dekker.one
Group work for a Monash Research Methods course
Merge branch 'master' of https://github.com/Dekker1/ResearchMethods
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*.dvi
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# these rules might exclude image files for figures etc.
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# *.ps
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# *.eps
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# *.pdf
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## Generated if empty string is given at "Please type another file name for output:"
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wk7/week7.pdf
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wk9/week9.pdf
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## Bibliography auxiliary files (bibtex/biblatex/biber):
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# algorithms
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# glossaries
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\documentclass[a4paper]{article}
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% To compile PDF run: latexmk -pdf {filename}.tex
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% Math package
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\usepackage{amsmath}
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%enable \cref{...} and \Cref{...} instead of \ref: Type of reference included in the link
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\usepackage[capitalise,nameinlink]{cleveref}
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% Enable that parameters of \cref{}, \ref{}, \cite{}, ... are linked so that a reader can click on the number an jump to the target in the document
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\usepackage{hyperref}
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% UTF-8 encoding
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\usepackage[T1]{fontenc}
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\usepackage[utf8]{inputenc} %support umlauts in the input
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% Easier compilation
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\usepackage{bookmark}
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\begin{document}
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\title{Week 7 - Evidence and experiments}
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\author{
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Jai Bheeman \and Kelvin Davis \and Jip J. Dekker \and Nelson Frew \and Tony
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Silvestere
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}
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\maketitle
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\section{Introduction} \label{sec:introduction}
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\section{Method} \label{sec:method}
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\section{Results} \label{sec:results}
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\section{Discussion} \label{sec:discussion}
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\end{document}
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Using matplotlib backend: MacOSX\n",
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"Populating the interactive namespace from numpy and matplotlib\n"
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}
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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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"from scipy import stats\n",
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"from matplotlib import colors\n",
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"\n",
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" <th class=\"col_heading level0 col0\" >ranking</th> \n",
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" </tr> <tr> \n",
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179
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow2_col4\" class=\"data row2 col4\" >-0.129971</td> \n",
180
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow2_col5\" class=\"data row2 col5\" >0.159385</td> \n",
181
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow2_col6\" class=\"data row2 col6\" >-0.0153843</td> \n",
182
" </tr> <tr> \n",
183
-
" <th id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67alevel0_row3\" class=\"row_heading level0 row3\" >tournplayed</th> \n",
184
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow3_col0\" class=\"data row3 col0\" >-0.244073</td> \n",
185
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow3_col1\" class=\"data row3 col1\" >-0.140033</td> \n",
186
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow3_col2\" class=\"data row3 col2\" >-0.004905</td> \n",
187
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow3_col3\" class=\"data row3 col3\" >1</td> \n",
188
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow3_col4\" class=\"data row3 col4\" >0.13293</td> \n",
189
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow3_col5\" class=\"data row3 col5\" >-0.139194</td> \n",
190
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow3_col6\" class=\"data row3 col6\" >-0.0712482</td> \n",
191
" </tr> <tr> \n",
192
-
" <th id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67alevel0_row4\" class=\"row_heading level0 row4\" >born</th> \n",
193
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow4_col0\" class=\"data row4 col0\" >0.17403</td> \n",
194
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow4_col1\" class=\"data row4 col1\" >-0.994296</td> \n",
195
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow4_col2\" class=\"data row4 col2\" >-0.129971</td> \n",
196
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow4_col3\" class=\"data row4 col3\" >0.13293</td> \n",
197
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow4_col4\" class=\"data row4 col4\" >1</td> \n",
198
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow4_col5\" class=\"data row4 col5\" >-0.163677</td> \n",
199
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow4_col6\" class=\"data row4 col6\" >0.0333731</td> \n",
200
" </tr> <tr> \n",
201
-
" <th id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67alevel0_row5\" class=\"row_heading level0 row5\" >weight</th> \n",
202
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow5_col0\" class=\"data row5 col0\" >-0.0826093</td> \n",
203
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow5_col1\" class=\"data row5 col1\" >0.157223</td> \n",
204
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow5_col2\" class=\"data row5 col2\" >0.159385</td> \n",
205
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow5_col3\" class=\"data row5 col3\" >-0.139194</td> \n",
206
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow5_col4\" class=\"data row5 col4\" >-0.163677</td> \n",
207
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow5_col5\" class=\"data row5 col5\" >1</td> \n",
208
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow5_col6\" class=\"data row5 col6\" >0.757689</td> \n",
209
" </tr> <tr> \n",
210
-
" <th id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67alevel0_row6\" class=\"row_heading level0 row6\" >height</th> \n",
211
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow6_col0\" class=\"data row6 col0\" >0.0196139</td> \n",
212
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow6_col1\" class=\"data row6 col1\" >-0.0282972</td> \n",
213
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow6_col2\" class=\"data row6 col2\" >-0.0153843</td> \n",
214
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow6_col3\" class=\"data row6 col3\" >-0.0712482</td> \n",
215
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow6_col4\" class=\"data row6 col4\" >0.0333731</td> \n",
216
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow6_col5\" class=\"data row6 col5\" >0.757689</td> \n",
217
-
" <td id=\"T_f0d22112_4e6c_11e8_ac07_2cf0ee20d67arow6_col6\" class=\"data row6 col6\" >1</td> \n",
218
" </tr></tbody> \n",
219
"</table> "
220
],
221
"text/plain": [
222
-
"<pandas.io.formats.style.Styler at 0x10f0b9240>"
223
]
224
},
225
"execution_count": 2,
···
251
{
252
"data": {
253
"text/plain": [
254
-
"[<matplotlib.lines.Line2D at 0x10f65e5f8>,\n",
255
-
" <matplotlib.lines.Line2D at 0x10f65e4e0>,\n",
256
-
