{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "from matplotlib import pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# DATA SOURCE\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "· From World University Ranking 2012-2016，We download the data of 2012-2016.\n", "\n", "https://data.world/hhaveliw/world-university-ranking-2016\n", "\n", "· Then from Times Higher Education：world university rankings 2017-2018，We manually copy-and-paste the data we need of 2017-2018 as supplementary.\n", "\n", "https://www.timeshighereducation.com/cn/world-university-rankings" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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World_RankUniversity_NameCountry%_Female_StudentsYear
01California Institute of TechnologyUnited States of America33.02012
12Harvard UniversityUnited States of AmericaNaN2012
22Stanford UniversityUnited States of America42.02012
34University of OxfordUnited Kingdom46.02012
45Princeton UniversityUnited States of America45.02012
56University of CambridgeUnited Kingdom46.02012
67Massachusetts Institute of TechnologyUnited States of America37.02012
78Imperial College LondonUnited Kingdom37.02012
89University of ChicagoUnited States of America42.02012
910University of California, BerkeleyUnited States of America50.02012
1011Yale UniversityUnited States of America50.02012
1112Columbia UniversityUnited States of AmericaNaN2012
1213University of California, Los AngelesUnited States of America52.02012
1314Johns Hopkins UniversityUnited States of America50.02012
1415ETH Zurich – Swiss Federal Institute of Tech...Switzerland31.02012
1516University of PennsylvaniaUnited States of America51.02012
1617University College LondonUnited Kingdom56.02012
1718University of MichiganUnited States of America48.02012
1819University of TorontoCanadaNaN2012
1920Cornell UniversityUnited States of America48.02012
2021Carnegie Mellon UniversityUnited States of America39.02012
2122Duke UniversityUnited States of America49.02012
2222University of British ColumbiaCanada54.02012
2324Georgia Institute of TechnologyUnited States of America31.02012
2425University of WashingtonUnited States of America53.02012
2526Northwestern UniversityUnited States of America48.02012
2627University of Wisconsin-MadisonUnited States of America51.02012
2728McGill UniversityCanada56.02012
2829University of Texas at AustinUnited States of America51.02012
2930University of TokyoJapanNaN2012
..................
2777350-400Justus Liebig University GiessenGermany61.02018
2778350-400University of KansasUnited States51.02018
2779350-400Kyushu UniversityJapan29.02018
2780350-400La Trobe UniversityAustralia63.02018
2781350-400Leibniz University of HanoverGermany41.02018
2782350-400Linköping UniversitySweden53.02018
2783350-400University of MacauMacao58.02018
2784350-400University of MalayaMalaysia66.02018
2785350-400Montpellier UniversityFrance53.02018
2786350-400Örebro UniversitySweden61.02018
2787350-400University of PaduaItaly55.02018
2788350-400University of PaviaItaly56.02018
2789350-400University of PisaItaly52.02018
2790350-400Royal Veterinary CollegeUnited Kingdom75.02018
2791350-400Sabancı UniversityTurkey41.02018
2792350-400University of SalernoItaly60.02018
2793350-400University of South CarolinaUnited States55.02018
2794350-400Stellenbosch UniversitySouth Africa54.02018
2795350-400University of StrasbourgFrance58.02018
2796350-400Sun Yat-sen UniversityChina51.02018
2797350-400Temple UniversityUnited States52.02018
2798350-400University of TriesteItaly56.02018
2799350-400Tulane UniversityUnited States56.02018
2800350-400University of TurkuFinland62.02018
2801350-400University College CorkIreland56.02018
2802350-400University of VermontUnited States56.02018
2803350-400Université de Versailles Saint-Quentin-en-Yvel...France59.02018
2804350-400University of WaikatoNew Zealand56.02018
2805350-400Wayne State UniversityUnited States58.02018
2806350-400York UniversityCanada56.02018
\n", "

2807 rows × 5 columns

\n", "
" ], "text/plain": [ " World_Rank University_Name \\\n", "0 1 California Institute of Technology \n", "1 2 Harvard University \n", "2 2 Stanford University \n", "3 4 University of Oxford \n", "4 5 Princeton University \n", "5 6 University of Cambridge \n", "6 7 Massachusetts Institute of Technology \n", "7 8 Imperial College London \n", "8 9 University of Chicago \n", "9 10 University of California, Berkeley \n", "10 11 Yale University \n", "11 12 Columbia University \n", "12 13 University of California, Los Angeles \n", "13 14 Johns Hopkins University \n", "14 15 ETH Zurich – Swiss Federal Institute of Tech... \n", "15 16 University of Pennsylvania \n", "16 17 University College London \n", "17 18 University of Michigan \n", "18 19 University of Toronto \n", "19 20 Cornell University \n", "20 21 Carnegie Mellon University \n", "21 22 Duke University \n", "22 22 University of British Columbia \n", "23 24 Georgia Institute of Technology \n", "24 25 University of Washington \n", "25 26 Northwestern University \n", "26 27 University of Wisconsin-Madison \n", "27 28 McGill University \n", "28 29 University of Texas at Austin \n", "29 30 University of Tokyo \n", "... ... ... \n", "2777 350-400 Justus Liebig University Giessen \n", "2778 350-400 University of Kansas \n", "2779 350-400 Kyushu University \n", "2780 350-400 La Trobe University \n", "2781 350-400 Leibniz University of Hanover \n", "2782 350-400 Linköping University \n", "2783 350-400 University of Macau \n", "2784 350-400 University of Malaya \n", "2785 350-400 Montpellier University \n", "2786 350-400 Örebro University \n", "2787 350-400 University of Padua \n", "2788 350-400 University of Pavia \n", "2789 350-400 University of Pisa \n", "2790 350-400 Royal Veterinary College \n", "2791 350-400 Sabancı University \n", "2792 350-400 University of Salerno \n", "2793 350-400 University of South Carolina \n", "2794 350-400 Stellenbosch University \n", "2795 350-400 University of Strasbourg \n", "2796 350-400 Sun Yat-sen University \n", "2797 350-400 Temple University \n", "2798 350-400 University of Trieste \n", "2799 350-400 Tulane University \n", "2800 350-400 University of Turku \n", "2801 350-400 University College Cork \n", "2802 350-400 University of Vermont \n", "2803 350-400 Université de Versailles