calc_stats(self, data: DataFrame) -> None: # pragma: no-cover - raise NotImplementedError + def calc_stats(self, data: DataFrame) -> None: + summarized = data.select(_nmissing=NMissing.expr, _nunique=NUnique.expr) + + self.statistics = [ + NMissing(summarized["_nmissing"].item()), + NUnique(summarized["_nunique"].item()), + ] class _DateProfile(ColumnProfile): - min: str | None = None - max: str | None = None _type: _TypeMap = _TypeMap.DATE - def fetch_stat_cols(self) -> tuple[str, ...]: - date_stats = ("min", "max") - return date_stats + super().fetch_stat_cols() - def calc_stats(self, data: DataFrame): - res = data.select(_min=nw.all().min(), _max=nw.all().max()).to_dict() + res = data.select(_min=MinStat.expr, _max=MaxStat.expr).to_dict() - ## Pull out elements: - self.min = str(res["_min"].item()) - self.max = str(res["_max"].item()) + self.statistics: list[Stat] = [MinStat(res["_min"].item()), MaxStat(res["_max"].item())] class _BoolProfile(ColumnProfile): - ntrue: int = -1 - nfalse: int = -1 _type: _TypeMap = _TypeMap.BOOL - def fetch_stat_cols(self) -> tuple[str, ...]: - bool_stat_cols = ("ntrue", "nfalse") - return bool_stat_cols + super().fetch_stat_cols() + def calc_stats(self, data: DataFrame) -> None: + res = data.select(_ntrue=NTrue.expr, _nfalse=NFalse.expr).to_dict() - def calc_stats(self, data: DataFrame): - self.ntrue: int = int(data.select(nw.all().sum()).item()) - # TODO: document trivalence - row_count = len(data) - self.nfalse = row_count - self.ntrue + self.statistics: list[Stat] = [NTrue(res["_ntrue"].item()), NFalse(res["_nfalse"].item())] class _StringProfile(ColumnProfile): - mean: float = -1 - std: float = -1 - min: float = -1 - max: float = -1 - p05: float = -1 - q_1: float = -1 - med: float = -1 - q_3: float = -1 - p95: float = -1 - iqr: float = -1 _type: _TypeMap = _TypeMap.STRING - def fetch_stat_cols(self) -> tuple[str, ...]: - str_stat_cols = ("mean", "std", "min", "max", "p05", "q_1", "med", "q_3", "p95", "iqr") - return super().fetch_stat_cols() + str_stat_cols - def calc_stats(self, data: DataFrame): - # cast as string to avoid ambiguity in cat/enum/etc. - col_str_len_data = data.select(nw.all().cast(nw.String).str.len_chars()) - self._calc_general_sats(col_str_len_data) + str_data = data.select(nw.all().cast(nw.String).str.len_chars()) + + summarized = str_data.select( + _mean=MeanStat.expr, + _median=MedianStat.expr, + _std=StdStat.expr, + _min=MinStat.expr, + _max=MaxStat.expr, + _p05=P05Stat.expr, + _q1=Q1Stat.expr, + _q3=Q3Stat.expr, + _p95=P95Stat.expr, + ) + + res = summarized.to_dict() + + stats: list[Stat] = [] + + stats.append(MeanStat(res["_mean"].item())) + stats.append(MedianStat(res["_median"].item())) + stats.append(StdStat(res["_std"].item())) + stats.append(MinStat(res["_min"].item())) + stats.append(MaxStat(res["_max"].item())) + stats.append(P05Stat(res["_p05"].item())) + stats.append(Q1Stat(res["_q1"].item())) + stats.append(Q3Stat(res["_q3"].item())) + stats.append(P95Stat(res["_p95"].item())) + + self.statistics.extend(stats) class _NumericProfile(ColumnProfile): - n_negative_vals: int = 0 - f_negative_vals: float = 0.0 - n_zero_vals: int = 0 - f_zero_vals: float = 0.0 - n_positive_vals: int = 0 - f_positive_vals: float = 0.0 - mean: float = -1 - std: float = -1 - min: float = -1 - max: float = -1 - p05: float = -1 - q_1: float = -1 - med: float = -1 - q_3: float = -1 - p95: float = -1 - iqr: float = -1 _type: _TypeMap = _TypeMap.NUMERIC - def fetch_stat_cols(self): - numeric_stat_cols = ( - "n_negative_vals", - "f_negative_vals", - "n_zero_vals", - "f_zero_vals", - "n_positive_vals", - "f_positive_vals", - "mean", - "std", - "min", - "max", - "p05", - "q_1", - "med", - "q_3", - "p95", - "iqr", + def calc_stats(self, data: DataFrame): + summarized = data.select( + _mean=MeanStat.expr, + _median=MedianStat.expr, + _std=StdStat.expr, + _min=MinStat.expr, + _max=MaxStat.expr, + _p05=P05Stat.expr, + _q1=Q1Stat.expr, + _q3=Q3Stat.expr, + _p95=P95Stat.expr, ) - return super().fetch_stat_cols() + numeric_stat_cols - def calc_stats(self, data: DataFrame): - self._calc_general_sats(data) + res = summarized.to_dict() + + stats: list[Stat] = [] + + stats.append(MeanStat(res["_mean"].item())) + stats.append(MedianStat(res["_median"].item())) + stats.append(StdStat(res["_std"].item())) + stats.append(MinStat(res["_min"].item())) + stats.append(MaxStat(res["_max"].item())) + stats.append(P05Stat(res["_p05"].item())) + stats.append(Q1Stat(res["_q1"].item())) + stats.append(Q3Stat(res["_q3"].item())) + stats.append(P95Stat(res["_p95"].item())) + + self.statistics.extend(stats) class _DataProfile: # TODO: feels redundant and weird @@ -334,18 +233,33 @@ def __init__( self.implementation = implementation self.column_profiles: list[ColumnProfile] = [] - def as_dataframe(self) -> DataFrame: - ## Convert list[dict] to dict[list]: - ## We remove nested types like `sample_data`. - ## We stringify all non-primitive types. + def as_dataframe(self, *, format_html: bool = False) -> DataFrame: + if format_html: + formatter = str + else: + formatter = lambda val: val # noqa: E731 + + cols: list[dict] = [] + for prof in self.column_profiles: + stat_vals = {} + for stat in prof.statistics: + if isinstance(stat.val, float): + val = round(stat.val, 5) + else: + val = stat.val + + stat_vals[stat.name] = formatter(val) + + stat_vals |= {"colname": prof.colname} + stat_vals |= {"coltype": str(prof.coltype)} + stat_vals |= {"sample_data": prof.sample_data} + stat_vals |= {"icon": _TypeMap.fetch_icon(prof._type)} + cols.append(stat_vals) - # TODO: I think this is overly complex - - col_dict = {col.colname: col._fetch_public_attrs() for col in self.column_profiles} - - result_dict = _pivot_to_dict(col_dict) - - return nw.from_dict(result_dict, backend=self.implementation) + try: + return self.implementation.to_native_namespace().DataFrame(cols) + except Exception: # TODO: There's a better way to do this + raise NotImplementedError def __repr__(self) -> str: # pragma: no cover return f"<_DataProfile(table_name={self.table_name}, row_count={self.row_count}, columns={self.columns})>" From 3e6ae9504df336e6851de76341a6a30d09e72035 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 23 Mar 2025 16:05:17 -0400 Subject: [PATCH 21/85] increase random examples --- tests/test_datascan.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_datascan.py b/tests/test_datascan.py index 9f301fccd..e7997bfca 100644 --- a/tests/test_datascan.py +++ b/tests/test_datascan.py @@ -17,7 +17,6 @@ @given(df=happy_path_df) -@settings(max_examples=5) def test_datascan_class_parametric(df) -> None: scanner = DataScan(data=df) @@ -44,6 +43,7 @@ def test_datascan_class_parametric(df) -> None: # TODO: Should contain many more cases +@pytest.mark.xfail(reason="Skip this until you figure out what to do w/stats") @given(df=pt.dataframes(min_size=5)) def test_datascan_json_output(df): scanner = DataScan(data=df) @@ -117,4 +117,4 @@ def test_compact_0_1_fmt(): if __name__ == "__main__": - pytest.main([__file__]) + pytest.main([__file__, "-x"]) From b5c4c17370f27229e11d28dee2499f8fd689dcfd Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 23 Mar 2025 16:05:34 -0400 Subject: [PATCH 22/85] add possible depracation to test previoew --- tests/test_validate.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tests/test_validate.py b/tests/test_validate.py index fd661be24..1de61903d 100644 --- a/tests/test_validate.py +++ b/tests/test_validate.py @@ -5246,6 +5246,7 @@ def test_preview_fails_head_tail_exceed_limit(): preview(small_table, n_head=100, n_tail=100, limit=300) +@pytest.mark.skip("Possibly deprecated") def test_preview_no_polars_duckdb_table(): small_table = load_dataset(dataset="small_table", tbl_type="duckdb") @@ -5353,6 +5354,7 @@ def test_missing_vals_tbl_no_polars(): missing_vals_tbl(small_table) +@pytest.mark.skip(reason="Possibly depracated") def test_missing_vals_tbl_using_ibis_no_pandas(): # Mock the absence of the pandas library with patch.dict(sys.modules, {"pandas": None}): @@ -5361,6 +5363,7 @@ def test_missing_vals_tbl_using_ibis_no_pandas(): missing_vals_tbl(small_table) +@pytest.mark.skip(reason="Possibly depracated") def test_missing_vals_tbl_using_ibis_no_polars(): # Mock the absence of the polars library with patch.dict(sys.modules, {"polars": None}): From 2362c4251b711584e2a05eef7ced046c47f04bae Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 23 Mar 2025 16:09:11 -0400 Subject: [PATCH 23/85] remove df lib narwhals/ibis branch --- pointblank/_datascan_utils.py | 7 ------- 1 file changed, 7 deletions(-) diff --git a/pointblank/_datascan_utils.py b/pointblank/_datascan_utils.py index 080c42952..86ccf1107 100644 --- a/pointblank/_datascan_utils.py +++ b/pointblank/_datascan_utils.py @@ -9,13 +9,6 @@ pass -def _to_df_lib(expr: any, df_lib: str) -> any: - if df_lib == "polars": - return expr.to_polars() - else: - return expr.to_pandas() - - def _round_to_sig_figs(value: float, sig_figs: int) -> float: if value == 0: return 0 From 63e96b80e0f833dcefb511b815b351cf1006af9b Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 23 Mar 2025 18:49:48 -0400 Subject: [PATCH 24/85] add .swp to git ignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 843a8a0ee..2872d7e66 100644 --- a/.gitignore +++ b/.gitignore @@ -123,3 +123,4 @@ docs/objects.json datasets/ /*.parquet .ruff_cache +.swp From d2ecaaed59f46669d156495924240db3e299db2b Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 23 Mar 2025 18:50:09 -0400 Subject: [PATCH 25/85] inline colname/coltype gt formatting --- pointblank/datascan.py | 16 +++++++++++++++- pointblank/scan_profile.py | 21 ++++----------------- tests/test_datascan.py | 2 +- 3 files changed, 20 insertions(+), 19 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 8b1fab32e..8c7c51c16 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -231,8 +231,22 @@ def get_tabular_report(self) -> GT: assert set(data.columns) == set(target_order), "Internal: fields calculated have no order." + ## Final Formatting: + formatted_data = data.with_columns( + colname=nw.concat_str( + nw.lit( + "
" + ), + nw.col("colname"), + nw.lit("
"), + nw.col("coltype"), + nw.lit("
"), + ) + ).to_native() + + ## GT Table: gt_tbl = ( - GT(data) + GT(formatted_data) .tab_header(title=html(combined_title)) .cols_align(align="right", columns=list(present_stat_cols)) .opt_table_font(font=google_font("IBM Plex Sans")) diff --git a/pointblank/scan_profile.py b/pointblank/scan_profile.py index 49781fec7..ccd2e7900 100644 --- a/pointblank/scan_profile.py +++ b/pointblank/scan_profile.py @@ -107,18 +107,12 @@ def sample_data(self) -> Sequence[Any]: @sample_data.setter def sample_data(self, value: object) -> None: # TODO: type guard this - if value is None: - self._sample_data = [] - return if isinstance(value, Sequence): self._sample_data = value return raise NotImplementedError def spawn_profile(self, _subprofile: type[ColumnProfile]) -> ColumnProfile: - # TODO: There might be an easier way to do this built in - if type[_subprofile] == type[ColumnProfile]: - return self # spawn self if no subprofile inst = _subprofile(coltype=self.coltype, colname=self.colname, statistics=self.statistics) # instantiate non-initializing properties inst.sample_data = self.sample_data @@ -256,17 +250,10 @@ def as_dataframe(self, *, format_html: bool = False) -> DataFrame: stat_vals |= {"icon": _TypeMap.fetch_icon(prof._type)} cols.append(stat_vals) - try: - return self.implementation.to_native_namespace().DataFrame(cols) - except Exception: # TODO: There's a better way to do this - raise NotImplementedError + # TODO: This is a bad way to do this + df_native = self.implementation.to_native_namespace().DataFrame(cols) + + return nw.from_native(df_native) def __repr__(self) -> str: # pragma: no cover return f"<_DataProfile(table_name={self.table_name}, row_count={self.row_count}, columns={self.columns})>" - - -def _fmt_col_header(name: str, _type: str) -> str: - return ( - f"
{name!s}
" - f"
{_type!s}
" - ) diff --git a/tests/test_datascan.py b/tests/test_datascan.py index e7997bfca..7ab76321f 100644 --- a/tests/test_datascan.py +++ b/tests/test_datascan.py @@ -22,7 +22,7 @@ def test_datascan_class_parametric(df) -> None: df_nw = nw.from_native(df) - summary_res = scanner.summary_data + summary_res = scanner.summary_data.to_native() ## Go through checks: cols = summary_res.select("colname").to_series().to_list() From 4ff3afaa729760e72b17cc979572a280ba97dcd1 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 23 Mar 2025 19:06:06 -0400 Subject: [PATCH 26/85] resolve pytest settings --- pointblank/scan_profile.py | 2 +- tests/test_compare.py | 6 +----- tests/test_datascan.py | 1 - tests/test_schema.py | 2 -- tests/test_validate.py | 7 ++++--- 5 files changed, 6 insertions(+), 12 deletions(-) diff --git a/pointblank/scan_profile.py b/pointblank/scan_profile.py index ccd2e7900..07b12d695 100644 --- a/pointblank/scan_profile.py +++ b/pointblank/scan_profile.py @@ -110,7 +110,7 @@ def sample_data(self, value: object) -> None: if isinstance(value, Sequence): self._sample_data = value return - raise NotImplementedError + raise NotImplementedError # pragma: no cover def spawn_profile(self, _subprofile: type[ColumnProfile]) -> ColumnProfile: inst = _subprofile(coltype=self.coltype, colname=self.colname, statistics=self.statistics) diff --git a/tests/test_compare.py b/tests/test_compare.py index e4368f3e0..e1346faea 100644 --- a/tests/test_compare.py +++ b/tests/test_compare.py @@ -6,18 +6,14 @@ from hypothesis import given -@pytest.mark.skip @given( dfa=pt.dataframes(min_size=100, max_size=1_000, allow_null=False), dfb=pt.dataframes(min_size=100, max_size=1_000, allow_null=False), ) +@pytest.mark.skip(reason="Not implemented") def test_compare_basic(dfa, dfb) -> None: comp = Compare(dfa, dfb) comp.compare() raise NotImplementedError - - -if __name__ == "__main__": - pytest.main([__file__]) diff --git a/tests/test_datascan.py b/tests/test_datascan.py index 7ab76321f..0c8429c2f 100644 --- a/tests/test_datascan.py +++ b/tests/test_datascan.py @@ -43,7 +43,6 @@ def test_datascan_class_parametric(df) -> None: # TODO: Should contain many more cases -@pytest.mark.xfail(reason="Skip this until you figure out what to do w/stats") @given(df=pt.dataframes(min_size=5)) def test_datascan_json_output(df): scanner = DataScan(data=df) diff --git a/tests/test_schema.py b/tests/test_schema.py index ee939c95c..fe874dcad 100644 --- a/tests/test_schema.py +++ b/tests/test_schema.py @@ -132,7 +132,6 @@ def test_schema_from_parquet_table(tbl_parquet): assert str(type(schema.tbl)) == "" -@pytest.mark.xfail def test_schema_from_duckdb_table(): schema = Schema(tbl=load_dataset(dataset="small_table", tbl_type="duckdb")) assert schema.columns == [ @@ -221,7 +220,6 @@ def test_get_dtype_list_small_table_pl(): ] -@pytest.mark.xfail def test_get_dtype_list_small_table_duckdb(): schema = Schema(tbl=load_dataset(dataset="small_table", tbl_type="duckdb")) diff --git a/tests/test_validate.py b/tests/test_validate.py index 1de61903d..0b578193c 100644 --- a/tests/test_validate.py +++ b/tests/test_validate.py @@ -5246,7 +5246,6 @@ def test_preview_fails_head_tail_exceed_limit(): preview(small_table, n_head=100, n_tail=100, limit=300) -@pytest.mark.skip("Possibly deprecated") def test_preview_no_polars_duckdb_table(): small_table = load_dataset(dataset="small_table", tbl_type="duckdb") @@ -5354,7 +5353,6 @@ def test_missing_vals_tbl_no_polars(): missing_vals_tbl(small_table) -@pytest.mark.skip(reason="Possibly depracated") def test_missing_vals_tbl_using_ibis_no_pandas(): # Mock the absence of the pandas library with patch.dict(sys.modules, {"pandas": None}): @@ -5363,7 +5361,6 @@ def test_missing_vals_tbl_using_ibis_no_pandas(): missing_vals_tbl(small_table) -@pytest.mark.skip(reason="Possibly depracated") def test_missing_vals_tbl_using_ibis_no_polars(): # Mock the absence of the polars library with patch.dict(sys.modules, {"polars": None}): @@ -8162,3 +8159,7 @@ def test_assert_passing_example() -> None: ) passing_validation.assert_passing() + + +if __name__ == "__main__": + test_missing_vals_tbl_no_polars() From c6339ec9039fc6b44c5e702d1ed06c2473c51b82 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 23 Mar 2025 19:49:01 -0400 Subject: [PATCH 27/85] Fix left borders --- pointblank/datascan.py | 6 +- uv.lock | 3145 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 3146 insertions(+), 5 deletions(-) create mode 100644 uv.lock diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 8c7c51c16..40105099a 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -277,10 +277,7 @@ def get_tabular_report(self) -> GT: ) .tab_style( style=style.borders(sides="left", style="none"), - locations=loc.body( - columns=["p05", "q_1", "median", "q_3", "p95", "max"], - rows=list(present_stat_cols), - ), + locations=loc.body(columns=list(present_stat_cols)), ) ## Formatting .cols_width( @@ -296,7 +293,6 @@ 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1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci-tests.yaml b/.github/workflows/ci-tests.yaml index fdf8c56be..bc488f7f0 100644 --- a/.github/workflows/ci-tests.yaml +++ b/.github/workflows/ci-tests.yaml @@ -29,7 +29,7 @@ jobs: pip install -e '.[dev]' - name: Install test dependencies run: | - pip install pytest pytest-cov pytest-snapshot pandas polars ibis-framework[duckdb,mysql,postgres,sqlite]>=9.5.0 chatlas shiny + pip install pytest pytest-randomly pytest-cov pytest-snapshot pandas polars ibis-framework[duckdb,mysql,postgres,sqlite]>=9.5.0 chatlas shiny - name: pytest unit tests run: | make test From da59a46c539c5374843801b9c7016201dca02c72 Mon Sep 17 00:00:00 2001 From: Richard Iannone Date: Mon, 24 Mar 2025 14:08:01 -0400 Subject: [PATCH 31/85] Update .github/workflows/ci-tests.yaml --- .github/workflows/ci-tests.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci-tests.yaml b/.github/workflows/ci-tests.yaml index bc488f7f0..d523f47e3 100644 --- a/.github/workflows/ci-tests.yaml +++ b/.github/workflows/ci-tests.yaml @@ -29,7 +29,7 @@ jobs: pip install -e '.[dev]' - name: Install test dependencies run: | - pip install pytest pytest-randomly pytest-cov pytest-snapshot pandas polars ibis-framework[duckdb,mysql,postgres,sqlite]>=9.5.0 chatlas shiny + pip install pytest pytest-randomly pytest-cov pytest-snapshot pandas polars ibis-framework[duckdb,mysql,postgres,sqlite]>=9.5.0 chatlas shiny hypothesis - name: pytest unit tests run: | make test From fde4f280885a010b0147ccdf31f45d63d5031adc Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Wed, 26 Mar 2025 21:30:35 -0400 Subject: [PATCH 32/85] allow dataframe construction with multiple types --- pointblank/scan_profile.py | 45 +++++++++++++++++++++++++------------- 1 file changed, 30 insertions(+), 15 deletions(-) diff --git a/pointblank/scan_profile.py b/pointblank/scan_profile.py index 07b12d695..fbbbe77db 100644 --- a/pointblank/scan_profile.py +++ b/pointblank/scan_profile.py @@ -133,6 +133,8 @@ class _DateProfile(ColumnProfile): def calc_stats(self, data: DataFrame): res = data.select(_min=MinStat.expr, _max=MaxStat.expr).to_dict() + data.select(_min=MinStat.expr) + self.statistics: list[Stat] = [MinStat(res["_min"].item()), MaxStat(res["_max"].item())] @@ -227,31 +229,44 @@ def __init__( self.implementation = implementation self.column_profiles: list[ColumnProfile] = [] - def as_dataframe(self, *, format_html: bool = False) -> DataFrame: - if format_html: - formatter = str - else: - formatter = lambda val: val # noqa: E731 - - cols: list[dict] = [] + def as_dataframe(self, *, strict: bool = True) -> DataFrame: + cols: list[dict] = [] # TODO: type hint for prof in self.column_profiles: stat_vals = {} for stat in prof.statistics: - if isinstance(stat.val, float): - val = round(stat.val, 5) - else: - val = stat.val - - stat_vals[stat.name] = formatter(val) + stat_vals[stat.name] = stat.val stat_vals |= {"colname": prof.colname} stat_vals |= {"coltype": str(prof.coltype)} - stat_vals |= {"sample_data": prof.sample_data} + stat_vals |= {"sample_data": str(prof.sample_data)} # TODO: not a good way to do this stat_vals |= {"icon": _TypeMap.fetch_icon(prof._type)} cols.append(stat_vals) + # Stringify if type mismatch + # Get all unique keys across all dictionaries + all_keys = set().union(*(d.keys() for d in cols)) + + for key in all_keys: + # Get all values for this key across all dictionaries + values = [d.get(key) for d in cols if key in d] + + # Check if all values are of the same type + if len(values) > 1: + first_type = type(values[0]) + all_same_type: bool = all(isinstance(v, first_type) for v in values[1:]) + if not all_same_type: + if strict: + msg = f"Some types in {key!s} stat are different. Turn off `strict` to bypass." + raise TypeError(msg) + for d in cols: + if key in d: + d[key] = str(d[key]) + # TODO: This is a bad way to do this - df_native = self.implementation.to_native_namespace().DataFrame(cols) + try: + df_native = self.implementation.to_native_namespace().DataFrame(cols) + except Exception: + raise NotImplementedError return nw.from_native(df_native) From e5d862489b380e7c040434886d0423551230f180 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Wed, 26 Mar 2025 21:31:02 -0400 Subject: [PATCH 33/85] add numeric and fraction formatting --- pointblank/datascan.py | 54 +++++++++++++++++++++++++++++++++--------- 1 file changed, 43 insertions(+), 11 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 40105099a..30370d7a6 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -205,7 +205,8 @@ def get_tabular_report(self) -> GT: # TODO: Ensure width is 905px in total - data = self.profile.as_dataframe(format_html=True) + data = self.profile.as_dataframe(strict=False) + # TODO: Type hint this # TODO: Remove all null columns # find what stat cols were used in the analysis @@ -231,22 +232,48 @@ def get_tabular_report(self) -> GT: assert set(data.columns) == set(target_order), "Internal: fields calculated have no order." + # TODO: min-SL? + # TODO: IQR + # TODO: Label formatting + ## Final Formatting: - formatted_data = data.with_columns( - colname=nw.concat_str( - nw.lit( - "
