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Exploratory data analysis of job rejection emails
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Job Rejection Data Analysis" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Let's dive into some interesting questions and analysis regarding job rejection emails. We have over 80 emails to work with including features such as `Time`, `Day`, `Subject`, and `Text`. A couple interesting questions that I thought of initially are the following:\n", | |
| "* What day of the week are most job rejections sent out?\n", | |
| "* What time do most job rejections get sent out?\n", | |
| "* What are common phrases and words used in the subject of the email?\n", | |
| "* What are common phrases and words used in the body of the email?\n", | |
| "* How do different companies rejection emails differ in sentiment and verbage?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 25, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# Imports\n", | |
| "import pandas as pd\n", | |
| "import numpy as np\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import seaborn as sns\n", | |
| "import re\n", | |
| "from nltk.corpus import stopwords\n", | |
| "from collections import Counter\n", | |
| "import nltk\n", | |
| "%matplotlib inline\n", | |
| "sns.set(style='whitegrid', palette='muted', font_scale=1.3)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style>\n", | |
| " .dataframe thead tr:only-child th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: left;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Date</th>\n", | |
| " <th>Time</th>\n", | |
| " <th>Day</th>\n", | |
| " <th>Hour</th>\n", | |
| " <th>Subject</th>\n", | |
| " <th>Text</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>2018-07-12 12:31:08</td>\n", | |
| " <td>12:31:08</td>\n", | |
| " <td>Thurs</td>\n", | |
| " <td>12</td>\n", | |
| " <td>Your IBM Application</td>\n", | |
| " <td>Ref: 110127BR - 2018 Data Scientist Internship...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>2018-06-12 16:30:28</td>\n", | |
| " <td>16:30:28</td>\n", | |
| " <td>Tues</td>\n", | |
| " <td>16</td>\n", | |
| " <td>Thank you from Workday!</td>\n", | |
| " <td><!doctype html><html xmlns:o=3D\"urn:schemas-mi...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>2018-05-17 08:43:38</td>\n", | |
| " <td>08:43:38</td>\n", | |
| " <td>Thurs</td>\n", | |
| " <td>8</td>\n", | |
| " <td>An Update Regarding Your Visa Job Application</td>\n", | |
| " <td>\\r\\nDear Conor,\\r\\nThank you for giving us the...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>2018-05-01 15:21:05</td>\n", | |
| " <td>15:21:05</td>\n", | |
| " <td>Tues</td>\n", | |
| " <td>15</td>\n", | |
| " <td>Thank you for your interest in Zynga for Inter...</td>\n", | |
| " <td><html><head>\\r\\n<meta http-equiv=3DContent-Typ...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>2018-04-26 14:49:02</td>\n", | |
| " <td>14:49:02</td>\n", | |
| " <td>Thurs</td>\n", | |
| " <td>14</td>\n", | |
| " <td>Your Application with Cambia Health Solutions</td>\n", | |
| " <td>Dear Conor,\\r\\n=C2=A0\\r\\nThank you for the int...</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Date Time Day Hour \\\n", | |
| "0 2018-07-12 12:31:08 12:31:08 Thurs 12 \n", | |
| "1 2018-06-12 16:30:28 16:30:28 Tues 16 \n", | |
| "2 2018-05-17 08:43:38 08:43:38 Thurs 8 \n", | |
| "3 2018-05-01 15:21:05 15:21:05 Tues 15 \n", | |
| "4 2018-04-26 14:49:02 14:49:02 Thurs 14 \n", | |
| "\n", | |
| " Subject \\\n", | |
| "0 Your IBM Application \n", | |
| "1 Thank you from Workday! \n", | |
| "2 An Update Regarding Your Visa Job Application \n", | |
| "3 Thank you for your interest in Zynga for Inter... \n", | |
| "4 Your Application with Cambia Health Solutions \n", | |
| "\n", | |
| " Text \n", | |
| "0 Ref: 110127BR - 2018 Data Scientist Internship... \n", | |
| "1 <!doctype html><html xmlns:o=3D\"urn:schemas-mi... \n", | |
| "2 \\r\\nDear Conor,\\r\\nThank you for giving us the... \n", | |
| "3 <html><head>\\r\\n<meta http-equiv=3DContent-Typ... \n", | |
| "4 Dear Conor,\\r\\n=C2=A0\\r\\nThank you for the int... " | |
| ] | |
| }, | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Read data\n", | |
| "df = pd.read_csv('rejections.csv')\n", | |
| "df.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "<class 'pandas.core.frame.DataFrame'>\n", | |
| "RangeIndex: 86 entries, 0 to 85\n", | |
| "Data columns (total 6 columns):\n", | |
| "Date 86 non-null object\n", | |
| "Time 86 non-null object\n", | |