Saint-Quentin-en-Yvel... \n", "2804 350-400 University of Waikato \n", "2805 350-400 Wayne State University \n", "2806 350-400 York University \n", "\n", " Country %_Female_Students Year \n", "0 United States of America 33.0 2012 \n", "1 United States of America NaN 2012 \n", "2 United States of America 42.0 2012 \n", "3 United Kingdom 46.0 2012 \n", "4 United States of America 45.0 2012 \n", "5 United Kingdom 46.0 2012 \n", "6 United States of America 37.0 2012 \n", "7 United Kingdom 37.0 2012 \n", "8 United States of America 42.0 2012 \n", "9 United States of America 50.0 2012 \n", "10 United States of America 50.0 2012 \n", "11 United States of America NaN 2012 \n", "12 United States of America 52.0 2012 \n", "13 United States of America 50.0 2012 \n", "14 Switzerland 31.0 2012 \n", "15 United States of America 51.0 2012 \n", "16 United Kingdom 56.0 2012 \n", "17 United States of America 48.0 2012 \n", "18 Canada NaN 2012 \n", "19 United States of America 48.0 2012 \n", "20 United States of America 39.0 2012 \n", "21 United States of America 49.0 2012 \n", "22 Canada 54.0 2012 \n", "23 United States of America 31.0 2012 \n", "24 United States of America 53.0 2012 \n", "25 United States of America 48.0 2012 \n", "26 United States of America 51.0 2012 \n", "27 Canada 56.0 2012 \n", "28 United States of America 51.0 2012 \n", "29 Japan NaN 2012 \n", "... ... ... ... \n", "2777 Germany 61.0 2018 \n", "2778 United States 51.0 2018 \n", "2779 Japan 29.0 2018 \n", "2780 Australia 63.0 2018 \n", "2781 Germany 41.0 2018 \n", "2782 Sweden 53.0 2018 \n", "2783 Macao 58.0 2018 \n", "2784 Malaysia 66.0 2018 \n", "2785 France 53.0 2018 \n", "2786 Sweden 61.0 2018 \n", "2787 Italy 55.0 2018 \n", "2788 Italy 56.0 2018 \n", "2789 Italy 52.0 2018 \n", "2790 United Kingdom 75.0 2018 \n", "2791 Turkey 41.0 2018 \n", "2792 Italy 60.0 2018 \n", "2793 United States 55.0 2018 \n", "2794 South Africa 54.0 2018 \n", "2795 France 58.0 2018 \n", "2796 China 51.0 2018 \n", "2797 United States 52.0 2018 \n", "2798 Italy 56.0 2018 \n", "2799 United States 56.0 2018 \n", "2800 Finland 62.0 2018 \n", "2801 Ireland 56.0 2018 \n", "2802 United States 56.0 2018 \n", "2803 France 59.0 2018 \n", "2804 New Zealand 56.0 2018 \n", "2805 United States 58.0 2018 \n", "2806 Canada 56.0 2018 \n", "\n", "[2807 rows x 5 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('school ranking.csv',)\n", "df" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def clean_us_name(s):\n", " if s == 'United States of America':\n", " return 'United States'\n", " else:\n", " return s\n", "df['Country'] = df['Country'].apply(clean_us_name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# World‘s Mean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "selecting female data & see means of each year.\n", "\n", "and then use the pivot table to come up with a new chart." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "50.15897634742148" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['%_Female_Students'].mean()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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e3qrcw6rKvSx6dTPNL70HwLDMVE4rzGF6YTanFeZw2ths8rPiZ4ZJd+cXL73Lfzy3nsmjhvKLzxYzOicj2s2KmuOGvpkNBpLcvS64Pwf4NvAUMA+4N7h9sovN3wByzGyEu9cCFwFlkWq8iHTP+zsbWF9Vxzf/YVLHsqQk4+T8IZycP4R/DIY7mlva2FBdx6rKPcGLwV7uX1ZLW3BINjo7veMFYHphDtMKsxmanhqNLh1TU0sr3/jDGp5YUcll00byg09OJzNtYF+pHk7vC4DFwdu7FOARd3/ezN4AHjOzG4HNwDUAZlYM3OLuN7l7q5l9BVhqoR0sB37RFx0RkeMrKa8C4JIpI4+5XlpKElPHZDN1TDbXf3gcAA3NLZRv29fxIrCqcg/PB/sDOHHE4I7zA6cV5jBl9NCoTkFcU9fILb9dzor39/Clj57CFy86JW5KGvYlc4+t0ZTi4mIvK9ObAZG+cPXPXmF/cyvP3XZ+RPa3p6GZtyr3dgwLrdqyh5q6JgBSkoxTR2YdMjQ0oWAIKf0wtcGarXtZ8GAZuxqa+eE1M7hs2qg+/57RZmbLO11Sf1QD+32OyABSW9fE8vd3c9vsUyK2z5zMNC6YMIILJozoWFa1t5FVlaHzA29V7uXZt7bx6OvvA5CemsTU0aEXgOljQ7fj8zIjeqL4T6u38+XHVjEsM1TScOqY+Ctp2JcU+iIDxAvrqnE//tBOb43MTmdk9siO7+PubN7ZEJwfCL0reOT1zfz65TYAhqanHPIiML0wh5HZ3T9R3Nbm/HhpBT9eWsGHinL4+Q3FjMgaFNG+JQKFvsgAUVJeRVFuJhNH9u/c8GbG+OGDGT98MHNnjAGgpbWNipp6Vm0JDQu9VbmHn//1XVqCM8X5WYM4rTCHGcELwWmF2eRkHn2K44bmFr782CqeW1PF1WcUcs+VUwfkB8/CodAXGQDqGg/yysadzDt3XExcc5+SnMSkUUOZNGoonz4rtKzxYCtrt+/jreCFYFXlHl5YV92xzbi8zI7zA9PHhk4UZ6alsHXPAW5eVMb6qn3ccdkkbjr/hJjoY6xS6IsMAMverqW5tY05fTy00xvpqcl8qGgYHyr64ENT+xoPsqZyb8e7geWbdvH0qm0AJBlMKMiitq6J5pY2fjX/TC48NT9azY8bCn2RAaCkvIrhQ9IOCdR4MDQ9lXNPHs65naY7rq1r6rha6K3KPeRkpnL3FVM5OT9xShr2JYW+SIJramnlL+tr+MSM0Qkxr86IrEHMnlSQMIXK+1vi1QITkUO8snEn+5tbY3poR/qPQl8kwZWUVzFkUArnnpQX7aZIDFDoiySw1jZnydpqZp06QpcwCqDQF0loyzfvZuf+5j7/QJbED4W+SAIrLa8iLTmJWaeOOP7KMiAo9EUSlLtTsraK807OIysGpz2W6FDoiySoddvr2LLrgIZ25BAKfZEEVVJehRm6nl0OodAXSVAl5VUUjxummSblEAp9kQTUXhZRQztyOIW+SAIqXRteWUQZeBT6IgmopLyKSaOGMjY3M9pNkRij0BdJMLV1TZRt3s2cyTqBK0dS6IskmP4qiyjxSaEvkmBKy6sYm5vBpFGaX16OpNAXSSB1jQd5eeNOLpk8UiUDpUsKfZEE8pegLOIlUzW0I11T6IskkJLyKvIGx19ZROk/Cn2RBNHU0spf3q7l4skFCVEWUfqGQl8kQbzyzk7qm1p01Y4ck0JfJEGUtpdFPFllEeXoFPoiCUBlESVcCn2RBLDi/d3sqG9mjoZ25DgU+iIJoGRNqCzihSqLKMcRVuib2SYzW21mK82sLFiWa2ZLzKwiuD3qNWJmNtTMKs3s/kg1XERC3J3StdWcq7KIEobuHOlf6O4z3L04eHw7sNTdTwGWBo+P5jvAiz1so4gcw/qqOt7f1aCrdiQsvRnemQssCu4vAq7oaiUzOwMoAEp78b1E5CjayyJ+VGURJQzhhr4DpWa23MwWBMsK3H17cL+KULAfwsySgP8EvtLrlopIl0rKqzmjSGURJTwpYa430923mlk+sMTM1nd+0t3dzLyL7b4A/MndK481+VPwQrIAoKioKMwmiciWXQ2s276POy6bFO2mSJwIK/TdfWtwW2Nmi4GzgGozG+Xu281sFFDTxabnAOeb2ReAIUCamdW7+yHj/+6+EFgIUFxc3NWLh4h0oaRcZRGle447vGNmg80sq/0+MAdYAzwFzAtWmwc8efi27n69uxe5+3hCQzwPHh74ItJzpeXVTByZRVGeyiJKeMIZ0y8A/mZmq4DXgWfd/XngXuBiM6sAPho8xsyKzeyXfdVgEQnZUd/EG5t36ShfuuW4wzvu/i4wvYvlO4HZXSwvA27qYvkDwAM9aaSIHOmFtaGyiHOm6KodCZ8+kSsSp0rKqygclsHkUUOj3RSJIwp9kThU39QSKos4RWURpXsU+iJx6C9v14TKImo8X7pJoS8Sh0rKq8kbnMYZ41QWUbpHoS8SZ5paWlm2voaPTlJZROk+hb5InOkoizhVV+1I9yn0ReJMaXk1g9OSOfek4dFuisQhhb5IHOkoizgxn/RUlUWU7lPoi8SRN9/fzY76Jl21Iz2m0BeJIyXlVaQmG7NUFlF6SKEvEifcnZLyas49aThDVRZRekihLxIn3q5WWUTpPYW+SJwoWVMdKos4OT/aTZE4ptAXiRMl5VV8qGgY+Vnp0W6KxDGFvkgc2LKrgbXb93GJplGWXlLoi8QBlUWUSFHoi8SB0rWhsojj8gZHuykS5xT6IjFuZ30TZZt2MUdH+RIBCn2RGPfCumraHOZM1ni+9J5CXyTGlZRXMyYngymjVRZRek+hLxLD6pta+FvFDpVFlIhR6IvEsL++XRuURdTQjkSGQl8khpWUV5E7OI3i8bnRbookCIW+SIxqbmkLyiLmqyyiRIxCXyRGvfLODuqaWvSBLIkohb5IjCoJyiKed7LKIkrkKPRFYlBbe1nEU1UWUSJLoS8Sg97cEiqLOEdX7UiEKfRFYlBJeTWpycaFEzV3vkSWQl8kxoTKIlZxjsoiSh9Q6IvEmLer69i8s0EfyJI+EVbom9kmM1ttZivNrCxYlmtmS8ysIrgd1sV2M8zsVTMrN7O3zOxTke6ASKIpLQ+VRbxYE6xJH+jOkf6F7j7D3YuDx7cDS939FGBp8PhwDcBn3X0KcClwn5nl9KrFIglOZRGlL/VmeGcusCi4vwi44vAV3H2Du1cE97cBNcCIXnxPkYS2ZVcD5dv2aRpl6TPhhr4DpWa23MwWBMsK3H17cL8KOOZfqZmdBaQB73Tx3AIzKzOzstra2jCbJJJ4StdWAyqLKH0nJcz1Zrr7VjPLB5aY2frOT7q7m5kfbWMzGwX8Fpjn7m2HP+/uC4GFAMXFxUfdj0iiKymv4tSCLMYPV1lE6RthHem7+9bgtgZYDJwFVAdh3h7qNV1ta2ZDgWeBO9z9tUg0WiQRtZdF1FU70peOG/pmNtjMstrvA3OANcBTwLxgtXnAk11sm0boReJBd388Uo0WSURL19WEyiJqaEf6UDhH+gXA38xsFfA68Ky7Pw/cC1xsZhXAR4PHmFmxmf0y2PYa4AJgfnC550ozmxHxXogkgJLyKpVFlD533DF9d38XmN7F8p3A7C6WlwE3BfcfAh7qfTNFElt9UwsvbdzB9R8uUllE6VP6RK5IDPjr27U0t7Tpqh3pcwp9kRhQujZUFvFMlUWUPqbQF4my5pY2/qyyiNJPFPoiUfbquzupa2xhzmQN7UjfU+iLRFlJeRWZacnMPEVlEaXvKfRFouiDsogjVBZR+oVCXySK3tyyh9q6Jl21I/1GoS8SRaXlVSqLKP1KoS8SJe1lEc8+MU9lEaXfKPRFomRDdT2bdjZoaEf6lUJfJEpKyqswQwVTpF8p9EWipHRtFaePzSF/qMoiSv9R6ItEQeXuBtZs3aehHel3Cn2RKCgtD5VF1Nz50t8U+iJRUFJexYSCIZygsojSzxT6Iv1sZ30Tb2zapaEdiQqFvkg/W7o+VBZRoS/RoNAX6WelKosoUaTQF+lH+5taeLFiBxdPLlBZRIkKhb5IP/rrBpVFlOhS6Iv0o5LyKoZlpnLm+GHRbooMUAp9kX7yQVnEAlKS9a8n0aG/PJF+8lpQFlFDOxJNCn2RfqKyiBILFPoi/aC9LOJHJqgsokSXQl+kH7y5ZQ81KosoMUChL9IPSsurSElSWUSJPoW+SB9rL4t4zkl5ZGeoLKJEl0JfpI9V1ITKImoaZYkFCn2RPlaypgpQWUSJDQp9kT5WsraK04tyKFBZRIkBYYW+mW0ys9VmttLMyoJluWa2xMwqgtsuP1duZvOCdSrMbF4kGy8S61QWUWJNd470L3T3Ge5eHDy+HVjq7qcAS4PHhzCzXOBO4MPAWcCdR3txEElES9aGyiIq9CVW9GZ4Zy6wKLi/CLiii3UuAZa4+y533w0sAS7txfcUiSsl5VWckq+yiBI7wg19B0rNbLmZLQiWFbj79uB+FdDVWaoxwJZOjyuDZYcwswVmVmZmZbW1tWE2SSS27drfzOvvqSyixJaUMNeb6e5bzSwfWGJm6zs/6e5uZt7TRrj7QmAhQHFxcY/3IxJLXlhXrbKIEnPCOtJ3963BbQ2wmND4fLWZjQIIbmu62HQrMLbT48JgmUjCay+LOHWMyiJK7Dhu6JvZYDPLar8PzAHWAE8B7VfjzAOe7GLzEmCOmQ0LTuDOCZaJJDSVRZRYFc7wTgGwOPjDTQEecffnzewN4DEzuxHYDFwDYGbFwC3ufpO77zKz7wBvBPv6trvvingvRGLMi0FZxDlT9IEsiS3HDX13fxeY3sXyncDsLpaXATd1evxr4Ne9a6ZIfGkvi3jW+NxoN0XkEPpErkiENbe0sXR9DbNVFlFikP4iRSJMZREllin0RSKsdG0VGanJnK+yiBKDFPoiEdTW5pSWqyyixC6FvkgErawMyiJO1VU7EpsU+iIRVBKURbzoVIW+xCaFvkiEuIeGds45KY/sTJVFlNik0BeJkI019by3Y7/KIkpMU+iLREhJeags4sWTNLQjsUuhLxIhJeXVzBibw8hslUWU2KXQF4mArXsOsHrrXn0gS2KeQl8kAkqDoZ1LNMGaxDiFvkgElJZXc0r+EE4cMSTaTRE5JoW+SC/t3t/M65t2aRpliQsKfZFeemFdNa1trvF8iQsKfZFeKimvZnR2OtPGZEe7KSLHpdAX6YWG5hZeqqhlzpSRKosocUGhL9ILL26opUllESWOKPRFeqGkvJoclUWUOKLQF+mhg61tLF1XzeyJKoso8UN/qSI99Nq7O9nX2KIPZElcUeiL9FBJeags4gUTRkS7KSJhU+iL9EBbm7NkrcoiSvxR6Iv0wKrKPVTva9JVOxJ3FPoiPVBSXk1KkjF7okJf4otCX6SbQmURqzj7RJVFlPij0Bfppo019by7Y7+u2pG4pNAX6abStdUAXDxZE6xJ/FHoi3RTSXkV01UWUeKUQl+kG7btOcBblXs1tCNxKyXcFc0sGSgDtrr75WZ2EfADIA1YDtzo7i1dbPc94B8IvcAsAW5zd49E4xNFS2sbFTX1rN66lzVb97J661627DpAwdBBFOVmMjY3k7HDMijMzWTssEwKh2Xo2vAo+aAsooZ2JD6FHfrAbcA6YKiZJQGLgNnuvsHMvg3MA37VeQMzOxc4DzgtWPQ34CPAX3rZ7rh1sLWNiur6jnBfvXUv67bvo6mlDYAhg1KYPHoosyfmU13XyIbqOv68vqbj+Xb5WYMYm5sZelHo9IIwNjeDUdkZJCdpmt++UFJezcn5QzhJZRElToUV+mZWSOho/R7g34A8oNndNwSrLAG+zmGhDziQTujdgAGpQHXvmx0fDra2saG6rlPA72Pd9n00dwr4KaOHcsPZ45hWmM3UMdmckDeYpMMCu63N2VHfxJbdDWzZdYD3dzWwZVcDW3Y38Pp7u3hy5QHaOr13SkkyRudkMDY3g6LcTAqHffBuYWxuJnmD0zT3ew+0l0X83AUnRrspIj0W7pH+fcC/A1nB4x1AipkVu3sZcDUw9vCN3P1VM1sGbCcU+ve7+7reNzv2NLccGvBrtu5lXVVdR8BnDUphypihzDvkfu7XAAAJ7ElEQVRnHFPHZDNtTDbjuwj4riQlGflD08kfms4Z4458/mBrG9v3NAYvCg2hF4XdB9iyq4Ela6vZUd98yPqZackUDssI3hkc+oIwNjeTIYO68wZw4Fi6vkZlESXuHfe/28wuB2rcfbmZzQJwdzezTwM/MrNBQCnQ2sW2JwOTgMJg0RIzO9/dXzpsvQXAAoCioqJedKd/tAf86k4Bv357Hc2tQcCnpzB1dDbzzx3fEfDjcjPDCvieSE1Ooigvk6K8zC6fb2huoTJ4EQi9SzjQ8QLx9/d2Ud906KmYYZmpwQtBJoXBu4X2F4gxORmkpSTO+X93p7m1jQPNrRw42EpDc2vH/cNvf//GFkZlp3NaocoiSvwK55DuPOATZnYZoaGaoWb2kLt/BjgfwMzmABO62PZK4DV3rw/Wew44Bzgk9N19IbAQoLi4OKZO8ja1tLKhqv6QgH+76oOAH5qewtQx2fzTeZ0CPi8zpoZPMtNSmFCQxYSCrCOec3f2NBxky+4jXxDWbt/HkrXVHX0FMIORQ9M7XhDGDsv84GRzbgYFWekRfXFra/NQ8HYK3w+CuYUDzW00NLfQ2L78sLBuONhKY/ORzzU0twbbtBwyNHY8X5h1Ukz9bkW6y7pzIU1wpP+V4OqdfHevCY70/wTc4+5/Pmz9TwE3A5cSGt55HrjP3Z8+2vcoLi72srKy7vckAppaWnm7qu6Qq2jerqrjYGvoZ5SdkcrUMUM7wn3amGyKcmMr4COtrc2prmvk/Z0fDBlt2d1AZfDiULWvkc5/QmnJSYwZlhEaPgreIeRkph5x1Nw5dA8cbONAc8sHyzutc/gJ7HAMSkkiIy2ZzNRk0tOSyUxLJiM1mYy0FDJSk8hMSyE9tfPy0G1mWuh+18+lkJGazNCMlIT+fUv8MrPl7l58vPV6M3j71WDoJwn4WXvgm1kxcIu73wQ8DlwErCZ0Uvf5YwV+f2o8eGTAb6g+NOCnjcnmxpkndgT82NyMAfcPn5RkjMoOXRH04S6eb2ppZevuA12+IKxZvZ3dDQcP3Z9x1NAtyEoNhXSnZZ0DOb09fNOSyEhNCQX7YetlpCb32TCaSCLo1pF+f+iLI/3Gg62sbw/4yg8CviV4X5+TGQr4zkfwhcMGXsD3hbrGg9Q1tnQcRaclJ+nnKtIH+uNIPyY1Hmxl7fZ9lHe6TLKiU8DnDk5j6phsLpw4oiPox+Qo4PtKVnoqWemaiVIkViRM6FftbWT+b16noqae1iDg84KAnz0xP3QUX5jN6Ox0BbyIDFgJE/p5Q9IoHJbBxZMLOoZpRingRUQOkTChn5qcxC/nnRntZoiIxLTE+ZSNiIgcl0JfRGQAUeiLiAwgCn0RkQFEoS8iMoAo9EVEBhCFvojIAKLQFxEZQGJuwjUzqwU292IXwwlV9op3idIPUF9iVaL0JVH6Ab3ryzh3H3G8lWIu9HvLzMrCmWku1iVKP0B9iVWJ0pdE6Qf0T180vCMiMoAo9EVEBpBEDP2F0W5AhCRKP0B9iVWJ0pdE6Qf0Q18SbkxfRESOLhGP9EVE5ChiPvTNbKyZLTOztWZWbma3BctzzWyJmVUEt8OC5RPN7FUzazKzrxxvP3Hal3Qze93MVgX7uSse+9Fpf8lm9qaZPdOf/Yh0X8xsk5mtNrOVZhbZQs/935ccM3vczNab2TozOyfe+mFmpwa/i/avfWb2pf7qRyT7Ejz3r8E+1pjZo2aW3qNGuXtMfwGjgA8F97OADcBk4HvA7cHy24HvBvfzgTOBe4CvHG8/cdoXA4YE91OBvwNnx1s/Ou3v34BHgGfi9e8reG4TMLy/+9BHfVkE3BTcTwNy4rEfnfaZDFQRupY97n4nwBjgPSAjePwYML8nbYr5I3133+7uK4L7dcA6Qj+AuYT+MAlurwjWqXH3N4CDYe6n30SwL+7u9cHD1OCr307ORKofAGZWCPwD8Mt+aPoRItmXaItUX8wsG7gA+FWwXrO77+mXTtBnv5PZwDvu3psPfnZbhPuSAmSYWQqQCWzrSZtiPvQ7M7PxwOmEjmwL3H178FQVUNDD/URFb/sSDImsBGqAJe4elb5E4HdyH/DvQFtftK87ItAXB0rNbLmZLeiTRoapl305AagFfhMMu/3SzAb3VVuPJVL/88CngUcj2rhu6k1f3H0r8APgfWA7sNfdS3vSjrgJfTMbAjwBfMnd93V+zkPvd8I60j3WfvpLJPri7q3uPgMoBM4ys6l90thj6G0/zOxyoMbdl/ddK8MTob+vme7+IeBjwK1mdkHkW3p8EehLCvAh4Gfufjqwn9AQRL+K4P98GvAJ4H8j3sgwReB/ZRihdwcnAKOBwWb2mZ60JS5C38xSCf3AHnb3PwSLq81sVPD8KEJHvD3ZT7+KVF/aBW+7lwGXRrqtxxKhfpwHfMLMNgG/Ay4ys4f6qMlHFanfSXA0hrvXAIuBs/qmxUcXob5UApWd3j0+TuhFoN9E+P/kY8AKd6+OfEuPL0J9+SjwnrvXuvtB4A/AuT1pT8yHvpkZobHFde7+w05PPQXMC+7PA57s4X76TQT7MsLMcoL7GcDFwPrIt/io3z8i/XD3r7t7obuPJ/T2+8/u3qOjl56K4O9ksJlltd8H5gBrIt/iY7YhUr+XKmCLmZ0aLJoNrI1wc48qUv3o5FqiNLQTwb68D5xtZpnBPmcTOj/QfT05+9ufX8BMQm993gJWBl+XAXnAUqACeAHIDdYfSehIZR+wJ7g/9Gj7idO+nAa8GexnDfCteOzHYfucRXSu3onU7+REYFXwVQ7cEa99CZ6bAZQF+/ojMCxO+zEY2Alk9/fvow/6chehg7s1wG+BQT1pkz6RKyIygMT88I6IiESOQl9EZABR6IuIDCAKfRGRAUShLyIygCj0ZcCzkL+Z2cc6LfukmT0fzXaJ9AVdsikCBNNY/C+huVFSCH0O4lJ3f6cX+0xx95YINVEkInSkLwK4+xrgaeBrwLeAB939HTObZ6HaBSvN7KdmlgRgZgvNrCyY3/xb7fsxs0ozu9fM3gSujEpnRI4hJdoNEIkhdwErgGagODj6vxI4191bzGwhoekiHiE0F/quYJrbZWb2uLu3T1VQ46GJykRijkJfJODu+83s90C9uzeZ2UcJFbQoC013QgawJVj9WjO7kdD/0GhChTHaQ//3/dtykfAp9EUO1cYHc/sb8Gt3/7+dVzCzU4DbgLPcfU8wM2jn0nX7+6WlIj2gMX2Ro3sBuMbMhgOYWZ6ZFRGaYK0O2BdMi3tJFNso0i060hc5CndfbaGi8y8EJ3APArcQmn1yLaEZDzcDL0evlSLdo0s2RUQGEA3viIgMIAp9EZEBRKEvIjKAKPRFRAYQhb6IyACi0BcRGUAU+iIiA4hCX0RkAPn/JV+ZYe+oeE4AAAAASUVORK5CYII=\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "female_data_group_by_year = df.groupby('Year').mean()\n", "female_data_group_by_year.plot()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountryAustraliaAustriaBelgiumBrazilCanadaChileChinaColombiaCyprusCzech Republic...SpainSwedenSwitzerlandTaiwanThailandTurkeyUnisted States of AmericaUnited Arab EmiratesUnited KingdomUnited States
