Upload M1L7 Code Along

Author: luckydoes

Mod1/lecture_code_alongs/M1L7-DataManipulation_STUDENT.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# M1L7 Data Types, Dates, Strings \n",
+    "\n",
+    " We'll be working with UFO sighting data.\n",
+    "\n",
+    "### **Dataset:** [UFO Sightings](https://www.kaggle.com/datasets/jonwright13/ufo-sightings-around-the-world-better?resource=download) -- This is also in your data folder \n",
+    "\n",
+    "### **Objectives:**\n",
+    "\n",
+    "- Change an object to a datetime object \n",
+    "- Use string methods to manipulate data \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 1:  Import pandas and numpy "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Import packages \n",
+    "\n",
+    "None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 2:  Load in the data and save it as `ufo`\n",
+    "\n",
+    "- The dataset is named `ufo-sightings.csv`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ufo = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3: Check column data types and the head of the data -- does the data/types make sense?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 4:  Convert the `Date` column to datetime \n",
+    "\n",
+    "- Even though we have columns for year, month, and hour; we still want to change Date_time to a datetime object \n",
+    "- Dates can come in many formats so we will use this format: '%Y-%m-%d %H:%M:%S'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ufo['Date_time'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Run this to see if the update worked \n",
+    "ufo.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 5:  Make the `Description` column all lowercase \n",
+    "\n",
+    "- Think about why would we want text all lowercase \n",
+    "\n",
+    "**Instructor Notes**\n",
+    "Feel free to talk about text analytics or LLMs or a simple case like states being different cases and you want to do aggregations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ufo['Description'] = None\n",
+    "print(ufo['Description'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 6:  Replace spaces with underscores in the `Encounter_Duration` column\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ufo['Encounter_Duration'] = None\n",
+    "print(ufo['Encounter_Duration'])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (learn-env)",
+   "language": "python",
+   "name": "learn-env"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}