" <matplotlib.lines.Line2D at 0x10f65e978>]"
257
]
258
},
259
"execution_count": 3,
···
262
},
263
{
264
"data": {
265
-
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pIIkTZmgbgtoepJqUqhKpotCUmNoepJp0xyFlodHFYUJTYlrPRKrJ3L3adSi77u5u7+3trXY1ZozDRxdD7upXjagi9cXM1rt791TbKVUlJdPoYpGZRamqOlHLqSCNLhaZWRQ46kCtTzSnHj6H1HKAFykXparqQK2nguqhh09PXz/L1tzFolW3sWzNXRVZXKnWF3ISKRfdcdSBpFNBca+aa72HT1J3bMUCfK0cC5FyUOCoA0mmgkJPsklO7hdXUid0tfXITKFUVR1IMhWUVFosybROvqBbrDxU6MSISaTRRMpJgaMOlDLRXNyTUlJXzUm226TMYpWHCgnwpQRQBRypFqWq6kRoKihu2imptFiSaZ1MgUGuhcpDhbT1hKbRar2nnTQ2BY4GFnJSWrl8cd5R4OVOi81pS7N7eDRveTEh7SJdBYJhVwXaiOIG+NAAqoZ4qSalqhpYyEkpqfUXCmWJimWPevr6WXnjhklpnZU3bpgyRVPL3YVD20WSarcRyUd3HA0sNO2UxHTiu4eOvNsoVg5w+dpHGM1OTi+NZp3L1z4yZY+v3i0DfH/dM2TcSZnxrtfWxhoToXd4BuRLtJW31SZHgxrlcLrjqBMhDaGNdqWdL7VVrHxcT18/N6/vP9imkXHn5vX9NdGYHHqHV6h1ptxTlmpQo+RTscBhZiea2d1m9qiZPWJmH4/K55nZnWb26+jr3KjczOzvzGyzmT1kZq+Z8LMujrb/tZldXKk616rQf95aXvazEbsYh1qxtIt7V53Hk2su4N5V59XE32dcrR87qY5KpqrGgE+6+y/N7GhgvZndCXwY+Km7rzGzVcAq4NPAW4FTosdZwDeBs8xsHnAZ0E3ugmq9ma11910VrHtNKaUhtFaX/QzpgTS3Pc2uPKmsue3FG9QbcWBe6LGIqxGPnZSuYoHD3bcCW6Pv95rZY0AXcCHw+9Fm1wD3kAscFwLf9dwCIfeZWaeZLYi2vdPdBwCi4HM+8P1K1b3WNOo/b9ygdtk7TmflTRsYzRxKyKRTxmXvOL3ofqE9uGpZ6LGISxNYSj6JNI6b2UJgKbAOOC4KKgDbgOOi77uAZybs9mxUVqh8xijln7eWGzaTmhMrpAdXkkL+RknND5ZU92ypLxUPHGZ2FHAz8Al3f9Em/Le6u5tZWdrzzOwS4BKAk046qRw/smaE/vPW8iCxJOfECunBlZRS/kZJpCFrfQJLqY6KBg4zS5MLGte6+y1R8fNmtsDdt0apqBei8n7gxAm7nxCV9XMotTVefs/h7+XuVwFXQW7p2DL+GlUX+s9by4PEkqxbLadbSjkOSd1N1mo7mVRPxQKH5W4trgYec/evTHhpLXAxsCb6euuE8o+a2XXkGsf3RMHlDuAvx3tfAW8BVleq3rUq5J83ybaRuCexJOtWyh1bpU/MocchybvJkONQyylSKV0l7ziWAR8CNprZg1HZZ8gFjBvM7CPAFuA90Wu3A28DNgNDwB8DuPuAmX0ReCDa7gvjDeVSXOiVdtx/+lqeE2tiHSr9O4UIPQ5J3bGFHIdaTpFKeVRsHIe7/9zdzd3PcPczo8ft7r7T3d/o7qe4+5vGg4Dn/Lm7n+zuS9y9d8LP+ra7vyJ6fKdSdW40Sc3WGtLXv5YHJ0Jy4xdCj0NSU46EHAeN/Wh8GjnewEIGAIb809fynFgQFgyTSqWtWNrFu17bdXCK9+lOh5LUVPEhx6FRu4/LIZqrqsElMVtrLc+JBWFpnaRSaYWmQ+l+2byixyapqeJDjkMtd0aQ8tAdh0wSModUraedQtI6Sf1OoWmdQlPCl3uq+HNPnR+rHGr/8yClU+CQSUL+6Wt5TiwIS+sk9TuFpnWSOjnf/avtscqh9j8PUjqlqmSS0CnIa7mvf2haJ4nfaVa6ieHRbN7yYpKaKj40sIWuWKkuvPVBgUMmCc25J6XWVwCM68DYkUGjWPm4pP5OSbb1JNmF93M9GycF3fefdSJfWrGk7O/TqJSqqhMh63GEqOWulKHTyyeZc4/7d8oWuOkpVD6u1rsLx5Xk5+5zPRv5t/uenhR0/+2+p/lcz8ayv1ejUuCoA0kuplPLXSlDTy5J5dx7+vq59PoHJ/2dLr3+waJ/p0KtLFN1qi1lxHmcwBbaXTiuJD9331/3TKxyOZJSVXVA8zrllHJySSLnvvqWhzg8wZSNygvt196SYnAkk7e8mJC/U+go8OsfeGbS1fn1DzxTtykxSK4rcyPTHUcdKOWE+bmejZy8+nYWrrqNk1ffPuXteC2ndUK6CpdSt5U3bph097Dyxg1F65ivkbtYOcBQnqBRrHzcyuWLSTdNvi9JN1nRv1PIHdvn//2RSWt+AIxmnM//+yNF6xdXSLffUEkNnmxkChx1IPSEGZLLTTKtEzf9lmRQu3ztI4we1tAwmnUuX1veE2ahxaSmtcjU4ee5Kc57IRcg+VYZLFYe6ocbtsYqL8X7zzoxVrkcSamqKoibAgmd3bVYLrdYD5IkuqGGpN+SXBsi34qBxcpDhS4ydeUdm/LeCdTCaPgQSR1v4OBnX72qwilwJKynr59Lb3jwYK+Z/t3DXHpDbvLgcp8wQ3O5tTydeC2PFwkRekUfcvwWHpM/cCw8pnDgMCDfp6XekzpfWrFEgaIEChwJ+8wtDx3R1TLrufJyr3yXMssbJIrlcpPqT9/cBPlS/81TJE9Dg1rc/ea2p/OevOe2F04htaebGMrzS7UXGcwX8jeCsLuHXzyRfzWCQuWQP2gUKw8VcryletTGkbB8J5Zi5aV4+fz2WOWQXH/6Qr9uscMQ2i25p6+flTcd1tB9U/GG7svecTrp1GGNzynjsnecXnCf1nT+nlCFyiH8rjCkMbnQjyz2VknNiRVyvKV6FDga2BPbh2KVQ3LjA0KEBrWQnkErlnZx5btfPamTwJXvfnXRu5SQtFPoiTlkDqkQIb23QqxY2sV7X3fipPEi733diQ2VlmwkSlUlzCz/FV4legKGXM12FkgZdBZJGSSV3goNaqHtCHHTgyHtAeeeOp9/u+/pvOXFJDlg7vDPSyXGO9T6VDcymQJHwj5w1kl5TxQfOOukovslNQFcSDojqQGKIUGtFHGPeUh7QOidQ0gbUUg7wuVrH8nbJnf52kfK+rdNcpAr1M6EigfGMjwzMMzTA4M8tWOILTsH2TIwxNM7h5jX0cJ7uk9keDTD4MgYQwcyDI1kGBoZY3Akw9CBMQZHxhgeyUx4nuHic17GpW+p7BT2ChwJC+kKmOQEcHsKdH8sVA5hV79tBWaFbSvSkLx/NP+guELlpejp6+eTN24gE501+3cP88kbNwC1cRcV0kZ02TtOZ+VNGyal7aZqR0iqm2ySd1DjgztHJ/xtV07xt81knce2vkjvUwOYGdte3M+WnbmT/dMDQ+w7MFb2ej6xY5DeLbsOPm8yaG9ppr0lRUdr9LWlmc72FrrmpmhvaaajJcWSEzrLXpfDKXCUKOTKJW5XwFqfciRkn5BZYUNGZgO0pIyRzJHX/S2pwkmkz/5g48GgMS6TdT77g411O9VGvqnYa6UdoZx3k79+fi+3b9zGjx7eyq+27Z3WPqNZ5xPXP8gnrn8w9vtNn9NMhmYytDBGMxnSjNFio8xmmNk2yEtaDrCoY4wT2kc45viTecXvvp/21mY6mrO0+n5sdBhGhmB0EEaHYWQQRoeisujR/mrguAr+HgocJQm9E4gbbEJWsIPcFUq+WVabiiTdQ3LuK5cvzvsPV6wBNXRW2BD5gkaxciDv/FHFykOFtnGE6Onr5/r7D5t36v7i806FjuOYzmd82579fO/+p7n2vi0TgkYGLAueCxi7hkZZuOq2Au+Spc2GmGu7ODa1g2PYxzEMcqwPMZ+9/Knt5Zj0ixxje5lnLzKPF2mx3N/Pc+/Efp/FCM3Ms31T/EaTDXkr7XYg1j7TNhg9tgMbPglNzZCNcUdzzkdh4RsqU7eIAkcJQu4EQm6TQ/v6h5yci039UOgu6cbeI0984+WFfqeQoBYyTiJJnW3pvCmcziLTh4Qcb4BlJ8/j3t8cOf5i2cnzCu4zeRqVLOCMepbLfvgAZyw6wLbBbWwb2pb7OriN5/Y9R8dvbcBS+3N7jByDZ5tpatmBNWVYcs2qgu8FwAI4agG8CPzvh3KPvE6Co4v/pKL2Ro8ni251NMXfpfBxO9zJIyN8cmA3rd5Kizst7rRGX1ucCd97SSfYA7TwvbFzaW7t4LWnnsBpL3sppNtzj5bxrx2QboN0x+SyClPgKEFIXrbYHEgF86uBff1DAk5ITjvfCaxYORDUkjxWIOIVKk9ayPQhUx3vTDbDzv07eWHoBZ4ffJ5n9z3L47se56lZfRz9ytyUMqMvvoqmlgGaWrfykDlLrinwZgvznzqzwIW3Fqj7hCEoTS07C/8iM8hvWlr4Hy99ybS2TdFESypNS1Oa1lQr6VQLralZtDaPf99KS6qFlqZD3z+3a5T7n9jL6GgbIwNvgMFW2h5JccXi2ll+V4GjBCH56ZATc+gdRy1PH12oVaJYa0VIyilJuXRLBmveS1PLDlJtW2huf4Kx9qdYcs0n8+5z9CsL/7wpr+gj6dkPB9R25mqmCTIpLJvGsulcWizbwqJj53Hi3Hm0pTtoaW5jf/YAgyOD7B3dy76Rfewb3cfekb0Mjg6S8emlLDNkGc4cYDhzAEannw5r6oRWwDOt+Fgno2T48s82kD3qFEYzo4xmRxnLjjGaHT30iMrPWnAW5510XuDRmR4FjhKETj4YV2gACEmdzDTuzvDYMDuGd/D84Fa27dnCkwObWHDCOmjdzr6WIwdLFjqhFwsCMn3mRlM2RVM2jXmahfPmgDUx6hlGshlGfYyR7CgHMgcYHovf62qMLKSykJr8v7Fp6Dk2FR4bWxWzXvrDg9/vBy77Rf7tmpuaSTelaW5qZk7rHAWOWpbUbK2ha2YPHsh/F1OoPFRzk+VNFzUXabAIaePIcVptiJektnF8+mkWpJ+ivaWfT131adY1ZxlIHTm9R7ET+hnfLXBVX0rSXUri5mRSY2RSY8Awj+97MfE6pCxFuimde6TSNFsz6VT64Ml5vHzS8wnbj38/6bUJ5fl+zvjjUzc+zI59GfAU7inwFHgzxx3dwS1/9jt5f44lvJaIAkeJkpitNfTOJqSvf4ip2h5GMiMMjg4yuH8XAwObefA3D/D6+fcx2rKD59v3suewbrHTuaLfHT0ePVhiQPFV86Q2NFszHS0dtDe305JqId2UZtO2QfDmCSfK8ZNmM+989Ql5T8zNTc38/U+fnLTt+PfmKf7xD19X8IT9s8cH+M7Pn+GFPWMcN7uDvzj3VC488ySam5pptmZSTdX7LK0+9xV5/98//aYlLDhqQdXqNZECRx1Ich2KnAw0HcBS+0mn9jCvdQtHz9rMuf/8BXbMOvJePihPf8z4N/U+QXdtamlqoSPdQXu6nY50Bx3pDvoHsvQPjE06wUKKU4/r5PdOWZC7Aj7savmvf7SZwQMcPJnn9mvmqNYWrv6js484KadTad79jXVs2zOKR++Dp4AmujrbuHdV/hTK0i/8uOCo9r/+3bcU/D2vXvvjgunYtyzMv19PXz9fu30Lw6O528qtA/DFtVtob55dE43Pyf+/x6fAUSfG72zcnZHsCPvH9rNtcBu7D+xm+9BOfvbEJn6xZTO/2f0UTS0vkJr1fNAJPd8+493KZbJZqVmTTswPP7sfsq14ZhaebYVsS+6rN/OZt74qbwrjo9/bMOkEO/GK+yeXnnfESXk8ZXLKZ+6gUNB9as0FBev8gX/5z0m93ZadPI9r339Owe0/e03+MRR7BqH7pd15X/v0m8/i0usfnNRBroni43ouOGNB3vEsF5xR/Ao7pCdb0tObNCIFjhK4Ozf98km++pNH2bZ3L8fNSfHhN3Sx7JQ57B/bz9DYEDuHd7JtaBvPDz7PtqFttC/aTFN6z8G+8RNNtxdNMenZJf+ImtKezdJhKdotzYH90JRtOXhCHn9kM218+LdfSUfrHDpmzWFWyxzSLR25R6qFd31j3cE88eGpkEc/f0HePHHhQWeFT8zF9vnwq/LvM7a3cO+cl895ecHXQu7Uevr6uf/JXZPK7n9yFz19/YUHAAZMytm7ZeA9JuOkAAAKWklEQVSI3nHZqLzQ+4SOZ9ldYKLKQuVQ2gzQSdwFJDnFUCgFjgl6+vr54r1fYzD7HG0tWbrmpWlrzTI0up/hsf0cyBxgJHOAMR8h4yO4RR/Ol0LHS2Ef8A+P5x6FpGYl8qvE1pbNctxYhqM8S0fWcydsdzqyWdqzTkdU3pHN5h4ebZN1mrMptmePYWe2k90+h10+h4FsJ7t8Dl+5+NxoYNL4AKXcYKVXfuln7KcFzzOzf8iJ+Y9+u/BVdnb/cwVfa08XXpuk0YSMIQqZ9PLadfkHhF677umCQSB0Tqw5BXoOFluzPWSfpOYug/q4I1LgiPT09bPq5oew4x8n1bqHA57iiV2Hpw+OptmPpoUsbZ5llmdoZ4wtcwqfmA7Xls0yP5Mhg9GfbqYjm6VrdIyusTEWjGU4NpNhbjbDnEx0op5wwp512IjUuNecz2Tn86zP5wU6uXDZmdAx/9Cj/Rhoaed3vraO/d7KEK0M00p2wok95IT+lcXn5y0fpkYjaANLasLCkGATKiRVFbJPUnOXQbITPoZS4Ihceccm9o9luG3bk5zetCXezkUGSMfWOhvmnwazu+Col0DHS1j9463s9Nns8DkMcDSDPov95E7uv1nzzoI/qtgJ/cLz8weBZ/ypUn8DSUB4d+baFLp0bMhaKyHpraTmLoNkJ74MpcARyf2hjBPtheIbts6BjmOhYz4/eirDDp/DTnIn9R0+J3eCZw53/++LoOUoaG6ZtHtI7vz7Pyq8j8xMSU4SmYSQKd8hbFaFWj8xJzWwuBQKHJHxD+AZB64+ovw3V7wt7z5/ViQI0D79SdNEZrrQLqghsyqEzEgcOktwiHzT37/rtVOPF0tycaramFa0BtTyvE4i9aTQMidFlj8p4b3y/9Bidxwhqy5+4Oz8K3QWKi9FoWV0e/r6i+6z+paN9O8exjnUE6vYPqVQ4IgUmr5jqmk9RGSyQnNOFpuLMvTEF3LBF7K+zZdWLOGDZ590MCClzPjg2SfFWpBtuor1qirnPqWom1SVmZ0PfJ3cvBLfcvc15fz59ZBXFGlUoV1Qk5zIM+7KnaFCelUl3ROrLu44zCwF/CPwVuA04P1mdlo532PF0i6uuGgJXZ1tGLk7jSsuqp3570UaWeiJL6Rrba0r1Eg/1fLNcfcpRb3ccbwe2OzuTwCY2XXAhUyc464MkpiwUKSepCx/iqnc7RWhPZ1CutbWupDsR9IZk7q44wC6gGcmPH82KjvIzC4xs14z692+vXAjVzkV6jNfr33pJXkfLNC4Wqg8aX/7njNjlUPh5WuLLWu7cvli2tKTZ6Sdzokv6SvtJIRkP5LOmNRL4JiSu1/l7t3u3j1/fuFudeX0h2fl/+cuVA5hJ4qQf0SAU16Sf+3hQuUAswpcShYqBzju6JZY5aFmt+af6rpQ+biQ4xByzEP2CW10DXmvkM/eiqVdfO29Z046IX3tvWcWPSFd+yfnHFGPZSfP49o/KTyZYuiJLyTghP4/JWnF0i7uXXUeT665gHtXnTetABCyTyjzOuhuambnAJe7+/Lo+WoAd78i3/bd3d3e29ubSN0+17NxUn/r95914pT/9CH75J3VtMg/4rg3f+Uefv3CobltT3lJB3de+vtF9zn1s7ezf0J+YlbK+NWX849lGXfWl+/k+b0jB58fd3QL6z775qL75BsMWWxmV4AzLvsPXjxw6HZ8dmuKhz6ff1qTiUKOQ8gxD/07hQh5r5DPXq0LGb+Q5N+pnpjZenfPP+3xxO3qJHA0A48DbwT6gQeAP3T3R/Jtn2TgEBFpFNMNHHXROO7uY2b2UeAOct1xv10oaIiISGXVReAAcPfbgdurXQ8RkZmuYRrHRUQkGQocIiISiwKHiIjEUhe9quIys+1AzNWY6saxwI5qV6IG6DgcomORo+OQU8pxeJm7TzkQriEDRyMzs97pdJdrdDoOh+hY5Og45CRxHJSqEhGRWBQ4REQkFgWO+nNVtStQI3QcDtGxyNFxyKn4cVAbh4iIxKI7DhERiUWBo4aZ2YlmdreZPWpmj5jZx6PyeWZ2p5n9Ovo6t9p1raQix+FyM+s3swejR/EpfOucmc0ys/vNbEN0HD4flS8ys3VmttnMrjez8s5nX2OKHIf/Y2ZPTvg8FF40pIGYWcrM+szsh9Hzin8elKqqYWa2AFjg7r80s6OB9cAK4MPAgLuvMbNVwFx3/3QVq1pRRY7De4B97v43Va1gQszMgA5332dmaeDnwMeBS4Fb3P06M/snYIO7