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zaltoprofen/Lorenz.ipynb

Created July 12, 2018 14:11
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affection.csv
Name Affection
Anna 3171
Ayumu 4455
Mami 4545
Konomi 4549
Arisa 4603
Azusa 4605
Reika 4691
Akane 4721
Hibiki 4753
Minako 4831
Yayoi 4851
Ritsuko 4877
Iori 4885
Haruka 4887
Iku 4921
Roco 4983
Ami 5036
Noriko 5145
Nao 5221
Matsuri 5225
Kana 5274
Emily 5738
Subaru 5793
Elena 5796
Miya 5890
Fuka 5958
Tomoka 6175
Tamaki 6437
Chizuru 6588
Kotoha 6633
Julia 7031
Serika 7658
Takane 8330
Yukiho 9031
Hinata 9402
Karen 9499
Makoto 11190
Tsubasa 11393
Umi 11456
Miki 12421
Sayoko 12573
Momoko 13094
Mirai 15312
Rio 16023
Chihaya 17547
Shizuka 20092
Kaori 24908
Mizuki 51318
Tsumugi 51600
Megumi 74365
Shiho 103644
Yuriko 106535
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Lorenz.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Affection</th>\n",
" <th>Share</th>\n",
" <th>AccumulatedShare</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Anna</td>\n",
" <td>3171</td>\n",
" <td>0.004174</td>\n",
" <td>0.004174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Ayumu</td>\n",
" <td>4455</td>\n",
" <td>0.005864</td>\n",
" <td>0.010039</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Mami</td>\n",
" <td>4545</td>\n",
" <td>0.005983</td>\n",
" <td>0.016022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Konomi</td>\n",
" <td>4549</td>\n",
" <td>0.005988</td>\n",
" <td>0.022010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Arisa</td>\n",
" <td>4603</td>\n",
" <td>0.006059</td>\n",
" <td>0.028069</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Azusa</td>\n",
" <td>4605</td>\n",
" <td>0.006062</td>\n",
" <td>0.034131</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Reika</td>\n",
" <td>4691</td>\n",
" <td>0.006175</td>\n",
" <td>0.040306</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Akane</td>\n",
" <td>4721</td>\n",
" <td>0.006215</td>\n",
" <td>0.046521</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Hibiki</td>\n",
" <td>4753</td>\n",
" <td>0.006257</td>\n",
" <td>0.052778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Minako</td>\n",
" <td>4831</td>\n",
" <td>0.006359</td>\n",
" <td>0.059137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Yayoi</td>\n",
" <td>4851</td>\n",
" <td>0.006386</td>\n",
" <td>0.065523</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Ritsuko</td>\n",
" <td>4877</td>\n",
" <td>0.006420</td>\n",
" <td>0.071943</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Iori</td>\n",
" <td>4885</td>\n",
" <td>0.006431</td>\n",
" <td>0.078373</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Haruka</td>\n",
" <td>4887</td>\n",
" <td>0.006433</td>\n",
" <td>0.084806</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Iku</td>\n",
" <td>4921</td>\n",
" <td>0.006478</td>\n",
" <td>0.091284</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Roco</td>\n",
" <td>4983</td>\n",
" <td>0.006560</td>\n",
" <td>0.097844</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Ami</td>\n",
" <td>5036</td>\n",
" <td>0.006629</td>\n",
" <td>0.104473</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Noriko</td>\n",
" <td>5145</td>\n",
" <td>0.006773</td>\n",
" <td>0.111246</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Nao</td>\n",
" <td>5221</td>\n",
" <td>0.006873</td>\n",
" <td>0.118119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Matsuri</td>\n",
" <td>5225</td>\n",
" <td>0.006878</td>\n",
" <td>0.124997</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Kana</td>\n",
" <td>5274</td>\n",