" + formatted_data = ( + nw.from_native(data) + .with_columns( + colname=nw.concat_str( + nw.lit( + "
" + ), + nw.col("colname"), + nw.lit("
"), + nw.col("coltype"), + nw.lit("
"), + ), + # TODO: These are supposted to be html formatted + __frac_n_unique=nw.col("n_unique") / nw.lit(self.profile.row_count), + __frac_n_missing=nw.col("n_missing") / nw.lit(self.profile.row_count), + ) + .with_columns( + n_unique=nw.concat_str( + nw.col("n_unique"), nw.lit("
"), nw.col("__frac_n_unique") + ), + n_missing=nw.concat_str( + nw.col("n_missing"), nw.lit("
"), nw.col("__frac_n_missing") ), - nw.col("colname"), - nw.lit("
"), - nw.col("coltype"), - nw.lit("
"), ) - ).to_native() + .drop("__frac_n_unique", "__frac_n_missing") + ) + + ## Determine Value Formatting Selectors: + # TODO: will they always be 64? might want strictly type cast this? + fmt_int: list[str] = formatted_data.select(nw.selectors.by_dtype(nw.dtypes.Int64)).columns + fmt_float: list[str] = formatted_data.select( + nw.selectors.by_dtype(nw.dtypes.Float64) + ).columns ## GT Table: gt_tbl = ( - GT(formatted_data) + GT(formatted_data.to_native()) .tab_header(title=html(combined_title)) .cols_align(align="right", columns=list(present_stat_cols)) .opt_table_font(font=google_font("IBM Plex Sans")) @@ -257,6 +284,10 @@ def get_tabular_report(self) -> GT: ) ## Order .cols_move_to_start(target_order) + ## Value Formatting + .fmt_integer(columns=fmt_int) + .fmt_number(columns=fmt_float) + .sub_missing(missing_text="-") ## Generic Styling .tab_style( style=style.text(size="10px"), @@ -295,6 +326,7 @@ def to_dict(self) -> dict: raise NotImplementedError def to_json(self) -> str: + # TODO: Might make sense to return it deconstructed then to dict? prof_dict = self.profile.as_dataframe().to_dict(as_series=False) return json.dumps(prof_dict, indent=4, default=str) From 19f5bdb08cedea294db8c495b0170359facc7d5e Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Thu, 27 Mar 2025 21:28:17 -0400 Subject: [PATCH 34/85] implement fully lazyframe support --- pointblank/datascan.py | 41 +- pointblank/scan_profile.py | 101 +- tests/test_datascan.py | 23 +- uv.lock | 3898 ++++++++++++++++++++++++++++++++++++ 4 files changed, 4001 insertions(+), 62 deletions(-) create mode 100644 uv.lock diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 30370d7a6..b590cd20e 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -9,14 +9,14 @@ from narwhals.typing import FrameT from pointblank._utils_html import _create_table_dims_html, _create_table_type_html -from pointblank.scan_profile import ColumnProfile, _DataProfile, _TypeMap +from pointblank.scan_profile import ColumnProfile, _as_physical, _DataProfile, _TypeMap from pointblank.scan_profile_stats import COLUMN_ORDER_REGISTRY if TYPE_CHECKING: from collections.abc import Mapping from narwhals.dataframe import DataFrame - from narwhals.typing import IntoDataFrame + from narwhals.typing import Frame, IntoFrame from pointblank.scan_profile_stats import StatGroup @@ -118,34 +118,29 @@ class DataScan: A DataScan object. """ - def __init__( - self, data: IntoDataFrame, tbl_name: str | None = None, *, force_collect: bool = True - ) -> None: - self.nw_data: DataFrame = nw.from_native(data) + def __init__(self, data: IntoFrame, tbl_name: str | None = None) -> None: + self.nw_data: Frame = nw.from_native(data) # # TODO: this must be wrong, investigate a more idiomatic way - is_lazy = self.nw_data._level == "lazy" - if is_lazy and not force_collect: # ? this should be allowed (eventually?) - msg = ( - "`DataScan` requires a dataframe to avoid unexpected computations " - "of the caller's execution graph. Please collect the data into a " - "narwhals compliant dataframe first or turn on `force_collect`." - ) - raise TypeError(msg) - if is_lazy and force_collect: - self.nw_data = self.nw_data.collect() + # is_lazy = self.nw_data._level == "lazy" + # if is_lazy and not force_collect: # ? this should be allowed (eventually?) + # msg = ( + # "`DataScan` requires a dataframe to avoid unexpected computations " + # "of the caller's execution graph. Please collect the data into a " + # "narwhals compliant dataframe first or turn on `force_collect`." + # ) + # raise TypeError(msg) + # if is_lazy and force_collect: + # self.nw_data = self.nw_data.collect() self.tbl_name: str | None = tbl_name self.profile: _DataProfile = self._generate_profile_df() def _generate_profile_df(self) -> _DataProfile: - row_count = len(self.nw_data) - columns: list[str] = self.nw_data.columns profile = _DataProfile( table_name=self.tbl_name, - row_count=row_count, columns=columns, implementation=self.nw_data.implementation, ) @@ -167,7 +162,9 @@ def _generate_profile_df(self) -> _DataProfile: ## Collect Sample Data: ## This is the most consistent way (i think) to get the samples out of the data. ## We can avoid writing our own logic to determine operations and rely on narwhals. - raw_vals: list[Any] = col_data.drop_nulls().head(5).to_dict()[column].to_list() + raw_vals: list[Any] = ( + _as_physical(col_data.drop_nulls().head(5)).to_dict()[column].to_list() + ) col_profile.sample_data = [str(x) for x in raw_vals] col_profile.calc_stats(col_data) @@ -177,11 +174,13 @@ def _generate_profile_df(self) -> _DataProfile: profile.column_profiles.append(sub_profile) + profile.set_row_count(self.nw_data) + return profile @property def summary_data(self) -> DataFrame: # TODO: Think this type hint is wrong - return self.profile.as_dataframe() + return self.profile.as_dataframe(strict=False) def get_tabular_report(self) -> GT: # Create the label, table type, and thresholds HTML fragments diff --git a/pointblank/scan_profile.py b/pointblank/scan_profile.py index fbbbe77db..434cbc1eb 100644 --- a/pointblank/scan_profile.py +++ b/pointblank/scan_profile.py @@ -8,6 +8,7 @@ from typing import TYPE_CHECKING, Any, TypedDict import narwhals as nw +from narwhals.dataframe import DataFrame from pointblank._constants import SVG_ICONS_FOR_DATA_TYPES from pointblank.scan_profile_stats import ( @@ -30,7 +31,7 @@ if TYPE_CHECKING: from collections.abc import MutableSequence - from narwhals.dataframe import DataFrame + from narwhals.typing import Frame ## Columns that pose risks for exceptions or don't have handlers @@ -91,7 +92,7 @@ def fetch_icon(cls, _type: _TypeMap) -> str: class _ColumnProfileABC(ABC): @abstractmethod - def calc_stats(self, data: DataFrame) -> None: ... + def calc_stats(self, data: Frame) -> None: ... @dataclass @@ -118,8 +119,10 @@ def spawn_profile(self, _subprofile: type[ColumnProfile]) -> ColumnProfile: inst.sample_data = self.sample_data return inst - def calc_stats(self, data: DataFrame) -> None: - summarized = data.select(_nmissing=NMissing.expr, _nunique=NUnique.expr) + def calc_stats(self, data: Frame) -> None: + summarized = _as_physical( + data.select(_nmissing=NMissing.expr, _nunique=NUnique.expr) + ).to_dict() self.statistics = [ NMissing(summarized["_nmissing"].item()), @@ -130,27 +133,35 @@ def calc_stats(self, data: DataFrame) -> None: class _DateProfile(ColumnProfile): _type: _TypeMap = _TypeMap.DATE - def calc_stats(self, data: DataFrame): - res = data.select(_min=MinStat.expr, _max=MaxStat.expr).to_dict() + def calc_stats(self, data: Frame): + res = data.select(_min=MinStat.expr, _max=MaxStat.expr) - data.select(_min=MinStat.expr) + physical = _as_physical(res).to_dict() - self.statistics: list[Stat] = [MinStat(res["_min"].item()), MaxStat(res["_max"].item())] + self.statistics: list[Stat] = [ + MinStat(physical["_min"].item()), + MaxStat(physical["_max"].item()), + ] class _BoolProfile(ColumnProfile): _type: _TypeMap = _TypeMap.BOOL - def calc_stats(self, data: DataFrame) -> None: - res = data.select(_ntrue=NTrue.expr, _nfalse=NFalse.expr).to_dict() + def calc_stats(self, data: Frame) -> None: + res = data.select(_ntrue=NTrue.expr, _nfalse=NFalse.expr) + + physical = _as_physical(res).to_dict() - self.statistics: list[Stat] = [NTrue(res["_ntrue"].item()), NFalse(res["_nfalse"].item())] + self.statistics: list[Stat] = [ + NTrue(physical["_ntrue"].item()), + NFalse(physical["_nfalse"].item()), + ] class _StringProfile(ColumnProfile): _type: _TypeMap = _TypeMap.STRING - def calc_stats(self, data: DataFrame): + def calc_stats(self, data: Frame): str_data = data.select(nw.all().cast(nw.String).str.len_chars()) summarized = str_data.select( @@ -165,19 +176,19 @@ def calc_stats(self, data: DataFrame): _p95=P95Stat.expr, ) - res = summarized.to_dict() + physical = _as_physical(summarized).to_dict() stats: list[Stat] = [] - stats.append(MeanStat(res["_mean"].item())) - stats.append(MedianStat(res["_median"].item())) - stats.append(StdStat(res["_std"].item())) - stats.append(MinStat(res["_min"].item())) - stats.append(MaxStat(res["_max"].item())) - stats.append(P05Stat(res["_p05"].item())) - stats.append(Q1Stat(res["_q1"].item())) - stats.append(Q3Stat(res["_q3"].item())) - stats.append(P95Stat(res["_p95"].item())) + stats.append(MeanStat(physical["_mean"].item())) + stats.append(MedianStat(physical["_median"].item())) + stats.append(StdStat(physical["_std"].item())) + stats.append(MinStat(physical["_min"].item())) + stats.append(MaxStat(physical["_max"].item())) + stats.append(P05Stat(physical["_p05"].item())) + stats.append(Q1Stat(physical["_q1"].item())) + stats.append(Q3Stat(physical["_q3"].item())) + stats.append(P95Stat(physical["_p95"].item())) self.statistics.extend(stats) @@ -185,8 +196,8 @@ def calc_stats(self, data: DataFrame): class _NumericProfile(ColumnProfile): _type: _TypeMap = _TypeMap.NUMERIC - def calc_stats(self, data: DataFrame): - summarized = data.select( + def calc_stats(self, data: Frame): + res = data.select( _mean=MeanStat.expr, _median=MedianStat.expr, _std=StdStat.expr, @@ -198,19 +209,19 @@ def calc_stats(self, data: DataFrame): _p95=P95Stat.expr, ) - res = summarized.to_dict() + summarized = _as_physical(res).to_dict() stats: list[Stat] = [] - stats.append(MeanStat(res["_mean"].item())) - stats.append(MedianStat(res["_median"].item())) - stats.append(StdStat(res["_std"].item())) - stats.append(MinStat(res["_min"].item())) - stats.append(MaxStat(res["_max"].item())) - stats.append(P05Stat(res["_p05"].item())) - stats.append(Q1Stat(res["_q1"].item())) - stats.append(Q3Stat(res["_q3"].item())) - stats.append(P95Stat(res["_p95"].item())) + stats.append(MeanStat(summarized["_mean"].item())) + stats.append(MedianStat(summarized["_median"].item())) + stats.append(StdStat(summarized["_std"].item())) + stats.append(MinStat(summarized["_min"].item())) + stats.append(MaxStat(summarized["_max"].item())) + stats.append(P05Stat(summarized["_p05"].item())) + stats.append(Q1Stat(summarized["_q1"].item())) + stats.append(Q3Stat(summarized["_q3"].item())) + stats.append(P95Stat(summarized["_p95"].item())) self.statistics.extend(stats) @@ -219,16 +230,25 @@ class _DataProfile: # TODO: feels redundant and weird def __init__( self, table_name: str | None, - row_count: int, columns: list[str], implementation: nw.Implementation, ): self.table_name: str | None = table_name - self.row_count: int = row_count self.columns: list[str] = columns self.implementation = implementation self.column_profiles: list[ColumnProfile] = [] + # TODO: annotate row count? + + def set_row_count(self, data: Frame) -> None: + assert self.columns # internal: cols should already be set + + slim = data.select(nw.col(self.columns[0])) + + physical = _as_physical(slim) + + self.row_count = len(physical) + def as_dataframe(self, *, strict: bool = True) -> DataFrame: cols: list[dict] = [] # TODO: type hint for prof in self.column_profiles: @@ -272,3 +292,12 @@ def as_dataframe(self, *, strict: bool = True) -> DataFrame: def __repr__(self) -> str: # pragma: no cover return f"<_DataProfile(table_name={self.table_name}, row_count={self.row_count}, columns={self.columns})>" + + +def _as_physical(data: Frame) -> DataFrame: + try: + # TODO: might be + return data.collect() # type: ignore[union-attr] + except AttributeError: + assert isinstance(data, DataFrame) # help mypy + return data diff --git a/tests/test_datascan.py b/tests/test_datascan.py index 60b465dd6..d510261dd 100644 --- a/tests/test_datascan.py +++ b/tests/test_datascan.py @@ -7,16 +7,26 @@ import polars as pl from pointblank.datascan import DataScan, col_summary_tbl +from pointblank import load_dataset from pointblank._datascan_utils import _compact_0_1_fmt, _compact_decimal_fmt, _compact_integer_fmt +from pointblank.scan_profile import _as_physical ## Setup Strategies: happy_path_df = pt.dataframes( - min_size=5, allowed_dtypes=[pl.Int64, pl.Float64, pl.String, pl.Categorical] + min_size=5, + allowed_dtypes=[pl.Int64, pl.Float64, pl.String, pl.Categorical, pl.Date, pl.Datetime], +) +happy_path_ldf = pt.dataframes( + min_size=5, + allowed_dtypes=[pl.Int64, pl.Float64, pl.String, pl.Categorical, pl.Date, pl.Datetime], + lazy=True, ) +# TODO: Generate a grid of different types (arrow, pandas, polars, etc.) -@given(df=happy_path_df) + +@given(happy_path_df | happy_path_ldf) def test_datascan_class_parametric(df) -> None: scanner = DataScan(data=df) @@ -24,6 +34,9 @@ def test_datascan_class_parametric(df) -> None: summary_res = scanner.summary_data.to_native() + physical_summary = _as_physical(scanner.summary_data) + physical_input = _as_physical(nw.from_native(df)) + ## Go through checks: cols = summary_res.select("colname").to_series().to_list() @@ -31,8 +44,9 @@ def test_datascan_class_parametric(df) -> None: df_cols = df_nw.columns assert set(cols) == set(df_cols), msg - msg = "return type must be same as input" - assert isinstance(summary_res, type(df)), msg + msg = "return type is the physical version of the input" + assert physical_input.implementation == physical_summary.implementation + assert isinstance(scanner.summary_data, nw.DataFrame) msg = "did not return correct amount of summary rows" assert len(summary_res) == len(cols) # only for happy path @@ -166,4 +180,3 @@ def 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[PATCH 36/85] implement stat level labeling --- pointblank/_utils_html.py | 10 ++++++++++ pointblank/datascan.py | 16 +++++++++++++--- pointblank/scan_profile.py | 30 ++++++++++++++++++------------ pointblank/scan_profile_stats.py | 25 ++++++++++++++++++++++++- 4 files changed, 65 insertions(+), 16 deletions(-) diff --git a/pointblank/_utils_html.py b/pointblank/_utils_html.py index 6108a4031..004ed79d7 100644 --- a/pointblank/_utils_html.py +++ b/pointblank/_utils_html.py @@ -1,9 +1,19 @@ from __future__ import annotations +from typing import Any + +from great_tables import html + from pointblank._constants import TABLE_TYPE_STYLES from pointblank._utils import _format_to_integer_value +def _make_sublabel(major: str, minor: str) -> Any: + return html( + f'{major!s}{minor!s}' + ) + + def _create_table_type_html( tbl_type: str | None, tbl_name: str | None, font_size: str = "10px" ) -> str: diff --git a/pointblank/datascan.py b/pointblank/datascan.py index b590cd20e..6c4f2a6a8 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -231,13 +231,22 @@ def get_tabular_report(self) -> GT: assert set(data.columns) == set(target_order), "Internal: fields calculated have no order." + label_map: dict[str, Any] = {} + for target_col in target_order: + try: + matching_stat = next( + stat for stat in COLUMN_ORDER_REGISTRY if target_col == stat.name + ) + except StopIteration: + continue + label_map[target_col] = matching_stat.label + # TODO: min-SL? # TODO: IQR - # TODO: Label formatting ## Final Formatting: formatted_data = ( - nw.from_native(data) + nw.from_native(data) # TODO: isn't it already native? .with_columns( colname=nw.concat_str( nw.lit( @@ -248,7 +257,6 @@ def get_tabular_report(self) -> GT: nw.col("coltype"), nw.lit(""), ), - # TODO: These are supposted to be html formatted __frac_n_unique=nw.col("n_unique") / nw.lit(self.profile.row_count), __frac_n_missing=nw.col("n_missing") / nw.lit(self.profile.row_count), ) @@ -283,6 +291,8 @@ def get_tabular_report(self) -> GT: ) ## Order .cols_move_to_start(target_order) + ## Labeling + .cols_label(label_map) ## Value Formatting .fmt_integer(columns=fmt_int) .fmt_number(columns=fmt_float) diff --git a/pointblank/scan_profile.py b/pointblank/scan_profile.py index 434cbc1eb..4191a8576 100644 --- a/pointblank/scan_profile.py +++ b/pointblank/scan_profile.py @@ -124,10 +124,12 @@ def calc_stats(self, data: Frame) -> None: data.select(_nmissing=NMissing.expr, _nunique=NUnique.expr) ).to_dict() - self.statistics = [ - NMissing(summarized["_nmissing"].item()), - NUnique(summarized["_nunique"].item()), - ] + self.statistics.extend( + [ + NMissing(summarized["_nmissing"].item()), + NUnique(summarized["_nunique"].item()), + ] + ) class _DateProfile(ColumnProfile): @@ -138,10 +140,12 @@ def calc_stats(self, data: Frame): physical = _as_physical(res).to_dict() - self.statistics: list[Stat] = [ - MinStat(physical["_min"].item()), - MaxStat(physical["_max"].item()), - ] + self.statistics.extend( + [ + MinStat(physical["_min"].item()), + MaxStat(physical["_max"].item()), + ] + ) class _BoolProfile(ColumnProfile): @@ -152,10 +156,12 @@ def calc_stats(self, data: Frame) -> None: physical = _as_physical(res).to_dict() - self.statistics: list[Stat] = [ - NTrue(physical["_ntrue"].item()), - NFalse(physical["_nfalse"].item()), - ] + self.statistics.extend( + [ + NTrue(physical["_ntrue"].item()), + NFalse(physical["_nfalse"].item()), + ] + ) class _StringProfile(ColumnProfile): diff --git a/pointblank/scan_profile_stats.py b/pointblank/scan_profile_stats.py index 215d3e478..1b5a01248 100644 --- a/pointblank/scan_profile_stats.py +++ b/pointblank/scan_profile_stats.py @@ -7,6 +7,8 @@ import narwhals as nw +from pointblank._utils_html import _make_sublabel + if TYPE_CHECKING: from typing import Any @@ -22,7 +24,15 @@ class Stat(ABC): name: ClassVar[str] group: ClassVar[StatGroup] expr: ClassVar[nw.Expr] - py_type: ClassVar[type] = float + label: ClassVar[str] + py_type: ClassVar[type] = float # TODO: why do we need this? + + def __eq__(self, value) -> bool: + if isinstance(value, str): + return value == self.name + if isinstance(value, Stat): + return value is self + return NotImplemented @dataclass(frozen=True) @@ -31,6 +41,7 @@ class MeanStat(Stat): name: ClassVar[str] = "mean" group = StatGroup.DESCR expr: ClassVar[nw.Expr] = nw.all().mean().cast(nw.Float64) + label: ClassVar[str] = "Mean" py_type: ClassVar[type] = float @@ -40,6 +51,7 @@ class StdStat(Stat): name: ClassVar[str] = "std" group = StatGroup.DESCR expr: ClassVar[nw.Expr] = nw.all().std().cast(nw.Float64) + label: ClassVar[str] = "SD" py_type: ClassVar[type] = float @@ -49,6 +61,7 @@ class MinStat(Stat): name: ClassVar[str] = "min" group = StatGroup.DESCR expr: ClassVar[nw.Expr] = nw.all().min().cast(nw.Float64) + label: ClassVar[str] = "Min" py_type: ClassVar[type] = float @@ -58,6 +71,7 @@ class MaxStat(Stat): name: ClassVar[str] = "max" group = StatGroup.DESCR expr: ClassVar[nw.Expr] = nw.all().max().cast(nw.Float64) + label: ClassVar[str] = "Max" py_type: ClassVar[type] = float @@ -67,6 +81,7 @@ class P05Stat(Stat): name: ClassVar[str] = "p05" group = StatGroup.DESCR expr: ClassVar[nw.Expr] = nw.all().quantile(0.005, interpolation="linear").cast(nw.Float64) + label: ClassVar[str] = _make_sublabel("P", "5") py_type: ClassVar[type] = float @@ -76,6 +91,7 @@ class Q1Stat(Stat): name: ClassVar[str] = "q_1" group = StatGroup.DESCR expr: ClassVar[nw.Expr] = nw.all().quantile(0.25, interpolation="linear").cast(nw.Float64) + label: ClassVar[str] = _make_sublabel("Q", "1") py_type: ClassVar[type] = float @@ -85,6 +101,7 @@ class MedianStat(Stat): name: ClassVar[str] = "median" group = StatGroup.DESCR expr: ClassVar[nw.Expr] = nw.all().median().cast(nw.Float64) + label: ClassVar[str] = "Med" py_type: ClassVar[type] = float @@ -94,6 +111,7 @@ class Q3Stat(Stat): name: ClassVar[str] = "q_3" group = StatGroup.DESCR expr: ClassVar[nw.Expr] = nw.all().quantile(0.75, interpolation="linear").cast(nw.Float64) + label: ClassVar[str] = _make_sublabel("Q", "3") py_type: ClassVar[type] = float @@ -103,6 +121,7 @@ class P95Stat(Stat): name: ClassVar[str] = "p95" group = StatGroup.DESCR expr: ClassVar[nw.Expr] = nw.all().quantile(0.95, interpolation="linear").cast(nw.Float64) + label: ClassVar[str] = _make_sublabel("P", "95") py_type: ClassVar[type] = float @@ -112,6 +131,7 @@ class NTrue(Stat): name: ClassVar[str] = "n_true" group = StatGroup.LOGIC expr: ClassVar[nw.Expr] = nw.all().sum().cast(nw.Boolean) + label: ClassVar[str] = _make_sublabel("True", "N") py_type: ClassVar[type] = int @@ -122,6 +142,7 @@ class NFalse(Stat): group = StatGroup.LOGIC # TODO: I don't think this is valid expr: ClassVar[nw.Expr] = ~nw.all().sum().cast(nw.Boolean) + label: ClassVar[str] = _make_sublabel("False", "N") py_type: ClassVar[type] = int @@ -131,6 +152,7 @@ class NMissing(Stat): name: ClassVar[str] = "n_missing" group = StatGroup.STRUCTURE expr: ClassVar[nw.Expr] = nw.all().null_count() + label: ClassVar[str] = _make_sublabel("Missing", "N") py_type: ClassVar[type] = int @@ -140,6 +162,7 @@ class NUnique(Stat): name: ClassVar[str] = "n_unique" group = StatGroup.STRUCTURE expr: ClassVar[nw.Expr] = nw.all().n_unique() + label: ClassVar[str] = _make_sublabel("UQ", "N") py_type: ClassVar[type] = int From 6f244bd661d17f0f17958b36622741bb19cb220a Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Thu, 27 Mar 2025 22:07:30 -0400 Subject: [PATCH 37/85] allow json returns to be non consistent types --- pointblank/datascan.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 6c4f2a6a8..e3a6f75b7 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -336,7 +336,7 @@ def to_dict(self) -> dict: def to_json(self) -> str: # TODO: Might make sense to return it deconstructed then to dict? - prof_dict = self.profile.as_dataframe().to_dict(as_series=False) + prof_dict = self.profile.as_dataframe(strict=False).to_dict(as_series=False) return json.dumps(prof_dict, indent=4, default=str) From bb3d9dfb6292e3c0695ee272b3c3db8676ebc44f Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Thu, 27 Mar 2025 22:09:20 -0400 Subject: [PATCH 38/85] remove defunct metadata class --- pointblank/scan_profile.py | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/pointblank/scan_profile.py b/pointblank/scan_profile.py index 4191a8576..439ecd27b 100644 --- a/pointblank/scan_profile.py +++ b/pointblank/scan_profile.py @@ -5,7 +5,7 @@ from collections.abc import Sequence from dataclasses import dataclass, field from enum import Enum -from typing import TYPE_CHECKING, Any, TypedDict +from typing import TYPE_CHECKING, Any import narwhals as nw from narwhals.dataframe import DataFrame @@ -34,15 +34,10 @@ from narwhals.typing import Frame -## Columns that pose risks for exceptions or don't have handlers +## Types that may cause unrecoverable errors and don't pose any value ILLEGAL_TYPES = ("struct",) -class _Metadata(TypedDict): # TODO: Need a better name - row_count: int - implementation: nw.Implementation - - class _TypeMap(Enum): # ! ordered; # TODO: consolidate w/other stats? NUMERIC = ("int", "float") From b1c230243e4f9639af0b3c54477deb53009d2dde Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Thu, 27 Mar 2025 22:10:53 -0400 Subject: [PATCH 39/85] remove defunct py_type attr --- pointblank/scan_profile.py | 1 - pointblank/scan_profile_stats.py | 14 -------------- 2 files changed, 15 deletions(-) diff --git a/pointblank/scan_profile.py b/pointblank/scan_profile.py index 439ecd27b..d5a75c843 100644 --- a/pointblank/scan_profile.py +++ b/pointblank/scan_profile.py @@ -102,7 +102,6 @@ def sample_data(self) -> Sequence[Any]: @sample_data.setter def sample_data(self, value: object) -> None: - # TODO: type guard this if isinstance(value, Sequence): self._sample_data = value return diff --git a/pointblank/scan_profile_stats.py b/pointblank/scan_profile_stats.py index 1b5a01248..2a66ac33b 100644 --- a/pointblank/scan_profile_stats.py +++ b/pointblank/scan_profile_stats.py @@ -25,7 +25,6 @@ class Stat(ABC): group: ClassVar[StatGroup] expr: ClassVar[nw.Expr] label: ClassVar[str] - py_type: ClassVar[type] = float # TODO: why do we need this? def __eq__(self, value) -> bool: if isinstance(value, str): @@ -42,7 +41,6 @@ class MeanStat(Stat): group = StatGroup.DESCR expr: ClassVar[nw.Expr] = nw.all().mean().cast(nw.Float64) label: ClassVar[str] = "Mean" - py_type: ClassVar[type] = float @dataclass(frozen=True) @@ -52,7 +50,6 @@ class StdStat(Stat): group = StatGroup.DESCR expr: ClassVar[nw.Expr] = nw.all().std().cast(nw.Float64) label: ClassVar[str] = "SD" - py_type: ClassVar[type] = float @dataclass(frozen=True) @@ -62,7 +59,6 @@ class MinStat(Stat): group = StatGroup.DESCR expr: ClassVar[nw.Expr] = nw.all().min().cast(nw.Float64) label: ClassVar[str] = "Min" - py_type: ClassVar[type] = float @dataclass(frozen=True) @@ -72,7 +68,6 @@ class MaxStat(Stat): group = StatGroup.DESCR expr: ClassVar[nw.Expr] = nw.all().max().cast(nw.Float64) label: ClassVar[str] = "Max" - py_type: ClassVar[type] = float @dataclass(frozen=True) @@ -82,7 +77,6 @@ class P05Stat(Stat): group = StatGroup.DESCR expr: ClassVar[nw.Expr] = nw.all().quantile(0.005, interpolation="linear").cast(nw.Float64) label: ClassVar[str] = _make_sublabel("P", "5") - py_type: ClassVar[type] = float @dataclass(frozen=True) @@ -92,7 +86,6 @@ class Q1Stat(Stat): group = StatGroup.DESCR expr: ClassVar[nw.Expr] = nw.all().quantile(0.25, interpolation="linear").cast(nw.Float64) label: ClassVar[str] = _make_sublabel("Q", "1") - py_type: ClassVar[type] = float @dataclass(frozen=True) @@ -102,7 +95,6 @@ class MedianStat(Stat): group = StatGroup.DESCR expr: ClassVar[nw.Expr] = nw.all().median().cast(nw.Float64) label: ClassVar[str] = "Med" - py_type: ClassVar[type] = float @dataclass(frozen=True) @@ -112,7 +104,6 @@ class Q3Stat(Stat): group = StatGroup.DESCR expr: ClassVar[nw.Expr] = nw.all().quantile(0.75, interpolation="linear").cast(nw.Float64) label: ClassVar[str] = _make_sublabel("Q", "3") - py_type: ClassVar[type] = float @dataclass(frozen=True) @@ -122,7 +113,6 @@ class P95Stat(Stat): group = StatGroup.DESCR expr: ClassVar[nw.Expr] = nw.all().quantile(0.95, interpolation="linear").cast(nw.Float64) label: ClassVar[str] = _make_sublabel("P", "95") - py_type: ClassVar[type] = float @dataclass(frozen=True) @@ -132,7 +122,6 @@ class NTrue(Stat): group = StatGroup.LOGIC expr: ClassVar[nw.Expr] = nw.all().sum().cast(nw.Boolean) label: ClassVar[str] = _make_sublabel("True", "N") - py_type: ClassVar[type] = int @dataclass(frozen=True) @@ -143,7 +132,6 @@ class NFalse(Stat): # TODO: I don't think this is valid expr: ClassVar[nw.Expr] = ~nw.all().sum().cast(nw.Boolean) label: ClassVar[str] = _make_sublabel("False", "N") - py_type: ClassVar[type] = int @dataclass(frozen=True) @@ -153,7 +141,6 @@ class NMissing(Stat): group = StatGroup.STRUCTURE expr: ClassVar[nw.Expr] = nw.all().null_count() label: ClassVar[str] = _make_sublabel("Missing", "N") - py_type: ClassVar[type] = int @dataclass(frozen=True) @@ -163,7 +150,6 @@ class NUnique(Stat): group = StatGroup.STRUCTURE expr: ClassVar[nw.Expr] = nw.all().n_unique() label: ClassVar[str] = _make_sublabel("UQ", "N") - py_type: ClassVar[type] = int COLUMN_ORDER_REGISTRY: tuple[type[Stat], ...] = ( From 8a6fc111257b0b37707612e7eab6fcb3c496be16 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Thu, 27 Mar 2025 22:12:38 -0400 Subject: [PATCH 40/85] remove defunct todos and uneccessary nw conversions --- pointblank/datascan.py | 21 +++------------------ 1 file changed, 3 insertions(+), 18 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index e3a6f75b7..9906e76c4 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -120,19 +120,6 @@ class DataScan: def __init__(self, data: IntoFrame, tbl_name: str | None = None) -> None: self.nw_data: Frame = nw.from_native(data) - - # # TODO: this must be wrong, investigate a more idiomatic way - # is_lazy = self.nw_data._level == "lazy" - # if is_lazy and not force_collect: # ? this should be allowed (eventually?) - # msg = ( - # "`DataScan` requires a dataframe to avoid unexpected computations " - # "of the caller's execution graph. Please collect the data into a " - # "narwhals compliant dataframe first or turn on `force_collect`." - # ) - # raise TypeError(msg) - # if is_lazy and force_collect: - # self.nw_data = self.nw_data.collect() - self.tbl_name: str | None = tbl_name self.profile: _DataProfile = self._generate_profile_df() @@ -179,7 +166,7 @@ def _generate_profile_df(self) -> _DataProfile: return profile @property - def summary_data(self) -> DataFrame: # TODO: Think this type hint is wrong + def summary_data(self) -> DataFrame: return self.profile.as_dataframe(strict=False) def get_tabular_report(self) -> GT: @@ -204,8 +191,7 @@ def get_tabular_report(self) -> GT: # TODO: Ensure width is 905px in total - data = self.profile.as_dataframe(strict=False) - # TODO: Type hint this + data: DataFrame = self.profile.as_dataframe(strict=False) # TODO: Remove all null columns # find what stat cols were used in the analysis @@ -246,8 +232,7 @@ def get_tabular_report(self) -> GT: ## Final Formatting: formatted_data = ( - nw.from_native(data) # TODO: isn't it already native? - .with_columns( + data.with_columns( colname=nw.concat_str( nw.lit( "
" From 6fcc860ccff4b212222fe0c21de8380721f27771 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Thu, 27 Mar 2025 22:23:07 -0400 Subject: [PATCH 41/85] implement IQR --- pointblank/datascan.py | 1 - pointblank/scan_profile.py | 21 +++++++++++++-------- pointblank/scan_profile_stats.py | 14 ++++++++++++++ 3 files changed, 27 insertions(+), 9 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 9906e76c4..2bcaa387d 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -228,7 +228,6 @@ def get_tabular_report(self) -> GT: label_map[target_col] = matching_stat.label # TODO: min-SL? - # TODO: IQR ## Final Formatting: formatted_data = ( diff --git a/pointblank/scan_profile.py b/pointblank/scan_profile.py index d5a75c843..e409859ef 100644 --- a/pointblank/scan_profile.py +++ b/pointblank/scan_profile.py @@ -12,6 +12,7 @@ from pointblank._constants import SVG_ICONS_FOR_DATA_TYPES from pointblank.scan_profile_stats import ( + IQRStat, MaxStat, MeanStat, MedianStat, @@ -203,25 +204,29 @@ def calc_stats(self, data: Frame): _std=StdStat.expr, _min=MinStat.expr, _max=MaxStat.expr, - _p05=P05Stat.expr, - _q1=Q1Stat.expr, - _q3=Q3Stat.expr, - _p95=P95Stat.expr, + _p_05=P05Stat.expr, + _q_1=Q1Stat.expr, + _q_3=Q3Stat.expr, + _p_95=P95Stat.expr, + ).with_columns( + _iqr=IQRStat.expr, # TODO: Need something to indicate this consistently ) summarized = _as_physical(res).to_dict() stats: list[Stat] = [] + # TODO: use a single extend to keep it consistent stats.append(MeanStat(summarized["_mean"].item())) stats.append(MedianStat(summarized["_median"].item())) stats.append(StdStat(summarized["_std"].item())) stats.append(MinStat(summarized["_min"].item())) stats.append(MaxStat(summarized["_max"].item())) - stats.append(P05Stat(summarized["_p05"].item())) - stats.append(Q1Stat(summarized["_q1"].item())) - stats.append(Q3Stat(summarized["_q3"].item())) - stats.append(P95Stat(summarized["_p95"].item())) + stats.append(P05Stat(summarized["_p_05"].item())) + stats.append(Q1Stat(summarized["_q_1"].item())) + stats.append(Q3Stat(summarized["_q_3"].item())) + stats.append(P95Stat(summarized["_p_95"].item())) + stats.append(IQRStat(summarized["_iqr"].item())) self.statistics.extend(stats) diff --git a/pointblank/scan_profile_stats.py b/pointblank/scan_profile_stats.py index 2a66ac33b..386e4961c 100644 --- a/pointblank/scan_profile_stats.py +++ b/pointblank/scan_profile_stats.py @@ -33,6 +33,10 @@ def __eq__(self, value) -> bool: return value is self return NotImplemented + @classmethod + def _fetch_priv_name(self) -> str: + return f"_{self.name}" + @dataclass(frozen=True) class MeanStat(Stat): @@ -115,6 +119,15 @@ class P95Stat(Stat): label: ClassVar[str] = _make_sublabel("P", "95") +@dataclass(frozen=True) +class IQRStat(Stat): + val: str + name: ClassVar[str] = "iqr" + group = StatGroup.DESCR + expr: ClassVar[nw.Expr] = nw.col(Q3Stat._fetch_priv_name()) - nw.col(Q1Stat._fetch_priv_name()) + label: ClassVar[str] = "IQR" + + @dataclass(frozen=True) class NTrue(Stat): val: int @@ -164,6 +177,7 @@ class NUnique(Stat): MedianStat, Q3Stat, P95Stat, + IQRStat, NTrue, NFalse, ) From 33564acb36dedadb45810970ace12c502b9bbf42 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sat, 29 Mar 2025 09:02:42 -0400 Subject: [PATCH 42/85] optional sample data in tabular reporting --- pointblank/datascan.py | 20 +++++++++++--------- pointblank/scan_profile_stats.py | 4 ++-- 2 files changed, 13 insertions(+), 11 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 2bcaa387d..ce870ef5f 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -169,7 +169,7 @@ def _generate_profile_df(self) -> _DataProfile: def summary_data(self) -> DataFrame: return self.profile.as_dataframe(strict=False) - def get_tabular_report(self) -> GT: + def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: # Create the label, table type, and thresholds HTML fragments table_type_html = _create_table_type_html( tbl_type=str(self.profile.implementation), tbl_name=self.tbl_name, font_size="10px" @@ -194,6 +194,9 @@ def get_tabular_report(self) -> GT: data: DataFrame = self.profile.as_dataframe(strict=False) # TODO: Remove all null columns + if not show_sample_data: + data = data.drop("sample_data") + # find what stat cols were used in the analysis non_stat_cols = ("icon", "colname", "coltype") # TODO: need a better place for this present_stat_cols: set[str] = set(data.columns) - set(non_stat_cols) @@ -213,10 +216,6 @@ def get_tabular_report(self) -> GT: right_border_cols.append(target_order[-1]) # add border to last stat col - target_order.append("sample_data") - - assert set(data.columns) == set(target_order), "Internal: fields calculated have no order." - label_map: dict[str, Any] = {} for target_col in target_order: try: @@ -227,8 +226,6 @@ def get_tabular_report(self) -> GT: continue label_map[target_col] = matching_stat.label - # TODO: min-SL? - ## Final Formatting: formatted_data = ( data.with_columns( @@ -241,8 +238,9 @@ def get_tabular_report(self) -> GT: nw.col("coltype"), nw.lit("
"), ), - __frac_n_unique=nw.col("n_unique") / nw.lit(self.profile.row_count), - __frac_n_missing=nw.col("n_missing") / nw.lit(self.profile.row_count), + # TODO: This is a very temporary solution + __frac_n_unique=(nw.col("n_unique") / nw.lit(self.profile.row_count)).round(5), + __frac_n_missing=(nw.col("n_missing") / nw.lit(self.profile.row_count)).round(5), ) .with_columns( n_unique=nw.concat_str( @@ -262,6 +260,9 @@ def get_tabular_report(self) -> GT: nw.selectors.by_dtype(nw.dtypes.Float64) ).columns + # TODO: Datetime Min/Max in wrong format + # TODO: Borders are incorrect, look at the example + ## GT Table: gt_tbl = ( GT(formatted_data.to_native()) @@ -277,6 +278,7 @@ def get_tabular_report(self) -> GT: .cols_move_to_start(target_order) ## Labeling .cols_label(label_map) + .cols_label(icon="", colname="Column", coltype="Type") ## Value Formatting .fmt_integer(columns=fmt_int) .fmt_number(columns=fmt_float) diff --git a/pointblank/scan_profile_stats.py b/pointblank/scan_profile_stats.py index 386e4961c..11b6764a7 100644 --- a/pointblank/scan_profile_stats.py +++ b/pointblank/scan_profile_stats.py @@ -61,7 +61,7 @@ class MinStat(Stat): val: str name: ClassVar[str] = "min" group = StatGroup.DESCR - expr: ClassVar[nw.Expr] = nw.all().min().cast(nw.Float64) + expr: ClassVar[nw.Expr] = nw.all().min() # don't cast as float, can be date label: ClassVar[str] = "Min" @@ -70,7 +70,7 @@ class MaxStat(Stat): val: str name: ClassVar[str] = "max" group = StatGroup.DESCR - expr: ClassVar[nw.Expr] = nw.all().max().cast(nw.Float64) + expr: ClassVar[nw.Expr] = nw.all().max() # don't cast as float, can be date label: ClassVar[str] = "Max" From 4194ef2174ad11d28a3e225d0f3ad04d787c9a99 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sat, 29 Mar 2025 10:47:19 -0400 Subject: [PATCH 43/85] fix borders --- pointblank/datascan.py | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index ce870ef5f..44b7cd39a 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -198,8 +198,9 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: data = data.drop("sample_data") # find what stat cols were used in the analysis - non_stat_cols = ("icon", "colname", "coltype") # TODO: need a better place for this + non_stat_cols = ("icon", "colname") # TODO: need a better place for this present_stat_cols: set[str] = set(data.columns) - set(non_stat_cols) + present_stat_cols.remove("coltype") target_order: list[str] = list(non_stat_cols) right_border_cols: list[str] = [non_stat_cols[-1]] @@ -211,7 +212,8 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: start_new_group: bool = last_group != cur_group if start_new_group: - last_col_added = target_order[-1] + last_group = cur_group + last_col_added = target_order[-2] # -2 since we don't include the current right_border_cols.append(last_col_added) right_border_cols.append(target_order[-1]) # add border to last stat col @@ -250,7 +252,7 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: nw.col("n_missing"), nw.lit("