Year
201244.027.054.048.045.0NaN29.0NaNNaN62.0...12.030.027.027.063.034.0NaNNaN37.025.0
201347.027.054.048.045.0NaN32.044.0NaN62.0...36.030.027.027.042.034.0NaNNaN37.026.0
201444.027.054.048.045.0NaN31.044.0NaN62.0...36.030.027.027.042.034.0NaNNaN37.026.0
201544.027.054.048.045.024.031.044.0NaN62.0...52.030.027.027.042.034.0NaNNaN37.026.0
201644.022.054.048.045.0NaN32.0NaN69.049.0...52.030.027.027.0NaN39.0NaNNaN37.026.0
201739.023.049.048.045.0NaN32.0NaN69.0NaN...52.031.027.033.0NaN39.0NaNNaN37.027.0
201843.029.050.048.046.0NaN22.0NaN57.0NaN...53.031.028.033.0NaN41.0NaN46.037.028.0
\n", "

7 rows × 54 columns

\n", "
" ], "text/plain": [ "Country Australia Austria Belgium Brazil Canada Chile China Colombia \\\n", "Year \n", "2012 44.0 27.0 54.0 48.0 45.0 NaN 29.0 NaN \n", "2013 47.0 27.0 54.0 48.0 45.0 NaN 32.0 44.0 \n", "2014 44.0 27.0 54.0 48.0 45.0 NaN 31.0 44.0 \n", "2015 44.0 27.0 54.0 48.0 45.0 24.0 31.0 44.0 \n", "2016 44.0 22.0 54.0 48.0 45.0 NaN 32.0 NaN \n", "2017 39.0 23.0 49.0 48.0 45.0 NaN 32.0 NaN \n", "2018 43.0 29.0 50.0 48.0 46.0 NaN 22.0 NaN \n", "\n", "Country Cyprus Czech Republic ... Spain Sweden Switzerland \\\n", "Year ... \n", "2012 NaN 62.0 ... 12.0 30.0 27.0 \n", "2013 NaN 62.0 ... 36.0 30.0 27.0 \n", "2014 NaN 62.0 ... 36.0 30.0 27.0 \n", "2015 NaN 62.0 ... 52.0 30.0 27.0 \n", "2016 69.0 49.0 ... 52.0 30.0 27.0 \n", "2017 69.0 NaN ... 52.0 31.0 27.0 \n", "2018 57.0 NaN ... 53.0 31.0 28.0 \n", "\n", "Country Taiwan Thailand Turkey Unisted States of America \\\n", "Year \n", "2012 27.0 63.0 34.0 NaN \n", "2013 27.0 42.0 34.0 NaN \n", "2014 27.0 42.0 34.0 NaN \n", "2015 27.0 42.0 34.0 NaN \n", "2016 27.0 NaN 39.0 NaN \n", "2017 33.0 NaN 39.0 NaN \n", "2018 33.0 NaN 41.0 NaN \n", "\n", "Country United Arab Emirates United Kingdom United States \n", "Year \n", "2012 NaN 37.0 25.0 \n", "2013 NaN 37.0 26.0 \n", "2014 NaN 37.0 26.0 \n", "2015 NaN 37.0 26.0 \n", "2016 NaN 37.0 26.0 \n", "2017 NaN 37.0 27.0 \n", "2018 46.0 37.0 28.0 \n", "\n", "[7 rows x 54 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.pivot_table(df, values='%_Female_Students', index='Country', columns='Year', aggfunc=np.min).T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Find out the relationship between rank and female ratio" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Firstly, we use the def fuction to delete the universities ranked in 201-400,\n", "\n", "and select the ratio and the rank top 200 university to draw a chart \n", "\n", "then use the diffrent colors to stress the top 20 universities." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def convert_rank(s):\n", " try:\n", " return int(s)\n", " except:\n", " return 201\n", "df['rank'] = df['World_Rank'].apply(convert_rank)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "myax = df[\n", " (df['rank'] < 201)\n", "].plot('rank', '%_Female_Students', kind='scatter', color='#B2FFDE', alpha=0.5)\n", "\n", "df[\n", " (df['rank'] < 15)\n", "].plot('rank', '%_Female_Students', kind='scatter', color='#FF5B3E', ax=myax, alpha=0.3)\n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "%_Female_Students 30.846154\n", "Year 2014.967391\n", "rank 144.576087\n", "dtype: float64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df['University_Name'].apply(lambda x: 'Technology'in x)].mean()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "%_Female_Students 51.513278\n", "Year 2015.003050\n", "rank 151.125048\n", "dtype: float64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df['University_Name'].apply(lambda x: 'Technology'not in x)].mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Continental" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "set up groups by continents\n", "\n", "see means of specific continents by years\n", "\n", "The line chart shows Asia is the continental with the lowest ratio, while the Austrialian has the highest ratio." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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World_RankUniversity_NameCountry%_Female_StudentsYearrankState
01California Institute of TechnologyUnited States33.020121Americas
12Harvard UniversityUnited StatesNaN20122Americas
22Stanford UniversityUnited States42.020122Americas
34University of OxfordUnited Kingdom46.020124Europe
45Princeton UniversityUnited States45.020125Americas
56University of CambridgeUnited Kingdom46.020126Europe
67Massachusetts Institute of TechnologyUnited States37.020127Americas
78Imperial College LondonUnited Kingdom37.020128Europe
89University of ChicagoUnited States42.020129Americas
910University of California, BerkeleyUnited States50.0201210Americas
1011Yale UniversityUnited States50.0201211Americas
1112Columbia UniversityUnited StatesNaN201212Americas
1213University of California, Los AngelesUnited States52.0201213Americas
1314Johns Hopkins UniversityUnited States50.0201214Americas
1415ETH Zurich – Swiss Federal Institute of Tech...Switzerland31.0201215Europe
1516University of PennsylvaniaUnited States51.0201216Americas
1617University College LondonUnited Kingdom56.0201217Europe
1718University of MichiganUnited States48.0201218Americas
1819University of TorontoCanadaNaN201219Americas
1920Cornell UniversityUnited States48.0201220Americas
2021Carnegie Mellon UniversityUnited States39.0201221Americas
2122Duke UniversityUnited States49.0201222Americas
2222University of British ColumbiaCanada54.0201222Americas
2324Georgia Institute of TechnologyUnited States31.0201224Americas
2425University of WashingtonUnited States53.0201225Americas
2526Northwestern UniversityUnited States48.0201226Americas
2627University of Wisconsin-MadisonUnited States51.0201227Americas
2728McGill UniversityCanada56.0201228Americas
2829University of Texas at AustinUnited States51.0201229Americas
2930University of TokyoJapanNaN201230Asia
........................
