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zeebonk/gist:b89df0a3a6b18d488f0f28cef5786078

Created July 30, 2018 12:49
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gistfile1.txt
{
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"import urllib.request\n",
"import json\n",
"from pprint import pprint\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Pandas Cookbook Links\n",
"\n",
"* https://www.youtube.com/watch?v=rNmn8bLFgdg&index=3&list=PLyBBc46Y6aAz54aOUgKXXyTcEmpMisAq3\n",
"* https://github.com/jvns/pandas-cookbook\n",
"* https://www.dataquest.io/blog/pandas-python-tutorial/\n",
"* https://jakevdp.github.io/PythonDataScienceHandbook/04.08-multiple-subplots.html"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read from URL\n",
"Skip the next one if you already have a file"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"event_json = \"raw_answers.json\"\n",
"json_url = \"https://app.nebu.com/app/rest/bigdataproxy?r=Rbmw9RJ%2FPLx3Lzhed8NbxQ%2B4WV4MUxrbGEvE94g5hwiukCZrGJQnlwMok8RXOVLfSr3xurwIV6uPuWd3h7R5NO0lYo98K02HoMoATG7B0ooMeZN9mmmiKwpRhPalUx8EEIuuAHgzVWXN07EApcMbBKSUH89Nd9TEvHQ7CDBvM8I%3D&q=SELECT+*+FROM+ParkMobile_ParkMobile_ParkMobile&format=json\"\n",
"\n",
"response = urllib.request.urlopen(json_url)\n",
"html = response.read()\n",
"html = html.decode('utf-8')\n",
"fh = open(event_json, 'w')\n",
"fh.write(html)\n",
"fh.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read the json file"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_json(event_json)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Clean up the dataframe\n",
"\n",
"Here we do some transformations on the data in order to clean it up"
]
},
{
"cell_type": "code",
"execution_count": 30,
"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>CODERESP</th>\n",
" <th>EINDDAT</th>\n",
" <th>EINDTIJD</th>\n",
" <th>Q1</th>\n",
" <th>SCORE_01</th>\n",
" <th>SCORE_02</th>\n",
" <th>SCORE_03</th>\n",
" <th>SCORE_04</th>\n",
" <th>SCORE_05</th>\n",
" <th>SCORE_06</th>\n",
" <th>...</th>\n",
" <th>SCORE_15</th>\n",
" <th>SCORE_16</th>\n",
" <th>THEFOUNDQ2CODE1</th>\n",
" <th>THEFOUNDQ2CODE2</th>\n",
" <th>THEFOUNDQ2CODE3</th>\n",
" <th>THEFOUNDQ2CODE4</th>\n",
" <th>THEFOUNDQ2CODE5</th>\n",
" <th>THEFOUNDQ2CODE6</th>\n",
" <th>THEFOUNDQ2CODE7</th>\n",
" <th>VULCEP</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1277</th>\n",
" <td>1196057186</td>\n",
" <td>2018-04-10</td>\n",
" <td>19:53:33</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1278</th>\n",
" <td>1323612836</td>\n",
" <td>2018-04-10</td>\n",
" <td>19:53:29</td>\n",
" <td>99999998.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>99999998.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1279</th>\n",
" <td>1385752055</td>\n",
" <td>2018-04-10</td>\n",
" <td>19:54:25</td>\n",
" <td>99999998.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>99999998.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1280</th>\n",
" <td>352185357</td>\n",
" <td>2018-04-10</td>\n",
" <td>19:59:09</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1281</th>\n",
" <td>1178161406</td>\n",
" <td>2018-04-10</td>\n",
" <td>19:56:49</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 28 columns</p>\n",
"</div>"
],
"text/plain": [
" CODERESP EINDDAT EINDTIJD Q1 SCORE_01 SCORE_02 \\\n",
"1277 1196057186 2018-04-10 19:53:33 2.0 NaN NaN \n",
"1278 1323612836 2018-04-10 19:53:29 99999998.0 NaN NaN \n",
"1279 1385752055 2018-04-10 19:54:25 99999998.0 NaN NaN \n",
