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Chapter01/DataPrep_Titanic.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_first_cabin(row):\n",
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" try:\n",
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" return row.split()[0]\n",
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" except:\n",
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" return np.nan "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"data = pd.read_csv('https://www.openml.org/data/get_csv/16826755/phpMYEkMl')\n",
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"data = data.replace('?', np.nan)\n",
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"data['cabin'] = data['cabin'].apply(get_first_cabin)\n",
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"data.to_csv('titanic.csv', index=False)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "feml",
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"language": "python",
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"name": "feml"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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": "3.7.3"
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},
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"toc": {
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": "block",
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"toc_window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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