2777350-400Justus Liebig University GiessenGermany61.02018201Europe
2778350-400University of KansasUnited States51.02018201Americas
2779350-400Kyushu UniversityJapan29.02018201Asia
2780350-400La Trobe UniversityAustralia63.02018201Australian
2781350-400Leibniz University of HanoverGermany41.02018201Europe
2782350-400Linköping UniversitySweden53.02018201Europe
2783350-400University of MacauMacao58.02018201Asia
2784350-400University of MalayaMalaysia66.02018201Asia
2785350-400Montpellier UniversityFrance53.02018201Europe
2786350-400Örebro UniversitySweden61.02018201Europe
2787350-400University of PaduaItaly55.02018201Europe
2788350-400University of PaviaItaly56.02018201Europe
2789350-400University of PisaItaly52.02018201Europe
2790350-400Royal Veterinary CollegeUnited Kingdom75.02018201Europe
2791350-400Sabancı UniversityTurkey41.02018201Asia
2792350-400University of SalernoItaly60.02018201Europe
2793350-400University of South CarolinaUnited States55.02018201Americas
2794350-400Stellenbosch UniversitySouth Africa54.02018201Africa
2795350-400University of StrasbourgFrance58.02018201Europe
2796350-400Sun Yat-sen UniversityChina51.02018201Asia
2797350-400Temple UniversityUnited States52.02018201Americas
2798350-400University of TriesteItaly56.02018201Europe
2799350-400Tulane UniversityUnited States56.02018201Americas
2800350-400University of TurkuFinland62.02018201Europe
2801350-400University College CorkIreland56.02018201Europe
2802350-400University of VermontUnited States56.02018201Americas
2803350-400Université de Versailles Saint-Quentin-en-Yvel...France59.02018201Europe
2804350-400University of WaikatoNew Zealand56.02018201Australian
2805350-400Wayne State UniversityUnited States58.02018201Americas
2806350-400York UniversityCanada56.02018201Americas
\n", "

2807 rows × 7 columns

\n", "
" ], "text/plain": [ " World_Rank University_Name \\\n", "0 1 California Institute of Technology \n", "1 2 Harvard University \n", "2 2 Stanford University \n", "3 4 University of Oxford \n", "4 5 Princeton University \n", "5 6 University of Cambridge \n", "6 7 Massachusetts Institute of Technology \n", "7 8 Imperial College London \n", "8 9 University of Chicago \n", "9 10 University of California, Berkeley \n", "10 11 Yale University \n", "11 12 Columbia University \n", "12 13 University of California, Los Angeles \n", "13 14 Johns Hopkins University \n", "14 15 ETH Zurich – Swiss Federal Institute of Tech... \n", "15 16 University of Pennsylvania \n", "16 17 University College London \n", "17 18 University of Michigan \n", "18 19 University of Toronto \n", "19 20 Cornell University \n", "20 21 Carnegie Mellon University \n", "21 22 Duke University \n", "22 22 University of British Columbia \n", "23 24 Georgia Institute of Technology \n", "24 25 University of Washington \n", "25 26 Northwestern University \n", "26 27 University of Wisconsin-Madison \n", "27 28 McGill University \n", "28 29 University of Texas at Austin \n", "29 30 University of Tokyo \n", "... ... ... \n", "2777 350-400 Justus Liebig University Giessen \n", "2778 350-400 University of Kansas \n", "2779 350-400 Kyushu University \n", "2780 350-400 La Trobe University \n", "2781 350-400 Leibniz University of Hanover \n", "2782 350-400 Linköping University \n", "2783 350-400 University of Macau \n", "2784 350-400 University of Malaya \n", "2785 350-400 Montpellier University \n", "2786 350-400 Örebro University \n", "2787 350-400 University of Padua \n", "2788 350-400 University of Pavia \n", "2789 350-400 University of Pisa \n", "2790 350-400 Royal Veterinary College \n", "2791 350-400 Sabancı University \n", "2792 350-400 University of Salerno \n", "2793 350-400 University of South Carolina \n", "2794 350-400 Stellenbosch University \n", "2795 350-400 University of Strasbourg \n", "2796 350-400 Sun Yat-sen University \n", "2797 350-400 Temple University \n", "2798 350-400 University of Trieste \n", "2799 350-400 Tulane University \n", "2800 350-400 University of Turku \n", "2801 350-400 University College Cork \n", "2802 350-400 University of Vermont \n", "2803 350-400 Université de Versailles Saint-Quentin-en-Yvel... \n", "2804 350-400 University of Waikato \n", "2805 350-400 Wayne State University \n", "2806 350-400 York University \n", "\n", " Country %_Female_Students Year rank State \n", "0 United States 33.0 2012 1 Americas \n", "1 United States NaN 2012 2 Americas \n", "2 United States 42.0 2012 2 Americas \n", "3 United Kingdom 46.0 2012 4 Europe \n", "4 United States 45.0 2012 5 Americas \n", "5 United Kingdom 46.0 2012 6 Europe \n", "6 United States 37.0 2012 7 Americas \n", "7 United Kingdom 37.0 2012 8 Europe \n", "8 United States 42.0 2012 9 Americas \n", "9 United States 50.0 2012 10 Americas \n", "10 United States 50.0 2012 11 Americas \n", "11 United States NaN 2012 12 Americas \n", "12 United States 52.0 2012 13 Americas \n", "13 United States 50.0 2012 14 Americas \n", "14 Switzerland 31.0 2012 15 Europe \n", "15 United States 51.0 2012 16 Americas \n", "16 United Kingdom 56.0 2012 17 Europe \n", "17 United States 48.0 2012 18 Americas \n", "18 Canada NaN 2012 19 Americas \n", "19 United States 48.0 2012 20 Americas \n", "20 United States 39.0 2012 21 Americas \n", "21 United States 49.0 2012 22 Americas \n", "22 Canada 54.0 2012 22 Americas \n", "23 United States 31.0 2012 24 Americas \n", "24 United States 53.0 2012 25 Americas \n", "25 United States 48.0 2012 26 Americas \n", "26 United States 51.0 2012 27 Americas \n", "27 Canada 56.0 2012 28 Americas \n", "28 United States 51.0 2012 29 Americas \n", "29 Japan NaN 2012 30 Asia \n", "... ... ... ... ... ... \n", "2777 Germany 61.0 2018 201 Europe \n", "2778 United States 51.0 2018 201 Americas \n", "2779 Japan 29.0 2018 201 Asia \n", "2780 Australia 63.0 2018 201 Australian \n", "2781 Germany 41.0 2018 201 Europe \n", "2782 Sweden 53.0 2018 201 Europe \n", "2783 Macao 58.0 2018 201 Asia \n", "2784 Malaysia 66.0 2018 201 Asia \n", "2785 France 53.0 2018 201 Europe \n", "2786 Sweden 61.0 2018 201 Europe \n", "2787 Italy 55.0 2018 201 Europe \n", "2788 Italy 56.0 2018 201 Europe \n", "2789 Italy 52.0 2018 201 Europe \n", "2790 United Kingdom 75.0 2018 201 Europe \n", "2791 Turkey 41.0 2018 201 Asia \n", "2792 Italy 60.0 2018 201 Europe \n", "2793 United States 55.0 2018 201 Americas \n", "2794 South Africa 54.0 2018 201 Africa \n", "2795 France 58.0 2018 201 Europe \n", "2796 China 51.0 2018 201 Asia \n", "2797 United States 52.0 2018 201 Americas \n", "2798 Italy 56.0 2018 201 Europe \n", "2799 United States 56.0 2018 201 Americas \n", "2800 Finland 62.0 2018 201 Europe \n", "2801 Ireland 56.0 2018 201 Europe \n", "2802 United States 56.0 2018 201 Americas \n", "2803 France 59.0 2018 201 Europe \n", "2804 New Zealand 56.0 2018 201 Australian \n", "2805 United States 58.0 2018 201 Americas \n", "2806 Canada 56.0 2018 201 Americas \n", "\n", "[2807 rows x 7 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "state_mapping = {\n", " 'China': 'Asia',\n", " 'India': 'Asia',\n", " 'Japan': 'Asia',\n", " 'South Korea': 'Asia',\n", " 'Taiwan': 'Asia',\n", " 'Hong Kong': 'Asia',\n", " 'Macau': 'Asia',\n", " 'Macao': 'Asia',\n", " 'Thailand': 'Asia',\n", " 'Turkey': 'Asia',\n", " 'Singapore': 'Asia',\n", " 'Malaysia': 'Asia',\n", " 'Saudi Arabia': 'Asia',\n", " 'United Arab Emirates': 'Asia',\n", " 'Maoco': 'Asia',\n", " 'Canada': 'Americas',\n", " 'United States': 'Americas',\n", " 'Unisted States of America': 'Americas',\n", " 'Canada': 'Americas',\n", " 'Colombia': 'Americas',\n", " 'Chile': 'Americas',\n", " 'Mexico': 'Americas',\n", " 'Brazil': 'Americas',\n", " 'United Kingdom':'Europe',\n", " 'Germany':'Europe',\n", " 'Italy':'Europe',\n", " 'Netherlands':'Europe',\n", " 'France':'Europe', \n", " 'Sweden':'Europe',\n", " 'Belgium':'Europe',\n", " 'Switzerland':'Europe',\n", " 'Spain':'Europe',\n", " 'Finland':'Europe',\n", " 'Denmark':'Europe',\n", " 'Norway':'Europe',\n", " 'Republic of Ireland':'Europe',\n", " 'Russian Federation':'Europe',\n", " 'Portugal':'Europe',\n", " 'Ireland':'Europe',\n", " 'Greece':'Europe',\n", " 'Iceland':'Europe',\n", " 'Estonia':'Europe',\n", " 'Poland':'Europe',\n", " 'Czech Republic':'Europe',\n", " 'Cyprus':'Europe',\n", " 'Luxembourg':'Europe',\n", " 'Danmark':'Europe', \n", " 'Hungary':'Europe',\n", " 'Austria':'Europe',\n", " 'Australia':'Australian',\n", " 'New Zealand':'Australian',\n", " 'South Africa':'Africa',\n", " 'Morocco':'Africa',\n", " 'Egypt':'Africa',\n", " 'Israel':'Middle_East',\n", " 'Iran':'Middle_East' \n", "}\n", "df['State'] = df['Country'].apply(lambda x: state_mapping.get(x, x))\n", "df" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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StateAfricaAmericasAsiaAustralianEuropeMiddle_East
Year
201246.025.013.044.012.027.0
201353.026.013.047.018.027.0
201453.026.013.044.018.027.0
201546.024.013.044.018.027.0
201653.026.013.044.018.035.0
201753.027.07.039.018.035.0
201853.028.014.043.020.032.0
\n", "
" ], "text/plain": [ "State Africa Americas Asia Australian Europe Middle_East\n", "Year \n", "2012 46.0 25.0 13.0 44.0 12.0 27.0\n", "2013 53.0 26.0 13.0 47.0 18.0 27.0\n", "2014 53.0 26.0 13.0 44.0 18.0 27.0\n", "2015 46.0 24.0 13.0 44.0 18.0 27.0\n", "2016 53.0 26.0 13.0 44.0 18.0 35.0\n", "2017 53.0 27.0 7.0 39.0 18.0 35.0\n", "2018 53.0 28.0 14.0 43.0 20.0 32.0" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.pivot_table(df, values='%_Female_Students', index='State', columns='Year', aggfunc=np.min).T" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = pd.pivot_table(df, values='%_Female_Students', index='State', columns='Year').T.plot()\n", "\n", "patches, labels = ax.get_legend_handles_labels()\n", "ax.legend(patches, labels, loc='upper right')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Asia" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we can find in the bar chart of Asia, means of the second country and the eight country dropped, meanwhile, the fourth country kept the lowest means all these years.\n", "in terms of that, we found out those three countries are Japan, Thailand and India" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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World_RankUniversity_NameCountry%_Female_StudentsYearrankState
2396201-400Toyota Technological InstituteJapan7.02017201Asia
530128Tokyo Institute of TechnologyJapan13.02013128Asia
926125Tokyo Institute of TechnologyJapan13.02014125Asia
1850201-250Tokyo Institute of TechnologyJapan13.02016201Asia
107108Tokyo Institute of TechnologyJapan13.02012108Asia
1161351-400Indian Institute of Technology KanpurIndia13.02014201Asia
2297201-400Tokyo Institute of TechnologyJapan13.02017201Asia
1344141Tokyo Institute of TechnologyJapan13.02015141Asia
2699251–300Tokyo Institute of TechnologyJapan14.02018201Asia
632226-250Indian Institute of Technology KharagpurIndia15.02013201Asia
1162351-400Indian Institute of Technology KharagpurIndia15.02014201Asia
1966351-400Indian Institute of Technology BombayIndia16.02016201Asia
656251-275Indian Institute of Technology BombayIndia16.02013201Asia
310301-350Indian Institute of Technology BombayIndia16.02012201Asia
1564351-400Indian Institute of Technology BombayIndia16.02015201Asia
2776350-400Indian Institute of Technology BombayIndia17.02018201Asia
1565351-400Indian Institute of Technology RoorkeeIndia17.02015201Asia
1163351-400Indian Institute of Technology RoorkeeIndia17.02014201Asia
2373201-400Indian Institute of Technology BombayIndia17.02017201Asia
759351-400Indian Institute of Technology RoorkeeIndia17.02013201Asia
1160351-400Indian Institute of Technology DelhiIndia18.02014201Asia
1479276-300Indian Institute of ScienceIndia19.02015201Asia
1870251-300Indian Institute of ScienceIndia19.02016201Asia
45150Pohang University of Science and TechnologySouth Korea20.0201350Asia
46968Korea Advanced Institute of Science and Techno...South Korea20.0201368Asia
5253Pohang University of Science and TechnologySouth Korea20.0201253Asia
85756Korea Advanced Institute of Science and Techno...South Korea20.0201456Asia
126766Pohang University of Science and TechnologySouth Korea20.0201566Asia
209289Korea Advanced Institute of Science and Techno...South Korea20.0201789Asia
9394Korea Advanced Institute of Science and Techno...South Korea20.0201294Asia
........................