"), nw.col("__frac_n_missing") ), ) - .drop("__frac_n_unique", "__frac_n_missing") + .drop("__frac_n_unique", "__frac_n_missing", "coltype") ) ## Determine Value Formatting Selectors: @@ -260,7 +262,7 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: nw.selectors.by_dtype(nw.dtypes.Float64) ).columns - # TODO: Datetime Min/Max in wrong format + # TODO: T/F N are incorrect # TODO: Borders are incorrect, look at the example ## GT Table: @@ -278,7 +280,7 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: .cols_move_to_start(target_order) ## Labeling .cols_label(label_map) - .cols_label(icon="", colname="Column", coltype="Type") + .cols_label(icon="", colname="Column") ## Value Formatting .fmt_integer(columns=fmt_int) .fmt_number(columns=fmt_float) @@ -301,10 +303,10 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: style=style.borders(sides="left", color="#E5E5E5", style="dashed"), locations=loc.body(columns=list(present_stat_cols)), ) - .tab_style( - style=style.borders(sides="left", style="none"), - locations=loc.body(columns=list(present_stat_cols)), - ) + # .tab_style( + # style=style.borders(sides="left", style="none"), + # locations=loc.body(columns=list(present_stat_cols)), + # ) ## Formatting .cols_width( icon="35px", colname="200px", **{stat_col: "50px" for stat_col in present_stat_cols} From 6f2965c11263eb97eb305da63b6bef897a2726f2 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sat, 29 Mar 2025 10:59:35 -0400 Subject: [PATCH 44/85] fix bool calculation and formatting --- pointblank/datascan.py | 3 --- pointblank/scan_profile_stats.py | 5 ++--- 2 files changed, 2 insertions(+), 6 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 44b7cd39a..0d7bdfcc9 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -262,9 +262,6 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: nw.selectors.by_dtype(nw.dtypes.Float64) ).columns - # TODO: T/F N are incorrect - # TODO: Borders are incorrect, look at the example - ## GT Table: gt_tbl = ( GT(formatted_data.to_native()) diff --git a/pointblank/scan_profile_stats.py b/pointblank/scan_profile_stats.py index 11b6764a7..4bb10e7fd 100644 --- a/pointblank/scan_profile_stats.py +++ b/pointblank/scan_profile_stats.py @@ -133,7 +133,7 @@ class NTrue(Stat): val: int name: ClassVar[str] = "n_true" group = StatGroup.LOGIC - expr: ClassVar[nw.Expr] = nw.all().sum().cast(nw.Boolean) + expr: ClassVar[nw.Expr] = nw.all().sum().cast(nw.Int64) label: ClassVar[str] = _make_sublabel("True", "N") @@ -142,8 +142,7 @@ class NFalse(Stat): val: int name: ClassVar[str] = "n_false" group = StatGroup.LOGIC - # TODO: I don't think this is valid - expr: ClassVar[nw.Expr] = ~nw.all().sum().cast(nw.Boolean) + expr: ClassVar[nw.Expr] = nw.all().filter(~nw.all()).count().cast(nw.Int64) label: ClassVar[str] = _make_sublabel("False", "N") From b1c83739c64102995c304ccef64491d65a88c420 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sat, 29 Mar 2025 11:03:03 -0400 Subject: [PATCH 45/85] drop all null stats --- pointblank/datascan.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 0d7bdfcc9..a691c2cc2 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -192,6 +192,15 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: # TODO: Ensure width is 905px in total data: DataFrame = self.profile.as_dataframe(strict=False) + + ## Remove all null columns: + all_null: list[str] = [] + for stat_name in data.iter_columns(): + col_len = len(stat_name.drop_nulls()) + if col_len == 0: + all_null.append(stat_name.name) + data = data.drop(all_null) + # TODO: Remove all null columns if not show_sample_data: @@ -300,10 +309,6 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: style=style.borders(sides="left", color="#E5E5E5", style="dashed"), locations=loc.body(columns=list(present_stat_cols)), ) - # .tab_style( - # style=style.borders(sides="left", style="none"), - # locations=loc.body(columns=list(present_stat_cols)), - # ) ## Formatting .cols_width( icon="35px", colname="200px", **{stat_col: "50px" for stat_col in present_stat_cols} From 2c1626b36a7a8891ab88e72bed84491a3c454996 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sat, 29 Mar 2025 11:04:52 -0400 Subject: [PATCH 46/85] annotate row count --- pointblank/scan_profile.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pointblank/scan_profile.py b/pointblank/scan_profile.py index e409859ef..ce6cb02fd 100644 --- a/pointblank/scan_profile.py +++ b/pointblank/scan_profile.py @@ -5,7 +5,7 @@ from collections.abc import Sequence from dataclasses import dataclass, field from enum import Enum -from typing import TYPE_CHECKING, Any +from typing import TYPE_CHECKING, Any, ClassVar import narwhals as nw from narwhals.dataframe import DataFrame @@ -232,6 +232,8 @@ def calc_stats(self, data: Frame): class _DataProfile: # TODO: feels redundant and weird + row_count: ClassVar[int] + def __init__( self, table_name: str | None, @@ -243,8 +245,6 @@ def __init__( self.implementation = implementation self.column_profiles: list[ColumnProfile] = [] - # TODO: annotate row count? - def set_row_count(self, data: Frame) -> None: assert self.columns # internal: cols should already be set From da1743058ad9a45af926ab0ff34d4e8a7190a12a Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 30 Mar 2025 12:45:23 -0400 Subject: [PATCH 47/85] fix inconsistent handling of false counts across libraries --- pointblank/scan_profile_stats.py | 9 ++++++++- tests/test_datascan.py | 13 +++++++++++++ 2 files changed, 21 insertions(+), 1 deletion(-) diff --git a/pointblank/scan_profile_stats.py b/pointblank/scan_profile_stats.py index 4bb10e7fd..0cd4ad1c0 100644 --- a/pointblank/scan_profile_stats.py +++ b/pointblank/scan_profile_stats.py @@ -142,7 +142,14 @@ class NFalse(Stat): val: int name: ClassVar[str] = "n_false" group = StatGroup.LOGIC - expr: ClassVar[nw.Expr] = nw.all().filter(~nw.all()).count().cast(nw.Int64) + ## Indirection to the point of absurdity: + ## - Reversing the bool results in inconsistent unsigned types, ie. False is max hexidecimal + ## - Filtering on `nw.all` is generally unavailable, at least for polars + ## - Replacing T/F w/Null is not interpreted correctly by Narwhals + ## - Swapping 0 -> 1 and 1 -> 0 is the most consistent, albiet odd + expr: ClassVar[nw.Expr] = ( + nw.all().cast(nw.Int64).replace_strict([1, 0], new=[0, 1]).sum().cast(nw.Int64) + ) label: ClassVar[str] = _make_sublabel("False", "N") diff --git a/tests/test_datascan.py b/tests/test_datascan.py index d510261dd..913a2e843 100644 --- a/tests/test_datascan.py +++ b/tests/test_datascan.py @@ -23,6 +23,19 @@ lazy=True, ) + +def _arrow_strat() -> None: + raise NotImplementedError + + +def _pandas_strat() -> None: + raise NotImplementedError + + +def _duckdb_strat() -> None: + raise NotImplementedError("This will be manual but it's necessary.") + + # TODO: Generate a grid of different types (arrow, pandas, polars, etc.) From aaf285d17b0d4f8b0f91840aa413963ae3dd692b Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 30 Mar 2025 12:46:06 -0400 Subject: [PATCH 48/85] remove html snapshot tests as it's no longer exactly the same --- tests/test_datascan.py | 24 ------------------------ 1 file changed, 24 deletions(-) diff --git a/tests/test_datascan.py b/tests/test_datascan.py index 913a2e843..32350db78 100644 --- a/tests/test_datascan.py +++ b/tests/test_datascan.py @@ -112,30 +112,6 @@ def test_col_summary_tbl_polars_categorical_column(): assert isinstance(tabular_output, GT) -def test_col_summary_tbl_pandas_snap(snapshot): - dataset = load_dataset(dataset="small_table", tbl_type="pandas") - col_summary_html = col_summary_tbl(dataset).as_raw_html() - - # Use the snapshot fixture to create and save the snapshot - snapshot.assert_match(col_summary_html, "col_summary_html_pandas.html") - - -def test_col_summary_tbl_polars_snap(snapshot): - dataset = load_dataset(dataset="small_table", tbl_type="polars") - col_summary_html = col_summary_tbl(dataset).as_raw_html() - - # Use the snapshot fixture to create and save the snapshot - snapshot.assert_match(col_summary_html, "col_summary_html_polars.html") - - -def test_col_summary_tbl_duckdb_snap(snapshot): - dataset = load_dataset(dataset="small_table", tbl_type="duckdb") - col_summary_html = col_summary_tbl(dataset).as_raw_html() - - # Use the snapshot fixture to create and save the snapshot - snapshot.assert_match(col_summary_html, "col_summary_html_duckdb.html") - - def test_datascan_class_raises(): with pytest.raises(TypeError): DataScan(data="not a DataFrame or Ibis Table") From 8e92bafa6239a14f5784cf4a2e5cf96caaee90eb Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 30 Mar 2025 13:08:58 -0400 Subject: [PATCH 49/85] fix dataframe implementation polars specific --- pointblank/_utils.py | 18 ++++++++++++++++++ pointblank/scan_profile.py | 18 ++++++++++-------- tests/test_datascan.py | 2 +- 3 files changed, 29 insertions(+), 9 deletions(-) diff --git a/pointblank/_utils.py b/pointblank/_utils.py index 59ce7b83e..64eb70e1e 100644 --- a/pointblank/_utils.py +++ b/pointblank/_utils.py @@ -2,6 +2,7 @@ import inspect import re +from collections import defaultdict from typing import TYPE_CHECKING, Any import narwhals as nw @@ -17,6 +18,23 @@ from pointblank._typing import AbsoluteBounds, Tolerance +def transpose_dicts(list_of_dicts: list[dict[str, Any]]) -> dict[str, list[Any]]: + if not list_of_dicts: + return {} + + # Get all unique keys across all dictionaries + all_keys = set() + for d in list_of_dicts: + all_keys.update(d.keys()) + + result = defaultdict(list) + for d in list_of_dicts: + for key in all_keys: + result[key].append(d.get(key)) # None is default for missing keys + + return dict(result) + + def _derive_single_bound(ref: int, tol: int | float) -> int: """Derive a single bound using the reference.""" if not isinstance(tol, float | int): diff --git a/pointblank/scan_profile.py b/pointblank/scan_profile.py index ce6cb02fd..8a0c9290b 100644 --- a/pointblank/scan_profile.py +++ b/pointblank/scan_profile.py @@ -11,6 +11,7 @@ from narwhals.dataframe import DataFrame from pointblank._constants import SVG_ICONS_FOR_DATA_TYPES +from pointblank._utils import transpose_dicts from pointblank.scan_profile_stats import ( IQRStat, MaxStat, @@ -255,6 +256,8 @@ def set_row_count(self, data: Frame) -> None: self.row_count = len(physical) def as_dataframe(self, *, strict: bool = True) -> DataFrame: + assert self.column_profiles + cols: list[dict] = [] # TODO: type hint for prof in self.column_profiles: stat_vals = {} @@ -278,7 +281,12 @@ def as_dataframe(self, *, strict: bool = True) -> DataFrame: # Check if all values are of the same type if len(values) > 1: first_type = type(values[0]) - all_same_type: bool = all(isinstance(v, first_type) for v in values[1:]) + + # use `type` instead of instance check because some types are sub + # classes of supers, ie. date is a subclass of datetime, so it's + # technically an instance. This however would fail most dataframe + # instantiations that require consistent types. + all_same_type: bool = all(type(v) is first_type for v in values[1:]) if not all_same_type: if strict: msg = f"Some types in {key!s} stat are different. Turn off `strict` to bypass." @@ -287,13 +295,7 @@ def as_dataframe(self, *, strict: bool = True) -> DataFrame: if key in d: d[key] = str(d[key]) - # TODO: This is a bad way to do this - try: - df_native = self.implementation.to_native_namespace().DataFrame(cols) - except Exception: - raise NotImplementedError - - return nw.from_native(df_native) + return nw.from_dict(transpose_dicts(cols), backend=self.implementation) def __repr__(self) -> str: # pragma: no cover return f"<_DataProfile(table_name={self.table_name}, row_count={self.row_count}, columns={self.columns})>" diff --git a/tests/test_datascan.py b/tests/test_datascan.py index 32350db78..6d4f6c4f8 100644 --- a/tests/test_datascan.py +++ b/tests/test_datascan.py @@ -70,7 +70,7 @@ def test_datascan_class_parametric(df) -> None: # TODO: Should contain many more cases -@given(df=pt.dataframes(min_size=5)) +@given(happy_path_df | happy_path_ldf) def test_datascan_json_output(df): scanner = DataScan(data=df) From 63d2f4bc80e623096a20764309a1287ba4d4c2e2 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 30 Mar 2025 13:12:56 -0400 Subject: [PATCH 50/85] clean up TODOs, extract label map creater --- pointblank/datascan.py | 31 +++++++++++++++++++------------ 1 file changed, 19 insertions(+), 12 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index a691c2cc2..30fd0b6c0 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -13,7 +13,7 @@ from pointblank.scan_profile_stats import COLUMN_ORDER_REGISTRY if TYPE_CHECKING: - from collections.abc import Mapping + from collections.abc import Mapping, Sequence from narwhals.dataframe import DataFrame from narwhals.typing import Frame, IntoFrame @@ -201,8 +201,6 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: all_null.append(stat_name.name) data = data.drop(all_null) - # TODO: Remove all null columns - if not show_sample_data: data = data.drop("sample_data") @@ -211,8 +209,12 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: present_stat_cols: set[str] = set(data.columns) - set(non_stat_cols) present_stat_cols.remove("coltype") + ## Assemble the target order and find what columns need borders. + ## Borders should be placed to divide the stat "groups" and create a + ## generally more aesthetically pleasing experience. target_order: list[str] = list(non_stat_cols) right_border_cols: list[str] = [non_stat_cols[-1]] + last_group: StatGroup = COLUMN_ORDER_REGISTRY[0].group for col in COLUMN_ORDER_REGISTRY: if col.name in present_stat_cols: @@ -227,15 +229,7 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: right_border_cols.append(target_order[-1]) # add border to last stat col - label_map: dict[str, Any] = {} - for target_col in target_order: - try: - matching_stat = next( - stat for stat in COLUMN_ORDER_REGISTRY if target_col == stat.name - ) - except StopIteration: - continue - label_map[target_col] = matching_stat.label + label_map: dict[str, Any] = self._build_label_map(target_order) ## Final Formatting: formatted_data = ( @@ -321,6 +315,19 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: return gt_tbl + @staticmethod + def _build_label_map(cols: Sequence[str]) -> dict[str, Any]: + label_map: dict[str, Any] = {} + for target_col in cols: + try: + matching_stat = next( + stat for stat in COLUMN_ORDER_REGISTRY if target_col == stat.name + ) + except StopIteration: + continue + label_map[target_col] = matching_stat.label + return label_map + def to_dict(self) -> dict: raise NotImplementedError From 0079f9b72b5835285fdf82a80b302255831e7a03 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 30 Mar 2025 13:14:16 -0400 Subject: [PATCH 51/85] clean up stat classes casting --- pointblank/datascan.py | 1 - pointblank/scan_profile_stats.py | 8 +++++--- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 30fd0b6c0..a3d14e99c 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -259,7 +259,6 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: ) ## Determine Value Formatting Selectors: - # TODO: will they always be 64? might want strictly type cast this? fmt_int: list[str] = formatted_data.select(nw.selectors.by_dtype(nw.dtypes.Int64)).columns fmt_float: list[str] = formatted_data.select( nw.selectors.by_dtype(nw.dtypes.Float64) diff --git a/pointblank/scan_profile_stats.py b/pointblank/scan_profile_stats.py index 0cd4ad1c0..40b77ae85 100644 --- a/pointblank/scan_profile_stats.py +++ b/pointblank/scan_profile_stats.py @@ -124,7 +124,9 @@ class IQRStat(Stat): val: str name: ClassVar[str] = "iqr" group = StatGroup.DESCR - expr: ClassVar[nw.Expr] = nw.col(Q3Stat._fetch_priv_name()) - nw.col(Q1Stat._fetch_priv_name()) + expr: ClassVar[nw.Expr] = nw.col(Q3Stat._fetch_priv_name()) - nw.col( + Q1Stat._fetch_priv_name() + ).cast(nw.Float64) label: ClassVar[str] = "IQR" @@ -158,7 +160,7 @@ class NMissing(Stat): val: int name: ClassVar[str] = "n_missing" group = StatGroup.STRUCTURE - expr: ClassVar[nw.Expr] = nw.all().null_count() + expr: ClassVar[nw.Expr] = nw.all().null_count().cast(nw.Int64) label: ClassVar[str] = _make_sublabel("Missing", "N") @@ -167,7 +169,7 @@ class NUnique(Stat): val: int name: ClassVar[str] = "n_unique" group = StatGroup.STRUCTURE - expr: ClassVar[nw.Expr] = nw.all().n_unique() + expr: ClassVar[nw.Expr] = nw.all().n_unique().cast(nw.Int64) label: ClassVar[str] = _make_sublabel("UQ", "N") From 45f9dc745f2931aed88d630f14bdc21538968c99 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 30 Mar 2025 13:16:55 -0400 Subject: [PATCH 52/85] standardize extension of stats instead of constant appends --- pointblank/datascan.py | 1 - pointblank/scan_profile.py | 56 ++++++++++++++++++-------------------- 2 files changed, 27 insertions(+), 30 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index a3d14e99c..f554bcc1d 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -331,7 +331,6 @@ def to_dict(self) -> dict: raise NotImplementedError def to_json(self) -> str: - # TODO: Might make sense to return it deconstructed then to dict? prof_dict = self.profile.as_dataframe(strict=False).to_dict(as_series=False) return json.dumps(prof_dict, indent=4, default=str) diff --git a/pointblank/scan_profile.py b/pointblank/scan_profile.py index 8a0c9290b..60e92b615 100644 --- a/pointblank/scan_profile.py +++ b/pointblank/scan_profile.py @@ -179,20 +179,19 @@ def calc_stats(self, data: Frame): ) physical = _as_physical(summarized).to_dict() - - stats: list[Stat] = [] - - stats.append(MeanStat(physical["_mean"].item())) - stats.append(MedianStat(physical["_median"].item())) - stats.append(StdStat(physical["_std"].item())) - stats.append(MinStat(physical["_min"].item())) - stats.append(MaxStat(physical["_max"].item())) - stats.append(P05Stat(physical["_p05"].item())) - stats.append(Q1Stat(physical["_q1"].item())) - stats.append(Q3Stat(physical["_q3"].item())) - stats.append(P95Stat(physical["_p95"].item())) - - self.statistics.extend(stats) + self.statistics.extend( + [ + MeanStat(physical["_mean"].item()), + MedianStat(physical["_median"].item()), + StdStat(physical["_std"].item()), + MinStat(physical["_min"].item()), + MaxStat(physical["_max"].item()), + P05Stat(physical["_p05"].item()), + Q1Stat(physical["_q1"].item()), + Q3Stat(physical["_q3"].item()), + P95Stat(physical["_p95"].item()), + ] + ) class _NumericProfile(ColumnProfile): @@ -215,21 +214,20 @@ def calc_stats(self, data: Frame): summarized = _as_physical(res).to_dict() - stats: list[Stat] = [] - - # TODO: use a single extend to keep it consistent - stats.append(MeanStat(summarized["_mean"].item())) - stats.append(MedianStat(summarized["_median"].item())) - stats.append(StdStat(summarized["_std"].item())) - stats.append(MinStat(summarized["_min"].item())) - stats.append(MaxStat(summarized["_max"].item())) - stats.append(P05Stat(summarized["_p_05"].item())) - stats.append(Q1Stat(summarized["_q_1"].item())) - stats.append(Q3Stat(summarized["_q_3"].item())) - stats.append(P95Stat(summarized["_p_95"].item())) - stats.append(IQRStat(summarized["_iqr"].item())) - - self.statistics.extend(stats) + self.statistics.extend( + [ + MeanStat(summarized["_mean"].item()), + MedianStat(summarized["_median"].item()), + StdStat(summarized["_std"].item()), + MinStat(summarized["_min"].item()), + MaxStat(summarized["_max"].item()), + P05Stat(summarized["_p_05"].item()), + Q1Stat(summarized["_q_1"].item()), + Q3Stat(summarized["_q_3"].item()), + P95Stat(summarized["_p_95"].item()), + IQRStat(summarized["_iqr"].item()), + ] + ) class _DataProfile: # TODO: feels redundant and weird From 3e13d585b4a871913717aa190ba64430b7c53deb Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 30 Mar 2025 13:29:27 -0400 Subject: [PATCH 53/85] add more tests for developer error in statistics --- tests/test_datascan.py | 23 +++++++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/tests/test_datascan.py b/tests/test_datascan.py index 6d4f6c4f8..a3650fe0b 100644 --- a/tests/test_datascan.py +++ b/tests/test_datascan.py @@ -10,6 +10,7 @@ from pointblank import load_dataset from pointblank._datascan_utils import _compact_0_1_fmt, _compact_decimal_fmt, _compact_integer_fmt from pointblank.scan_profile import _as_physical +from pointblank.scan_profile_stats import StatGroup, COLUMN_ORDER_REGISTRY ## Setup Strategies: @@ -50,7 +51,7 @@ def test_datascan_class_parametric(df) -> None: physical_summary = _as_physical(scanner.summary_data) physical_input = _as_physical(nw.from_native(df)) - ## Go through checks: + ## High Level Checks: cols = summary_res.select("colname").to_series().to_list() msg = "cols must be the same" @@ -67,7 +68,25 @@ def test_datascan_class_parametric(df) -> None: msg = "contains sample data" assert "sample_data" in summary_res.columns - # TODO: Should contain many more cases + ## More Granular Checks: + cols_that_must_be_there = ("n_missing", "n_unique", "icon", "colname", "sample_data", "coltype") + for col in cols_that_must_be_there: + assert col in summary_res.columns, f"Missing column: {col}" + + # this also catches developer error in syncing the calculations and stat classes + # for example if dev adds a new stat to `scan_profile_stats.py` and does not add + # it to the `calc_stats` method, this test will fail since it never calculated the + # statistic. + msg = "If a single of a group is there, they should all be there." + for group in StatGroup: + stats_that_should_be_present: list[str] = [ + stat.name for stat in COLUMN_ORDER_REGISTRY if group == stat.group + ] + any_in_summary = any( + col for col in stats_that_should_be_present if col in summary_res.columns + ) + if any_in_summary: + assert all(stat in summary_res.columns for stat in stats_that_should_be_present), msg @given(happy_path_df | happy_path_ldf) From 8de208f4905b0e11ae9a9a2c48dcceac73fbd2f1 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 30 Mar 2025 13:29:48 -0400 Subject: [PATCH 54/85] add iqr to string length --- pointblank/scan_profile.py | 27 ++++++++++++++------------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/pointblank/scan_profile.py b/pointblank/scan_profile.py index 60e92b615..b58abe154 100644 --- a/pointblank/scan_profile.py +++ b/pointblank/scan_profile.py @@ -5,7 +5,7 @@ from collections.abc import Sequence from dataclasses import dataclass, field from enum import Enum -from typing import TYPE_CHECKING, Any, ClassVar +from typing import TYPE_CHECKING, Any import narwhals as nw from narwhals.dataframe import DataFrame @@ -172,10 +172,12 @@ def calc_stats(self, data: Frame): _std=StdStat.expr, _min=MinStat.expr, _max=MaxStat.expr, - _p05=P05Stat.expr, - _q1=Q1Stat.expr, - _q3=Q3Stat.expr, - _p95=P95Stat.expr, + _p_05=P05Stat.expr, + _q_1=Q1Stat.expr, + _q_3=Q3Stat.expr, + _p_95=P95Stat.expr, + ).with_columns( + _iqr=IQRStat.expr, ) physical = _as_physical(summarized).to_dict() @@ -186,10 +188,11 @@ def calc_stats(self, data: Frame): StdStat(physical["_std"].item()), MinStat(physical["_min"].item()), MaxStat(physical["_max"].item()), - P05Stat(physical["_p05"].item()), - Q1Stat(physical["_q1"].item()), - Q3Stat(physical["_q3"].item()), - P95Stat(physical["_p95"].item()), + P05Stat(physical["_p_05"].item()), + Q1Stat(physical["_q_1"].item()), + Q3Stat(physical["_q_3"].item()), + P95Stat(physical["_p_95"].item()), + IQRStat(physical["_iqr"].item()), ] ) @@ -231,8 +234,6 @@ def calc_stats(self, data: Frame): class _DataProfile: # TODO: feels redundant and weird - row_count: ClassVar[int] - def __init__( self, table_name: str | None, @@ -256,7 +257,7 @@ def set_row_count(self, data: Frame) -> None: def as_dataframe(self, *, strict: bool = True) -> DataFrame: assert self.column_profiles - cols: list[dict] = [] # TODO: type hint + cols: list[dict[str, Any]] = [] for prof in self.column_profiles: stat_vals = {} for stat in prof.statistics: @@ -301,7 +302,7 @@ def __repr__(self) -> str: # pragma: no cover def _as_physical(data: Frame) -> DataFrame: try: - # TODO: might be + # TODO: might be a built in way to do this return data.collect() # type: ignore[union-attr] except AttributeError: assert isinstance(data, DataFrame) # help mypy From 3c0804a7e1189492aba399c0817b0902f8559de7 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 30 Mar 2025 13:55:05 -0400 Subject: [PATCH 55/85] add pandas and arrow strategies --- tests/test_datascan.py | 43 ++++++++++++++++++++++++++---------------- 1 file changed, 27 insertions(+), 16 deletions(-) diff --git a/tests/test_datascan.py b/tests/test_datascan.py index a3650fe0b..277d10ff1 100644 --- a/tests/test_datascan.py +++ b/tests/test_datascan.py @@ -1,19 +1,29 @@ +from __future__ import annotations + import pytest import narwhals as nw -from hypothesis import given, settings +from hypothesis import given, settings, strategies as st import polars.testing.parametric as pt from great_tables import GT +from typing import TYPE_CHECKING import polars as pl from pointblank.datascan import DataScan, col_summary_tbl -from pointblank import load_dataset from pointblank._datascan_utils import _compact_0_1_fmt, _compact_decimal_fmt, _compact_integer_fmt from pointblank.scan_profile import _as_physical from pointblank.scan_profile_stats import StatGroup, COLUMN_ORDER_REGISTRY +if TYPE_CHECKING: + import pyarrow as pa + import pandas as pd + ## Setup Strategies: +## Generate df and ldf happy paths using polars. +## Also generate pandas and arrow strategies which should smoke test any complete mistakes +## or inconsistent handling in narwhals. Really checking the consistency among packages is +## too much the job of narwhals, and we should avoid stepping on their testing suite. happy_path_df = pt.dataframes( min_size=5, allowed_dtypes=[pl.Int64, pl.Float64, pl.String, pl.Categorical, pl.Date, pl.Datetime], @@ -25,34 +35,32 @@ ) -def _arrow_strat() -> None: - raise NotImplementedError - - -def _pandas_strat() -> None: - raise NotImplementedError - +@st.composite +def _arrow_strat(draw) -> pa.Table: + polars_df = draw(happy_path_df) + return nw.from_native(polars_df).to_arrow() -def _duckdb_strat() -> None: - raise NotImplementedError("This will be manual but it's necessary.") +@st.composite +def _pandas_strat(draw) -> pd.DataFrame: + polars_df = draw(happy_path_df) + return nw.from_native(polars_df).to_pandas() -# TODO: Generate a grid of different types (arrow, pandas, polars, etc.) - -@given(happy_path_df | happy_path_ldf) +@given(happy_path_df | happy_path_ldf | _arrow_strat() | _pandas_strat()) +@settings(deadline=None) # too variant to enforce deadline def test_datascan_class_parametric(df) -> None: scanner = DataScan(data=df) df_nw = nw.from_native(df) - summary_res = scanner.summary_data.to_native() + summary_res = scanner.summary_data physical_summary = _as_physical(scanner.summary_data) physical_input = _as_physical(nw.from_native(df)) ## High Level Checks: - cols = summary_res.select("colname").to_series().to_list() + cols = summary_res.select("colname").to_dict()["colname"].to_list() msg = "cols must be the same" df_cols = df_nw.columns @@ -89,6 +97,9 @@ def test_datascan_class_parametric(df) -> None: assert all(stat in summary_res.columns for stat in stats_that_should_be_present), msg +# TODO: Check stats and expressions themselves by determinstic casing + + @given(happy_path_df | happy_path_ldf) def test_datascan_json_output(df): scanner = DataScan(data=df) From 7218e96726384c5d1899e44473250c61fedd7d81 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 30 Mar 2025 13:55:22 -0400 Subject: [PATCH 56/85] move min/max to structure --- pointblank/scan_profile_stats.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pointblank/scan_profile_stats.py b/pointblank/scan_profile_stats.py index 40b77ae85..93bc59a2d 100644 --- a/pointblank/scan_profile_stats.py +++ b/pointblank/scan_profile_stats.py @@ -60,7 +60,7 @@ class StdStat(Stat): class MinStat(Stat): val: str name: ClassVar[str] = "min" - group = StatGroup.DESCR + group = StatGroup.STRUCTURE expr: ClassVar[nw.Expr] = nw.all().min() # don't cast as float, can be date label: ClassVar[str] = "Min" @@ -69,7 +69,7 @@ class MinStat(Stat): class MaxStat(Stat): val: str name: ClassVar[str] = "max" - group = StatGroup.DESCR + group = StatGroup.STRUCTURE expr: ClassVar[nw.Expr] = nw.all().max() # don't cast as float, can be date label: ClassVar[str] = "Max" From 832ac2ed00117701ebc3ceb15fbaf174bb1e099c Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 30 Mar 2025 14:02:21 -0400 Subject: [PATCH 57/85] remove pandas datetime tests --- tests/test_datascan.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_datascan.py b/tests/test_datascan.py index 277d10ff1..e660be485 100644 --- a/tests/test_datascan.py +++ b/tests/test_datascan.py @@ -26,7 +26,7 @@ ## too much the job of narwhals, and we should avoid stepping on their testing suite. happy_path_df = pt.dataframes( min_size=5, - allowed_dtypes=[pl.Int64, pl.Float64, pl.String, pl.Categorical, pl.Date, pl.Datetime], + allowed_dtypes=[pl.Int64, pl.Float64, pl.String, pl.Categorical, pl.Date], ) happy_path_ldf = pt.dataframes( min_size=5, From 524ad9cd0124f00b6b8ef4e88a01f7393a206173 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 30 Mar 2025 17:57:38 -0400 Subject: [PATCH 58/85] generically type datascan init --- pointblank/datascan.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index f554bcc1d..bfb3eae78 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -16,7 +16,7 @@ from collections.abc import Mapping, Sequence from narwhals.dataframe import DataFrame - from narwhals.typing import Frame, IntoFrame + from narwhals.typing import Frame, IntoFrameT from pointblank.scan_profile_stats import StatGroup @@ -118,7 +118,8 @@ class DataScan: A DataScan object. """ - def __init__(self, data: IntoFrame, tbl_name: str | None = None) -> None: + # TODO: This needs to be generically typed at the class level, ie. DataScan[T] + def __init__(self, data: IntoFrameT, tbl_name: str | None = None) -> None: self.nw_data: Frame = nw.from_native(data) self.tbl_name: str | None = tbl_name self.profile: _DataProfile = self._generate_profile_df() @@ -166,8 +167,8 @@ def _generate_profile_df(self) -> _DataProfile: return profile @property - def summary_data(self) -> DataFrame: - return self.profile.as_dataframe(strict=False) + def summary_data(self) -> IntoFrameT: + return self.profile.as_dataframe(strict=False).to_native() def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: # Create the label, table type, and thresholds HTML fragments From a2eaa733537af65d84fcf60bfc57f419d9a9676b Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 30 Mar 2025 17:57:52 -0400 Subject: [PATCH 59/85] add deterministic test of values --- tests/test_datascan.py | 69 ++++++++++++++++++++++++++++++++++++------ 1 file changed, 59 insertions(+), 10 deletions(-) diff --git a/tests/test_datascan.py b/tests/test_datascan.py index e660be485..3cf53a7d4 100644 --- a/tests/test_datascan.py +++ b/tests/test_datascan.py @@ -3,11 +3,12 @@ import pytest import narwhals as nw -from hypothesis import given, settings, strategies as st -import polars.testing.parametric as pt +from hypothesis import given, settings, strategies as st, example +import polars.testing.parametric as ptp from great_tables import GT -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, NamedTuple import polars as pl +import polars.testing as pt from pointblank.datascan import DataScan, col_summary_tbl from pointblank._datascan_utils import _compact_0_1_fmt, _compact_decimal_fmt, _compact_integer_fmt @@ -24,11 +25,13 @@ ## Also generate pandas and arrow strategies which should smoke test any complete mistakes ## or inconsistent handling in narwhals. Really checking the consistency among packages is ## too much the job of narwhals, and we should avoid stepping on their testing suite. -happy_path_df = pt.dataframes( +## LDF gets a datetime check because eager datetime values are not easily handled by pandas. +## We need the coverage of datetimes generally and that is checked by the ldf, just not for eager. +happy_path_df = ptp.dataframes( min_size=5, allowed_dtypes=[pl.Int64, pl.Float64, pl.String, pl.Categorical, pl.Date], ) -happy_path_ldf = pt.dataframes( +happy_path_ldf = ptp.dataframes( min_size=5, allowed_dtypes=[pl.Int64, pl.Float64, pl.String, pl.Categorical, pl.Date, pl.Datetime], lazy=True, @@ -54,9 +57,8 @@ def test_datascan_class_parametric(df) -> None: df_nw = nw.from_native(df) - summary_res = scanner.summary_data + summary_res: nw.DataFrame = nw.from_native(scanner.summary_data) - physical_summary = _as_physical(scanner.summary_data) physical_input = _as_physical(nw.from_native(df)) ## High Level Checks: @@ -67,8 +69,7 @@ def test_datascan_class_parametric(df) -> None: assert set(cols) == set(df_cols), msg msg = "return type is the physical version of the input" - assert physical_input.implementation == physical_summary.implementation - assert isinstance(scanner.summary_data, nw.DataFrame) + assert physical_input.implementation == summary_res.implementation msg = "did not return correct amount of summary rows" assert len(summary_res) == len(cols) # only for happy path @@ -97,7 +98,55 @@ def test_datascan_class_parametric(df) -> None: assert all(stat in summary_res.columns for stat in stats_that_should_be_present), msg -# TODO: Check stats and expressions themselves by determinstic casing +## Deterministic Casing: +class _Case(NamedTuple): + data: pl.DataFrame + should_be: pl.DataFrame + + +case1 = _Case( + data=pl.DataFrame( + { + "bool_col": [True, False, True, False, True], + "numeric_col": [1.5, 2.3, 3.1, 4.7, 5.2], + } + ), + should_be=pl.DataFrame( + { + "colname": ["bool_col", "numeric_col"], + "std": [None, 1.57], + "mean": [None, 3.36], + "max": [None, 5.2], + "q_1": [None, 2.3], + "p95": [None, 5.1], + "n_missing": [0, 0], + "n_false": [2, None], + "median": [None, 3.1], + "n_true": [3, None], + "iqr": [None, 2.4], + "p05": [None, 1.516], + "n_unique": [2, 5], + "q_3": [None, 4.7], + "min": [None, 1.5], + } + ), +) + + +@pytest.mark.parametrize("case", [case1]) +def test_deterministic_calculations(case: _Case) -> None: + scanner = DataScan(case.data) + + output = scanner.summary_data.drop("icon", "coltype", "sample_data") + + pt.assert_frame_equal( + case.should_be, + output, + check_row_order=False, + check_column_order=False, + check_exact=False, + atol=0.01, + ) @given(happy_path_df | happy_path_ldf) From b81dd3905bec3433030bbf42db2f4592feb66d54 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 30 Mar 2025 18:03:44 -0400 Subject: [PATCH 60/85] add col table summry examples --- tests/test_datascan.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/tests/test_datascan.py b/tests/test_datascan.py index 3cf53a7d4..18e142551 100644 --- a/tests/test_datascan.py +++ b/tests/test_datascan.py @@ -9,6 +9,7 @@ from typing import TYPE_CHECKING, NamedTuple import polars as pl import polars.testing as pt +import pointblank as pb from pointblank.datascan import DataScan, col_summary_tbl from pointblank._datascan_utils import _compact_0_1_fmt, _compact_decimal_fmt, _compact_integer_fmt @@ -159,7 +160,9 @@ def test_datascan_json_output(df): @given(df=happy_path_df) -@settings(max_examples=5) +@example(df=pb.load_dataset(dataset="small_table", tbl_type="polars")) +@example(df=pb.load_dataset(dataset="nycflights", tbl_type="duckdb")) +@settings(deadline=None) def test_col_summary_tbl(df): col_summary = col_summary_tbl(df) From 6d225cc96c4762fe04be128d5e4e5e42e89e4e53 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 30 Mar 2025 18:05:37 -0400 Subject: [PATCH 61/85] decrease rounding decimals in visual --- pointblank/datascan.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index bfb3eae78..c8210986c 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -245,8 +245,8 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: nw.lit(""), ), # TODO: This is a very temporary solution - __frac_n_unique=(nw.col("n_unique") / nw.lit(self.profile.row_count)).round(5), - __frac_n_missing=(nw.col("n_missing") / nw.lit(self.profile.row_count)).round(5), + __frac_n_unique=(nw.col("n_unique") / nw.lit(self.profile.row_count)).round(2), + __frac_n_missing=(nw.col("n_missing") / nw.lit(self.profile.row_count)).round(2), ) .with_columns( n_unique=nw.concat_str( @@ -283,7 +283,7 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: .cols_label(icon="", colname="Column") ## Value Formatting .fmt_integer(columns=fmt_int) - .fmt_number(columns=fmt_float) + .fmt_number(columns=fmt_float, decimals=2) .sub_missing(missing_text="-") ## Generic Styling .tab_style( From 2c66c0aec3553f5faef70089a64ea0c7faee7f8a Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 30 Mar 2025 18:11:30 -0400 Subject: [PATCH 62/85] remove metadata only test --- tests/test_datascan.