| "Day 86 non-null object\n", | |
| "Hour 86 non-null int64\n", | |
| "Subject 86 non-null object\n", | |
| "Text 86 non-null object\n", | |
| "dtypes: int64(1), object(5)\n", | |
| "memory usage: 4.1+ KB\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Data overview\n", | |
| "df.info()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "As you can see, we have a little over 80 different job rejections to review from my internship search over the course of the last year. For a little background, let's go over my internship search by the numbers and get a better feel for what we're working with.\n", | |
| "* Applications: 234\n", | |
| "* Replies: 93\n", | |
| "* Rejections: 90 \n", | |
| "* Offers: 3" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Which Companies Are Included?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "Update - Data Analytics Intern - Undergraduate & Masters Degrees,\\r\\n 1765077 2\n", | |
| "Your application to Software Engineer, Intern 2018 (San Francisco)\\r\\n at Airbnb 2\n", | |
| "Your Uber Application 2\n", | |
| "Thanks for your Application! 2\n", | |
| "Illumina: Status of your application 2\n", | |
| "BlackRock | We Wish You Success 2\n", | |
| "Thank you for your interest 2\n", | |
| "Your application for Data Scientist Intern - New York at Spotify. 1\n", | |
| "Your application to Airware: Data Science Intern - Fall 1\n", | |
| "Important information about your application to PlayStation 1\n", | |
| "Thank You for Your Interest in Expedia 1\n", | |
| "Thank you for your interest in TripAdvisor 1\n", | |
| "Thanks for your interest in Wealthfront, Conor 1\n", | |
| "Your application for Data Scientist: Ads & Markets Analytics -\\r\\n Summer Internship - New York at Spotify. 1\n", | |
| "Update on Your Application for Interns@swissre 2018 - Data\\r\\n Scientist Intern 1\n", | |
| "Important information about your application to LendUp 1\n", | |
| "Your OpenAI application 1\n", | |
| "b'Avvo, Inc. - Big Data Engineer Intern \\xe2\\x80\\x93 Summer 2018 Update (Conor Dewey)' 1\n", | |
| "Update on your application at Squarespace 1\n", | |
| "Your application for Software Developer Intern - Seattle & San\\r\\n Francisco at Redfin 1\n", | |
| "Conor Dewey - Medallia Application 1\n", | |
| "Your application for Intern-Big Data Engineer at Palo Alto Networks 1\n", | |
| "Thank you from Thumbtack 1\n", | |
| "CareDash Application 1\n", | |
| "UPDATE: Status of Your Application With Red Hat 1\n", | |
| "Bazaarvoice - Data Analyst Intern 1\n", | |
| "RE: Application for Data Science Graduation Program and Internships\\r\\n from Conor Dewey 1\n", | |
| "Thank You - LiveRamp 1\n", | |
| "Thank you for your interest in Twilio 1\n", | |
| "Thanks for your interest in Wish, Conor 1\n", | |
| " ..\n", | |
| "Thank you for your application - Asana 1\n", | |
| "Your application for Data Engineer Intern - Boston at Spotify. 1\n", | |
| "Merck 2018 MRL Data Scientist Intern - Boston 1\n", | |
| "Interview Follow-Up: Your application for Data Science Intern at\\r\\n Chegg 1\n", | |
| "Pandora Media, Inc.- Your application for Intern 2018 - Product\\r\\n Analytics 1\n", | |
| "Atlassian - 2018 Summer Software Developer Intern - Austin, TX Application 1\n", | |
| "Thanks for your interest in Cogo Labs, Conor 1\n", | |
| "P&G 1\n", | |
| "Yext - Software Engineering Internship Application (Conor Dewey) 1\n", | |
| "Your Zocdoc Application 1\n", | |
| "Thanks for your interest in TripAdvisor 1\n", | |
| "2018 Summer League Rookie Program Update 1\n", | |
| "Your IBM Application 1\n", | |
| "Your Application for Intern, Big Data Developer 1\n", | |
| "An Update Regarding Your Visa Job Application 1\n", | |
| "b'Bloomberg Application Follow-up' 1\n", | |
| "GitHub Application Follow Up for Conor Dewey 1\n", | |
| "Thank you from Workday! 1\n", | |
| "b'Twitter Application Status' 1\n", | |
| "Thank you for your interest in Definitive Healthcare 1\n", | |
| "Your application for Software Engineer (Intern) at Pinterest 1\n", | |
| "Following up on your application to Logikcull 1\n", | |
| "Thank you for your interest in Zynga for Intern/Co-op Software\\r\\n Engineer - Current Students (Summer 2018) 1\n", | |
| "Salesforce: Thank You! 1\n", | |
| "Thank you for your interest in Visa 1\n", | |
| "Note from Addepar 1\n", | |
| "Your Application with Cambia Health Solutions 1\n", | |
| "Your application for Intern Data Scientist at ExtraHop Networks,\\r\\n Inc. 1\n", | |
| "Thank you for your interest in Datto 1\n", | |