"1280 352185357 2018-04-10 19:59:09 2.0 NaN NaN \n",
"1281 1178161406 2018-04-10 19:56:49 1.0 NaN NaN \n",
"\n",
" SCORE_03 SCORE_04 SCORE_05 SCORE_06 ... SCORE_15 SCORE_16 \\\n",
"1277 NaN NaN NaN NaN ... NaN NaN \n",
"1278 NaN NaN NaN NaN ... NaN NaN \n",
"1279 NaN NaN NaN NaN ... NaN NaN \n",
"1280 NaN NaN NaN NaN ... NaN NaN \n",
"1281 NaN NaN NaN NaN ... NaN NaN \n",
"\n",
" THEFOUNDQ2CODE1 THEFOUNDQ2CODE2 THEFOUNDQ2CODE3 THEFOUNDQ2CODE4 \\\n",
"1277 NaN NaN NaN NaN \n",
"1278 NaN NaN NaN NaN \n",
"1279 NaN NaN NaN NaN \n",
"1280 NaN NaN NaN NaN \n",
"1281 NaN NaN NaN NaN \n",
"\n",
" THEFOUNDQ2CODE5 THEFOUNDQ2CODE6 THEFOUNDQ2CODE7 VULCEP \n",
"1277 NaN NaN NaN 3.0 \n",
"1278 NaN NaN NaN 99999998.0 \n",
"1279 NaN NaN NaN 99999998.0 \n",
"1280 NaN NaN NaN 4.0 \n",
"1281 NaN NaN NaN 2.0 \n",
"\n",
"[5 rows x 28 columns]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# fill all data that is empty with NaN\n",
"data.replace('',np.nan,inplace=True)\n",
"\n",
"metacols = ['CODERESP', 'EINDDAT', 'EINDTIJD']\n",
"intcols = ['THEFOUNDQ2CODE1', 'THEFOUNDQ2CODE2' , 'THEFOUNDQ2CODE3', 'THEFOUNDQ2CODE4', 'THEFOUNDQ2CODE5', 'THEFOUNDQ2CODE6', 'THEFOUNDQ2CODE7', 'VULCEP']\n",
"checkbox_columns = ['THEFOUNDQ2CODE1', 'THEFOUNDQ2CODE2',\n",
" 'THEFOUNDQ2CODE3', 'THEFOUNDQ2CODE4', 'THEFOUNDQ2CODE5',\n",
" 'THEFOUNDQ2CODE6', 'THEFOUNDQ2CODE7', 'VULCEP']\n",
"scorecols = []\n",
"for i in range(1,15):\n",
" s = 'SCORE_' + str(i)\n",
" q = 'SCORE_' + str(i).zfill(2)\n",
" data.rename(columns={s:q}, inplace=True)\n",
" scorecols.append(q)\n",
"\n",
"data = data.sort_index(axis=1)\n",
"\n",
"for column in data.columns:\n",
" if column not in metacols:\n",
" data[column] = data[column].astype('float')\n",
"\n",
"# change date column to date\n",
"data['EINDDAT'] = pd.to_datetime(data['EINDDAT'], format='%d.%m.%Y')\n",
"\n",
"data.tail()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Latest Entry = 2018-04-17 00:00:00\n"
]
}
],
"source": [
"print('Latest Entry = ' + str(data.EINDDAT.max()))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of scores for CEP 1 = 288\n"
]
}
],
"source": [
"print('Number of scores for CEP 1 = ' + str(data.SCORE_01.dropna().count()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Max responses per day (inc NaN)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"EINDDAT\n",
"2018-03-09 10\n",
"2018-03-12 7\n",
"2018-03-15 4\n",
"2018-03-29 13\n",
"2018-03-30 43\n",
"2018-03-31 50\n",
"2018-04-01 66\n",
"2018-04-02 49\n",
"2018-04-03 32\n",
"2018-04-04 5\n",
"2018-04-05 85\n",
"2018-04-06 100\n",
"2018-04-07 94\n",
"2018-04-08 100\n",
"2018-04-09 34\n",
"2018-04-10 113\n",
"2018-04-11 140\n",
"2018-04-12 135\n",
"2018-04-13 68\n",
"2018-04-14 26\n",
"2018-04-15 33\n",
"2018-04-16 47\n",
"2018-04-17 28\n",
"Name: CODERESP, dtype: int64"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.groupby('EINDDAT').count()['CODERESP']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Score per brand per CEP"
]
},
{
"cell_type": "code",
"execution_count": 27,
"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>ParkMobile</th>\n",
" <th>YellowBrick</th>\n",
" <th>Parkline</th>\n",
" <th>ANWB</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>CEP1</th>\n",
" <td>60.178100</td>\n",
" <td>55.091325</td>\n",
" <td>55.088544</td>\n",
" <td>45.471458</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CEP2</th>\n",
" <td>60.557715</td>\n",
" <td>57.886856</td>\n",