1408201-225Korea UniversitySouth KoreaNaN2015201Asia
1418201-225University of Science and Technology of ChinaChinaNaN2015201Asia
1427201-225Yonsei UniversitySouth KoreaNaN2015201Asia
1520301-350Renmin University of ChinaChinaNaN2015201Asia
1562351-400Hanyang UniversitySouth KoreaNaN2015201Asia
1601351-400Wuhan UniversityChinaNaN2015201Asia
164442Peking UniversityChinaNaN201642Asia
164543University of TokyoJapanNaN201643Asia
166159Hong Kong University of Science and TechnologyHong KongNaN201659Asia
168785Seoul National UniversitySouth KoreaNaN201685Asia
169088Kyoto UniversityJapanNaN201688Asia
1839201-250University of Science and Technology of ChinaChinaNaN2016201Asia
1876251-300Korea UniversitySouth KoreaNaN2016201Asia
1951301-350Yonsei UniversitySouth KoreaNaN2016201Asia
1953351-400Bilkent UniversityTurkeyNaN2016201Asia
1963351-400Hanyang UniversitySouth KoreaNaN2016201Asia
204239The University of TokyoJapanNaN201739Asia
205249Hong Kong University of Science and TechnologyHong KongNaN201749Asia
207672Seoul National UniversitySouth KoreaNaN201772Asia
207976Chinese University of Hong KongHong KongNaN201776Asia
209591Kyoto UniversityJapanNaN201791Asia
2144137Sungkyunkwan University (SKKU)South KoreaNaN2017137Asia
2157153University of Science and Technology of ChinaChinaNaN2017153Asia
2303201-400Yonsei UniversitySouth KoreaNaN2017201Asia
2369201-400Hanyang UniversitySouth KoreaNaN2017201Asia
244944Hong Kong University of Science and TechnologyHong KongNaN201844Asia
245146The University of TokyoJapanNaN201846Asia
246358Chinese University of Hong KongHong KongNaN201858Asia
248075Seoul National UniversitySouth KoreaNaN201875Asia
2773350-400Hanyang UniversitySouth KoreaNaN2018201Asia
\n", "

345 rows × 7 columns

\n", "
" ], "text/plain": [ " World_Rank University_Name \\\n", "2396 201-400 Toyota Technological Institute \n", "530 128 Tokyo Institute of Technology \n", "926 125 Tokyo Institute of Technology \n", "1850 201-250 Tokyo Institute of Technology \n", "107 108 Tokyo Institute of Technology \n", "1161 351-400 Indian Institute of Technology Kanpur \n", "2297 201-400 Tokyo Institute of Technology \n", "1344 141 Tokyo Institute of Technology \n", "2699 251–300 Tokyo Institute of Technology \n", "632 226-250 Indian Institute of Technology Kharagpur \n", "1162 351-400 Indian Institute of Technology Kharagpur \n", "1966 351-400 Indian Institute of Technology Bombay \n", "656 251-275 Indian Institute of Technology Bombay \n", "310 301-350 Indian Institute of Technology Bombay \n", "1564 351-400 Indian Institute of Technology Bombay \n", "2776 350-400 Indian Institute of Technology Bombay \n", "1565 351-400 Indian Institute of Technology Roorkee \n", "1163 351-400 Indian Institute of Technology Roorkee \n", "2373 201-400 Indian Institute of Technology Bombay \n", "759 351-400 Indian Institute of Technology Roorkee \n", "1160 351-400 Indian Institute of Technology Delhi \n", "1479 276-300 Indian Institute of Science \n", "1870 251-300 Indian Institute of Science \n", "451 50 Pohang University of Science and Technology \n", "469 68 Korea Advanced Institute of Science and Techno... \n", "52 53 Pohang University of Science and Technology \n", "857 56 Korea Advanced Institute of Science and Techno... \n", "1267 66 Pohang University of Science and Technology \n", "2092 89 Korea Advanced Institute of Science and Techno... \n", "93 94 Korea Advanced Institute of Science and Techno... \n", "... ... ... \n", "1408 201-225 Korea University \n", "1418 201-225 University of Science and Technology of China \n", "1427 201-225 Yonsei University \n", "1520 301-350 Renmin University of China \n", "1562 351-400 Hanyang University \n", "1601 351-400 Wuhan University \n", "1644 42 Peking University \n", "1645 43 University of Tokyo \n", "1661 59 Hong Kong University of Science and Technology \n", "1687 85 Seoul National University \n", "1690 88 Kyoto University \n", "1839 201-250 University of Science and Technology of China \n", "1876 251-300 Korea University \n", "1951 301-350 Yonsei University \n", "1953 351-400 Bilkent University \n", "1963 351-400 Hanyang University \n", "2042 39 The University of Tokyo \n", "2052 49 Hong Kong University of Science and Technology \n", "2076 72 Seoul National University \n", "2079 76 Chinese University of Hong Kong \n", "2095 91 Kyoto University \n", "2144 137 Sungkyunkwan University (SKKU) \n", "2157 153 University of Science and Technology of China \n", "2303 201-400 Yonsei University \n", "2369 201-400 Hanyang University \n", "2449 44 Hong Kong University of Science and Technology \n", "2451 46 The University of Tokyo \n", "2463 58 Chinese University of Hong Kong \n", "2480 75 Seoul National University \n", "2773 350-400 Hanyang University \n", "\n", " Country %_Female_Students Year rank State \n", "2396 Japan 7.0 2017 201 Asia \n", "530 Japan 13.0 2013 128 Asia \n", "926 Japan 13.0 2014 125 Asia \n", "1850 Japan 13.0 2016 201 Asia \n", "107 Japan 13.0 2012 108 Asia \n", "1161 India 13.0 2014 201 Asia \n", "2297 Japan 13.0 2017 201 Asia \n", "1344 Japan 13.0 2015 141 Asia \n", "2699 Japan 14.0 2018 201 Asia \n", "632 India 15.0 2013 201 Asia \n", "1162 India 15.0 2014 201 Asia \n", "1966 India 16.0 2016 201 Asia \n", "656 India 16.0 2013 201 Asia \n", "310 India 16.0 2012 201 Asia \n", "1564 India 16.0 2015 201 Asia \n", "2776 India 17.0 2018 201 Asia \n", "1565 India 17.0 2015 201 Asia \n", "1163 India 17.0 2014 201 Asia \n", "2373 India 17.0 2017 201 Asia \n", "759 India 17.0 2013 201 Asia \n", "1160 India 18.0 2014 201 Asia \n", "1479 India 19.0 2015 201 Asia \n", "1870 India 19.0 2016 201 Asia \n", "451 South Korea 20.0 2013 50 Asia \n", "469 South Korea 20.0 2013 68 Asia \n", "52 South Korea 20.0 2012 53 Asia \n", "857 South Korea 20.0 2014 56 Asia \n", "1267 South Korea 20.0 2015 66 Asia \n", "2092 South Korea 20.0 2017 89 Asia \n", "93 South Korea 20.0 2012 94 Asia \n", "... ... ... ... ... ... \n", "1408 South Korea NaN 2015 201 Asia \n", "1418 China NaN 2015 201 Asia \n", "1427 South Korea NaN 2015 201 Asia \n", "1520 China NaN 2015 201 Asia \n", "1562 South Korea NaN 2015 201 Asia \n", "1601 China NaN 2015 201 Asia \n", "1644 China NaN 2016 42 Asia \n", "1645 Japan NaN 2016 43 Asia \n", "1661 Hong Kong NaN 2016 59 Asia \n", "1687 South Korea NaN 2016 85 Asia \n", "1690 Japan NaN 2016 88 Asia \n", "1839 China NaN 2016 201 Asia \n", "1876 South Korea NaN 2016 201 Asia \n", "1951 South Korea NaN 2016 201 Asia \n", "1953 Turkey NaN 2016 201 Asia \n", "1963 South Korea NaN 2016 201 Asia \n", "2042 Japan NaN 2017 39 Asia \n", "2052 Hong Kong NaN 2017 49 Asia \n", "2076 South Korea NaN 2017 72 Asia \n", "2079 Hong Kong NaN 2017 76 Asia \n", "2095 Japan NaN 2017 91 Asia \n", "2144 South Korea NaN 2017 137 Asia \n", "2157 China NaN 2017 153 Asia \n", "2303 South Korea NaN 2017 201 Asia \n", "2369 South Korea NaN 2017 201 Asia \n", "2449 Hong Kong NaN 2018 44 Asia \n", "2451 Japan NaN 2018 46 Asia \n", "2463 Hong Kong NaN 2018 58 Asia \n", "2480 South Korea NaN 2018 75 Asia \n", "2773 South Korea NaN 2018 201 Asia \n", "\n", "[345 rows x 7 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Asia_data= df[df['State'] =='Asia']\n", "Asia_data.sort_values(by='%_Female_Students', ascending=True)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[2009, 2019, 10, 60]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = pd.pivot_table(Asia_data, values='%_Female_Students', index='Country', columns='Year').T.plot(figsize=(10,10))\n", "ax.axis([2009, 2019, 10, 60])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Australian" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we can find in the bar chart of Australian that there are only 2 countries,while the New Zealand is always the higher one.The reason may be that Austiralia has more universities than New Zealand which bring down the mean." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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World_RankUniversity_NameCountry%_Female_StudentsYearrankState
2394201-400Swinburne University of TechnologyAustralia39.02017201Australian
2539134University of AdelaideAustralia43.02018134Australian
1577351-400Swinburne University of TechnologyAustralia44.02015201Australian
1992351-400Swinburne University of TechnologyAustralia44.02016201Australian
385350-400Swinburne University of TechnologyAustralia44.02012201Australian
1178351-400Swinburne University of TechnologyAustralia44.02014201Australian
249085University of New South WalesAustralia46.0201885Australian
208178University of New South WalesAustralia46.0201778Australian
917114University of New South WalesAustralia47.02014114Australian
2146142University of AdelaideAustralia47.02017142Australian
48685University of New South WalesAustralia47.0201385Australian
168582University of New South WalesAustralia47.0201682Australian
1311109University of New South WalesAustralia47.02015109Australian
174173University of New South WalesAustralia47.02012173Australian
1365164University of AdelaideAustralia48.02015164Australian
1017201-225University of AdelaideAustralia48.02014201Australian
1752149University of AdelaideAustralia48.02016149Australian
578176University of AdelaideAustralia48.02013176Australian
212201-225University of AdelaideAustralia48.02012201Australian
2517112University of Western AustraliaAustralia49.02018112Australian
1502276-300University of WollongongAustralia50.02015201Australian
2302201-400University of WollongongAustralia50.02017201Australian
2359201-400University of CanterburyNew Zealand50.02017201Australian
746301-350University of WollongongAustralia50.02013201Australian
969168University of Western AustraliaAustralia50.02014168Australian
1903251-300University of WollongongAustralia50.02016201Australian
1098276-300University of WollongongAustralia50.02014201Australian
591190University of Western AustraliaAustralia50.02013190Australian
1360157University of Western AustraliaAustralia50.02015157Australian
2761350-400University of CanterburyNew Zealand50.02018201Australian
........................