" <td>0.006943</td>\n",
" <td>0.131939</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Emily</td>\n",
" <td>5738</td>\n",
" <td>0.007553</td>\n",
" <td>0.139493</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Subaru</td>\n",
" <td>5793</td>\n",
" <td>0.007626</td>\n",
" <td>0.147119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Elena</td>\n",
" <td>5796</td>\n",
" <td>0.007630</td>\n",
" <td>0.154748</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Miya</td>\n",
" <td>5890</td>\n",
" <td>0.007753</td>\n",
" <td>0.162502</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>Fuka</td>\n",
" <td>5958</td>\n",
" <td>0.007843</td>\n",
" <td>0.170345</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>Tomoka</td>\n",
" <td>6175</td>\n",
" <td>0.008129</td>\n",
" <td>0.178473</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>Tamaki</td>\n",
" <td>6437</td>\n",
" <td>0.008474</td>\n",
" <td>0.186947</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Chizuru</td>\n",
" <td>6588</td>\n",
" <td>0.008672</td>\n",
" <td>0.195619</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Kotoha</td>\n",
" <td>6633</td>\n",
" <td>0.008732</td>\n",
" <td>0.204351</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>Julia</td>\n",
" <td>7031</td>\n",
" <td>0.009255</td>\n",
" <td>0.213606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>Serika</td>\n",
" <td>7658</td>\n",
" <td>0.010081</td>\n",
" <td>0.223687</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>Takane</td>\n",
" <td>8330</td>\n",
" <td>0.010965</td>\n",
" <td>0.234653</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>Yukiho</td>\n",
" <td>9031</td>\n",
" <td>0.011888</td>\n",
" <td>0.246541</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>Hinata</td>\n",
" <td>9402</td>\n",
" <td>0.012377</td>\n",
" <td>0.258917</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>Karen</td>\n",
" <td>9499</td>\n",
" <td>0.012504</td>\n",
" <td>0.271422</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>Makoto</td>\n",
" <td>11190</td>\n",
" <td>0.014730</td>\n",
" <td>0.286152</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>Tsubasa</td>\n",
" <td>11393</td>\n",
" <td>0.014998</td>\n",
" <td>0.301150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>Umi</td>\n",
" <td>11456</td>\n",
" <td>0.015080</td>\n",
" <td>0.316230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>Miki</td>\n",
" <td>12421</td>\n",
" <td>0.016351</td>\n",
" <td>0.332581</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>Sayoko</td>\n",
" <td>12573</td>\n",
" <td>0.016551</td>\n",
" <td>0.349132</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>Momoko</td>\n",
" <td>13094</td>\n",
" <td>0.017237</td>\n",
" <td>0.366368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>Mirai</td>\n",
" <td>15312</td>\n",
" <td>0.020156</td>\n",
" <td>0.386525</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>Rio</td>\n",
" <td>16023</td>\n",
" <td>0.021092</td>\n",
" <td>0.407617</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>Chihaya</td>\n",
" <td>17547</td>\n",
" <td>0.023099</td>\n",
" <td>0.430716</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>Shizuka</td>\n",
" <td>20092</td>\n",
" <td>0.026449</td>\n",
" <td>0.457164</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>Kaori</td>\n",
" <td>24908</td>\n",
" <td>0.032788</td>\n",
" <td>0.489953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>Mizuki</td>\n",
" <td>51318</td>\n",
" <td>0.067554</td>\n",
" <td>0.557507</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>Tsumugi</td>\n",
