Created
February 4, 2018 18:43
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Columns containing multiple bits of information
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "toc": "true" | |
| }, | |
| "source": [ | |
| "# Table of Contents\n", | |
| " <p><div class=\"lev1 toc-item\"><a href=\"#Concat-with-expand\" data-toc-modified-id=\"Concat-with-expand-1\"><span class=\"toc-item-num\">1 </span>Concat with expand</a></div><div class=\"lev1 toc-item\"><a href=\"#Zip\" data-toc-modified-id=\"Zip-2\"><span class=\"toc-item-num\">2 </span>Zip</a></div><div class=\"lev1 toc-item\"><a href=\"#differing-num-splits\" data-toc-modified-id=\"differing-num-splits-3\"><span class=\"toc-item-num\">3 </span>differing num splits</a></div>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Different ways of dealing with a column that contains multiple bits of information." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2018-02-04T18:42:13.085179Z", | |
| "start_time": "2018-02-04T18:42:12.569897Z" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import pandas as pd" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2018-02-04T18:42:13.093513Z", | |
| "start_time": "2018-02-04T18:42:13.087487Z" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "df = pd.DataFrame({\n", | |
| " 'name': ['Daniel', 'Julia', 'Eric'],\n", | |
| " 'tags': ['male,student', 'female,nurse', 'male,accountant']\n", | |
| " })" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2018-02-04T18:42:13.139725Z", | |
| "start_time": "2018-02-04T18:42:13.096442Z" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>name</th>\n", | |
| " <th>tags</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>Daniel</td>\n", | |
| " <td>male,student</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>Julia</td>\n", | |
| " <td>female,nurse</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>Eric</td>\n", | |
| " <td>male,accountant</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " name tags\n", | |
| "0 Daniel male,student\n", | |
| "1 Julia female,nurse\n", | |
| "2 Eric male,accountant" | |
| ] | |
| }, | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Concat with expand" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2018-02-04T18:42:13.149011Z", | |
| "start_time": "2018-02-04T18:42:13.143132Z" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "col_split_expand = df['tags'].str.split(',', expand=True)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2018-02-04T18:42:13.158738Z", | |
| "start_time": "2018-02-04T18:42:13.153747Z" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "df_concat = pd.concat([df, col_split_expand], axis=1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2018-02-04T18:42:13.179610Z", | |
| "start_time": "2018-02-04T18:42:13.162810Z" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>name</th>\n", | |
| " <th>tags</th>\n", | |
| " <th>0</th>\n", | |
| " <th>1</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>Daniel</td>\n", | |
| " <td>male,student</td>\n", | |
| " <td>male</td>\n", | |
| " <td>student</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>Julia</td>\n", | |
| " <td>female,nurse</td>\n", | |
| " <td>female</td>\n", | |
| " <td>nurse</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>Eric</td>\n", | |
| " <td>male,accountant</td>\n", | |
| " <td>male</td>\n", | |
| " <td>accountant</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " name tags 0 1\n", | |
| "0 Daniel male,student male student\n", | |
| "1 Julia female,nurse female nurse\n", | |
| "2 Eric male,accountant male accountant" | |
| ] | |
| }, | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df_concat" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2018-02-04T18:28:21.347512Z", | |
| "start_time": "2018-02-04T18:28:21.341501Z" | |
| } | |
| }, | |
| "source": [ | |
| "# Zip" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2018-02-04T18:42:13.190003Z", | |
| "start_time": "2018-02-04T18:42:13.183231Z" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "df = pd.DataFrame({\n", | |
| " 'name': ['Daniel', 'Julia', 'Eric'],\n", | |
| " 'tags': ['male,student', 'female,nurse', 'male,accountant']\n", | |
| " })\n", | |
| "col_split = df['tags'].str.split(',')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2018-02-04T18:42:13.199644Z", | |
| "start_time": "2018-02-04T18:42:13.193101Z" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "df['sex'], df['occupation'] = zip(*col_split)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2018-02-04T18:42:13.226734Z", | |
| "start_time": "2018-02-04T18:42:13.204293Z" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>name</th>\n", | |
| " <th>tags</th>\n", | |
| " <th>sex</th>\n", | |
| " <th>occupation</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>Daniel</td>\n", | |
| " <td>male,student</td>\n", | |
| " <td>male</td>\n", | |
| " <td>student</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>Julia</td>\n", | |
| " <td>female,nurse</td>\n", | |
| " <td>female</td>\n", | |
| " <td>nurse</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>Eric</td>\n", | |
| " <td>male,accountant</td>\n", | |
| " <td>male</td>\n", | |
| " <td>accountant</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " name tags sex occupation\n", | |
| "0 Daniel male,student male student\n", | |
| "1 Julia female,nurse female nurse\n", | |
| "2 Eric male,accountant male accountant" | |
| ] | |
| }, | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2018-02-04T18:34:03.826602Z", | |
| "start_time": "2018-02-04T18:34:03.823486Z" | |
| } | |
| }, | |
| "source": [ | |
| "# differing num splits" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2018-02-04T18:42:13.233696Z", | |
| "start_time": "2018-02-04T18:42:13.229294Z" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "df = pd.DataFrame({\n", | |
| " 'name': ['Daniel', 'Julia', 'Eric'],\n", | |
| " 'tags': ['male,student,reserach associate,data engineer', 'female,student,nurse', 'male,accountant']\n", | |