f7Oada2kIsfhT4EfuvtNVa1gwszsUqAbmO3ubzezG6jw50F3HDXM3be6+y+j7/cCj5Fb+fBC4Jpos2vInUQbVpHjMKN4zr7oaTp6OHAeMH6ynAmfh0LHYcYxsxOAC4BvRc+NBD4PChx1wswWAkuBdcBx7r41emkbcFyVqpW4w44DwEfN7CEz+3ajp+zgYFriQeAF4E7gN8Budx+LNjliWeVGdPhxcPfxz8OXo8/DV82stYpVTMrXgE8B2ej5MSTweVDgqANmdhRwM/AJd39x4mueyzXOiKutPMfhm8DJwJnAVuBvq1i9RLh7xt3PBE4AXg+cWuUqVcXhx8HMXgWsJnc8XgfMAxo2fQtgZm8HXnD39Um/twJHjYtyuDcD17r7LVHx81Hefzz//0K16peUfMfB3Z+PTiBZ4F/InUhnBHffDdwNnAN0RqtkQu5E2l+1iiVswnE4P0ppursfAL5D438elgHvNLOngOvIpai+TgKfBwWOGhblK68GHnP3r0x4aS1wcfT9xcCtSdctSYWOw3jwjPwB8HDSdUuSmc03s87o+zbgzeTae+4G3h1tNhM+D/mOw68mXEwZubx+Q38e3H21u5/g7guB9wF3ufsHSODzoF5VNczM3gD8P2Ajh3KYnyGX378BOIncLMDvcfeBqlQyAUWOw/vJpakceAr47xPafhqOmZ1BrrEzRe6i7wZ3/4KZvZzcFec8oA/4YHTV3ZCKHIe7gPmAAQ8CfzqhEb2hmdnvA/8r6lVV8c+DAoeIiMSiVJWIiMSiwCEiIrEocIiISCwKHCIiEosCh4iIxKLAISIisShwiIhILAocImVmZj1mtj5aK+KSqOwjZvZ4tI7Ev5jZP0Tl883sZjN7IHosq27tRaamAYAiZWZm89x9IJoO4wFgOXAv8BpgL3AXuTUSPmpm3wO+4e4/N7OTgDvc/ZVVq7zINDRPvYmIxPQxM/uD6PsTgQ8B/3d8WhgzuxH4rej1NwGn5aZXAmC2mR01U6bKkPqkwCFSRtGcQW8CznH3ITO7B/gVUOguogk42933J1NDkdKpjUOkvOYAu6KgcSpwNtAB/J6ZzY2mu37XhO1/DPzF+JOZsk621DcFDpHy+g+g2cweA9YA95FbD+EvgfvJtXU8BeyJtv8Y0B2tWvcouXWzRWqaGsdFEjDebhHdcfwA+La7/6Da9RIJoTsOkWRcHq2R/TDwJNBT5fqIBNMdh4iIxKI7DhERiUWBQ0REYlHgEBGRWBQ4REQkFgUOERGJRYFDRERi+f/E49Gjcv6XzgAAAABJRU5ErkJggg==\n",
266
"text/plain": [
267
-
"<Figure size 432x288 with 1 Axes>"
268
]
269
},
270
"metadata": {},
···
292
{
293
"data": {
294
"text/plain": [
295
-
"-578.3045372326807"
296
]
297
},
298
"execution_count": 4,
299
"metadata": {},
300
"output_type": "execute_result"
301
}
302
],
303
"source": [
304
-
"poly1d(polyfit(data[\"age\"], data[\"points\"], 2))(0)"
305
]
306
},
307
{
···
312
{
313
"data": {
314
"text/plain": [
315
-
"[<matplotlib.lines.Line2D at 0x10f6e7048>]"
316
]
317
},
318
"execution_count": 5,
···
321
},
322
{
323
"data": {
324
-
"image/png": "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\n",
325
"text/plain": [
326
-
"<Figure size 432x288 with 1 Axes>"
327
]
328
},
329
"metadata": {},
···
331
}
332
],
333
"source": [
334
-
"plot(xu, p1(xu))"
335
]
336
},
337
{
338
"cell_type": "code",
339
"execution_count": 6,
340
"metadata": {},
341
"outputs": [
342
{
343
"data": {
344
"text/plain": [
345
-
"[<matplotlib.lines.Line2D at 0x10f7b0128>]"
346
]
347
},
348
-
"execution_count": 6,
349
"metadata": {},
350
"output_type": "execute_result"
351
},
352
{
353
"data": {
354
-
"image/png": 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\n",
355
"text/plain": [
356
-
"<Figure size 432x288 with 1 Axes>"
357
]
358
},
359
"metadata": {},
···
361
}
362
],
363
"source": [
364
-
"plot(xu, p2(xu))"
365
]
366
},
367
{
368
"cell_type": "code",
369
-
"execution_count": 7,
370
"metadata": {},
371
"outputs": [
372
{
373
"data": {
374
"text/plain": [
375
-
"<matplotlib.collections.PolyCollection at 0x10f806b70>"
376
]
377
},
378
-
"execution_count": 7,
379
"metadata": {},
380
"output_type": "execute_result"
381
},
382
{
383
"data": {
384
-
"image/png": 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\n",
385
"text/plain": [
386
-
"<Figure size 432x288 with 1 Axes>"
387
]
388
},
389
"metadata": {},
···
391
}
392
],
393
"source": [
394
-
"nbins = 15\n",
395
-
"title('Hexbin')\n",
396
-
"hexbin(x, y, gridsize=nbins, cmap=plt.cm.BuGn_r)"
397
]
398
},
399
{
400
"cell_type": "code",
401
-
"execution_count": 8,
402
"metadata": {},
403
"outputs": [
404
{
405
"data": {
406
"text/plain": [
407
-
"Text(0,0.5,'points')"
408
]
409
},
410
-
"execution_count": 8,
411
"metadata": {},
412
"output_type": "execute_result"
413
},
414
{
415
"data": {
416
-
"image/png": 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\n",
417
"text/plain": [
418
-
"<Figure size 432x288 with 1 Axes>"
419
]
420
},
421
"metadata": {},
···
423
}
424
],
425
"source": [
426
-
"plot(x, y, linestyle='', marker='o', markersize=0.7)\n",
427
-
"xlabel(\"age\")\n",
428
-
"ylabel(\"points\")"
429
]
430
},
431
{
···
437
},
438
{
439
"cell_type": "code",
440
-
"execution_count": 9,
441
"metadata": {},
442
"outputs": [
443
{
···
458
},
459
{
460
"cell_type": "code",
461
-
"execution_count": 10,
462
"metadata": {},
463
"outputs": [
464
{
···
482
},
483
{
484
"cell_type": "code",
485
-
"execution_count": 11,
486
"metadata": {},
487
"outputs": [
488
{
···
506
},
507
{
508
"cell_type": "code",
509
-
"execution_count": 12,
510
"metadata": {},
511
"outputs": [
512
{
···
527
},
528
{
529
"cell_type": "code",
530
-
"execution_count": 13,
531
"metadata": {},
532
"outputs": [
533
{
···
548
},
549
{
550
"cell_type": "code",
551
-
"execution_count": null,
552
"metadata": {},
553
-
"outputs": [],
554
"source": [
555
"# Calculate observations\n",
556
"minR = data[\"ranking\"].min()\n",
···
576
},
577
{
578
"cell_type": "code",
579
-
"execution_count": 14,
580
"metadata": {},
581
"outputs": [
582
{
···
607
},
608
{
609
"cell_type": "code",
610
-
"execution_count": 15,
611
"metadata": {},
612
"outputs": [
613
{
···
628
},
629
{
630
"cell_type": "code",
631
-
"execution_count": 55,
632
"metadata": {},
633
"outputs": [
634
{
635
-
"data": {
636
-
"text/plain": [
637
-
"Power_divergenceResult(statistic=7.7069814692682215, pvalue=0.3591396627749135)"
638
-
]
639
-
},
640
-
"execution_count": 55,
641
-
"metadata": {},
642
-
"output_type": "execute_result"
643
}
644
],
645
"source": [
···
692
"name": "python",
693
"nbconvert_exporter": "python",
694
"pygments_lexer": "ipython3",
695
-
"version": "3.6.5"
696
}
697
},
698
"nbformat": 4,
···
16
"name": "stdout",
17
"output_type": "stream",
18
"text": [
19
+
"Using matplotlib backend: Qt5Agg\n",
20
"Populating the interactive namespace from numpy and matplotlib\n"
21
]
22
}
···
27
"import pandas as pd\n",
28
"import numpy as np\n",
29
"import matplotlib.pyplot as plt\n",
30
+
"import seaborn as sns\n",
31
"from scipy import stats\n",
32
"from matplotlib import colors\n",
33
"\n",
···
43
"data": {
44
"text/html": [
45
"<style type=\"text/css\" >\n",
46
+
" #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row0_col0 {\n",
47
" background-color: #fc7f00;\n",
48
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row0_col1 {\n",
49
" background-color: #ffcc09;\n",
50
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row0_col2 {\n",
51
" background-color: #faec2c;\n",
52
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row0_col3 {\n",
53
" background-color: #ffd20d;\n",
54
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row0_col4 {\n",
55
" background-color: #ffb300;\n",
56
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row0_col5 {\n",
57
" background-color: #ffc404;\n",
58
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row0_col6 {\n",
59
" background-color: #ffbc00;\n",
60
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row1_col0 {\n",
61
" background-color: #ffcc09;\n",
62
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row1_col1 {\n",
63
" background-color: #fc7f00;\n",
64
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row1_col2 {\n",
65
" background-color: #ffb600;\n",
66
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row1_col3 {\n",
67
" background-color: #ffc908;\n",
68
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row1_col4 {\n",
69
" background-color: #e4ff7a;\n",
70
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row1_col5 {\n",
71
" background-color: #ffb400;\n",
72
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row1_col6 {\n",
73
" background-color: #ffbf01;\n",
74
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row2_col0 {\n",
75
" background-color: #faec2c;\n",
76
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row2_col1 {\n",
77
" background-color: #ffb600;\n",
78
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row2_col2 {\n",
79
" background-color: #fc7f00;\n",
80
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row2_col3 {\n",
81
" background-color: #ffbd00;\n",
82
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row2_col4 {\n",
83
" background-color: #ffc907;\n",
84
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row2_col5 {\n",
85
" background-color: #ffb400;\n",
86
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row2_col6 {\n",
87
" background-color: #ffbf01;\n",
88
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row3_col0 {\n",
89
" background-color: #ffd20d;\n",
90
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row3_col1 {\n",
91
" background-color: #ffc908;\n",
92
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row3_col2 {\n",
93
" background-color: #ffbd00;\n",
94