755351-400Deakin UniversityAustralia60.02013201Australian
1510301-350Deakin UniversityAustralia60.02015201Australian
2744301-350University of TasmaniaAustralia60.02018201Australian
355350-400Deakin UniversityAustralia60.02012201Australian
1110301-350Deakin UniversityAustralia60.02014201Australian
1912301-350Deakin UniversityAustralia60.02016201Australian
2643201–250University of South AustraliaAustralia62.02018201Australian
2626201–250James Cook UniversityAustralia62.02018201Australian
370350-400Massey UniversityNew Zealand62.02012201Australian
767351-400Massey UniversityNew Zealand62.02013201Australian
2780350-400La Trobe UniversityAustralia63.02018201Australian
756351-400Flinders UniversityAustralia63.02013201Australian
365350-400La Trobe UniversityAustralia63.02012201Australian
356350-400Flinders UniversityAustralia63.02012201Australian
2273201-400James Cook UniversityAustralia63.02017201Australian
2366201-400Flinders UniversityAustralia63.02017201Australian
1872251-300James Cook UniversityAustralia63.02016201Australian
1969351-400La Trobe UniversityAustralia63.02016201Australian
2715301-350Charles Darwin UniversityAustralia63.02018201Australian
2719301-350Flinders UniversityAustralia63.02018201Australian
2380201-400La Trobe UniversityAustralia63.02017201Australian
1866251-300Flinders UniversityAustralia63.02016201Australian
2264201-400Charles Darwin UniversityAustralia66.02017201Australian
1155351-400Charles Darwin UniversityAustralia67.02014201Australian
754351-400Charles Darwin UniversityAustralia67.02013201Australian
1507301-350Charles Darwin UniversityAustralia67.02015201Australian
1862251-300Charles Darwin UniversityAustralia67.02016201Australian
307301-350Charles Darwin UniversityAustralia67.02012201Australian
358350-400Griffith UniversityAustraliaNaN2012201Australian
1868251-300Griffith UniversityAustraliaNaN2016201Australian
\n", "

180 rows × 7 columns

\n", "
" ], "text/plain": [ " World_Rank University_Name Country \\\n", "2394 201-400 Swinburne University of Technology Australia \n", "2539 134 University of Adelaide Australia \n", "1577 351-400 Swinburne University of Technology Australia \n", "1992 351-400 Swinburne University of Technology Australia \n", "385 350-400 Swinburne University of Technology Australia \n", "1178 351-400 Swinburne University of Technology Australia \n", "2490 85 University of New South Wales Australia \n", "2081 78 University of New South Wales Australia \n", "917 114 University of New South Wales Australia \n", "2146 142 University of Adelaide Australia \n", "486 85 University of New South Wales Australia \n", "1685 82 University of New South Wales Australia \n", "1311 109 University of New South Wales Australia \n", "174 173 University of New South Wales Australia \n", "1365 164 University of Adelaide Australia \n", "1017 201-225 University of Adelaide Australia \n", "1752 149 University of Adelaide Australia \n", "578 176 University of Adelaide Australia \n", "212 201-225 University of Adelaide Australia \n", "2517 112 University of Western Australia Australia \n", "1502 276-300 University of Wollongong Australia \n", "2302 201-400 University of Wollongong Australia \n", "2359 201-400 University of Canterbury New Zealand \n", "746 301-350 University of Wollongong Australia \n", "969 168 University of Western Australia Australia \n", "1903 251-300 University of Wollongong Australia \n", "1098 276-300 University of Wollongong Australia \n", "591 190 University of Western Australia Australia \n", "1360 157 University of Western Australia Australia \n", "2761 350-400 University of Canterbury New Zealand \n", "... ... ... ... \n", "755 351-400 Deakin University Australia \n", "1510 301-350 Deakin University Australia \n", "2744 301-350 University of Tasmania Australia \n", "355 350-400 Deakin University Australia \n", "1110 301-350 Deakin University Australia \n", "1912 301-350 Deakin University Australia \n", "2643 201–250 University of South Australia Australia \n", "2626 201–250 James Cook University Australia \n", "370 350-400 Massey University New Zealand \n", "767 351-400 Massey University New Zealand \n", "2780 350-400 La Trobe University Australia \n", "756 351-400 Flinders University Australia \n", "365 350-400 La Trobe University Australia \n", "356 350-400 Flinders University Australia \n", "2273 201-400 James Cook University Australia \n", "2366 201-400 Flinders University Australia \n", "1872 251-300 James Cook University Australia \n", "1969 351-400 La Trobe University Australia \n", "2715 301-350 Charles Darwin University Australia \n", "2719 301-350 Flinders University Australia \n", "2380 201-400 La Trobe University Australia \n", "1866 251-300 Flinders University Australia \n", "2264 201-400 Charles Darwin University Australia \n", "1155 351-400 Charles Darwin University Australia \n", "754 351-400 Charles Darwin University Australia \n", "1507 301-350 Charles Darwin University Australia \n", "1862 251-300 Charles Darwin University Australia \n", "307 301-350 Charles Darwin University Australia \n", "358 350-400 Griffith University Australia \n", "1868 251-300 Griffith University Australia \n", "\n", " %_Female_Students Year rank State \n", "2394 39.0 2017 201 Australian \n", "2539 43.0 2018 134 Australian \n", "1577 44.0 2015 201 Australian \n", "1992 44.0 2016 201 Australian \n", "385 44.0 2012 201 Australian \n", "1178 44.0 2014 201 Australian \n", "2490 46.0 2018 85 Australian \n", "2081 46.0 2017 78 Australian \n", "917 47.0 2014 114 Australian \n", "2146 47.0 2017 142 Australian \n", "486 47.0 2013 85 Australian \n", "1685 47.0 2016 82 Australian \n", "1311 47.0 2015 109 Australian \n", "174 47.0 2012 173 Australian \n", "1365 48.0 2015 164 Australian \n", "1017 48.0 2014 201 Australian \n", "1752 48.0 2016 149 Australian \n", "578 48.0 2013 176 Australian \n", "212 48.0 2012 201 Australian \n", "2517 49.0 2018 112 Australian \n", "1502 50.0 2015 201 Australian \n", "2302 50.0 2017 201 Australian \n", "2359 50.0 2017 201 Australian \n", "746 50.0 2013 201 Australian \n", "969 50.0 2014 168 Australian \n", "1903 50.0 2016 201 Australian \n", "1098 50.0 2014 201 Australian \n", "591 50.0 2013 190 Australian \n", "1360 50.0 2015 157 Australian \n", "2761 50.0 2018 201 Australian \n", "... ... ... ... ... \n", "755 60.0 2013 201 Australian \n", "1510 60.0 2015 201 Australian \n", "2744 60.0 2018 201 Australian \n", "355 60.0 2012 201 Australian \n", "1110 60.0 2014 201 Australian \n", "1912 60.0 2016 201 Australian \n", "2643 62.0 2018 201 Australian \n", "2626 62.0 2018 201 Australian \n", "370 62.0 2012 201 Australian \n", "767 62.0 2013 201 Australian \n", "2780 63.0 2018 201 Australian \n", "756 63.0 2013 201 Australian \n", "365 63.0 2012 201 Australian \n", "356 63.0 2012 201 Australian \n", "2273 63.0 2017 201 Australian \n", "2366 63.0 2017 201 Australian \n", "1872 63.0 2016 201 Australian \n", "1969 63.0 2016 201 Australian \n", "2715 63.0 2018 201 Australian \n", "2719 63.0 2018 201 Australian \n", "2380 63.0 2017 201 Australian \n", "1866 63.0 2016 201 Australian \n", "2264 66.0 2017 201 Australian \n", "1155 67.0 2014 201 Australian \n", "754 67.0 2013 201 Australian \n", "1507 67.0 2015 201 Australian \n", "1862 67.0 2016 201 Australian \n", "307 67.0 2012 201 Australian \n", "358 NaN 2012 201 Australian \n", "1868 NaN 2016 201 Australian \n", "\n", "[180 rows x 7 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Australian_data= df[df['State'] =='Australian']\n", "Australian_data.sort_values(by='%_Female_Students', ascending=True)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Australian=Australian_data[Australian_data['State'] == 'Australian']\n", "pd.pivot_table(Australian, values='%_Female_Students', index='Country', columns='Year', aggfunc=np.min).T.plot.bar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Middle East" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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World_RankUniversity_NameCountry%_Female_StudentsYearrankState
322301-350Sharif University of TechnologyIran27.02012201Middle_East
723301-350Sharif University of TechnologyIran27.02013201Middle_East
1065251-275Sharif University of TechnologyIran27.02014201Middle_East
1523301-350Sharif University of TechnologyIran27.02015201Middle_East
2709301-350Babol Noshirvani University of TechnologyIran32.02018201Middle_East
2746301-350Technion Israel Institute of TechnologyIsrael34.02018201Middle_East
207201-225Technion Israel Institute of TechnologyIsrael35.02012201Middle_East
595193Technion Israel Institute of TechnologyIsrael35.02013193Middle_East
2349201-400Technion Israel Institute of TechnologyIsrael35.02017201Middle_East
1014201-225Technion Israel Institute of TechnologyIsrael35.02014201Middle_East
1410201-225Technion Israel Institute of TechnologyIsrael35.02015201Middle_East
1944301-350Technion Israel Institute of TechnologyIsrael35.02016201Middle_East
1566351-400Isfahan University of TechnologyIran39.02015201Middle_East
2649201–250Tel Aviv UniversityIsrael54.02018201Middle_East
2623201–250Hebrew University of JerusalemIsrael55.02018201Middle_East
120121Hebrew University of JerusalemIsrael55.02012121Middle_East
992191Hebrew University of JerusalemIsrael55.02014191Middle_East
538137Hebrew University of JerusalemIsrael55.02013137Middle_East
1780178Hebrew University of JerusalemIsrael55.02016178Middle_East