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/test_datascan.py b/tests/test_datascan.py index 18e142551..35ce9d0e1 100644 --- a/tests/test_datascan.py +++ b/tests/test_datascan.py @@ -161,7 +161,6 @@ def test_datascan_json_output(df): @given(df=happy_path_df) @example(df=pb.load_dataset(dataset="small_table", tbl_type="polars")) -@example(df=pb.load_dataset(dataset="nycflights", tbl_type="duckdb")) @settings(deadline=None) def test_col_summary_tbl(df): col_summary = col_summary_tbl(df) From 3b75f7fc4f3a1302ea6882a3ef156c72b4c701ae Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Mon, 31 Mar 2025 20:47:51 -0400 Subject: [PATCH 63/85] re-impliment assistant scan endpoint --- pointblank/assistant.py | 4 +--- pointblank/datascan.py | 3 --- 2 files changed, 1 insertion(+), 6 deletions(-) diff --git a/pointblank/assistant.py b/pointblank/assistant.py index dfef752c9..21b929780 100644 --- a/pointblank/assistant.py +++ b/pointblank/assistant.py @@ -176,9 +176,7 @@ def assistant( if data is not None: scan = DataScan(data=data) - scan_dict = scan.to_dict() - - tbl_type = scan_dict["tbl_type"] + tbl_type: str = scan.profile.implementation.name.lower() tbl_json = scan.to_json() if tbl_name is not None: diff --git a/pointblank/datascan.py b/pointblank/datascan.py index c8210986c..c62eec323 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -328,9 +328,6 @@ def _build_label_map(cols: Sequence[str]) -> dict[str, Any]: label_map[target_col] = matching_stat.label return label_map - def to_dict(self) -> dict: - raise NotImplementedError - def to_json(self) -> str: prof_dict = self.profile.as_dataframe(strict=False).to_dict(as_series=False) From eb91321e9fa176402dc137c4706a582526f63f85 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Wed, 2 Apr 2025 18:56:25 -0400 Subject: [PATCH 64/85] implement ibis intake --- pointblank/datascan.py | 28 ++++++++++++++++++++++++- tests/test_datascan.py | 47 +++++++++++++++++++++++++++++++++++------- 2 files changed, 67 insertions(+), 8 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index c62eec323..f50044bab 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -6,6 +6,7 @@ import narwhals as nw from great_tables import GT, google_font, html, loc, style +from narwhals.dataframe import LazyFrame from narwhals.typing import FrameT from pointblank._utils_html import _create_table_dims_html, _create_table_type_html @@ -120,7 +121,32 @@ class DataScan: # TODO: This needs to be generically typed at the class level, ie. DataScan[T] def __init__(self, data: IntoFrameT, tbl_name: str | None = None) -> None: - self.nw_data: Frame = nw.from_native(data) + as_native = nw.from_native(data) + + if as_native.implementation.name == "IBIS" and as_native._level == "lazy": + assert isinstance(as_native, LazyFrame) # help mypy + + ibis_native = as_native.to_native() + + valid_conversion_methods = ("to_pyarrow", "to_pandas", "to_polars") + for conv_method in valid_conversion_methods: + try: + valid_native = getattr(ibis_native, conv_method)() + except (NotImplementedError, ImportError, ModuleNotFoundError): + continue + break + else: + msg = ( + "To use `ibis` as input, you must have one of arrow, pandas, polars or numpy " + "available in the process. Until `ibis` is fully supported by Narwhals, this is " + "necessary. Additionally, the data must be collected in order to calculate some " + "structural statistics, which may be performance detrimental." + ) + raise ImportError(msg) + as_native = nw.from_native(valid_native) + + self.nw_data: Frame = nw.from_native(as_native) + self.tbl_name: str | None = tbl_name self.profile: _DataProfile = self._generate_profile_df() diff --git a/tests/test_datascan.py b/tests/test_datascan.py index 35ce9d0e1..7a8360384 100644 --- a/tests/test_datascan.py +++ b/tests/test_datascan.py @@ -13,7 +13,6 @@ from pointblank.datascan import DataScan, col_summary_tbl from pointblank._datascan_utils import _compact_0_1_fmt, _compact_decimal_fmt, _compact_integer_fmt -from pointblank.scan_profile import _as_physical from pointblank.scan_profile_stats import StatGroup, COLUMN_ORDER_REGISTRY if TYPE_CHECKING: @@ -52,6 +51,15 @@ def _pandas_strat(draw) -> pd.DataFrame: @given(happy_path_df | happy_path_ldf | _arrow_strat() | _pandas_strat()) +@example(pb.load_dataset("small_table", "polars")) +@example(pb.load_dataset("small_table", "pandas")) +@example(pb.load_dataset("small_table", "duckdb")) +@example(pb.load_dataset("game_revenue", "polars")) +@example(pb.load_dataset("game_revenue", "pandas")) +@example(pb.load_dataset("game_revenue", "duckdb")) +@example(pb.load_dataset("nycflights", "polars")) +@example(pb.load_dataset("nycflights", "pandas")) +@example(pb.load_dataset("nycflights", "duckdb")) @settings(deadline=None) # too variant to enforce deadline def test_datascan_class_parametric(df) -> None: scanner = DataScan(data=df) @@ -60,8 +68,6 @@ def test_datascan_class_parametric(df) -> None: summary_res: nw.DataFrame = nw.from_native(scanner.summary_data) - physical_input = _as_physical(nw.from_native(df)) - ## High Level Checks: cols = summary_res.select("colname").to_dict()["colname"].to_list() @@ -70,7 +76,13 @@ def test_datascan_class_parametric(df) -> None: assert set(cols) == set(df_cols), msg msg = "return type is the physical version of the input" - assert physical_input.implementation == summary_res.implementation + try: + assert df_nw.implementation == summary_res.implementation + except AssertionError: + if df_nw.implementation.name == "IBIS" and df_nw._level == "lazy": + pass # this is actually expected, the summary will come back in another type + else: + raise AssertionError msg = "did not return correct amount of summary rows" assert len(summary_res) == len(cols) # only for happy path @@ -150,7 +162,17 @@ def test_deterministic_calculations(case: _Case) -> None: ) -@given(happy_path_df | happy_path_ldf) +@given(happy_path_df | happy_path_ldf | _arrow_strat() | _pandas_strat()) +@example(pb.load_dataset("small_table", "polars")) +@example(pb.load_dataset("small_table", "pandas")) +@example(pb.load_dataset("small_table", "duckdb")) +@example(pb.load_dataset("game_revenue", "polars")) +@example(pb.load_dataset("game_revenue", "pandas")) +@example(pb.load_dataset("game_revenue", "duckdb")) +@example(pb.load_dataset("nycflights", "polars")) +@example(pb.load_dataset("nycflights", "pandas")) +@example(pb.load_dataset("nycflights", "duckdb")) +@settings(deadline=None) def test_datascan_json_output(df): scanner = DataScan(data=df) @@ -159,8 +181,19 @@ def test_datascan_json_output(df): assert isinstance(profile_json, str) -@given(df=happy_path_df) -@example(df=pb.load_dataset(dataset="small_table", tbl_type="polars")) +# TODO: paramaterize the whole 9 yards, ie. all datasets/types plus happy paths + + +@given(happy_path_df | happy_path_ldf | _arrow_strat() | _pandas_strat()) +@example(pb.load_dataset("small_table", "polars")) +@example(pb.load_dataset("small_table", "pandas")) +@example(pb.load_dataset("small_table", "duckdb")) +@example(pb.load_dataset("game_revenue", "polars")) +@example(pb.load_dataset("game_revenue", "pandas")) +@example(pb.load_dataset("game_revenue", "duckdb")) +@example(pb.load_dataset("nycflights", "polars")) +@example(pb.load_dataset("nycflights", "pandas")) +@example(pb.load_dataset("nycflights", "duckdb")) @settings(deadline=None) def test_col_summary_tbl(df): col_summary = col_summary_tbl(df) From fbae10820c8e253dec19e4f1164d97071ea951ad Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sat, 5 Apr 2025 08:04:18 -0400 Subject: [PATCH 65/85] rename UQ and NA --- pointblank/datascan.py | 8 ++++++++ pointblank/scan_profile_stats.py | 4 ++-- 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index f50044bab..76023895e 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -335,6 +335,14 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: ) ) + # TODO: + # - mean and SD in own cat + # - min, med, max to span percentiles + # - IQR in separate category at the end + # - T/F in the UQ for bools (can remove them as stat types) + # - datetime value formatting weirdness + # - datetime column formatting weirdness + # If the version of `great_tables` is `>=0.17.0` then disable Quarto table processing if version("great_tables") >= "0.17.0": gt_tbl = gt_tbl.tab_options(quarto_disable_processing=True) diff --git a/pointblank/scan_profile_stats.py b/pointblank/scan_profile_stats.py index 93bc59a2d..5c544202f 100644 --- a/pointblank/scan_profile_stats.py +++ b/pointblank/scan_profile_stats.py @@ -161,7 +161,7 @@ class NMissing(Stat): name: ClassVar[str] = "n_missing" group = StatGroup.STRUCTURE expr: ClassVar[nw.Expr] = nw.all().null_count().cast(nw.Int64) - label: ClassVar[str] = _make_sublabel("Missing", "N") + label: ClassVar[str] = "NA" @dataclass(frozen=True) @@ -170,7 +170,7 @@ class NUnique(Stat): name: ClassVar[str] = "n_unique" group = StatGroup.STRUCTURE expr: ClassVar[nw.Expr] = nw.all().n_unique().cast(nw.Int64) - label: ClassVar[str] = _make_sublabel("UQ", "N") + label: ClassVar[str] = "UQ" COLUMN_ORDER_REGISTRY: tuple[type[Stat], ...] = ( From d8ea301d587acc3c5d01427d4de4d0cc9c2be15b Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sat, 5 Apr 2025 08:07:26 -0400 Subject: [PATCH 66/85] SD and mean in their own category --- pointblank/datascan.py | 2 +- pointblank/scan_profile_stats.py | 14 +++++++++----- 2 files changed, 10 insertions(+), 6 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 76023895e..5a3c9e7b9 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -336,12 +336,12 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: ) # TODO: - # - mean and SD in own cat # - min, med, max to span percentiles # - IQR in separate category at the end # - T/F in the UQ for bools (can remove them as stat types) # - datetime value formatting weirdness # - datetime column formatting weirdness + # - center all column headers # If the version of `great_tables` is `>=0.17.0` then disable Quarto table processing if version("great_tables") >= "0.17.0": diff --git a/pointblank/scan_profile_stats.py b/pointblank/scan_profile_stats.py index 5c544202f..a36512fe2 100644 --- a/pointblank/scan_profile_stats.py +++ b/pointblank/scan_profile_stats.py @@ -15,10 +15,14 @@ class StatGroup(Enum): DESCR = auto() + SUMMARY = auto() STRUCTURE = auto() LOGIC = auto() +# TODO: Make sure all these subclasses are suffixed w/`Stat` + + class Stat(ABC): val: Any name: ClassVar[str] @@ -42,16 +46,16 @@ def _fetch_priv_name(self) -> str: class MeanStat(Stat): val: str name: ClassVar[str] = "mean" - group = StatGroup.DESCR + group = StatGroup.SUMMARY expr: ClassVar[nw.Expr] = nw.all().mean().cast(nw.Float64) label: ClassVar[str] = "Mean" @dataclass(frozen=True) -class StdStat(Stat): +class StdStat(Stat): # TODO: Rename this SD for consistency val: str name: ClassVar[str] = "std" - group = StatGroup.DESCR + group = StatGroup.SUMMARY expr: ClassVar[nw.Expr] = nw.all().std().cast(nw.Float64) label: ClassVar[str] = "SD" @@ -176,10 +180,10 @@ class NUnique(Stat): COLUMN_ORDER_REGISTRY: tuple[type[Stat], ...] = ( NUnique, NMissing, - MinStat, - MaxStat, MeanStat, StdStat, + MinStat, + MaxStat, P05Stat, Q1Stat, MedianStat, From 5c10c98a9b1c232fba8b6c785624007a92c48668 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sat, 5 Apr 2025 08:09:15 -0400 Subject: [PATCH 67/85] fix order of min/max --- pointblank/datascan.py | 1 - pointblank/scan_profile_stats.py | 6 +++--- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 5a3c9e7b9..c6d54aa28 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -336,7 +336,6 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: ) # TODO: - # - min, med, max to span percentiles # - IQR in separate category at the end # - T/F in the UQ for bools (can remove them as stat types) # - datetime value formatting weirdness diff --git a/pointblank/scan_profile_stats.py b/pointblank/scan_profile_stats.py index a36512fe2..01420a7d5 100644 --- a/pointblank/scan_profile_stats.py +++ b/pointblank/scan_profile_stats.py @@ -64,7 +64,7 @@ class StdStat(Stat): # TODO: Rename this SD for consistency class MinStat(Stat): val: str name: ClassVar[str] = "min" - group = StatGroup.STRUCTURE + group = StatGroup.DESCR expr: ClassVar[nw.Expr] = nw.all().min() # don't cast as float, can be date label: ClassVar[str] = "Min" @@ -73,7 +73,7 @@ class MinStat(Stat): class MaxStat(Stat): val: str name: ClassVar[str] = "max" - group = StatGroup.STRUCTURE + group = StatGroup.DESCR expr: ClassVar[nw.Expr] = nw.all().max() # don't cast as float, can be date label: ClassVar[str] = "Max" @@ -183,12 +183,12 @@ class NUnique(Stat): MeanStat, StdStat, MinStat, - MaxStat, P05Stat, Q1Stat, MedianStat, Q3Stat, P95Stat, + MaxStat, IQRStat, NTrue, NFalse, From 97018bf948c9b932530cd7a5412c6dacf02745b9 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sat, 5 Apr 2025 08:10:09 -0400 Subject: [PATCH 68/85] IQR in dedicated cat --- pointblank/scan_profile_stats.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/pointblank/scan_profile_stats.py b/pointblank/scan_profile_stats.py index 01420a7d5..222fdccdf 100644 --- a/pointblank/scan_profile_stats.py +++ b/pointblank/scan_profile_stats.py @@ -18,6 +18,7 @@ class StatGroup(Enum): SUMMARY = auto() STRUCTURE = auto() LOGIC = auto() + IQR = auto() # TODO: Make sure all these subclasses are suffixed w/`Stat` @@ -127,7 +128,7 @@ class P95Stat(Stat): class IQRStat(Stat): val: str name: ClassVar[str] = "iqr" - group = StatGroup.DESCR + group = StatGroup.IQR expr: ClassVar[nw.Expr] = nw.col(Q3Stat._fetch_priv_name()) - nw.col( Q1Stat._fetch_priv_name() ).cast(nw.Float64) @@ -189,7 +190,7 @@ class NUnique(Stat): Q3Stat, P95Stat, MaxStat, - IQRStat, NTrue, NFalse, + IQRStat, ) From 4a43125893e40f68d8149fc0f7e04b21909f9733 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sat, 5 Apr 2025 08:20:29 -0400 Subject: [PATCH 69/85] align column values to right and headers to center --- pointblank/datascan.py | 6 ++++-- pointblank/scan_profile_stats.py | 2 +- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index c6d54aa28..8c9eee2b3 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -307,6 +307,10 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: ## Labeling .cols_label(label_map) .cols_label(icon="", colname="Column") + .cols_align("center", columns=list(present_stat_cols)) + .tab_style( + style=style.text(align="right"), locations=loc.body(columns=list(present_stat_cols)) + ) ## Value Formatting .fmt_integer(columns=fmt_int) .fmt_number(columns=fmt_float, decimals=2) @@ -336,11 +340,9 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: ) # TODO: - # - IQR in separate category at the end # - T/F in the UQ for bools (can remove them as stat types) # - datetime value formatting weirdness # - datetime column formatting weirdness - # - center all column headers # If the version of `great_tables` is `>=0.17.0` then disable Quarto table processing if version("great_tables") >= "0.17.0": diff --git a/pointblank/scan_profile_stats.py b/pointblank/scan_profile_stats.py index 222fdccdf..d6bc4919a 100644 --- a/pointblank/scan_profile_stats.py +++ b/pointblank/scan_profile_stats.py @@ -179,8 +179,8 @@ class NUnique(Stat): COLUMN_ORDER_REGISTRY: tuple[type[Stat], ...] = ( - NUnique, NMissing, + NUnique, MeanStat, StdStat, MinStat, From d6bff9d8a507b0d98f5a7cb352426eee66155043 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sat, 5 Apr 2025 08:31:04 -0400 Subject: [PATCH 70/85] make calculations aware of column name --- pointblank/scan_profile.py | 62 ++++++++++++++++++-------------- pointblank/scan_profile_stats.py | 40 +++++++++++++-------- 2 files changed, 61 insertions(+), 41 deletions(-) diff --git a/pointblank/scan_profile.py b/pointblank/scan_profile.py index b58abe154..1e58d06a3 100644 --- a/pointblank/scan_profile.py +++ b/pointblank/scan_profile.py @@ -117,7 +117,7 @@ def spawn_profile(self, _subprofile: type[ColumnProfile]) -> ColumnProfile: def calc_stats(self, data: Frame) -> None: summarized = _as_physical( - data.select(_nmissing=NMissing.expr, _nunique=NUnique.expr) + data.select(_col=self.colname).select(_nmissing=NMissing.expr, _nunique=NUnique.expr) ).to_dict() self.statistics.extend( @@ -132,7 +132,7 @@ class _DateProfile(ColumnProfile): _type: _TypeMap = _TypeMap.DATE def calc_stats(self, data: Frame): - res = data.select(_min=MinStat.expr, _max=MaxStat.expr) + res = data.rename({self.colname: "_col"}).select(_min=MinStat.expr, _max=MaxStat.expr) physical = _as_physical(res).to_dict() @@ -148,7 +148,7 @@ class _BoolProfile(ColumnProfile): _type: _TypeMap = _TypeMap.BOOL def calc_stats(self, data: Frame) -> None: - res = data.select(_ntrue=NTrue.expr, _nfalse=NFalse.expr) + res = data.rename({self.colname: "_col"}).select(_ntrue=NTrue.expr, _nfalse=NFalse.expr) physical = _as_physical(res).to_dict() @@ -166,18 +166,22 @@ class _StringProfile(ColumnProfile): def calc_stats(self, data: Frame): str_data = data.select(nw.all().cast(nw.String).str.len_chars()) - summarized = str_data.select( - _mean=MeanStat.expr, - _median=MedianStat.expr, - _std=StdStat.expr, - _min=MinStat.expr, - _max=MaxStat.expr, - _p_05=P05Stat.expr, - _q_1=Q1Stat.expr, - _q_3=Q3Stat.expr, - _p_95=P95Stat.expr, - ).with_columns( - _iqr=IQRStat.expr, + summarized = ( + str_data.rename({self.colname: "_col"}) + .select( + _mean=MeanStat.expr, + _median=MedianStat.expr, + _std=StdStat.expr, + _min=MinStat.expr, + _max=MaxStat.expr, + _p_05=P05Stat.expr, + _q_1=Q1Stat.expr, + _q_3=Q3Stat.expr, + _p_95=P95Stat.expr, + ) + .with_columns( + _iqr=IQRStat.expr, + ) ) physical = _as_physical(summarized).to_dict() @@ -201,18 +205,22 @@ class _NumericProfile(ColumnProfile): _type: _TypeMap = _TypeMap.NUMERIC def calc_stats(self, data: Frame): - res = data.select( - _mean=MeanStat.expr, - _median=MedianStat.expr, - _std=StdStat.expr, - _min=MinStat.expr, - _max=MaxStat.expr, - _p_05=P05Stat.expr, - _q_1=Q1Stat.expr, - _q_3=Q3Stat.expr, - _p_95=P95Stat.expr, - ).with_columns( - _iqr=IQRStat.expr, # TODO: Need something to indicate this consistently + res = ( + data.rename({self.colname: "_col"}) + .select( + _mean=MeanStat.expr, + _median=MedianStat.expr, + _std=StdStat.expr, + _min=MinStat.expr, + _max=MaxStat.expr, + _p_05=P05Stat.expr, + _q_1=Q1Stat.expr, + _q_3=Q3Stat.expr, + _p_95=P95Stat.expr, + ) + .with_columns( + _iqr=IQRStat.expr, # TODO: Need something to indicate this consistently + ) ) summarized = _as_physical(res).to_dict() diff --git a/pointblank/scan_profile_stats.py b/pointblank/scan_profile_stats.py index d6bc4919a..5cf0ff5b0 100644 --- a/pointblank/scan_profile_stats.py +++ b/pointblank/scan_profile_stats.py @@ -19,9 +19,11 @@ class StatGroup(Enum): STRUCTURE = auto() LOGIC = auto() IQR = auto() + FREQ = auto() # TODO: Make sure all these subclasses are suffixed w/`Stat` +# TODO: Replace all the nw.all w/_col class Stat(ABC): @@ -48,7 +50,7 @@ class MeanStat(Stat): val: str name: ClassVar[str] = "mean" group = StatGroup.SUMMARY - expr: ClassVar[nw.Expr] = nw.all().mean().cast(nw.Float64) + expr: ClassVar[nw.Expr] = nw.col("_col").mean().cast(nw.Float64) label: ClassVar[str] = "Mean" @@ -57,7 +59,7 @@ class StdStat(Stat): # TODO: Rename this SD for consistency val: str name: ClassVar[str] = "std" group = StatGroup.SUMMARY - expr: ClassVar[nw.Expr] = nw.all().std().cast(nw.Float64) + expr: ClassVar[nw.Expr] = nw.col("_col").std().cast(nw.Float64) label: ClassVar[str] = "SD" @@ -66,7 +68,7 @@ class MinStat(Stat): val: str name: ClassVar[str] = "min" group = StatGroup.DESCR - expr: ClassVar[nw.Expr] = nw.all().min() # don't cast as float, can be date + expr: ClassVar[nw.Expr] = nw.col("_col").min() # don't cast as float, can be date label: ClassVar[str] = "Min" @@ -75,7 +77,7 @@ class MaxStat(Stat): val: str name: ClassVar[str] = "max" group = StatGroup.DESCR - expr: ClassVar[nw.Expr] = nw.all().max() # don't cast as float, can be date + expr: ClassVar[nw.Expr] = nw.col("_col").max() # don't cast as float, can be date label: ClassVar[str] = "Max" @@ -84,7 +86,9 @@ class P05Stat(Stat): val: str name: ClassVar[str] = "p05" group = StatGroup.DESCR - expr: ClassVar[nw.Expr] = nw.all().quantile(0.005, interpolation="linear").cast(nw.Float64) + expr: ClassVar[nw.Expr] = ( + nw.col("_col").quantile(0.005, interpolation="linear").cast(nw.Float64) + ) label: ClassVar[str] = _make_sublabel("P", "5") @@ -93,7 +97,7 @@ class Q1Stat(Stat): val: str name: ClassVar[str] = "q_1" group = StatGroup.DESCR - expr: ClassVar[nw.Expr] = nw.all().quantile(0.25, interpolation="linear").cast(nw.Float64) + expr: ClassVar[nw.Expr] = nw.col("_col").quantile(0.25, interpolation="linear").cast(nw.Float64) label: ClassVar[str] = _make_sublabel("Q", "1") @@ -102,7 +106,7 @@ class MedianStat(Stat): val: str name: ClassVar[str] = "median" group = StatGroup.DESCR - expr: ClassVar[nw.Expr] = nw.all().median().cast(nw.Float64) + expr: ClassVar[nw.Expr] = nw.col("_col").median().cast(nw.Float64) label: ClassVar[str] = "Med" @@ -111,7 +115,7 @@ class Q3Stat(Stat): val: str name: ClassVar[str] = "q_3" group = StatGroup.DESCR - expr: ClassVar[nw.Expr] = nw.all().quantile(0.75, interpolation="linear").cast(nw.Float64) + expr: ClassVar[nw.Expr] = nw.col("_col").quantile(0.75, interpolation="linear").cast(nw.Float64) label: ClassVar[str] = _make_sublabel("Q", "3") @@ -120,7 +124,7 @@ class P95Stat(Stat): val: str name: ClassVar[str] = "p95" group = StatGroup.DESCR - expr: ClassVar[nw.Expr] = nw.all().quantile(0.95, interpolation="linear").cast(nw.Float64) + expr: ClassVar[nw.Expr] = nw.col("_col").quantile(0.95, interpolation="linear").cast(nw.Float64) label: ClassVar[str] = _make_sublabel("P", "95") @@ -140,12 +144,12 @@ class NTrue(Stat): val: int name: ClassVar[str] = "n_true" group = StatGroup.LOGIC - expr: ClassVar[nw.Expr] = nw.all().sum().cast(nw.Int64) + expr: ClassVar[nw.Expr] = nw.col("_col").sum().cast(nw.Int64) label: ClassVar[str] = _make_sublabel("True", "N") @dataclass(frozen=True) -class NFalse(Stat): +class NFalse(Stat): # TODO remove this ? val: int name: ClassVar[str] = "n_false" group = StatGroup.LOGIC @@ -160,12 +164,20 @@ class NFalse(Stat): label: ClassVar[str] = _make_sublabel("False", "N") +@dataclass(frozen=True) +class FreqStats(Stat): + val: list[dict[str, int]] + name: ClassVar[str] = "freqs" + group = StatGroup.FREQ + expr: ClassVar[nw.Expr] = nw.len().over("_col") + + @dataclass(frozen=True) class NMissing(Stat): val: int name: ClassVar[str] = "n_missing" group = StatGroup.STRUCTURE - expr: ClassVar[nw.Expr] = nw.all().null_count().cast(nw.Int64) + expr: ClassVar[nw.Expr] = nw.col("_col").null_count().cast(nw.Int64) label: ClassVar[str] = "NA" @@ -174,13 +186,13 @@ class NUnique(Stat): val: int name: ClassVar[str] = "n_unique" group = StatGroup.STRUCTURE - expr: ClassVar[nw.Expr] = nw.all().n_unique().cast(nw.Int64) + expr: ClassVar[nw.Expr] = nw.col("_col").n_unique().cast(nw.Int64) label: ClassVar[str] = "UQ" COLUMN_ORDER_REGISTRY: tuple[type[Stat], ...] = ( - NMissing, NUnique, + NMissing, MeanStat, StdStat, MinStat, From d8890ffa06905e263dda12f1291747370abb9150 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 13 Apr 2025 21:07:41 -0400 Subject: [PATCH 71/85] Implement frequencies as a skeleton concept; incorporating bools --- pointblank/datascan.py | 85 ++++++++++++++++++++++++++++++-- pointblank/scan_profile.py | 32 ++++++------ pointblank/scan_profile_stats.py | 57 ++++++--------------- tests/test_datascan.py | 31 ++++++------ 4 files changed, 129 insertions(+), 76 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 8c9eee2b3..f5446e623 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -1,5 +1,6 @@ from __future__ import annotations +import contextlib import json from importlib.metadata import version from typing import TYPE_CHECKING, Any @@ -235,6 +236,8 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: non_stat_cols = ("icon", "colname") # TODO: need a better place for this present_stat_cols: set[str] = set(data.columns) - set(non_stat_cols) present_stat_cols.remove("coltype") + with contextlib.suppress(KeyError): + present_stat_cols.remove("freqs") # TODO: currently used for html but no displayed? ## Assemble the target order and find what columns need borders. ## Borders should be placed to divide the stat "groups" and create a @@ -270,21 +273,86 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: nw.col("coltype"), nw.lit(""), ), - # TODO: This is a very temporary solution + # TODO: This is a temporary solution? __frac_n_unique=(nw.col("n_unique") / nw.lit(self.profile.row_count)).round(2), __frac_n_missing=(nw.col("n_missing") / nw.lit(self.profile.row_count)).round(2), ) + # Format the unique and missing pct strings .with_columns( n_unique=nw.concat_str( - nw.col("n_unique"), nw.lit("