| "Your BuzzFeed Application 1\n", | |
| "Name: Subject, Length: 79, dtype: int64" | |
| ] | |
| }, | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Subject list\n", | |
| "df['Subject'].value_counts()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## What Day Are Job Rejections Most Frequent?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
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JJ59o0qRJatiw4Q23N2nSROPHj9fWrVsrZDgAAIC7Xakx9tNPP6lJkyalPoCvr69+/PHH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\nsn5Nr127Vo899pj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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x1a19761710>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# Visualization\n", | |
| "plt.figure(figsize=(10,8))\n", | |
| "sns.countplot(df['Day'], order=['Mon', 'Tues', 'Weds', 'Thurs', 'Fri', 'Sat', 'Sun']);\n", | |
| "plt.xlabel('Day of the Week')\n", | |
| "plt.ylabel('Count');" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Key Insights\n", | |
| "* Rejections most frequently came on Wednesdays\n", | |
| "* Thursdays also appear to be a popular day for bad news\n", | |
| "* Sundays are smooth sailing but apparently Saturdays are fair game..." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Who Sends Rejection Emails on Saturdays?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style>\n", | |
| " .dataframe thead tr:only-child th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: left;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Date</th>\n", | |
| " <th>Time</th>\n", | |
| " <th>Day</th>\n", | |
| " <th>Hour</th>\n", | |
| " <th>Subject</th>\n", | |
| " <th>Text</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>46</th>\n", | |
| " <td>2017-12-16 15:04:42</td>\n", | |
| " <td>15:04:42</td>\n", | |
| " <td>Sat</td>\n", | |
| " <td>15</td>\n", | |
| " <td>Important information about your application t...</td>\n", | |
| " <td>Hi Conor,\\r\\nThank you for applying to Softwar...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>63</th>\n", | |
| " <td>2017-11-04 07:27:04</td>\n", | |
| " <td>07:27:04</td>\n", | |
| " <td>Sat</td>\n", | |
| " <td>7</td>\n", | |
| " <td>Thank you for your interest in TripAdvisor</td>\n", | |
| " <td>Hi Conor,\\r\\n\\r\\nThank you for your interest i...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>69</th>\n", | |
| " <td>2017-09-30 13:02:53</td>\n", | |
| " <td>13:02:53</td>\n", | |
| " <td>Sat</td>\n", | |
| " <td>13</td>\n", | |
| " <td>Thank you for your interest</td>\n", | |
| " <td><div><img src=\"https://performancemanager4.suc...</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Date Time Day Hour \\\n", | |
| "46 2017-12-16 15:04:42 15:04:42 Sat 15 \n", | |
| "63 2017-11-04 07:27:04 07:27:04 Sat 7 \n", | |
| "69 2017-09-30 13:02:53 13:02:53 Sat 13 \n", | |
| "\n", | |
| " Subject \\\n", | |
| "46 Important information about your application t... \n", | |
| "63 Thank you for your interest in TripAdvisor \n", | |
| "69 Thank you for your interest \n", | |
| "\n", | |
| " Text \n", | |
| "46 Hi Conor,\\r\\nThank you for applying to Softwar... \n", | |
| "63 Hi Conor,\\r\\n\\r\\nThank you for your interest i... \n", | |
| "69 <div><img src=\"https://performancemanager4.suc... " | |
| ] | |
| }, | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Subset data\n", | |
| "sat = df.loc[df['Day'] == 'Sat']\n", | |
| "sat.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Important information about your application to LendUp\n", | |
| "Hi Conor,\r\n", | |
| "Thank you for applying to Software Engineering Intern. After reviewing with the team, we have decided not to move forward with your candidacy. Due to the number of applications we receive we are not able to provide specific feedback.\r\n", | |
| "\r\n", | |
| "We will keep your details on file and let you know if any suitable openings arise.\r\n", | |
| "\r\n", | |
| "Thanks again for your interest in LendUp and we wish you the best of luck in your search!\r\n", | |
| "\r\n", | |
| "Regards,\r\n", | |
| "LendUp Recruiting Team\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# First offender: LendUp\n", | |
| "print(sat.iloc[0]['Subject'])\n", | |
| "print(sat.iloc[0]['Text'])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Thank you for your interest in TripAdvisor\n", | |
| "Hi Conor,\r\n", | |
| "\r\n", | |
| "Thank you for your interest in the 17-18 Campus - Machine Learning Intern p=\r\n", | |
| "osition at TripAdvisor. We have received your application and it is current=\r\n", | |