" <td>51600</td>\n",
" <td>0.067925</td>\n",
" <td>0.625432</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>Megumi</td>\n",
" <td>74365</td>\n",
" <td>0.097893</td>\n",
" <td>0.723325</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>Shiho</td>\n",
" <td>103644</td>\n",
" <td>0.136435</td>\n",
" <td>0.859759</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>Yuriko</td>\n",
" <td>106535</td>\n",
" <td>0.140241</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Affection Share AccumulatedShare\n",
"0 Anna 3171 0.004174 0.004174\n",
"1 Ayumu 4455 0.005864 0.010039\n",
"2 Mami 4545 0.005983 0.016022\n",
"3 Konomi 4549 0.005988 0.022010\n",
"4 Arisa 4603 0.006059 0.028069\n",
"5 Azusa 4605 0.006062 0.034131\n",
"6 Reika 4691 0.006175 0.040306\n",
"7 Akane 4721 0.006215 0.046521\n",
"8 Hibiki 4753 0.006257 0.052778\n",
"9 Minako 4831 0.006359 0.059137\n",
"10 Yayoi 4851 0.006386 0.065523\n",
"11 Ritsuko 4877 0.006420 0.071943\n",
"12 Iori 4885 0.006431 0.078373\n",
"13 Haruka 4887 0.006433 0.084806\n",
"14 Iku 4921 0.006478 0.091284\n",
"15 Roco 4983 0.006560 0.097844\n",
"16 Ami 5036 0.006629 0.104473\n",
"17 Noriko 5145 0.006773 0.111246\n",
"18 Nao 5221 0.006873 0.118119\n",
"19 Matsuri 5225 0.006878 0.124997\n",
"20 Kana 5274 0.006943 0.131939\n",
"21 Emily 5738 0.007553 0.139493\n",
"22 Subaru 5793 0.007626 0.147119\n",
"23 Elena 5796 0.007630 0.154748\n",
"24 Miya 5890 0.007753 0.162502\n",
"25 Fuka 5958 0.007843 0.170345\n",
"26 Tomoka 6175 0.008129 0.178473\n",
"27 Tamaki 6437 0.008474 0.186947\n",
"28 Chizuru 6588 0.008672 0.195619\n",
"29 Kotoha 6633 0.008732 0.204351\n",
"30 Julia 7031 0.009255 0.213606\n",
"31 Serika 7658 0.010081 0.223687\n",
"32 Takane 8330 0.010965 0.234653\n",
"33 Yukiho 9031 0.011888 0.246541\n",
"34 Hinata 9402 0.012377 0.258917\n",
"35 Karen 9499 0.012504 0.271422\n",
"36 Makoto 11190 0.014730 0.286152\n",
"37 Tsubasa 11393 0.014998 0.301150\n",
"38 Umi 11456 0.015080 0.316230\n",
"39 Miki 12421 0.016351 0.332581\n",
"40 Sayoko 12573 0.016551 0.349132\n",
"41 Momoko 13094 0.017237 0.366368\n",
"42 Mirai 15312 0.020156 0.386525\n",
"43 Rio 16023 0.021092 0.407617\n",
"44 Chihaya 17547 0.023099 0.430716\n",
"45 Shizuka 20092 0.026449 0.457164\n",
"46 Kaori 24908 0.032788 0.489953\n",
"47 Mizuki 51318 0.067554 0.557507\n",
"48 Tsumugi 51600 0.067925 0.625432\n",
"49 Megumi 74365 0.097893 0.723325\n",
"50 Shiho 103644 0.136435 0.859759\n",
"51 Yuriko 106535 0.140241 1.000000"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('affection.csv')\n",
"df = df.sort_values(by='Affection')\n",
"df['Share'] = df.Affection / df.Affection.sum()\n",
"df['AccumulatedShare'] = np.cumsum(df.Share)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x10d309438>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"x = np.linspace(0, 1, len(df))\n",
"y = np.asarray(df.AccumulatedShare)\n",
"ax.plot(x, y, label='Lorenz Curve')\n",
"ax.plot([0, 1], [0, 1])\n",
"ax.set_xlim([0, 1])\n",
"ax.set_ylim([0, 1])\n",
"ax.set_aspect(1)\n",
"ax.legend()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gini coef: 0.5384438874521849\n"
]
}
],
"source": [
"dx = x[1:] - x[:-1]\n",
"dy = y[1:] - y[:-1]\n",
"\n",
"gini = 1 - 2 * np.sum(dx * (dy / 2 + y[:-1]))\n",
"print('Gini coef:', gini)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x114c3bd30>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot(x='Name', y='Share', kind='barh', figsize=(8, 10))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df."
]
}
],
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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