" <td>58.271627</td>\n",
" <td>45.938872</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CEP3</th>\n",
" <td>60.001952</td>\n",
" <td>59.445897</td>\n",
" <td>57.675663</td>\n",
" <td>48.375319</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CEP4</th>\n",
" <td>62.763675</td>\n",
" <td>60.513585</td>\n",
" <td>59.395238</td>\n",
" <td>46.316433</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ParkMobile YellowBrick Parkline ANWB\n",
"CEP1 60.178100 55.091325 55.088544 45.471458\n",
"CEP2 60.557715 57.886856 58.271627 45.938872\n",
"CEP3 60.001952 59.445897 57.675663 48.375319\n",
"CEP4 62.763675 60.513585 59.395238 46.316433"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"PM_SCORE = ['SCORE_01', 'SCORE_02', 'SCORE_03', 'SCORE_04']\n",
"YB_SCORE = ['SCORE_05', 'SCORE_06', 'SCORE_07', 'SCORE_08']\n",
"PL_SCORE = ['SCORE_09', 'SCORE_10', 'SCORE_11', 'SCORE_12']\n",
"AN_SCORE = ['SCORE_13', 'SCORE_14', 'SCORE_15', 'SCORE_16']\n",
"names = ['ParkMobile','YellowBrick','Parkline','ANWB']\n",
"\n",
"competition = [PM_SCORE, YB_SCORE, PL_SCORE, AN_SCORE]\n",
"\n",
"result = pd.DataFrame(index=['CEP1','CEP2','CEP3','CEP4'],columns=['ParkMobile','YellowBrick','Parkline','ANWB'])\n",
"\n",
"i = 0\n",
"for name in names:\n",
" result[name] = data.loc[3:,competition[i]].mean().values\n",
" i = i + 1\n",
"# result.plot(kind='barH', subplots=True, figsize=(8,10), legend=False)\n",
"result"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1800x432 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"names = result.columns\n",
"values = []\n",
"\n",
"plt.figure(1, figsize=(25, 6))\n",
"current_palette = sns.color_palette(\"muted\")\n",
"\n",
"for i in range (1,5):\n",
" CEP = 'CEP' + str(i)\n",
" values.append(result.loc[CEP])\n",
" plt.subplot(140+i)\n",
" plt.bar(names, values[i-1], color=current_palette)\n",
" plt.title(CEP, loc='left', fontsize=12, fontweight=0)\n",
" plt.ylim(40,65)\n",
" \n",
"\n",
"plt.suptitle('Plotting the CEP\\'s')\n",
"plt.show()\n",
"#result.plot(kind='barh', figsize=(8,10), legend=True, table=True)\n",
"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10a7f0198>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set_palette(\"deep\", desat=.6)\n",
"# Set up the matplotlib figure\n",
"f, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(8, 6), sharex=True)\n",
"sns.barplot(result.PM, ax=ax1)\n",
"# ax1.set_ylabel(\"PM\")\n",
"sns.barplot(result.YB, ax=ax2)\n",
"# ax2.set_ylabel(\"YB\")\n",
"sns.barplot(result.PL, ax=ax3)\n",
"# ax3.set_ylabel(\"PL\")\n",
"sns.barplot(result.AN, ax=ax4)\n",
"# ax4.set_ylabel(\"AN\")\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,0,'AN')"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x1080 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set_palette(\"deep\", desat=.6)\n",
"# Set up the matplotlib figure\n",
"f, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(10, 15), sharex=True)\n",
"sns.violinplot(data.SCORE_01.dropna(), ax=ax1)\n",
"ax1.set_xlabel(\"PM\")\n",
"sns.violinplot(data.SCORE_05.dropna(), ax=ax2)\n",
"ax2.set_xlabel(\"YB\")\n",
"sns.violinplot(data.SCORE_09.dropna(), ax=ax3)\n",
"ax3.set_xlabel(\"PL\")\n",
"sns.violinplot(data.SCORE_13.dropna(), ax=ax4)\n",
"ax4.set_xlabel(\"AN\")\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10adaae10>"
]
},
"execution_count": 20,
"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": [
"sns.kdeplot(data.SCORE_04.dropna(), shade=True)\n",