| "ly being reviewed by the hiring team. We will be in touch if there is inter=\r\n", | |
| "est in moving forward.\r\n", | |
| "\r\n", | |
| "In the meantime please be sure to visit our careers page ( http://www.tripa=\r\n", | |
| "dvisor.com/careers ) for more information about:\r\n", | |
| "\r\n", | |
| "- our benefits and unique perks ( http://www.tripadvisor.com/careers/benefi=\r\n", | |
| "ts )\r\n", | |
| "\r\n", | |
| "- who we are and what we value ( http://www.tripadvisor.com/careers/culture=\r\n", | |
| " )\r\n", | |
| "\r\n", | |
| "We encourage you to learn more about our people, our offices, our =E2=80=9C=\r\n", | |
| "speed wins=E2=80=9D culture and the work we do by visiting our Twitter ( ht=\r\n", | |
| "tps://twitter.com/gotripadvisor ),Instagram ( https://www.instagram.com/got=\r\n", | |
| "ripadvisor/ ) and LinkedIn ( https://www.linkedin.com/company/tripadvisor/c=\r\n", | |
| "areers ) pages. These pages provide an inside glimpse of life at TripAdviso=\r\n", | |
| "r offices around the world.\r\n", | |
| "\r\n", | |
| "Thanks again for your interest in joining the TripAdvisor team!\r\n", | |
| "\r\n", | |
| "Best regards,\r\n", | |
| "\r\n", | |
| "Your TripAdvisor Recruiters\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Second offender: TripAdvisor\n", | |
| "print(sat.iloc[1]['Subject'])\n", | |
| "print(sat.iloc[1]['Text'])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Thank you for your interest\n", | |
| "<div><img src=\"https://performancemanager4.successfactors.com/doc/custom/ElectronicArts/EA_Logo.png\" /></div>\r\n", | |
| "\r\n", | |
| "<div> \r\n", | |
| "<p>Dear Conor:</p>\r\n", | |
| "\r\n", | |
| "<p>Thank you for applying to the Machine Learning Scientist Intern (Summer 2018) position. We’ve had a very competitive pool of applicants. We appreciate your interest in the role but we have decided not to move your application forward.</p>\r\n", | |
| "\r\n", | |
| "<p>We will keep your resume on file for future opportunities, and we encourage you to check our website often and apply for other EA positions.</p>\r\n", | |
| "\r\n", | |
| "<p>Best Regards,<br />\r\n", | |
| "The Global Recruiting Team at EA</p>\r\n", | |
| "</div>\r\n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Third offender: EA\n", | |
| "print(sat.iloc[2]['Subject'])\n", | |
| "print(sat.iloc[2]['Text'])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## What Times Are Job Rejections Most Frequent?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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WKCUlRSNGjCioeQEAAIqUHMtYmTJltG7dOs2aNUszZ87UtWvX5OTkJMuyVL58eXXt2lUj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\n3bp1k7Ozs3x8fNS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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x1a197614e0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# Visualization\n", | |
| "plt.figure(figsize=(10,8))\n", | |
| "sns.countplot(df['Hour'], order=np.arange(0,24));\n", | |
| "plt.xlabel('Hour of the Day (EST)')\n", | |
| "plt.ylabel('Count');" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Key Insights\n", | |
| "* We seem to see the normal distribution that we would expect\n", | |
| "* There are spikes at 9am and 12am signaling the start of the workday for EST and PST respectively\n", | |
| "* Only one rejection came in well after end of day for EST and PST..." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Who Sent Out the Late Email?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style>\n", | |
| " .dataframe thead tr:only-child th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: left;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Date</th>\n", | |
| " <th>Time</th>\n", | |
| " <th>Day</th>\n", | |
| " <th>Hour</th>\n", | |
| " <th>Subject</th>\n", | |
| " <th>Text</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>79</th>\n", | |
| " <td>2017-09-21 22:08:37</td>\n", | |
| " <td>22:08:37</td>\n", | |
| " <td>Thurs</td>\n", | |
| " <td>22</td>\n", | |
| " <td>P&G</td>\n", | |
| " <td><!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.01//...</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Date Time Day Hour Subject \\\n", | |
| "79 2017-09-21 22:08:37 22:08:37 Thurs 22 P&G \n", | |
| "\n", | |
| " Text \n", | |
| "79 <!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.01//... " | |
| ] | |
| }, | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Subset data\n", | |
| "late = df.loc[df['Hour'] == 22]\n", | |
