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ch5_pandas.ipynb

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{
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"nbformat": 4,
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"nbformat_minor": 2,
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"metadata": {
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"language_info": {
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"name": "python",
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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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"version": "3.7.5-final"
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},
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"orig_nbformat": 2,
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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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"npconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": 3,
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"kernelspec": {
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"name": "python37564bit42731fe29a1c4072b88558cb14c14b00",
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"display_name": "Python 3.7.5 64-bit"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# getting started with pandas"
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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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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"from pandas import Series, DataFrame"
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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": 5,
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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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"np.random.seed(12345)\n",
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"import matplotlib.pyplot as plt\n",
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"plt.rc('figure', figsize=(10, 6))\n",
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"PREVIOUS_MAX_ROWS = pd.options.display.max_rows\n",
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"pd.options.display.max_rows = 20\n",
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"np.set_printoptions(precision=4, suppress=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## intro to pandas data structure"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Series"
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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": 6,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": "0 4\n1 8\n2 -5\n3 2\ndtype: int64"
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},
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"metadata": {},
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"execution_count": 6
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}
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],
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"source": [
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"obj = pd.Series([4, 8, -5, 2])\n",
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"obj"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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]
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}

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