"# plt.hist(data.SCORE_1.dropna(), 20, color=sns.desaturate('indicvanred',1))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10ae1bda0>"
]
},
"execution_count": 21,
"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": [
"sns.kdeplot(data.SCORE_08.dropna(), shade=True)\n",
"# plt.hist(data.SCORE_1.dropna(), 20, color=sns.desaturate('indicvanred',1))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/edwin/newCode/blauw/venv/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
" warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10ae8a320>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(data.SCORE_01.dropna())\n",
"# plt.hist(data.SCORE_1.dropna(), 20, color=sns.desaturate('indianred',1))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/edwin/newCode/blauw/venv/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
" warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10b3867b8>"
]
},
"execution_count": 23,
"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": [
"data.SCORE_13 = data.SCORE_13.astype(float)\n",
"sns.distplot(data.SCORE_13.dropna())\n",
"# plt.hist(data.SCORE_1.dropna(), 20, color=sns.desaturate('indianred',1))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,0,'AN')"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x720 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#data.SCORE_5 = data.SCORE_5.astype(float)\n",
"#sns.boxplot(data.SCORE_5)\n",
"\n",
"\n",
"sns.set_palette(\"deep\", desat=.6)\n",
"# Set up the matplotlib figure\n",
"f, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(10, 10), sharex=True)\n",
"sns.boxplot(data.SCORE_01.dropna(), ax=ax1)\n",
"ax1.set_xlabel(\"PM\")\n",
"sns.boxplot(data.SCORE_05.dropna(), ax=ax2)\n",
"ax2.set_xlabel(\"YB\")\n",
"sns.boxplot(data.SCORE_09.dropna(), ax=ax3)\n",
"ax3.set_xlabel(\"PL\")\n",
"sns.boxplot(data.SCORE_13.dropna(), ax=ax4)\n",
"ax4.set_xlabel(\"AN\")\n"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [],
"source": [
"# pd.set_option('max_columns',100)\n",
"testset = data.loc[3:,'Q1':'SCORE_16'].dropna() \n",
"output = testset.loc[3:,].groupby('Q1').mean()\n",
"# output.sort_index(ascending = False, axis=0)\n",
"output.to_clipboard()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"\n",
"CEP1 = ['SCORE_01', 'SCORE_05', 'SCORE_09', 'SCORE_13']\n",
"CEP2 = ['SCORE_02', 'SCORE_06', 'SCORE_10', 'SCORE_14']\n",
"CEP3 = ['SCORE_03', 'SCORE_07', 'SCORE_11', 'SCORE_15']\n",
"CEP4 = ['SCORE_04', 'SCORE_08', 'SCORE_12', 'SCORE_16']\n",
"\n",
"OutputCEP1 = data.loc[3:,].groupby('Q1')[CEP1].mean()\n",
"OutputCEP2 = data.loc[3:,].groupby('Q1')[CEP2].mean()\n",
"OutputCEP3 = data.loc[3:,].groupby('Q1')[CEP3].mean()\n",
"OutputCEP4 = data.loc[3:,].groupby('Q1')[CEP4].mean()\n",
"\n",
"OutputCEPX = data.loc[3:,CEP1].mean()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "\"None of [['SCORE_1', 'SCORE_2', 'SCORE_3', 'SCORE_4']] are in the [columns]\"",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-28-38eab8bd65b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\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[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcompetition\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0mi\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[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/newCode/blauw/venv/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1365\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1366\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1367\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1368\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1369\u001b[0m \u001b[0;31m# we by definition only have the 0th axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/newCode/blauw/venv/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m 861\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 862\u001b[0m \u001b[0;31m# no multi-index, so validate all of the indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 863\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_valid_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 864\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 865\u001b[0m \u001b[0;31m# ugly hack for GH #836\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/newCode/blauw/venv/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_has_valid_tuple\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 202\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 203\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Too many indexers'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 204\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_valid_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\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 205\u001b[0m raise ValueError(\"Location based indexing can only have \"\n\u001b[1;32m 206\u001b[0m \u001b[0;34m\"[{types}] types\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/newCode/blauw/venv/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_has_valid_type\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1470\u001b[0m raise KeyError(\n\u001b[1;32m 1471\u001b[0m u\"None of [{key}] are in the [{axis}]\".format(\n\u001b[0;32m-> 1472\u001b[0;31m key=key, axis=self.obj._get_axis_name(axis)))\n\u001b[0m\u001b[1;32m 1473\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1474\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: \"None of [['SCORE_1', 'SCORE_2', 'SCORE_3', 'SCORE_4']] are in the [columns]\""
]
}
],
"source": [
"PM_SCORE = ['SCORE_1', 'SCORE_2', 'SCORE_3', 'SCORE_4']\n",
"YB_SCORE = ['SCORE_5', 'SCORE_6', 'SCORE_7', 'SCORE_8']\n",
"PL_SCORE = ['SCORE_9', 'SCORE_10', 'SCORE_11', 'SCORE_12']\n",
"AN_SCORE = ['SCORE_13', 'SCORE_14', 'SCORE_15', 'SCORE_16']\n",
"names = ['PM','YB','PL','AN']\n",
"\n",
"competition = [PM_SCORE, YB_SCORE, PL_SCORE, AN_SCORE]\n",
"\n",
"result = pd.DataFrame(index=['CEP1','CEP2','CEP3','CEP4'],columns=['PM','YB','PL','AN'])\n",
"\n",
"i = 0\n",
"for name in names:\n",
" result[name] = data.loc[3:,competition[i]].mean().values\n",
" i = i + 1\n",
"\n",
"result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"result.plot.bar(ylim=40)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"OutputCEPX.plot(kind='bar')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.hist(data.SCORE_1)\n",
"\n",
"# data['SCORE_1'].hist()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data['SCORE_13'].plot(kind='hist')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data.loc[3:,['VULCEP', 'THEFOUNDQ2CODE1']].groupby(['VULCEP','THEFOUNDQ2CODE1'])['THEFOUNDQ2CODE1'].count()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"code = data[data['VULCEP'] == '1'].loc[3:,['THEFOUNDQ2CODE1']]\n",
"code['THEFOUNDQ2CODE1'].value_counts().plot(kind='bar')"
]
}
],
"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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