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row3_col3 {\n",
95
" background-color: #fc7f00;\n",
96
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row3_col4 {\n",
97
" background-color: #ffb500;\n",
98
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row3_col5 {\n",
99
" background-color: #ffc908;\n",
100
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row3_col6 {\n",
101
" background-color: #ffc304;\n",
102
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row4_col0 {\n",
103
" background-color: #ffb300;\n",
104
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row4_col1 {\n",
105
" background-color: #e4ff7a;\n",
106
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row4_col2 {\n",
107
" background-color: #ffc907;\n",
108
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row4_col3 {\n",
109
" background-color: #ffb500;\n",
110
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row4_col4 {\n",
111
" background-color: #fc7f00;\n",
112
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row4_col5 {\n",
113
" background-color: #ffcc09;\n",
114
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row4_col6 {\n",
115
" background-color: #ffbb00;\n",
116
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row5_col0 {\n",
117
" background-color: #ffc404;\n",
118
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row5_col1 {\n",
119
" background-color: #ffb400;\n",
120
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row5_col2 {\n",
121
" background-color: #ffb400;\n",
122
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row5_col3 {\n",
123
" background-color: #ffc908;\n",
124
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row5_col4 {\n",
125
" background-color: #ffcc09;\n",
126
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row5_col5 {\n",
127
" background-color: #fc7f00;\n",
128
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row5_col6 {\n",
129
" background-color: #fd8f00;\n",
130
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row6_col0 {\n",
131
" background-color: #ffbc00;\n",
132
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row6_col1 {\n",
133
" background-color: #ffbf01;\n",
134
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row6_col2 {\n",
135
" background-color: #ffbf01;\n",
136
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row6_col3 {\n",
137
" background-color: #ffc304;\n",
138
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row6_col4 {\n",
139
" background-color: #ffbb00;\n",
140
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row6_col5 {\n",
141
" background-color: #fd8f00;\n",
142
+
" } #T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row6_col6 {\n",
143
" background-color: #fc7f00;\n",
144
" }</style> \n",
145
+
"<table id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6\" > \n",
146
"<thead> <tr> \n",
147
" <th class=\"blank level0\" ></th> \n",
148
" <th class=\"col_heading level0 col0\" >ranking</th> \n",
···
154
" <th class=\"col_heading level0 col6\" >height</th> \n",
155
" </tr></thead> \n",
156
"<tbody> <tr> \n",
157
+
" <th id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6level0_row0\" class=\"row_heading level0 row0\" >ranking</th> \n",
158
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row0_col0\" class=\"data row0 col0\" >1</td> \n",
159
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row0_col1\" class=\"data row0 col1\" >-0.165935</td> \n",
160
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row0_col2\" class=\"data row0 col2\" >-0.586707</td> \n",
161
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row0_col3\" class=\"data row0 col3\" >-0.244073</td> \n",
162
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row0_col4\" class=\"data row0 col4\" >0.17403</td> \n",
163
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row0_col5\" class=\"data row0 col5\" >-0.0826093</td> \n",
164
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row0_col6\" class=\"data row0 col6\" >0.0196139</td> \n",
165
" </tr> <tr> \n",
166
+
" <th id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6level0_row1\" class=\"row_heading level0 row1\" >age</th> \n",
167
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row1_col0\" class=\"data row1 col0\" >-0.165935</td> \n",
168
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row1_col1\" class=\"data row1 col1\" >1</td> \n",
169
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row1_col2\" class=\"data row1 col2\" >0.121731</td> \n",
170
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row1_col3\" class=\"data row1 col3\" >-0.140033</td> \n",
171
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row1_col4\" class=\"data row1 col4\" >-0.994296</td> \n",
172
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row1_col5\" class=\"data row1 col5\" >0.157223</td> \n",
173
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row1_col6\" class=\"data row1 col6\" >-0.0282972</td> \n",
174
" </tr> <tr> \n",
175
+
" <th id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6level0_row2\" class=\"row_heading level0 row2\" >points</th> \n",
176
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row2_col0\" class=\"data row2 col0\" >-0.586707</td> \n",
177
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row2_col1\" class=\"data row2 col1\" >0.121731</td> \n",
178
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row2_col2\" class=\"data row2 col2\" >1</td> \n",
179
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row2_col3\" class=\"data row2 col3\" >-0.004905</td> \n",
180
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row2_col4\" class=\"data row2 col4\" >-0.129971</td> \n",
181
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row2_col5\" class=\"data row2 col5\" >0.159385</td> \n",
182
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row2_col6\" class=\"data row2 col6\" >-0.0153843</td> \n",
183
" </tr> <tr> \n",
184
+
" <th id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6level0_row3\" class=\"row_heading level0 row3\" >tournplayed</th> \n",
185
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row3_col0\" class=\"data row3 col0\" >-0.244073</td> \n",
186
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row3_col1\" class=\"data row3 col1\" >-0.140033</td> \n",
187
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row3_col2\" class=\"data row3 col2\" >-0.004905</td> \n",
188
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row3_col3\" class=\"data row3 col3\" >1</td> \n",
189
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row3_col4\" class=\"data row3 col4\" >0.13293</td> \n",
190
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row3_col5\" class=\"data row3 col5\" >-0.139194</td> \n",
191
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row3_col6\" class=\"data row3 col6\" >-0.0712482</td> \n",
192
" </tr> <tr> \n",
193
+
" <th id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6level0_row4\" class=\"row_heading level0 row4\" >born</th> \n",
194
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row4_col0\" class=\"data row4 col0\" >0.17403</td> \n",
195
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row4_col1\" class=\"data row4 col1\" >-0.994296</td> \n",
196
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row4_col2\" class=\"data row4 col2\" >-0.129971</td> \n",
197
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row4_col3\" class=\"data row4 col3\" >0.13293</td> \n",
198
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row4_col4\" class=\"data row4 col4\" >1</td> \n",
199
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row4_col5\" class=\"data row4 col5\" >-0.163677</td> \n",
200
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row4_col6\" class=\"data row4 col6\" >0.0333731</td> \n",
201
" </tr> <tr> \n",
202
+
" <th id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6level0_row5\" class=\"row_heading level0 row5\" >weight</th> \n",
203
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row5_col0\" class=\"data row5 col0\" >-0.0826093</td> \n",
204
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row5_col1\" class=\"data row5 col1\" >0.157223</td> \n",
205
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row5_col2\" class=\"data row5 col2\" >0.159385</td> \n",
206
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row5_col3\" class=\"data row5 col3\" >-0.139194</td> \n",
207
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row5_col4\" class=\"data row5 col4\" >-0.163677</td> \n",
208