"), nw.col("__frac_n_unique") + nw.col("n_unique"), + nw.lit("
"), + nw.col("__frac_n_unique"), ), n_missing=nw.concat_str( - nw.col("n_missing"), nw.lit("
"), nw.col("__frac_n_missing") + nw.col("n_missing"), + nw.lit("
"), + nw.col("__frac_n_missing"), ), ) + # TODO: Should be able to use selectors for this .drop("__frac_n_unique", "__frac_n_missing", "coltype") ) + if "freqs" in formatted_data.columns: # TODO: don't love this arbitrary check + # Extract HTML freqs: + try: + formatted_data = formatted_data.with_columns( + __freq_true=nw.col("freqs").struct.field("True"), + __freq_false=nw.col("freqs").struct.field("False"), + ) + except Exception as e: + pass # struct not available in the implementation, ie. numpy Pandas + else: + freq_ser: nw.Series = formatted_data["freqs"] + trues: list[int | None] = [] + falses: list[int | None] = [] + for freq in freq_ser: + try: + trues.append(freq["True"]) + falses.append(freq["False"]) + except (KeyError, TypeError): + trues.append(None) + falses.append(None) + true_ser: nw.Series = nw.new_series( + name="__freq_true", values=trues, backend=self.profile.implementation + ) + false_ser: nw.Series = nw.new_series( + name="__freq_false", values=falses, backend=self.profile.implementation + ) + formatted_data = formatted_data.with_columns( + __freq_true=true_ser, __freq_false=false_ser + ) + + formatted_data = ( + formatted_data.with_columns( + # for bools, UQs are represented as percentages + __pct_true=(nw.col("__freq_true") / self.profile.row_count).round(2), + __pct_false=(nw.col("__freq_false") / self.profile.row_count).round(2), + ) + .with_columns( + __bool_unique_html=nw.concat_str( + nw.lit("T"), + nw.col("__pct_true"), + nw.lit("
F"), + nw.col("__pct_false"), + ), + ) + .with_columns( + n_unique=nw.when(~nw.col("__bool_unique_html").is_null()) + .then(nw.col("__bool_unique_html")) + .otherwise(nw.col("n_unique")) + ) + .drop( + "__freq_true", + "__freq_false", + "__bool_unique_html", + "freqs", + "__pct_true", + "__pct_false", + ) + ) + ## Determine Value Formatting Selectors: fmt_int: list[str] = formatted_data.select(nw.selectors.by_dtype(nw.dtypes.Int64)).columns fmt_float: list[str] = formatted_data.select( @@ -314,7 +382,6 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: ## Value Formatting .fmt_integer(columns=fmt_int) .fmt_number(columns=fmt_float, decimals=2) - .sub_missing(missing_text="-") ## Generic Styling .tab_style( style=style.text(size="10px"), @@ -333,16 +400,24 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: style=style.borders(sides="left", color="#E5E5E5", style="dashed"), locations=loc.body(columns=list(present_stat_cols)), ) - ## Formatting + # ## Formatting .cols_width( icon="35px", colname="200px", **{stat_col: "50px" for stat_col in present_stat_cols} ) ) + if "PYARROW" != formatted_data.implementation.name: + # TODO: this is more proactive than it should be IMO; would like to see EAFP + gt_tbl = gt_tbl.sub_missing(missing_text="-") + # https://github.com/posit-dev/great-tables/issues/667 + # TODO: # - T/F in the UQ for bools (can remove them as stat types) # - datetime value formatting weirdness # - datetime column formatting weirdness + # - very small percentages should get a less than + # - ANY .00 should be rounded to int, ie. 1.00 -> 1 + # - add the SL back # If the version of `great_tables` is `>=0.17.0` then disable Quarto table processing if version("great_tables") >= "0.17.0": diff --git a/pointblank/scan_profile.py b/pointblank/scan_profile.py index 1e58d06a3..efc4f1d3f 100644 --- a/pointblank/scan_profile.py +++ b/pointblank/scan_profile.py @@ -13,14 +13,13 @@ from pointblank._constants import SVG_ICONS_FOR_DATA_TYPES from pointblank._utils import transpose_dicts from pointblank.scan_profile_stats import ( + FreqStat, IQRStat, MaxStat, MeanStat, MedianStat, MinStat, - NFalse, NMissing, - NTrue, NUnique, P05Stat, P95Stat, @@ -148,16 +147,21 @@ class _BoolProfile(ColumnProfile): _type: _TypeMap = _TypeMap.BOOL def calc_stats(self, data: Frame) -> None: - res = data.rename({self.colname: "_col"}).select(_ntrue=NTrue.expr, _nfalse=NFalse.expr) + group_by_contexts = ( + data.rename({self.colname: "_col"}).group_by("_col").agg(_freq=FreqStat.expr) + ) - physical = _as_physical(res).to_dict() + summarized_groupby = _as_physical(group_by_contexts).to_dict() - self.statistics.extend( - [ - NTrue(physical["_ntrue"].item()), - NFalse(physical["_nfalse"].item()), - ] - ) + # TODO: Need a real way to do this + col_vals: list[Any] = summarized_groupby["_col"].to_list() + freqs: list[int] = summarized_groupby["_freq"].to_list() + + freq_dict: dict[str, int] = { + str(colval): freq for colval, freq in zip(col_vals, freqs, strict=True) + } + + self.statistics.extend([FreqStat(freq_dict)]) class _StringProfile(ColumnProfile): @@ -166,6 +170,8 @@ class _StringProfile(ColumnProfile): def calc_stats(self, data: Frame): str_data = data.select(nw.all().cast(nw.String).str.len_chars()) + # TODO: We should get an FreqStat here; estimate cardinality first + summarized = ( str_data.rename({self.colname: "_col"}) .select( @@ -218,13 +224,11 @@ def calc_stats(self, data: Frame): _q_3=Q3Stat.expr, _p_95=P95Stat.expr, ) - .with_columns( - _iqr=IQRStat.expr, # TODO: Need something to indicate this consistently - ) + # TODO: need a consistent way to indicate this + .with_columns(_iqr=IQRStat.expr) ) summarized = _as_physical(res).to_dict() - self.statistics.extend( [ MeanStat(summarized["_mean"].item()), diff --git a/pointblank/scan_profile_stats.py b/pointblank/scan_profile_stats.py index 5cf0ff5b0..406544cad 100644 --- a/pointblank/scan_profile_stats.py +++ b/pointblank/scan_profile_stats.py @@ -50,7 +50,7 @@ class MeanStat(Stat): val: str name: ClassVar[str] = "mean" group = StatGroup.SUMMARY - expr: ClassVar[nw.Expr] = nw.col("_col").mean().cast(nw.Float64) + expr: ClassVar[nw.Expr] = nw.col("_col").mean() label: ClassVar[str] = "Mean" @@ -59,7 +59,7 @@ class StdStat(Stat): # TODO: Rename this SD for consistency val: str name: ClassVar[str] = "std" group = StatGroup.SUMMARY - expr: ClassVar[nw.Expr] = nw.col("_col").std().cast(nw.Float64) + expr: ClassVar[nw.Expr] = nw.col("_col").std() label: ClassVar[str] = "SD" @@ -86,9 +86,7 @@ class P05Stat(Stat): val: str name: ClassVar[str] = "p05" group = StatGroup.DESCR - expr: ClassVar[nw.Expr] = ( - nw.col("_col").quantile(0.005, interpolation="linear").cast(nw.Float64) - ) + expr: ClassVar[nw.Expr] = nw.col("_col").quantile(0.005, interpolation="linear") label: ClassVar[str] = _make_sublabel("P", "5") @@ -97,7 +95,7 @@ class Q1Stat(Stat): val: str name: ClassVar[str] = "q_1" group = StatGroup.DESCR - expr: ClassVar[nw.Expr] = nw.col("_col").quantile(0.25, interpolation="linear").cast(nw.Float64) + expr: ClassVar[nw.Expr] = nw.col("_col").quantile(0.25, interpolation="linear") label: ClassVar[str] = _make_sublabel("Q", "1") @@ -106,7 +104,7 @@ class MedianStat(Stat): val: str name: ClassVar[str] = "median" group = StatGroup.DESCR - expr: ClassVar[nw.Expr] = nw.col("_col").median().cast(nw.Float64) + expr: ClassVar[nw.Expr] = nw.col("_col").median() label: ClassVar[str] = "Med" @@ -115,7 +113,7 @@ class Q3Stat(Stat): val: str name: ClassVar[str] = "q_3" group = StatGroup.DESCR - expr: ClassVar[nw.Expr] = nw.col("_col").quantile(0.75, interpolation="linear").cast(nw.Float64) + expr: ClassVar[nw.Expr] = nw.col("_col").quantile(0.75, interpolation="linear") label: ClassVar[str] = _make_sublabel("Q", "3") @@ -124,7 +122,7 @@ class P95Stat(Stat): val: str name: ClassVar[str] = "p95" group = StatGroup.DESCR - expr: ClassVar[nw.Expr] = nw.col("_col").quantile(0.95, interpolation="linear").cast(nw.Float64) + expr: ClassVar[nw.Expr] = nw.col("_col").quantile(0.95, interpolation="linear") label: ClassVar[str] = _make_sublabel("P", "95") @@ -133,43 +131,17 @@ class IQRStat(Stat): val: str name: ClassVar[str] = "iqr" group = StatGroup.IQR - expr: ClassVar[nw.Expr] = nw.col(Q3Stat._fetch_priv_name()) - nw.col( - Q1Stat._fetch_priv_name() - ).cast(nw.Float64) + expr: ClassVar[nw.Expr] = nw.col(Q3Stat._fetch_priv_name()) - nw.col(Q1Stat._fetch_priv_name()) label: ClassVar[str] = "IQR" @dataclass(frozen=True) -class NTrue(Stat): - val: int - name: ClassVar[str] = "n_true" - group = StatGroup.LOGIC - expr: ClassVar[nw.Expr] = nw.col("_col").sum().cast(nw.Int64) - label: ClassVar[str] = _make_sublabel("True", "N") - - -@dataclass(frozen=True) -class NFalse(Stat): # TODO remove this ? - val: int - name: ClassVar[str] = "n_false" - group = StatGroup.LOGIC - ## Indirection to the point of absurdity: - ## - Reversing the bool results in inconsistent unsigned types, ie. False is max hexidecimal - ## - Filtering on `nw.all` is generally unavailable, at least for polars - ## - Replacing T/F w/Null is not interpreted correctly by Narwhals - ## - Swapping 0 -> 1 and 1 -> 0 is the most consistent, albiet odd - expr: ClassVar[nw.Expr] = ( - nw.all().cast(nw.Int64).replace_strict([1, 0], new=[0, 1]).sum().cast(nw.Int64) - ) - label: ClassVar[str] = _make_sublabel("False", "N") - - -@dataclass(frozen=True) -class FreqStats(Stat): - val: list[dict[str, int]] +class FreqStat(Stat): + val: dict[str, int] # the key must be stringified name: ClassVar[str] = "freqs" group = StatGroup.FREQ - expr: ClassVar[nw.Expr] = nw.len().over("_col") + expr: ClassVar[nw.Expr] = nw.len() + label: ClassVar[str] = "Freq" @dataclass(frozen=True) @@ -191,8 +163,8 @@ class NUnique(Stat): COLUMN_ORDER_REGISTRY: tuple[type[Stat], ...] = ( - NUnique, NMissing, + NUnique, MeanStat, StdStat, MinStat, @@ -202,7 +174,6 @@ class NUnique(Stat): Q3Stat, P95Stat, MaxStat, - NTrue, - NFalse, + FreqStat, IQRStat, ) diff --git a/tests/test_datascan.py b/tests/test_datascan.py index 7a8360384..b2e0d5c29 100644 --- a/tests/test_datascan.py +++ b/tests/test_datascan.py @@ -3,6 +3,7 @@ import pytest import narwhals as nw +import polars.selectors as cs from hypothesis import given, settings, strategies as st, example import polars.testing.parametric as ptp from great_tables import GT @@ -120,6 +121,7 @@ class _Case(NamedTuple): case1 = _Case( data=pl.DataFrame( { + # TODO: Make the bool tri-valent "bool_col": [True, False, True, False, True], "numeric_col": [1.5, 2.3, 3.1, 4.7, 5.2], } @@ -133,14 +135,13 @@ class _Case(NamedTuple): "q_1": [None, 2.3], "p95": [None, 5.1], "n_missing": [0, 0], - "n_false": [2, None], "median": [None, 3.1], - "n_true": [3, None], "iqr": [None, 2.4], "p05": [None, 1.516], "n_unique": [2, 5], "q_3": [None, 4.7], "min": [None, 1.5], + "freqs": [{"True": 3, "False": 2}, None], } ), ) @@ -152,14 +153,19 @@ def test_deterministic_calculations(case: _Case) -> None: output = scanner.summary_data.drop("icon", "coltype", "sample_data") - pt.assert_frame_equal( - case.should_be, - output, - check_row_order=False, - check_column_order=False, - check_exact=False, - atol=0.01, - ) + check_settings = { + "check_row_order": False, + "check_column_order": False, + "check_exact": False, + "atol": 0.01, + } + + pt.assert_frame_equal(case.should_be, output, check_dtypes=False, **check_settings) + + output_clean = output.drop("freqs") # TODO: make this dynamic, ie. a a struct? + should_be_clean = case.should_be.drop("freqs") + + pt.assert_frame_equal(should_be_clean, output_clean, check_dtypes=True, **check_settings) @given(happy_path_df | happy_path_ldf | _arrow_strat() | _pandas_strat()) @@ -181,9 +187,7 @@ def test_datascan_json_output(df): assert isinstance(profile_json, str) -# TODO: paramaterize the whole 9 yards, ie. all datasets/types plus happy paths - - +@example(pb.load_dataset("nycflights", "duckdb")) # ! move this back to the normal spot @given(happy_path_df | happy_path_ldf | _arrow_strat() | _pandas_strat()) @example(pb.load_dataset("small_table", "polars")) @example(pb.load_dataset("small_table", "pandas")) @@ -193,7 +197,6 @@ def test_datascan_json_output(df): @example(pb.load_dataset("game_revenue", "duckdb")) @example(pb.load_dataset("nycflights", "polars")) @example(pb.load_dataset("nycflights", "pandas")) -@example(pb.load_dataset("nycflights", "duckdb")) @settings(deadline=None) def test_col_summary_tbl(df): col_summary = col_summary_tbl(df) From b5b5c155317ed95a6df4e089bf8369044a937bbc Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Tue, 15 Apr 2025 20:54:03 -0400 Subject: [PATCH 72/85] drop trailing dec marks and zeros --- pointblank/datascan.py | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index f5446e623..31895742b 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -381,7 +381,9 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: ) ## Value Formatting .fmt_integer(columns=fmt_int) - .fmt_number(columns=fmt_float, decimals=2) + .fmt_number( + columns=fmt_float, decimals=2, drop_trailing_dec_mark=True, drop_trailing_zeros=True + ) ## Generic Styling .tab_style( style=style.text(size="10px"), @@ -411,13 +413,10 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: gt_tbl = gt_tbl.sub_missing(missing_text="-") # https://github.com/posit-dev/great-tables/issues/667 - # TODO: - # - T/F in the UQ for bools (can remove them as stat types) - # - datetime value formatting weirdness - # - datetime column formatting weirdness - # - very small percentages should get a less than - # - ANY .00 should be rounded to int, ie. 1.00 -> 1 - # - add the SL back + # TODO: datetime value formatting weirdness + # TODO: datetime column formatting weirdness + # TODO: very small percentages should get a less than + # TODO: add the SL back # If the version of `great_tables` is `>=0.17.0` then disable Quarto table processing if version("great_tables") >= "0.17.0": From 8f0330d11ddaf15963dd1d2ddd7e988510419baf Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sat, 19 Apr 2025 17:01:58 -0400 Subject: [PATCH 73/85] early terminations in compact fmt --- pointblank/_datascan_utils.py | 31 ++++++++++++++++++------------- 1 file changed, 18 insertions(+), 13 deletions(-) diff --git a/pointblank/_datascan_utils.py b/pointblank/_datascan_utils.py index 86ccf1107..eee5ef7d5 100644 --- a/pointblank/_datascan_utils.py +++ b/pointblank/_datascan_utils.py @@ -43,18 +43,23 @@ def _compact_decimal_fmt(value: float | int) -> str: return formatted -def _compact_0_1_fmt(value: float | int) -> str: +def _compact_0_1_fmt(value: float | int | None) -> str | None: + if value is None: + return value + if value == 0: - formatted = " 0.00" - elif value == 1: - formatted = " 1.00" - elif abs(value) < 1 and abs(value) >= 0.01: - formatted = " " + fmt_number(value, decimals=2)[0] - elif abs(value) < 0.01: - formatted = "<0.01" - elif abs(value) > 0.99: - formatted = ">0.99" - else: - formatted = fmt_number(value, n_sigfig=3)[0] + return " 0.00" - return formatted + if value == 1: + return " 1.00" + + if abs(value) < 1 and abs(value) >= 0.01: + return " " + fmt_number(value, decimals=2)[0] + + if abs(value) < 0.01: + return "<0.01" + + if abs(value) > 0.99: + return ">0.99" + + return fmt_number(value, n_sigfig=3)[0] From 9f4b8e0b7e916d8ee0578030f569a67a63f96a17 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sat, 19 Apr 2025 17:02:16 -0400 Subject: [PATCH 74/85] impliment custom fraction formatting --- pointblank/datascan.py | 79 +++++++++++++++++++++++++++--------------- 1 file changed, 51 insertions(+), 28 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 31895742b..094bdf51d 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -261,24 +261,51 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: label_map: dict[str, Any] = self._build_label_map(target_order) + def _fmt_frac(vec) -> list[str | None]: # TODO: rename, document, to utils? + res: list[str | None] = [] + for x in vec: + if x is None: + res.append(x) + continue + + if x < 0.01: + res.append("<.01") + continue + + if int(x) == x: # can remove trailing 0s w/o loss + res.append(str(int(x))) + continue + + res.append(str(round(x, 2))) + + return nw.new_series("foo", values=res, backend=self.profile.implementation) + ## Final Formatting: - formatted_data = ( - data.with_columns( - colname=nw.concat_str( - nw.lit( - "
" - ), - nw.col("colname"), - nw.lit("
"), - nw.col("coltype"), - nw.lit("
"), + formatted_data = data.with_columns( + colname=nw.concat_str( + nw.lit( + "
" ), - # TODO: This is a temporary solution? - __frac_n_unique=(nw.col("n_unique") / nw.lit(self.profile.row_count)).round(2), - __frac_n_missing=(nw.col("n_missing") / nw.lit(self.profile.row_count)).round(2), - ) + nw.col("colname"), + nw.lit("
"), + nw.col("coltype"), + nw.lit("
"), + ), + __frac_n_unique=nw.col("n_unique") / nw.lit(self.profile.row_count), + __frac_n_missing=nw.col("n_missing") / nw.lit(self.profile.row_count), + ) + + # format fractions: + # this is an anti-pattern but there's no serious alternative + for _fmt_col in ("__frac_n_unique", "__frac_n_missing"): + formatted: nw.Series = _fmt_frac(formatted_data[_fmt_col]) + formatted_data = formatted_data.drop(_fmt_col) + formatted_data = formatted_data.with_columns(formatted.alias(_fmt_col)) + + formatted_data = ( + # TODO: This is a temporary solution? # Format the unique and missing pct strings - .with_columns( + formatted_data.with_columns( n_unique=nw.concat_str( nw.col("n_unique"), nw.lit("
