{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "from matplotlib import pyplot as pt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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CountryCityNumber of Male MembersNumber of Female MembersAccept
0CanadaVancouver23True
1USAnn Arbor23False
2BrazilFortaleza20False
3USDallas64True
4CanadaLondon21True
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" ], "text/plain": [ " Country City Number of Male Members Number of Female Members Accept\n", "0 Canada Vancouver 2 3 True\n", "1 US Ann Arbor 2 3 False\n", "2 Brazil Fortaleza 2 0 False\n", "3 US Dallas 6 4 True\n", "4 Canada London 2 1 True" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "teams = pd.read_csv(\"group-applications.csv\")\n", "teams.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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TotalAccept
Australia11
Brazil31
Canada74
France11
Germany22
Greece11
Israel10
Netherlands10
UK20
US252
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" ], "text/plain": [ " Total Accept\n", "Australia 1 1\n", "Brazil 3 1\n", "Canada 7 4\n", "France 1 1\n", "Germany 2 2\n", "Greece 1 1\n", "Israel 1 0\n", "Netherlands 1 0\n", "UK 2 0\n", "US 25 2" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t = teams.groupby([\"Country\"])\n", "a = teams[(teams[\"Accept\"] == True)].groupby([\"Country\"])\n", "teams_group = pd.concat([t.size(), a.size()], axis=1, keys=[\"Total\", \"Accept\"]).fillna(0)\n", "teams_group" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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BB9OnTx922GEHHn30UQAOP/xw5s6dy7777kvv3r256KKLcvrtRSQqFewqrrvuOg477DAO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\nlHX7HnYtB6NA/Xv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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "teams_group.plot(title=\"Number of groups by countries\", \n", " kind='bar')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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Number of men that appliedNumber of men that were selectedNumber of women that appliedNumber of women that were selected
Australia1111
Brazil3131
Canada7474
France1111
Germany2222
Greece1111
Israel1010
Netherlands1010
UK2020
US252252
\n", "
" ], "text/plain": [ " Number of men that applied Number of men that were selected \\\n", "Australia 1 1 \n", "Brazil 3 1 \n", "Canada 7 4 \n", "France 1 1 \n", "Germany 2 2 \n", "Greece 1 1 \n", "Israel 1 0 \n", "Netherlands 1 0 \n", "UK 2 0 \n", "US 25 2 \n", "\n", " Number of women that applied Number of women that were selected \n", "Australia 1 1 \n", "Brazil 3 1 \n", "Canada 7 4 \n", "France 1 1 \n", "Germany 2 2 \n", "Greece 1 1 \n", "Israel 1 0 \n", "Netherlands 1 0 \n", "UK 2 0 \n", "US 25 2 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m_t = t[\"Number of Male Members\"].size()\n", "m_a = a[\"Number of Male Members\"].size()\n", "f_t = t[\"Number of Female Members\"].size()\n", "f_a = a[\"Number of Female Members\"].size()\n", "teams_gender = pd.concat([m_t, m_a, f_t, f_a], axis=1, keys=[\"Number of men that applied\", \"Number of men that were selected\", \"Number of women that applied\", \"Number of women that were selected\"]).fillna(0)\n", "teams_gender" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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F0OrVq5du3LhRN27cWCnySCg02O2336579+7VadOmaevWrfXiiy/WkpIS/eqr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\nsC8AAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "teams_gender.plot(title=\"Individual applications by gender\",\n", " kind='bar')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Total 44\n", "Accept 12\n", "dtype: float64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "teams_group.sum()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "people = pd.read_csv(\"individual-applications.csv\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TotalAccept
Australia44
Brazil103
Canada4312
France76
Germany33
Greece44
India31
Israel30
Mexico20
Netherlands40
South Africa10
Switzerland11
UK160
US12715
\n", "
" ], "text/plain": [ " Total Accept\n", "Australia 4 4\n", "Brazil 10 3\n", "Canada 43 12\n", "France 7 6\n", "Germany 3 3\n", "Greece 4 4\n", "India 3 1\n", "Israel 3 0\n", "Mexico 2 0\n", "Netherlands 4 0\n", "South Africa 1 0\n", "Switzerland 1 1\n", "UK 16 0\n", "US 127 15" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t = people.groupby([\"Country\"]).size()\n", "a = people[(people[\"Accept\"] == True)].groupby([\"Country\"]).size()\n", "people_group = pd.concat([t, a], axis=1, keys=[\"Total\", \"Accept\"]).fillna(0)\n", "people_group" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "people_group.plot(title=\"Individual application by countries\",\n", " kind='bar')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Total 228\n", "Accept 49\n", "dtype: float64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "people_group.sum()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "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": "3.4.3+" } }, "nbformat": 4, "nbformat_minor": 0 }