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row5_col5\" class=\"data row5 col5\" >1</td> \n",
209
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row5_col6\" class=\"data row5 col6\" >0.757689</td> \n",
210
" </tr> <tr> \n",
211
+
" <th id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6level0_row6\" class=\"row_heading level0 row6\" >height</th> \n",
212
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row6_col0\" class=\"data row6 col0\" >0.0196139</td> \n",
213
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row6_col1\" class=\"data row6 col1\" >-0.0282972</td> \n",
214
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row6_col2\" class=\"data row6 col2\" >-0.0153843</td> \n",
215
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row6_col3\" class=\"data row6 col3\" >-0.0712482</td> \n",
216
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row6_col4\" class=\"data row6 col4\" >0.0333731</td> \n",
217
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row6_col5\" class=\"data row6 col5\" >0.757689</td> \n",
218
+
" <td id=\"T_2eeaf31a_4f3b_11e8_82d6_780cb8866ae6row6_col6\" class=\"data row6 col6\" >1</td> \n",
219
" </tr></tbody> \n",
220
"</table> "
221
],
222
"text/plain": [
223
+
"<pandas.io.formats.style.Styler at 0x7f5347ae08d0>"
224
]
225
},
226
"execution_count": 2,
···
252
{
253
"data": {
254
"text/plain": [
255
+
"[<matplotlib.lines.Line2D at 0x7f533f51c438>,\n",
256
+
" <matplotlib.lines.Line2D at 0x7f533f51c2b0>,\n",
257
+
" <matplotlib.lines.Line2D at 0x7f533f51c6d8>]"
258
]
259
},
260
"execution_count": 3,
···
263
},
264
{
265
"data": {
266
+
"image/png": 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\n",
267
"text/plain": [
268
+
"<matplotlib.figure.Figure at 0x7f5347ae0278>"
269
]
270
},
271
"metadata": {},
···
293
{
294
"data": {
295
"text/plain": [
296
+
"<matplotlib.collections.PolyCollection at 0x7f533f09f6a0>"
297
]
298
},
299
"execution_count": 4,
300
"metadata": {},
301
"output_type": "execute_result"
302
+
},
303
+
{
304
+
"data": {
305
+
"image/png": 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\n",
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"<matplotlib.figure.Figure at 0x7f537c151940>"
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]
309
+
},
310
+
"metadata": {},
311
+
"output_type": "display_data"
312
}
313
],
314
"source": [
315
+
"nbins = 15\n",
316
+
"title('Hexbin')\n",
317
+
"hexbin(x, y, gridsize=nbins, cmap=plt.cm.BuGn_r)"
318
]
319
},
320
{
···
325
{
326
"data": {
327
"text/plain": [
328
+
"Text(0,0.5,'points')"
329
]
330
},
331
"execution_count": 5,
···
334
},
335
{
336
"data": {
337
+
"image/png": 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\n",
338
"text/plain": [
339
+
"<matplotlib.figure.Figure at 0x7f533f4f14e0>"
340
]
341
},
342
"metadata": {},
···
344
}
345
],
346
"source": [
347
+
"plot(x, y, linestyle='', marker='o', markersize=0.7)\n",
348
+
"xlabel(\"age\")\n",
349
+
"ylabel(\"points\")"
350
+
]
351
+
},
352
+
{
353
+
"cell_type": "markdown",
354
+
"metadata": {},
355
+
"source": [
356
+
"## Visual Exploration\n",
357
+
"Here we will use visualisation methods to form an intuitive understanding of the relationships we are investigating."
358
]
359
},
360
{
361
"cell_type": "code",
362
"execution_count": 6,
363
"metadata": {},
364
+
"outputs": [],
365
+
"source": [
366
+
"# Separate data by gender\n",
367
+
"data_male = data.loc[data[\"gender\"] == \"M\"]\n",
368
+
"data_female = data.loc[data[\"gender\"] == \"F\"]"
369
+
]
370
+
},
371
+
{
372
+
"cell_type": "code",
373
+
"execution_count": 7,
374
+
"metadata": {},
375
"outputs": [
376
{
377
"data": {
378
"text/plain": [
379
+
"Text(0.5,1,'Scatter plots of Points vs Height')"
380
]
381
},
382
+
"execution_count": 7,
383
"metadata": {},
384
"output_type": "execute_result"
385
},
386
{
387
"data": {
388
+
"image/png": 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\n",
389
"text/plain": [
390
+
"<matplotlib.figure.Figure at 0x7f533f05edd8>"
391
]
392
},
393
"metadata": {},
···
395
}
396
],
397
"source": [
398
+
"plot(\"height\", \"points\", data=data_male, linestyle='', marker='o', markersize=1.5, alpha=0.3, label=\"Male\")\n",
399
+
"plot(\"height\", \"points\", data=data_female, linestyle='', marker='o', markersize=1.5, alpha=0.3, label=\"Female\")\n",
400
+
"xlabel(\"height\")\n",
401
+
"ylabel(\"points\")\n",
402
+
"legend()\n",
403
+
"plt.title(\"Scatter plots of Points vs Height\")"
404
]
405
},
406
{
407
"cell_type": "code",
408
+
"execution_count": 8,
409
"metadata": {},
410
"outputs": [
411
{
412
"data": {
413
"text/plain": [
414
+
"Text(0.5,1,'Scatter plots of Points vs Height')"
415
]
416
},
417
+
"execution_count": 8,
418
"metadata": {},
419
"output_type": "execute_result"
420
},
421
{
422
"data": {
423
+
"image/png": 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\n",
424
"text/plain": [
425
+
"<matplotlib.figure.Figure at 0x7f533efeeef0>"
426
]
427
},
428
"metadata": {},
···
430
}
431
],
432
"source": [
433
+
"# TODO: remove the outliers\n",
434
+
"data_female_clean = data_female[np.abs(data_female.height-data_female.height.mean())<=(3*data_female.height.std())]\n",
435
+
"\n",
436
+
"plot(\"height\", \"points\", data=data_male, linestyle='', marker='o', markersize=3, alpha=0.3, label=\"Male\")\n",
437
+
"plot(\"height\", \"points\", data=data_female_clean, linestyle='', marker='o', markersize=3, alpha=0.3, label=\"Female\")\n",
438
+
"xlabel(\"height\")\n",
439
+
"ylabel(\"points\")\n",
440
+
"legend()\n",
441
+
"plt.title(\"Scatter plots of Points vs Height\")"
442
]
443
},
444
{
445
"cell_type": "code",
446
+
"execution_count": 9,
447
"metadata": {},
448
"outputs": [
449
{
450
"data": {
451
"text/plain": [
452
+
"Text(0.5,1,'Scatter plots of Ranking vs Height')"
453
]
454
},
455
+
"execution_count": 9,
456
"metadata": {},
457
"output_type": "execute_result"
458
},
459
{
460
"data": {
461
+
"image/png": 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\n",
462
"text/plain": [
463
+
"<matplotlib.figure.Figure at 0x7f533f0afc18>"
464
]
465
},
466
"metadata": {},
···
468
}
469
],
470
"source": [
471
+
"plot(\"height\", \"ranking\", data=data_male, linestyle='', marker='o', markersize=3, alpha=0.3, label=\"Male\")\n",
472
+
"plot(\"height\", \"ranking\", data=data_female_clean, linestyle='', marker='o', markersize=3, alpha=0.3, label=\"Female\")\n",
473
+
"xlabel(\"height\")\n",
474
+
"ylabel(\"ranking\")\n",
475
+
"legend()\n",
476
+
"plt.title(\"Scatter plots of Ranking vs Height\")"
477
+
]
478
+
},
479
+
{
480
+
"cell_type": "markdown",
481
+
"metadata": {},
482
+
"source": [
483
+
"### Handedness exploration"
484
+
]
485
+
},
486
+
{
487
+
"cell_type": "code",
488
+
"execution_count": 10,
489
+
"metadata": {},
490
+
"outputs": [
491
+
{
492
+
"name": "stdout",
493
+
"output_type": "stream",
494
+
"text": [
495
+
"P(R|M) = 0.8728070175438597\n",
496
+
"P(L|M) = 0.125\n",
497
+
"P(R|F) = 0.9224489795918367\n",
498
+
"P(L|F) = 0.07755102040816327\n"
499
+
]
500
+
}
501
+
],
502
+
"source": [
503
+
"print(\"P(R|M) =\", (data_male[\"hand\"] == \"R\").mean())\n",
504
+
"print(\"P(L|M) =\", (data_male[\"hand\"] == \"L\").mean())\n",
505
+
"print(\"P(R|F) =\", (data_female[\"hand\"] == \"R\").mean())\n",
506
+
"print(\"P(L|F) =\", (data_female[\"hand\"] == \"L\").mean())"
507
+
]
508
+
},
509
+
{
510
+
"cell_type": "code",
511
+
"execution_count": 11,
512
+
"metadata": {},
513
+
"outputs": [
514
+
{
515
+
"data": {
516
+
"text/plain": [
517
+
"18.999995"
518
+
]
519
+
},
520
+
"execution_count": 11,
521
+
"metadata": {},
522
+
"output_type": "execute_result"
523
+
}
524
+
],
525
+
"source": [
526
+
"len(data_female) * 0.077551"
527
+
]
528
+
},
529
+
{
530
+
"cell_type": "code",
531
+
"execution_count": 12,
532
+
"metadata": {},
533
+
"outputs": [],
534
+
"source": [
535
+
"data_right = data.loc[data[\"hand\"] == \"R\"]\n",
536
+
"data_left = data.loc[data[\"hand\"] == \"L\"]"
537
+
]
538
+
},
539
+
{
540
+
"cell_type": "code",
541
+
"execution_count": 13,
542
+
"metadata": {},
543
+
"outputs": [
544
+
{
545
+
"data": {
546
+
"text/plain": [
547
+
"Text(0.5,1,'P(points)')"
548
+
]
549
+
},
550
+
"execution_count": 13,
551
+
"metadata": {},
552
+
"output_type": "execute_result"
553
+
},
554
+
{
555
+
"data": {
556
+
"image/png": 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\n",
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7f5347ac1cf8>"
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]
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+
},
561
+
"metadata": {},