| " })" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2018-02-04T18:42:13.247175Z", | |
| "start_time": "2018-02-04T18:42:13.237616Z" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
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| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>name</th>\n", | |
| " <th>tags</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>Daniel</td>\n", | |
| " <td>male,student,reserach associate,data engineer</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>Julia</td>\n", | |
| " <td>female,student,nurse</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>Eric</td>\n", | |
| " <td>male,accountant</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " name tags\n", | |
| "0 Daniel male,student,reserach associate,data engineer\n", | |
| "1 Julia female,student,nurse\n", | |
| "2 Eric male,accountant" | |
| ] | |
| }, | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2018-02-04T18:42:13.264964Z", | |
| "start_time": "2018-02-04T18:42:13.250462Z" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>0</th>\n", | |
| " <th>1</th>\n", | |
| " <th>2</th>\n", | |
| " <th>3</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>male</td>\n", | |
| " <td>student</td>\n", | |
| " <td>reserach associate</td>\n", | |
| " <td>data engineer</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>female</td>\n", | |
| " <td>student</td>\n", | |
| " <td>nurse</td>\n", | |
| " <td>None</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>male</td>\n", | |
| " <td>accountant</td>\n", | |
| " <td>None</td>\n", | |
| " <td>None</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " 0 1 2 3\n", | |
| "0 male student reserach associate data engineer\n", | |
| "1 female student nurse None\n", | |
| "2 male accountant None None" | |
| ] | |
| }, | |
| "execution_count": 12, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "split_expand = df['tags'].str.split(',', expand=True)\n", | |
| "split_expand" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2018-02-04T18:42:13.273385Z", | |
| "start_time": "2018-02-04T18:42:13.267263Z" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "RangeIndex(start=0, stop=4, step=1)" | |
| ] | |
| }, | |
| "execution_count": 13, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "split_expand.columns" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2018-02-04T18:42:13.282327Z", | |
| "start_time": "2018-02-04T18:42:13.276386Z" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "split_expand.columns = ['tag_{}'.format(x) for x in range(len(split_expand.columns))]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2018-02-04T18:42:13.290191Z", | |
| "start_time": "2018-02-04T18:42:13.285133Z" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "df_diff_concat = pd.concat([df, split_expand], axis=1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 16, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2018-02-04T18:42:13.309831Z", | |
| "start_time": "2018-02-04T18:42:13.294209Z" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>name</th>\n", | |
| " <th>tags</th>\n", | |
| " <th>tag_0</th>\n", | |
| " <th>tag_1</th>\n", | |
| " <th>tag_2</th>\n", | |
| " <th>tag_3</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>Daniel</td>\n", | |
| " <td>male,student,reserach associate,data engineer</td>\n", | |
| " <td>male</td>\n", | |
| " <td>student</td>\n", | |
| " <td>reserach associate</td>\n", | |
| " <td>data engineer</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>Julia</td>\n", | |
| " <td>female,student,nurse</td>\n", | |
| " <td>female</td>\n", | |
| " <td>student</td>\n", | |
| " <td>nurse</td>\n", | |
| " <td>None</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>Eric</td>\n", | |
| " <td>male,accountant</td>\n", | |
| " <td>male</td>\n", | |
| " <td>accountant</td>\n", | |
| " <td>None</td>\n", | |
| " <td>None</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " name tags tag_0 tag_1 \\\n", | |
| "0 Daniel male,student,reserach associate,data engineer male student \n", | |
| "1 Julia female,student,nurse female student \n", | |
| "2 Eric male,accountant male accountant \n", | |
| "\n", | |
| " tag_2 tag_3 \n", | |
| "0 reserach associate data engineer \n", | |
| "1 nurse None \n", | |
| "2 None None " | |
| ] | |
| }, | |
| "execution_count": 16, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df_diff_concat" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python (root)", | |
| "language": "python", | |
| "name": "other-env" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
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| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
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| "toc": { | |
| "colors": { | |
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| "moveMenuLeft": true, | |
| "nav_menu": { | |
| "height": "66px", | |
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| "navigate_menu": true, | |
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| "threshold": 4, | |
| "toc_cell": true, | |
| "toc_section_display": "block", | |
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| "widenNotebook": false | |
| }, | |
| "varInspector": { | |
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| "lenVar": 40 | |
| }, | |
| "kernels_config": { | |
| "python": { | |
| "delete_cmd_postfix": "", | |
| "delete_cmd_prefix": "del ", | |
| "library": "var_list.py", | |
| "varRefreshCmd": "print(var_dic_list())" | |
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| "r": { | |
| "delete_cmd_postfix": ") ", | |
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| "types_to_exclude": [ | |
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| ], | |
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| "nbformat": 4, | |
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| } |
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