| "late.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Looks like P&G sent out an automated rejection email at 10pm EST on a Thursday... Shame... " | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Common Subject Words & Phrases" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Tokenize and remove stop words\n", | |
| "subject_str = df['Subject'].to_string()\n", | |
| "words = nltk.word_tokenize(subject_str)\n", | |
| "clean = [word for word in words if word not in stopwords.words('english')]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 40, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Remove punctuation and numbers\n", | |
| "punctuation = re.compile(r'[-.?!,\":;()|0-9]')\n", | |
| "clean = [punctuation.sub(\"\", word) for word in clean]\n", | |
| "clean = [word.lower() for word in clean if len(word) > 0]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 41, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Get most common words\n", | |
| "words = [item[0] for item in Counter(clean).most_common(10)]\n", | |
| "counts = [item[1] for item in Counter(clean).most_common(10)]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 59, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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+YmJitGjRIg0ZMsT+XwwAAICdFdlfXDl48KA6deqk77//XnFxcRoyZIjCwsI0duxY7d+/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\nXQ2dYWFh+dbSt29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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x1a22bde0b8>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# Visualization\n", | |
| "plt.figure(figsize=(10,5))\n", | |
| "sns.barplot(counts, words);\n", | |
| "plt.ylabel('');\n", | |
| "plt.xlabel('Number of Occurrences');" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Key Insights\n", | |
| "* It appears that common practice was some form of 'Thank you for your interest'\n", | |
| "* Next most popular was a variation of 'Update on your application for x role'" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Common Body Words & Phrases" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 52, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Tokenize and remove stop words\n", | |
| "subject_str = df['Text'].to_string()\n", | |
| "words = nltk.word_tokenize(subject_str)\n", | |
| "clean = [word for word in words if word not in stopwords.words('english')]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 53, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# Remove punctuation and numbers\n", | |
| "punctuation = re.compile(r'[-.?!,\":;()<>//``//]|0-9]')\n", | |
| "clean = [punctuation.sub(\"\", word) for word in clean]\n", | |
| "clean = [word.lower() for word in clean if len(word) > 0]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 55, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Get most common words\n", | |
| "words = [item[0] for item in Counter(clean).most_common(10)]\n", | |
| "counts = [item[1] for item in Counter(clean).most_common(10)]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 60, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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+YmJitGjRIg0ZMsT+XwwAAICdFdlfXDl48KA6deqk77//XnFxcRoyZIjCwsI0duxY7d+/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\nXQ2dYWFh+dbSt29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CLl26ZNu/a9euecZ/8cUXJUkfffRRgWM///zzev755xUUFCR/f391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\nAANCIgAAAAwIiQAAADD4P5HCy0crz0j6AAAAAElFTkSuQmCC\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x1a22cca6a0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# Visualization\n", | |
| "plt.figure(figsize=(10,5))\n", | |
| "sns.barplot(counts, words);\n", | |
| "plt.ylabel('');\n", | |
| "plt.xlabel('Number of Occurrences');" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Key Insights\n", | |
| "* Less to report here, more of the same\n", | |
| "* More than half of the emails included my name in them" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Wrapping Up\n", | |
| "That's it for now. Moving forward, I think it would be interesting to continue this analysis further and look into sentiment and fundamental differences between key companies. For more on this analysis, stay tuned for an accompanying blog post breaking it down further." | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.6.6" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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