562
+
"output_type": "display_data"
563
+
}
564
+
],
565
+
"source": [
566
+
"sns.distplot(data_right[\"points\"], kde=True, hist=False, label=\"Right\")\n",
567
+
"sns.distplot(data_left[\"points\"], kde=True, hist=False, label=\"Left\")\n",
568
+
"title(\"P(points)\")"
569
+
]
570
+
},
571
+
{
572
+
"cell_type": "code",
573
+
"execution_count": 14,
574
+
"metadata": {},
575
+
"outputs": [],
576
+
"source": [
577
+
"data_right_male = data_right.loc[data_right[\"gender\"] == \"M\"]\n",
578
+
"data_right_female = data_right.loc[data_right[\"gender\"] == \"F\"]\n",
579
+
"data_left_male = data_left.loc[data_left[\"gender\"] == \"M\"]\n",
580
+
"data_left_female = data_left.loc[data_left[\"gender\"] == \"F\"]"
581
+
]
582
+
},
583
+
{
584
+
"cell_type": "code",
585
+
"execution_count": 15,
586
+
"metadata": {},
587
+
"outputs": [
588
+
{
589
+
"data": {
590
+
"text/plain": [
591
+
"Text(0.5,1,'P(points|female)')"
592
+
]
593
+
},
594
+
"execution_count": 15,
595
+
"metadata": {},
596
+
"output_type": "execute_result"
597
+
},
598
+
{
599
+
"data": {
600
+
"image/png": 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\n",
601
+
"text/plain": [
602
+
"<matplotlib.figure.Figure at 0x7f533cb5cac8>"
603
+
]
604
+
},
605
+
"metadata": {},
606
+
"output_type": "display_data"
607
+
}
608
+
],
609
+
"source": [
610
+
"figsize(12,4)\n",
611
+
"subplot(121)\n",
612
+
"sns.distplot(data_right_male[\"points\"], kde=True, hist=False, label=\"Right\")\n",
613
+
"sns.distplot(data_left_male[\"points\"], kde=True, hist=False, label=\"Left\")\n",
614
+
"title(\"P(points|male)\")\n",
615
+
"\n",
616
+
"subplot(122)\n",
617
+
"sns.distplot(data_right_female[\"points\"], kde=True, hist=False, label=\"Right\")\n",
618
+
"sns.distplot(data_left_female[\"points\"], kde=True, hist=False, label=\"Left\")\n",
619
+
"title(\"P(points|female)\")"
620
]
621
},
622
{
···
628
},
629
{
630
"cell_type": "code",
631
+
"execution_count": 16,
632
"metadata": {},
633
"outputs": [
634
{
···
649
},
650
{
651
"cell_type": "code",
652
+
"execution_count": 17,
653
"metadata": {},
654
"outputs": [
655
{
···
673
},
674
{
675
"cell_type": "code",
676
+
"execution_count": 18,
677
"metadata": {},
678
"outputs": [
679
{
···
697
},
698
{
699
"cell_type": "code",
700
+
"execution_count": 19,
701
"metadata": {},
702
"outputs": [
703
{
···
718
},
719
{
720
"cell_type": "code",
721
+
"execution_count": 20,
722
"metadata": {},
723
"outputs": [
724
{
···
739
},
740
{
741
"cell_type": "code",
742
+
"execution_count": 21,
743
"metadata": {},
744
+
"outputs": [
745
+
{
746
+
"ename": "NameError",
747
+
"evalue": "name 'dataM' is not defined",
748
+
"output_type": "error",
749
+
"traceback": [
750
+
"\u001b[0;31m--------------------------------------------------------------------------\u001b[0m",
751
+
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
752
+
"\u001b[0;32m<ipython-input-21-284410c792e4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mgroups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mincr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmaxR\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mminR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mgroups\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mobservations\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mdataM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogical_and\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogical_and\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"hand\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"L\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ranking\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mincr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ranking\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mincr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroups\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mdataM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogical_and\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogical_and\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"hand\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"R\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ranking\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mincr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ranking\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mincr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroups\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mlh_prod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"hand\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"L\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
753
+
"\u001b[0;32m<ipython-input-21-284410c792e4>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mgroups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mincr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmaxR\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mminR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mgroups\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mobservations\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mdataM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogical_and\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogical_and\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"hand\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"L\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ranking\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mincr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ranking\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mincr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroups\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mdataM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogical_and\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogical_and\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"hand\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"R\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ranking\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mincr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ranking\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mincr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroups\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mlh_prod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"hand\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"L\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
754
+
"\u001b[0;31mNameError\u001b[0m: name 'dataM' is not defined"
755
+
]
756
+
}
757
+
],
758
"source": [
759
"# Calculate observations\n",
760
"minR = data[\"ranking\"].min()\n",
···
780
},
781
{
782
"cell_type": "code",
783
+
"execution_count": 22,
784
"metadata": {},
785
"outputs": [
786
{
···
811
},
812
{
813
"cell_type": "code",
814
+
"execution_count": 23,
815
"metadata": {},
816
"outputs": [
817
{
···
832
},
833
{
834
"cell_type": "code",
835
+
"execution_count": 24,
836
"metadata": {},
837
"outputs": [
838
{
839
+
"ename": "NameError",
840
+
"evalue": "name 'dataM' is not defined",
841
+
"output_type": "error",
842
+
"traceback": [
843
+
"\u001b[0;31m--------------------------------------------------------------------------\u001b[0m",
844
+
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
845
+
"\u001b[0;32m<ipython-input-24-284410c792e4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mgroups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mincr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmaxR\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mminR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mgroups\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mobservations\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mdataM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogical_and\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogical_and\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"hand\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"L\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ranking\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mincr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ranking\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mincr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroups\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mdataM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogical_and\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogical_and\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"hand\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"R\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ranking\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mincr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ranking\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mincr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroups\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mlh_prod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"hand\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"L\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