"), @@ -301,7 +328,7 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: __freq_true=nw.col("freqs").struct.field("True"), __freq_false=nw.col("freqs").struct.field("False"), ) - except Exception as e: + except Exception: # TODO: should be narrowed if possible pass # struct not available in the implementation, ie. numpy Pandas else: freq_ser: nw.Series = formatted_data["freqs"] @@ -327,8 +354,8 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: formatted_data = ( formatted_data.with_columns( # for bools, UQs are represented as percentages - __pct_true=(nw.col("__freq_true") / self.profile.row_count).round(2), - __pct_false=(nw.col("__freq_false") / self.profile.row_count).round(2), + __pct_true=(nw.col("__freq_true") / self.profile.row_count), + __pct_false=(nw.col("__freq_false") / self.profile.row_count), ) .with_columns( __bool_unique_html=nw.concat_str( @@ -366,10 +393,7 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: .cols_align(align="right", columns=list(present_stat_cols)) .opt_table_font(font=google_font("IBM Plex Sans")) .opt_align_table_header(align="left") - .tab_style( - style=style.text(font=google_font("IBM Plex Mono")), - locations=loc.body(), - ) + .tab_style(style=style.text(font=google_font("IBM Plex Mono")), locations=loc.body()) ## Order .cols_move_to_start(target_order) ## Labeling @@ -382,17 +406,16 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: ## Value Formatting .fmt_integer(columns=fmt_int) .fmt_number( - columns=fmt_float, decimals=2, drop_trailing_dec_mark=True, drop_trailing_zeros=True + columns=fmt_float, + decimals=2, + drop_trailing_dec_mark=True, + drop_trailing_zeros=True, ## Generic Styling ) - ## Generic Styling .tab_style( style=style.text(size="10px"), locations=loc.body(columns=list(present_stat_cols)), ) - .tab_style( - style=style.text(size="12px"), - locations=loc.body(columns="colname"), - ) + .tab_style(style=style.text(size="12px"), locations=loc.body(columns="colname")) ## Borders .tab_style( style=style.borders(sides="right", color="#D3D3D3", style="solid"), From 0d94ea7aab29a6849a2d58444b7b5b813c71c81c Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sat, 19 Apr 2025 17:02:34 -0400 Subject: [PATCH 75/85] put min/max in dedicated stat group called bounds --- pointblank/scan_profile_stats.py | 5 +++-- tests/test_datascan.py | 3 ++- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/pointblank/scan_profile_stats.py b/pointblank/scan_profile_stats.py index 406544cad..d62bcd28e 100644 --- a/pointblank/scan_profile_stats.py +++ b/pointblank/scan_profile_stats.py @@ -20,6 +20,7 @@ class StatGroup(Enum): LOGIC = auto() IQR = auto() FREQ = auto() + BOUNDS = auto() # TODO: Make sure all these subclasses are suffixed w/`Stat` @@ -67,7 +68,7 @@ class StdStat(Stat): # TODO: Rename this SD for consistency class MinStat(Stat): val: str name: ClassVar[str] = "min" - group = StatGroup.DESCR + group = StatGroup.BOUNDS expr: ClassVar[nw.Expr] = nw.col("_col").min() # don't cast as float, can be date label: ClassVar[str] = "Min" @@ -76,7 +77,7 @@ class MinStat(Stat): class MaxStat(Stat): val: str name: ClassVar[str] = "max" - group = StatGroup.DESCR + group = StatGroup.BOUNDS expr: ClassVar[nw.Expr] = nw.col("_col").max() # don't cast as float, can be date label: ClassVar[str] = "Max" diff --git a/tests/test_datascan.py b/tests/test_datascan.py index b2e0d5c29..4fa0a5468 100644 --- a/tests/test_datascan.py +++ b/tests/test_datascan.py @@ -109,7 +109,8 @@ def test_datascan_class_parametric(df) -> None: col for col in stats_that_should_be_present if col in summary_res.columns ) if any_in_summary: - assert all(stat in summary_res.columns for stat in stats_that_should_be_present), msg + for stat in stats_that_should_be_present: + assert stat in summary_res.columns, f"{msg}: Missing {stat}" ## Deterministic Casing: From 63cb10180e65d41cb9a5637c4e78e77ac7c3c961 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sat, 19 Apr 2025 17:04:08 -0400 Subject: [PATCH 76/85] annotate min/max should return to descr at some point --- pointblank/datascan.py | 1 - 1 file changed, 1 deletion(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 094bdf51d..145551206 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -438,7 +438,6 @@ def _fmt_frac(vec) -> list[str | None]: # TODO: rename, document, to utils? # TODO: datetime value formatting weirdness # TODO: datetime column formatting weirdness - # TODO: very small percentages should get a less than # TODO: add the SL back # If the version of `great_tables` is `>=0.17.0` then disable Quarto table processing From 307d0a5f1f2deb26bb3e2d3bc2ea401cfe16fa4d Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sat, 19 Apr 2025 17:08:00 -0400 Subject: [PATCH 77/85] fix unformatted t/f percentages --- pointblank/datascan.py | 16 +++++++++++----- pointblank/scan_profile_stats.py | 4 ++-- 2 files changed, 13 insertions(+), 7 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 145551206..1a53edba7 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -351,13 +351,19 @@ def _fmt_frac(vec) -> list[str | None]: # TODO: rename, document, to utils? __freq_true=true_ser, __freq_false=false_ser ) + ## format pct true values + formatted_data = formatted_data.with_columns( + # for bools, UQs are represented as percentages + __pct_true=nw.col("__freq_true") / self.profile.row_count, + __pct_false=nw.col("__freq_false") / self.profile.row_count, + ) + for _fmt_col in ("__pct_true", "__pct_false"): + formatted: nw.Series = _fmt_frac(formatted_data[_fmt_col]) + formatted_data = formatted_data.drop(_fmt_col) + formatted_data = formatted_data.with_columns(formatted.alias(_fmt_col)) + formatted_data = ( formatted_data.with_columns( - # for bools, UQs are represented as percentages - __pct_true=(nw.col("__freq_true") / self.profile.row_count), - __pct_false=(nw.col("__freq_false") / self.profile.row_count), - ) - .with_columns( __bool_unique_html=nw.concat_str( nw.lit("T"), nw.col("__pct_true"), diff --git a/pointblank/scan_profile_stats.py b/pointblank/scan_profile_stats.py index d62bcd28e..63b57fb34 100644 --- a/pointblank/scan_profile_stats.py +++ b/pointblank/scan_profile_stats.py @@ -68,7 +68,7 @@ class StdStat(Stat): # TODO: Rename this SD for consistency class MinStat(Stat): val: str name: ClassVar[str] = "min" - group = StatGroup.BOUNDS + group = StatGroup.BOUNDS # TODO: These should get put back in DESCR once datetime p* expr: ClassVar[nw.Expr] = nw.col("_col").min() # don't cast as float, can be date label: ClassVar[str] = "Min" @@ -77,7 +77,7 @@ class MinStat(Stat): class MaxStat(Stat): val: str name: ClassVar[str] = "max" - group = StatGroup.BOUNDS + group = StatGroup.BOUNDS # TODO: These should get put back in DESCR once datetime p* expr: ClassVar[nw.Expr] = nw.col("_col").max() # don't cast as float, can be date label: ClassVar[str] = "Max" From 1c1db3792066e24ff68eba682f2905bca1135b5e Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 20 Apr 2025 18:39:08 -0400 Subject: [PATCH 78/85] move _fmt_frac to utils --- pointblank/_utils_html.py | 20 ++++++++++++++++++++ pointblank/datascan.py | 31 +++++++------------------------ 2 files changed, 27 insertions(+), 24 deletions(-) diff --git a/pointblank/_utils_html.py b/pointblank/_utils_html.py index 004ed79d7..c540f53ed 100644 --- a/pointblank/_utils_html.py +++ b/pointblank/_utils_html.py @@ -8,6 +8,26 @@ from pointblank._utils import _format_to_integer_value +def _fmt_frac(vec) -> list[str | None]: + res: list[str | None] = [] + for x in vec: + if x is None: + res.append(x) + continue + + if x < 0.01: + res.append("<.01") + continue + + if int(x) == x: # can remove trailing 0s w/o loss + res.append(str(int(x))) + continue + + res.append(str(round(x, 2))) + + return res + + def _make_sublabel(major: str, minor: str) -> Any: return html( f'{major!s}{minor!s}' diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 1a53edba7..83ce91a07 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -10,7 +10,7 @@ from narwhals.dataframe import LazyFrame from narwhals.typing import FrameT -from pointblank._utils_html import _create_table_dims_html, _create_table_type_html +from pointblank._utils_html import _create_table_dims_html, _create_table_type_html, _fmt_frac from pointblank.scan_profile import ColumnProfile, _as_physical, _DataProfile, _TypeMap from pointblank.scan_profile_stats import COLUMN_ORDER_REGISTRY @@ -261,25 +261,6 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: label_map: dict[str, Any] = self._build_label_map(target_order) - def _fmt_frac(vec) -> list[str | None]: # TODO: rename, document, to utils? - res: list[str | None] = [] - for x in vec: - if x is None: - res.append(x) - continue - - if x < 0.01: - res.append("<.01") - continue - - if int(x) == x: # can remove trailing 0s w/o loss - res.append(str(int(x))) - continue - - res.append(str(round(x, 2))) - - return nw.new_series("foo", values=res, backend=self.profile.implementation) - ## Final Formatting: formatted_data = data.with_columns( colname=nw.concat_str( @@ -298,7 +279,10 @@ def _fmt_frac(vec) -> list[str | None]: # TODO: rename, document, to utils? # format fractions: # this is an anti-pattern but there's no serious alternative for _fmt_col in ("__frac_n_unique", "__frac_n_missing"): - formatted: nw.Series = _fmt_frac(formatted_data[_fmt_col]) + _formatted: list[str | None] = _fmt_frac(formatted_data[_fmt_col]) + formatted: nw.Series = nw.new_series( + "foo", values=_formatted, backend=self.profile.implementation + ) formatted_data = formatted_data.drop(_fmt_col) formatted_data = formatted_data.with_columns(formatted.alias(_fmt_col)) @@ -433,18 +417,17 @@ def _fmt_frac(vec) -> list[str | None]: # TODO: rename, document, to utils? ) # ## Formatting .cols_width( - icon="35px", colname="200px", **{stat_col: "50px" for stat_col in present_stat_cols} + icon="35px", colname="200px", **{stat_col: "60px" for stat_col in present_stat_cols} ) ) if "PYARROW" != formatted_data.implementation.name: - # TODO: this is more proactive than it should be IMO; would like to see EAFP + # TODO: this is more proactive than it should be gt_tbl = gt_tbl.sub_missing(missing_text="-") # https://github.com/posit-dev/great-tables/issues/667 # TODO: datetime value formatting weirdness # TODO: datetime column formatting weirdness - # TODO: add the SL back # If the version of `great_tables` is `>=0.17.0` then disable Quarto table processing if version("great_tables") >= "0.17.0": From a5282fabf287a20c4183854c43487eab5684dad9 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 20 Apr 2025 18:59:23 -0400 Subject: [PATCH 79/85] account for 0s in fraction formatting --- pointblank/_utils_html.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/pointblank/_utils_html.py b/pointblank/_utils_html.py index c540f53ed..bbed4876a 100644 --- a/pointblank/_utils_html.py +++ b/pointblank/_utils_html.py @@ -15,6 +15,10 @@ def _fmt_frac(vec) -> list[str | None]: res.append(x) continue + if x == 0: + res.append("0") + continue + if x < 0.01: res.append("<.01") continue From f0feae04b340dc052abbccc447054602f3885662 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 20 Apr 2025 18:59:45 -0400 Subject: [PATCH 80/85] bug where _fmt_frac does not convert to series --- pointblank/datascan.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 83ce91a07..5b7d055a3 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -342,7 +342,10 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: __pct_false=nw.col("__freq_false") / self.profile.row_count, ) for _fmt_col in ("__pct_true", "__pct_false"): - formatted: nw.Series = _fmt_frac(formatted_data[_fmt_col]) + _formatted: list[str] = _fmt_frac(formatted_data[_fmt_col]) + formatted = nw.new_series( + name=_fmt_col, values=_formatted, backend=self.profile.implementation + ) formatted_data = formatted_data.drop(_fmt_col) formatted_data = formatted_data.with_columns(formatted.alias(_fmt_col)) From 507bbb25ddd98ee9e8ef9d81ad4491480608e183 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 20 Apr 2025 19:09:43 -0400 Subject: [PATCH 81/85] account for arbitrary types in integer formatting --- pointblank/_utils_html.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/pointblank/_utils_html.py b/pointblank/_utils_html.py index bbed4876a..7538c58e8 100644 --- a/pointblank/_utils_html.py +++ b/pointblank/_utils_html.py @@ -23,8 +23,14 @@ def _fmt_frac(vec) -> list[str | None]: res.append("<.01") continue - if int(x) == x: # can remove trailing 0s w/o loss - res.append(str(int(x))) + try: + intx: int = int(x) + except ValueError: # generic object, ie. NaN + res.append(str(x)) + continue + + if intx == x: # can remove trailing 0s w/o loss + res.append(str(intx)) continue res.append(str(round(x, 2))) From a7dc59b1b1b3d1b9e9273dfb87da1f30ee4fddb7 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 20 Apr 2025 19:10:05 -0400 Subject: [PATCH 82/85] account for tables that don't need freqs --- pointblank/datascan.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 5b7d055a3..e4ef8f6e4 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -313,8 +313,7 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: __freq_false=nw.col("freqs").struct.field("False"), ) except Exception: # TODO: should be narrowed if possible - pass # struct not available in the implementation, ie. numpy Pandas - else: + # if no struct implimentation exists, it must be done manually freq_ser: nw.Series = formatted_data["freqs"] trues: list[int | None] = [] falses: list[int | None] = [] @@ -342,7 +341,7 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: __pct_false=nw.col("__freq_false") / self.profile.row_count, ) for _fmt_col in ("__pct_true", "__pct_false"): - _formatted: list[str] = _fmt_frac(formatted_data[_fmt_col]) + _formatted: list[str | None] = _fmt_frac(formatted_data[_fmt_col]) formatted = nw.new_series( name=_fmt_col, values=_formatted, backend=self.profile.implementation ) From 68b7e659e102033d05f2e2f2f5e3cee7e8cb0b15 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 20 Apr 2025 19:20:48 -0400 Subject: [PATCH 83/85] remove testing artifact --- pointblank/datascan.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index e4ef8f6e4..864e41e4b 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -281,7 +281,7 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: for _fmt_col in ("__frac_n_unique", "__frac_n_missing"): _formatted: list[str | None] = _fmt_frac(formatted_data[_fmt_col]) formatted: nw.Series = nw.new_series( - "foo", values=_formatted, backend=self.profile.implementation + _fmt_col, values=_formatted, backend=self.profile.implementation ) formatted_data = formatted_data.drop(_fmt_col) formatted_data = formatted_data.with_columns(formatted.alias(_fmt_col)) From 614b3a16a49d59c842c369635c4c75f8bf1ec18b Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sat, 26 Apr 2025 11:07:51 -0400 Subject: [PATCH 84/85] add datetime and date formatting to gt --- pointblank/datascan.py | 31 +++++++++++++++++++++++++------ 1 file changed, 25 insertions(+), 6 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 864e41e4b..128c3b07d 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -276,6 +276,20 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: __frac_n_missing=nw.col("n_missing") / nw.lit(self.profile.row_count), ) + ## Pull out type indicies: + # TODO: This should get a dedicated mini-class + # TODO: Needs a type guard too + datetime_idx: list[int] = ( + formatted_data.select(__tmp_idx=nw.col("coltype") == nw.lit("Datetime"))["__tmp_idx"] + .arg_true() + .to_list() + ) + date_idx: list[int] = ( + formatted_data.select(__tmp_idx=nw.col("coltype") == nw.lit("Date"))["__tmp_idx"] + .arg_true() + .to_list() + ) + # format fractions: # this is an anti-pattern but there's no serious alternative for _fmt_col in ("__frac_n_unique", "__frac_n_missing"): @@ -401,13 +415,13 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: columns=fmt_float, decimals=2, drop_trailing_dec_mark=True, - drop_trailing_zeros=True, ## Generic Styling + drop_trailing_zeros=True, ) - .tab_style( - style=style.text(size="10px"), - locations=loc.body(columns=list(present_stat_cols)), + .fmt_datetime( + # TODO: This is lazy and I should come up with a better solution + columns=[c for c in present_stat_cols if c in ("min", "max")], + rows=datetime_idx, ) - .tab_style(style=style.text(size="12px"), locations=loc.body(columns="colname")) ## Borders .tab_style( style=style.borders(sides="right", color="#D3D3D3", style="solid"), @@ -417,7 +431,12 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: style=style.borders(sides="left", color="#E5E5E5", style="dashed"), locations=loc.body(columns=list(present_stat_cols)), ) - # ## Formatting + ## Formatting + .tab_style( + style=style.text(size="10px"), + locations=loc.body(columns=list(present_stat_cols)), + ) + .tab_style(style=style.text(size="12px"), locations=loc.body(columns="colname")) .cols_width( icon="35px", colname="200px", **{stat_col: "60px" for stat_col in present_stat_cols} ) From 6c12b51b068dbb87484fdbb1c871947010ae7749 Mon Sep 17 00:00:00 2001 From: Tyler Riccio Date: Sun, 27 Apr 2025 18:51:12 -0400 Subject: [PATCH 85/85] add SL footnote --- pointblank/datascan.py | 20 ++++++++++++++------ 1 file changed, 14 insertions(+), 6 deletions(-) diff --git a/pointblank/datascan.py b/pointblank/datascan.py index 128c3b07d..f9d5afd90 100644 --- a/pointblank/datascan.py +++ b/pointblank/datascan.py @@ -278,14 +278,19 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: ## Pull out type indicies: # TODO: This should get a dedicated mini-class - # TODO: Needs a type guard too + # TODO: Technically ne a type guard too datetime_idx: list[int] = ( - formatted_data.select(__tmp_idx=nw.col("coltype") == nw.lit("Datetime"))["__tmp_idx"] + formatted_data.select( + __tmp_idx=nw.col("coltype").str.contains("Datetime", literal=True) + )["__tmp_idx"] .arg_true() .to_list() ) date_idx: list[int] = ( - formatted_data.select(__tmp_idx=nw.col("coltype") == nw.lit("Date"))["__tmp_idx"] + formatted_data.select( + __tmp_idx=nw.col("coltype").str.contains("Date", literal=True) + & ~nw.col("coltype").str.contains("Datetime", literal=True) + )["__tmp_idx"] .arg_true() .to_list() ) @@ -396,6 +401,7 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: gt_tbl = ( GT(formatted_data.to_native()) .tab_header(title=html(combined_title)) + .tab_source_note(source_note="String columns statistics regard the string's length.") .cols_align(align="right", columns=list(present_stat_cols)) .opt_table_font(font=google_font("IBM Plex Sans")) .opt_align_table_header(align="left") @@ -422,6 +428,11 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: columns=[c for c in present_stat_cols if c in ("min", "max")], rows=datetime_idx, ) + .fmt_date( + # TODO: This is lazy and I should come up with a better solution + columns=[c for c in present_stat_cols if c in ("min", "max")], + rows=date_idx, + ) ## Borders .tab_style( style=style.borders(sides="right", color="#D3D3D3", style="solid"), @@ -447,9 +458,6 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT: gt_tbl = gt_tbl.sub_missing(missing_text="-") # https://github.com/posit-dev/great-tables/issues/667 - # TODO: datetime value formatting weirdness - # TODO: datetime column formatting weirdness - # If the version of `great_tables` is `>=0.17.0` then disable Quarto table processing if version("great_tables") >= "0.17.0": gt_tbl = gt_tbl.tab_options(quarto_disable_processing=True)