Update project files

Links Analysis Cross-Platforms/helpers.py

@@ -0,0 +1,110 @@
+
+import pandas as pd
+from datetime import datetime
+import numpy as np
+import re
+from collections import Counter
+
+
+class URLS:
+    def __init__(self, df):
+        self.df = df
+
+    def datetime_from_timestamp(self, timestamp_column):
+        """
+        converts time stamp to ddatetime object
+        """
+        return self.df[timestamp_column].apply(
+            lambda x: x if np.isnan(x) else datetime.fromtimestamp(x)
+        )
+
+    def months_filteration(self, datetime_column, start, end):
+        """
+        Filter dataframe due to time, get data between two dates
+        """
+        return (
+            self.df[
+                (self.df[datetime_column].astype(str) >= start)
+                & (self.df[datetime_column].astype(str) < end)
+            ]
+            .reset_index()
+            .drop(columns="index")
+        )
+
+
+def get_urls(string):
+    """
+    Get links from text
+    """
+    regex = r"(?i)\b((?:https?://|www\d{0,3}[.]|[a-z0-9.\-]+[.][a-z]{2,4}/)(?:[^\s()<>]+|\(([^\s()<>]+|(\([^\s()<>]+\)))*\))+(?:\(([^\s()<>]+|(\([^\s()<>]+\)))*\)|[^\s`!()\[\]{};:'\".,<>?«»“”‘’]))"
+    url = re.findall(regex, string)
+    urls = [x[0] for x in url]
+    return urls
+
+
+def urls_df(filtered_df, column="n_urls"):
+    return filtered_df[filtered_df[column] > 0].reset_index().drop(columns="index")
+
+
+def one_link_df(df, n_urls_column="n_urls", url_column="url", urls="urls"):
+    one_link_df = df[df[n_urls_column] == 1]
+    one_link_df[url_column] = one_link_df[urls].apply(lambda x: x[0])
+    one_link_df = one_link_df.reset_index().drop(columns="index")
+    return one_link_df.reset_index().drop(columns="index")
+
+
+def one_link_urls(one_link_df, column="urls"):
+    return one_link_df[column].values
+
+
+def one_link_urls_with_count(one_link_df, user_column, url_column="url"):
+    users = []
+    for url in one_link_df[url_column].values:
+        n = one_link_df[one_link_df.url == url][user_column].nunique()
+        if n != 0:
+            users.append(n)
+
+    one_link_df["n_users"] = users
+
+    temp_urls = []
+    for i in one_link_df[["url", "n_users"]].values:
+        temp_urls.append((i[0], i[1]))
+    data_last = dict(Counter(temp_urls))
+    links_last = []
+    users_last = []
+    count_last = []
+    for url_users, count in data_last.items():
+        links_last.append(url_users[0])
+        count_last.append(count)
+        users_last.append(url_users[1])
+
+    return (
+        pd.DataFrame({"link": links_last, "count": count_last, "n_users": users_last})
+        .sort_values("n_users", ascending=False)
+        .reset_index()
+        .drop(columns="index")
+    )
+
+
+def df_more_than_link(df):
+    return df[df.n_urls > 1].reset_index().drop(columns="index")
+
+
+def more_than_one_link_with_count(df, column_user, column_urls):
+    user_link_m = []
+    for user_links in df[[column_user, column_urls]].values:
+        for link in user_links[1]:
+            user_link_m.append((user_links[0], link))
+    temp = pd.DataFrame(
+        {"link": [i[1] for i in user_link_m], "user": [i[0] for i in user_link_m]}
+    )
+    links = temp["link"].unique()
+    last_links = []
+    last_u = []
+    last_c = []
+    for link in links:
+        last_c.append(temp[temp["link"] == link].shape[0])
+        last_u.append(temp[temp["link"] == link]["user"].nunique())
+        last_links.append(link)
+
+    return pd.DataFrame({"link": last_links, "count": last_c, "n_users": last_u})