846
+
"\u001b[0;32m<ipython-input-24-284410c792e4>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mgroups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mincr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmaxR\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mminR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mgroups\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mobservations\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mdataM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogical_and\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogical_and\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"hand\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"L\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ranking\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mincr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ranking\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mincr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroups\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mdataM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogical_and\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogical_and\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"hand\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"R\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ranking\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mincr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ranking\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mincr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroups\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mlh_prod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"hand\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"L\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mNameError\u001b[0m: name 'dataM' is not defined"
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]
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}
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],
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"source": [
···
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.4"
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}
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},
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"nbformat": 4,
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wk8/week8.tex
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wk8/week8.tex
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···
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\documentclass[a4paper]{article}
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+
% To compile PDF run: latexmk -pdf {filename}.tex
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% Math package
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\usepackage{amsmath}
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%enable \cref{...} and \Cref{...} instead of \ref: Type of reference included in the link
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\usepackage[capitalise,nameinlink]{cleveref}
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% Enable that parameters of \cref{}, \ref{}, \cite{}, ... are linked so that a reader can click on the number an jump to the target in the document
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\usepackage{hyperref}
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% UTF-8 encoding
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\usepackage[T1]{fontenc}
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\usepackage[utf8]{inputenc} %support umlauts in the input
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% Easier compilation
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\usepackage{bookmark}
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\begin{document}
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\title{Week 8 - Quantitative data analysis}
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\author{
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Jai Bheeman \and Kelvin Davis \and Jip J. Dekker \and Nelson Frew \and Tony
20
+
Silvestere
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+
}
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+
\maketitle
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+
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\section{Introduction} \label{sec:introduction}
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+
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\section{Method} \label{sec:method}
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\section{Results} \label{sec:results}
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\section{Discussion} \label{sec:discussion}
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\end{document}
wk9/Tennis players 2017-09 final.xlsx
wk9/Tennis players 2017-09 final.xlsx
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wk9/pearson.png
wk9/pearson.png
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wk9/spearman.png
wk9/spearman.png
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wk9/week9.tex
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wk9/week9.tex
···
···
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+
\documentclass[a4paper]{article}
2
+
% To compile PDF run: latexmk -pdf {filename}.tex
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+
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+
% Math package
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+
\usepackage{amsmath}
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+
%enable \cref{...} and \Cref{...} instead of \ref: Type of reference included in the link
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+
\usepackage[capitalise,nameinlink]{cleveref}
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+
% Enable that parameters of \cref{}, \ref{}, \cite{}, ... are linked so that a reader can click on the number an jump to the target in the document
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+
\usepackage{hyperref}
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+
% UTF-8 encoding
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\usepackage[T1]{fontenc}
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+
\usepackage[utf8]{inputenc} %support umlauts in the input
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+
% Easier compilation
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+
\usepackage{bookmark}
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\usepackage{graphicx}
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\begin{document}
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\title{Week 9 - Correlation and Regression}
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\author{
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Jai Bheeman \and Kelvin Davis \and Jip J. Dekker \and Nelson Frew \and Tony
21
+
Silvestere
22
+
}
23
+
\maketitle
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+
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\section{Introduction} \label{sec:introduction}
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We present a report on the relationship between the heights and weights of the
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top tennis players as catalogued in provided data. We use statistical analysis
28
+
techniques to numerically describe the characteristics of the data, to see how
29
+
trends are exhibited within the data set. We conclude the report with a brief
30
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discussion of the implications of the analysis and provide insights on
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potential correlations that may exist.
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\section{Method} \label{sec:method}