1406201-225Hebrew University of JerusalemIsrael55.02015201Middle_East
2190186Hebrew University of JerusalemIsrael57.02017186Middle_East
304301-350Bar-Ilan UniversityIsrael59.02012201Middle_East
165166Tel Aviv UniversityIsraelNaN2012166Middle_East
559158Tel Aviv UniversityIsraelNaN2013158Middle_East
1001199Tel Aviv UniversityIsraelNaN2014199Middle_East
1390188Tel Aviv UniversityIsraelNaN2015188Middle_East
1846201-250Tel Aviv UniversityIsraelNaN2016201Middle_East
2250201-400Tel Aviv UniversityIsraelNaN2017201Middle_East
\n", "
" ], "text/plain": [ " World_Rank University_Name Country \\\n", "322 301-350 Sharif University of Technology Iran \n", "723 301-350 Sharif University of Technology Iran \n", "1065 251-275 Sharif University of Technology Iran \n", "1523 301-350 Sharif University of Technology Iran \n", "2709 301-350 Babol Noshirvani University of Technology Iran \n", "2746 301-350 Technion Israel Institute of Technology Israel \n", "207 201-225 Technion Israel Institute of Technology Israel \n", "595 193 Technion Israel Institute of Technology Israel \n", "2349 201-400 Technion Israel Institute of Technology Israel \n", "1014 201-225 Technion Israel Institute of Technology Israel \n", "1410 201-225 Technion Israel Institute of Technology Israel \n", "1944 301-350 Technion Israel Institute of Technology Israel \n", "1566 351-400 Isfahan University of Technology Iran \n", "2649 201–250 Tel Aviv University Israel \n", "2623 201–250 Hebrew University of Jerusalem Israel \n", "120 121 Hebrew University of Jerusalem Israel \n", "992 191 Hebrew University of Jerusalem Israel \n", "538 137 Hebrew University of Jerusalem Israel \n", "1780 178 Hebrew University of Jerusalem Israel \n", "1406 201-225 Hebrew University of Jerusalem Israel \n", "2190 186 Hebrew University of Jerusalem Israel \n", "304 301-350 Bar-Ilan University Israel \n", "165 166 Tel Aviv University Israel \n", "559 158 Tel Aviv University Israel \n", "1001 199 Tel Aviv University Israel \n", "1390 188 Tel Aviv University Israel \n", "1846 201-250 Tel Aviv University Israel \n", "2250 201-400 Tel Aviv University Israel \n", "\n", " %_Female_Students Year rank State \n", "322 27.0 2012 201 Middle_East \n", "723 27.0 2013 201 Middle_East \n", "1065 27.0 2014 201 Middle_East \n", "1523 27.0 2015 201 Middle_East \n", "2709 32.0 2018 201 Middle_East \n", "2746 34.0 2018 201 Middle_East \n", "207 35.0 2012 201 Middle_East \n", "595 35.0 2013 193 Middle_East \n", "2349 35.0 2017 201 Middle_East \n", "1014 35.0 2014 201 Middle_East \n", "1410 35.0 2015 201 Middle_East \n", "1944 35.0 2016 201 Middle_East \n", "1566 39.0 2015 201 Middle_East \n", "2649 54.0 2018 201 Middle_East \n", "2623 55.0 2018 201 Middle_East \n", "120 55.0 2012 121 Middle_East \n", "992 55.0 2014 191 Middle_East \n", "538 55.0 2013 137 Middle_East \n", "1780 55.0 2016 178 Middle_East \n", "1406 55.0 2015 201 Middle_East \n", "2190 57.0 2017 186 Middle_East \n", "304 59.0 2012 201 Middle_East \n", "165 NaN 2012 166 Middle_East \n", "559 NaN 2013 158 Middle_East \n", "1001 NaN 2014 199 Middle_East \n", "1390 NaN 2015 188 Middle_East \n", "1846 NaN 2016 201 Middle_East \n", "2250 NaN 2017 201 Middle_East " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ME_data= df[df['State'] =='Middle_East']\n", "ME_data.sort_values(by='%_Female_Students', ascending=True)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Iran=ME_data[ME_data['Country'] == 'Iran']\n", "pd.pivot_table(Iran, values='%_Female_Students', index='University_Name', columns='Year', aggfunc=np.min).T.plot.bar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# female ratio rank " ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Country\n", "Poland 67.000000\n", "Malaysia 66.000000\n", "Iceland 66.000000\n", "Estonia 66.000000\n", "Cyprus 65.000000\n", "Greece 62.714286\n", "Czech Republic 59.400000\n", "Macau 58.000000\n", "Macao 58.000000\n", "Finland 56.767347\n", "Ireland 56.625000\n", "Saudi Arabia 56.000000\n", "Spain 55.985771\n", "Republic of Ireland 55.720000\n", "Norway 55.071429\n", "New Zealand 55.026190\n", "Canada 54.967737\n", "Australia 54.885981\n", "Belgium 54.408163\n", "South Africa 54.261905\n", "Sweden 53.947619\n", "Hong Kong 53.821429\n", "Italy 53.379991\n", "United Kingdom 53.213112\n", "Mexico 51.000000\n", "Hungary 51.000000\n", "Luxembourg 50.333333\n", "United States 49.657508\n", "Austria 49.445238\n", "Germany 49.404508\n", "Denmark 49.342857\n", "Singapore 48.785714\n", "France 48.596226\n", "Netherlands 48.549451\n", "Switzerland 48.215986\n", "Brazil 48.000000\n", "Danmark 48.000000\n", "Thailand 47.250000\n", "Portugal 47.216667\n", "Israel 46.190476\n", "United Arab Emirates 46.000000\n", "Egypt 46.000000\n", "Morocco 46.000000\n", "Turkey 44.876190\n", "Colombia 44.000000\n", "Russian Federation 43.095238\n", "China 42.002551\n", "Taiwan 34.991497\n", "South Korea 30.195918\n", "Iran 29.200000\n", "Japan 28.251330\n", "Chile 24.000000\n", "India 19.042857\n", "Unisted States of America NaN\n", "dtype: float64" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.pivot_table(df, values='%_Female_Students', index='Country', columns='Year').T.mean().sort_values(ascending=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Canada" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We pick Canada as an example to demonstrate that why this country have that kind of high female ratio at the same time have the base of so much universities.\n", "\n", "And we find out the following possible reasons.\n", "\n", "First is the high eduacation investment of the Cannada, in addition, there are many policy which are benifitial for the immigration and for the well-eduacted foreign woman to study in Canada. The last reason is the feminist movement which incresase the right of woman." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Canada_data= df[df['Country'] =='Canada']\n", "ax = pd.pivot_table(Canada_data, values='%_Female_Students', index='Country', columns='Year').T.plot()\n", "ax" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# India" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We sort the value of lowest ratio, find out the India is constantly the lowest one.\n", "\n", "The reasons are follow, First, India has most the technology university which have lower rate of female students. It is also true for the whole world.\n", "\n", "Another reason is the weak education for the Indian woman. \n", "And the prejudice hinder the Indian woman to get the higher education." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Country\n", "India 19.042857\n", "Chile 24.000000\n", "Japan 28.251330\n", "Iran 29.200000\n", "South Korea 30.195918\n", "Taiwan 34.991497\n", "China 42.002551\n", "Russian Federation 43.095238\n", "Colombia 44.000000\n", "Turkey 44.876190\n", "Morocco 46.000000\n", "Egypt 46.000000\n", "United Arab Emirates 46.000000\n", "Israel 46.190476\n", "Portugal 47.216667\n", "Thailand 47.250000\n", "Danmark 48.000000\n", "Brazil 48.000000\n", "Switzerland 48.215986\n", "Netherlands 48.549451\n", "France 48.596226\n", "Singapore 48.785714\n", "Denmark 49.342857\n", "Germany 49.404508\n", "Austria 49.445238\n", "United States 49.657508\n", "Luxembourg 50.333333\n", "Hungary 51.000000\n", "Mexico 51.000000\n", "United Kingdom 53.213112\n", "Italy 53.379991\n", "Hong Kong 53.821429\n", "Sweden 53.947619\n", "South Africa 54.261905\n", "Belgium 54.408163\n", "Australia 54.885981\n", "Canada 54.967737\n", "New Zealand 55.026190\n", "Norway 55.071429\n", "Republic of Ireland 55.720000\n", "Spain 55.985771\n", "Saudi Arabia 56.000000\n", "Ireland 56.625000\n", "Finland 56.767347\n", "Macao 58.000000\n", "Macau 58.000000\n", "Czech Republic 59.400000\n", "Greece 62.714286\n", "Cyprus 65.000000\n", "Estonia 66.000000\n", "Iceland 66.000000\n", "Malaysia 66.000000\n", "Poland 67.000000\n", "Unisted States of America NaN\n", "dtype: float64" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.pivot_table(df, values='%_Female_Students', index='Country', columns='Year').T.mean().sort_values(ascending=True)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "India_data= df[df['Country'] =='India']\n", "ax = pd.pivot_table(India_data, values='%_Female_Students', index='Country', columns='Year').T.plot()\n", "ax" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "%_Female_Students 16.153846\n", "Year 2014.461538\n", "rank 201.000000\n", "dtype: float64" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "India_data[India_data['University_Name'].apply(lambda x: 'Technology'in x)].mean()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "%_Female_Students 28.500000\n", "Year 2015.833333\n", "rank 201.000000\n", "dtype: float64" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "India_data[India_data['University_Name'].apply(lambda x: 'Technology'not in x)].mean()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "%_Female_Students 30.846154\n", "Year 2014.967391\n", "rank 144.576087\n", "dtype: float64" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df['University_Name'].apply(lambda x: 'Technology'in x)].mean()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "%_Female_Students 51.513278\n", "Year 2015.003050\n", "rank 151.125048\n", "dtype: float64" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df['University_Name'].apply(lambda x: 'Technology'not in x)].mean()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }