698 lines
25 KiB
Plaintext
698 lines
25 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Groupby operations"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"Some imports:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false,
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"try:\n",
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" import seaborn\n",
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"except ImportError:\n",
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" pass\n",
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"\n",
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"pd.options.display.max_rows = 10"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"## Some 'theory': the groupby operation (split-apply-combine)\n",
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"\n",
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"The \"group by\" concept: we want to **apply the same function on subsets of your dataframe, based on some key to split the dataframe in subsets**\n",
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"\n",
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"This operation is also referred to as the \"split-apply-combine\" operation, involving the following steps:\n",
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"\n",
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"* **Splitting** the data into groups based on some criteria\n",
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"* **Applying** a function to each group independently\n",
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"* **Combining** the results into a data structure\n",
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"\n",
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"<img src=\"img/splitApplyCombine.png\">\n",
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"\n",
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"Similar to SQL `GROUP BY`"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"The example of the image in pandas syntax:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>data</th>\n",
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" <th>key</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>0</td>\n",
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" <td>A</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>5</td>\n",
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" <td>B</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>10</td>\n",
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" <td>C</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>5</td>\n",
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" <td>A</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>10</td>\n",
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" <td>B</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>15</td>\n",
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" <td>C</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>10</td>\n",
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" <td>A</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>15</td>\n",
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" <td>B</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>20</td>\n",
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" <td>C</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" data key\n",
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"0 0 A\n",
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"1 5 B\n",
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"2 10 C\n",
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"3 5 A\n",
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"4 10 B\n",
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"5 15 C\n",
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"6 10 A\n",
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"7 15 B\n",
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"8 20 C"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df = pd.DataFrame({'key':['A','B','C','A','B','C','A','B','C'],\n",
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" 'data': [0, 5, 10, 5, 10, 15, 10, 15, 20]})\n",
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"df"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Using the filtering and reductions operations we have seen in the previous notebooks, we could do something like:\n",
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"\n",
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"\n",
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" df[df['key'] == \"A\"].sum()\n",
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" df[df['key'] == \"B\"].sum()\n",
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" ...\n",
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"\n",
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"But pandas provides the `groupby` method to do this:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false,
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>data</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>key</th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>A</th>\n",
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" <td>15</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>B</th>\n",
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" <td>30</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>C</th>\n",
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" <td>45</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" data\n",
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"key \n",
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"A 15\n",
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"B 30\n",
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"C 45"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.groupby('key').aggregate(np.sum) # 'sum'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>data</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>key</th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>A</th>\n",
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" <td>15</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>B</th>\n",
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" <td>30</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>C</th>\n",
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" <td>45</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" data\n",
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"key \n",
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"A 15\n",
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"B 30\n",
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"C 45"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.groupby('key').sum()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"And many more methods are available. "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"## And now applying this on some real data"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We go back to the titanic survival data:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"df = pd.read_csv(\"data/titanic.csv\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>PassengerId</th>\n",
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" <th>Survived</th>\n",
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" <th>Pclass</th>\n",
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" <th>Name</th>\n",
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" <th>Sex</th>\n",
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" <th>Age</th>\n",
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" <th>SibSp</th>\n",
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" <th>Parch</th>\n",
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" <th>Ticket</th>\n",
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" <th>Fare</th>\n",
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" <th>Cabin</th>\n",
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" <th>Embarked</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>3</td>\n",
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" <td>Braund, Mr. Owen Harris</td>\n",
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" <td>male</td>\n",
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" <td>22.0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>A/5 21171</td>\n",
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" <td>7.2500</td>\n",
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" <td>NaN</td>\n",
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" <td>S</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>2</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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" <td>female</td>\n",