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Provided with a set of 132 unique records of the top 200 male tennis players,
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we sought to investigate the relationship between the height of particular
36
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individuals with their respective weights. We conducted basic statistical
37
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correlation analyses of the two variables with both Pearson's and Spearman's
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correlation coefficients to achieve this. Further, to understand the
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+
correlations more deeply, we carried out these correlation tests on the full
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population of cleaned data (removed duplicates etc), alongside several random
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samples and samples of ranking ranges within the top 200. To this end, we made
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+
use of Microsoft Excel tools and functions of the Python library SciPy.
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We specifically have made use of these separate statistical analysis tools in the
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interest of sanity checking our findings. To do this, we simply replicated the
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correlation tests within other software environments.
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\section{Results} \label{sec:results}
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We performed separate statistical analyses on 10 different samples of the
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population, as well as the population itself. This included 11 separate
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subsets of the rankings:
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\begin{itemize}
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\item The top 20 entries
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\item The middle 20 entries
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\item The bottom 20 entries
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\item The top 50 entries
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\item The bottom 50 entries
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\item 5 randomly chosen sets of 20 entries
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+
\end{itemize}
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\vspace{1em}
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Table \ref{tab:excel_results} shows the the results for the conducted tests.
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\begin{table}[ht]
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+
\centering
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+
\label{tab:excel_results}
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+
\begin{tabular}{|l|r|r|}
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+
\hline
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+
\textbf{Test Set} & \textbf{Pearson's Coefficient} & \textbf{Spearman's Coefficient} \\
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+
\hline
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+
\textbf{Full Population} & 0.77953 & 0.73925 \\
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+
\textbf{Top 20} & 0.80743 & 0.80345 \\
72
+
\textbf{Middle 20} & 0.54134 & 0.36565 \\
73
+
\textbf{Bottom 20} & 0.84046 & 0.88172 \\
74
+
\textbf{Top 50} & 0.80072 & 0.78979 \\
75
+
\textbf{Bottom 50} & 0.84237 & 0.81355 \\
76
+
\textbf{Random Set \#1} & 0.84243 & 0.80237 \\
77
+
\textbf{Random Set \#2} & 0.56564 & 0.58714 \\
78
+
\textbf{Random Set \#3} & 0.59223 & 0.63662 \\
79
+
\textbf{Random Set \#4} & 0.65091 & 0.58471 \\
80
+
\textbf{Random Set \#5} & 0.86203 & 0.77832
81
+
\\ \hline
82
+
\end{tabular}
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+
\caption{Table showing the correlation coefficients between height and
84
+
weight using different test sets. All data is rounded to 5 decimal
85
+
places}
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+
\end{table}
87
+
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+
\begin{figure}[ht]
89
+
\centering
90
+
\label{fig:scipy}
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+
\includegraphics[width=0.6\textwidth]{pearson.png}
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+
\includegraphics[width=0.6\textwidth]{spearman.png}
93
+
\caption{The Pearsion (top) and Spearman (bottom) correlations coefficients
94
+
of the data set as computed by the Pandas Python library}
95
+
\end{figure}
96
+
97
+
\section{Discussion} \label{sec:discussion}
98
+
The results generally indicate that there is a fairly strong positive
99
+
correlation between the weight and weight of an individual tennis player,
100
+
within the top 200 male players. The population maintains a strong positive
101
+
correlation with both Pearson's and Spearman's correlation coefficients,
102
+
indicating that a relationship may exist. Our population samples show
103
+
promising consistency with this, with 6 seperate samples having values above
104
+
0.6 with both techniques. The sample taken from the middle 20 players,
105
+
however, shows a relatively weaker correlation compared with the top 20 and
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+
middle 20, which provides some insight into the distribution of the strongest
107
+
correlated heights and weights amongst the rankings. All five random samples
108
+
of 20 taken from the population indicate however that there does appear to be
109
+
a consistent trend through the population, which corresponds accurately with
110
+
the coefficients on the general population.
111
+
112
+
113
+
\end{document}
+252
wk9/wk9.ipynb
+252
wk9/wk9.ipynb
···
···
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{
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{
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"text": [
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"Using matplotlib backend: MacOSX\n",
13
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"Populating the interactive namespace from numpy and matplotlib\n"
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]
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}
16
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],
17
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"source": [
18
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"%pylab\n",
19
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"%matplotlib inline\n",
20
+
"import pandas as pd\n",
21
+
"import numpy as np\n",
22
+
"import matplotlib.pyplot as plt\n",
23
+
"from scipy import stats\n",
24
+
"from matplotlib import colors\n",
25
+
"\n",
26
+
"data = pd.read_csv(\"Tennis players 2017-09.csv\")"
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]
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75
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76
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116
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"def background_gradient(s, m, M, cmap='Wistia', low=0, high=0):\n",
117
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" rng = M - m\n",
118
+
" norm = colors.Normalize(m - (rng * low),\n",
119
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120
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121
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122
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" return ['background-color: %s' % color for color in c]\n",
123
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"\n",
124
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"data = data[[\"SEX\", \"DOB\", \"RANK\", \"HANDED\", \"Country\", \"HEIGHT\", \"Weight\"]]\n",
125
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"data.drop_duplicates\n",
126
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"\n",
127
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128
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181
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182
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188
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189
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200
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