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" <td>38.0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>PC 17599</td>\n",
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" <td>71.2833</td>\n",
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" <td>C85</td>\n",
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" <td>C</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>3</td>\n",
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" <td>1</td>\n",
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" <td>3</td>\n",
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" <td>Heikkinen, Miss. Laina</td>\n",
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" <td>female</td>\n",
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" <td>26.0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>STON/O2. 3101282</td>\n",
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" <td>7.9250</td>\n",
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" <td>NaN</td>\n",
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" <td>S</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>4</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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" <td>female</td>\n",
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" <td>35.0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>113803</td>\n",
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" <td>53.1000</td>\n",
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" <td>C123</td>\n",
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" <td>S</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>5</td>\n",
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" <td>0</td>\n",
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" <td>3</td>\n",
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" <td>Allen, Mr. William Henry</td>\n",
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" <td>male</td>\n",
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" <td>35.0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>373450</td>\n",
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" <td>8.0500</td>\n",
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" <td>NaN</td>\n",
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" <td>S</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" PassengerId Survived Pclass \\\n",
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"0 1 0 3 \n",
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"1 2 1 1 \n",
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"2 3 1 3 \n",
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"3 4 1 1 \n",
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"4 5 0 3 \n",
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"\n",
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" Name Sex Age SibSp \\\n",
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"0 Braund, Mr. Owen Harris male 22.0 1 \n",
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"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
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"2 Heikkinen, Miss. Laina female 26.0 0 \n",
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"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
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"4 Allen, Mr. William Henry male 35.0 0 \n",
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"\n",
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" Parch Ticket Fare Cabin Embarked \n",
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"0 0 A/5 21171 7.2500 NaN S \n",
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"1 0 PC 17599 71.2833 C85 C \n",
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"2 0 STON/O2. 3101282 7.9250 NaN S \n",
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"3 0 113803 53.1000 C123 S \n",
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"4 0 373450 8.0500 NaN S "
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<div class=\"alert alert-success\">\n",
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" <b>EXERCISE</b>: Using groupby(), calculate the average age for each sex.\n",
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"</div>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
|
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"clear_cell": true,
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Sex\n",
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"female 27.915709\n",
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"male 30.726645\n",
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"Name: Age, dtype: float64"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"df.groupby('Sex')['Age'].mean()"
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]
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},
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{
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|
"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<div class=\"alert alert-success\">\n",
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" <b>EXERCISE</b>: Calculate the average survival ratio for all passengers.\n",
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"</div>"
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]
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},
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{
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"cell_type": "code",
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|
"execution_count": 8,
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|
"metadata": {
|
|
"clear_cell": true,
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"collapsed": false
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},
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"outputs": [
|
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{
|
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"data": {
|
|
"text/plain": [
|
|
"0.38383838383838381"
|
|
]
|
|
},
|
|
"execution_count": 8,
|
|
"metadata": {},
|
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"df['Survived'].sum() / len(df['Survived'])"
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]
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},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div class=\"alert alert-success\">\n",
|
|
" <b>EXERCISE</b>: Calculate this survival ratio for all passengers younger that 25 (remember: filtering/boolean indexing).\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {
|
|
"clear_cell": true,
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0.41196013289036543"
|
|
]
|
|
},
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df25 = df[df['Age'] <= 25]\n",
|
|
"df25['Survived'].sum() / len(df25['Survived'])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div class=\"alert alert-success\">\n",
|
|
" <b>EXERCISE</b>: Is there a difference in this survival ratio between the sexes? (tip: write the above calculation of the survival ratio as a function)\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {
|
|
"clear_cell": true,
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def survival_ratio(survived):\n",
|
|
" return survived.sum() / len(survived)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {
|
|
"clear_cell": true,
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Sex\n",
|
|
"female 0.742038\n",
|
|
"male 0.188908\n",
|
|
"Name: Survived, dtype: float64"
|
|
]
|
|
},
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df.groupby('Sex')['Survived'].aggregate(survival_ratio)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div class=\"alert alert-success\">\n",
|
|
" <b>EXERCISE</b>: Make a bar plot of the survival ratio for the different classes ('Pclass' column).\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {
|
|
"clear_cell": true,
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7fa3df924518>"
|
|
]
|
|
},
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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xMusRCorXWyafz209xqjGmhoZ1lRmrKnsFdp6yhhhv98/7Mw3HA7L5/OltxOJhO688059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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7fa4103b5898>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"df.groupby('Pclass')['Survived'].aggregate(survival_ratio).plot(kind='bar')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"If you are ready, more groupby exercises can be found in the \"Advanded groupby operations\" notebook."
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"celltoolbar": "Nbtutor - export exercises",
|
|
"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": "3.5.2"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|