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544 lines
56 KiB
544 lines
56 KiB
2 years ago
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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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"___\n",
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"\n",
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"<a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo.png'/></a>\n",
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"___\n",
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"<center><em>Copyright Pierian Data</em></center>\n",
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"<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
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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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"# Pandas Time Series Exercise Set #1 - Solution\n",
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"\n",
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"For this set of exercises we'll use a dataset containing monthly milk production values in pounds per cow from January 1962 to December 1975.\n",
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"\n",
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"<div class=\"alert alert-danger\" style=\"margin: 10px\"><strong>IMPORTANT NOTE!</strong> Make sure you don't run the cells directly above the example output shown, <br>otherwise you will end up writing over the example output!</div>"
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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": 16,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"168\n",
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" Date Production\n",
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"0 1962-01 589\n",
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"1 1962-02 561\n",
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"2 1962-03 640\n",
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"3 1962-04 656\n",
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"4 1962-05 727\n"
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]
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}
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],
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"source": [
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"# RUN THIS CELL\n",
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"import pandas as pd\n",
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"%matplotlib inline\n",
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"\n",
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"df = pd.read_csv('../Data/monthly_milk_production.csv', encoding='utf8')\n",
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"title = \"Monthly milk production: pounds per cow. Jan '62 - Dec '75\"\n",
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"\n",
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"print(len(df))\n",
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"print(df.head())"
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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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"So <tt>df</tt> has 168 records and 2 columns."
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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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"### 1. What is the current data type of the Date column?\n",
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"HINT: We show how to list column dtypes in the first set of DataFrame lectures."
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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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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# CODE HERE\n"
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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": 17,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Date object\n",
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"Production int64\n",
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"dtype: object"
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]
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# DON'T WRITE HERE\n",
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"df.dtypes"
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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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"### 2. Change the Date column to a datetime format"
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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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"collapsed": true
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Date datetime64[ns]\n",
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"Production int64\n",
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"dtype: object"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# DON'T WRITE HERE\n",
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"df['Date']=pd.to_datetime(df['Date'])\n",
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"df.dtypes"
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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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"### 3. Set the Date column to be the new index"
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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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"collapsed": true
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Production</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Date</th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>1962-01-01</th>\n",
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" <td>589</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1962-02-01</th>\n",
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" <td>561</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1962-03-01</th>\n",
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" <td>640</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1962-04-01</th>\n",
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" <td>656</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1962-05-01</th>\n",
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" <td>727</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Production\n",
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"Date \n",
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"1962-01-01 589\n",
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"1962-02-01 561\n",
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"1962-03-01 640\n",
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"1962-04-01 656\n",
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"1962-05-01 727"
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]
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},
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# DON'T WRITE HERE\n",
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"df.set_index('Date',inplace=True)\n",
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"df.head()"
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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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"### 4. Plot the DataFrame with a simple line plot. What do you notice about the plot?"
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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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"collapsed": true
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
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"data": {
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],
|
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"source": [
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"# DON'T WRITE HERE\n",
|
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|
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"df.plot();\n",
|
||
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"\n",
|
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"# THE PLOT SHOWS CONSISTENT SEASONALITY, AS WELL AS AN UPWARD TREND"
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"cell_type": "markdown",
|
||
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"metadata": {},
|
||
|
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"source": [
|
||
|
|
"### 5. Add a column called 'Month' that takes the month value from the index\n",
|
||
|
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"HINT: You have to call <tt>df.index</tt> as <tt>df['Date']</tt> won't work.\n",
|
||
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"\n",
|
||
|
|
"<strong>BONUS: See if you can obtain the <em>name</em> of the month instead of a number!</strong>"
|
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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": 28,
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" }\n",
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"\n",
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" }\n",
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"\n",
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" }\n",
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"</style>\n",
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" <thead>\n",
|
||
|
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" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
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" <th>Production</th>\n",
|
||
|
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" <th>Month</th>\n",
|
||
|
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" </tr>\n",
|
||
|
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" <tr>\n",
|
||
|
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" <th>Date</th>\n",
|
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|
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" <th></th>\n",
|
||
|
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" <th></th>\n",
|
||
|
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" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1962-01-01</th>\n",
|
||
|
|
" <td>589</td>\n",
|
||
|
|
" <td>1</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1962-02-01</th>\n",
|
||
|
|
" <td>561</td>\n",
|
||
|
|
" <td>2</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1962-03-01</th>\n",
|
||
|
|
" <td>640</td>\n",
|
||
|
|
" <td>3</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1962-04-01</th>\n",
|
||
|
|
" <td>656</td>\n",
|
||
|
|
" <td>4</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1962-05-01</th>\n",
|
||
|
|
" <td>727</td>\n",
|
||
|
|
" <td>5</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" Production Month\n",
|
||
|
|
"Date \n",
|
||
|
|
"1962-01-01 589 1\n",
|
||
|
|
"1962-02-01 561 2\n",
|
||
|
|
"1962-03-01 640 3\n",
|
||
|
|
"1962-04-01 656 4\n",
|
||
|
|
"1962-05-01 727 5"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 28,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# DON'T WRITE HERE\n",
|
||
|
|
"df['Month']=df.index.month\n",
|
||
|
|
"df.head()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 22,
|
||
|
|
"metadata": {},
|
||
|
|
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|
||
|
|
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|
||
|
|
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||
|
|
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|
||
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|
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|
||
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|
" }\n",
|
||
|
|
"\n",
|
||
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|
||
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|
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|
||
|
|
" }\n",
|
||
|
|
"\n",
|
||
|
|
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|
||
|
|
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|
||
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|
" }\n",
|
||
|
|
"</style>\n",
|
||
|
|
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|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>Production</th>\n",
|
||
|
|
" <th>Month</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Date</th>\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1962-01-01</th>\n",
|
||
|
|
" <td>589</td>\n",
|
||
|
|
" <td>January</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1962-02-01</th>\n",
|
||
|
|
" <td>561</td>\n",
|
||
|
|
" <td>February</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1962-03-01</th>\n",
|
||
|
|
" <td>640</td>\n",
|
||
|
|
" <td>March</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1962-04-01</th>\n",
|
||
|
|
" <td>656</td>\n",
|
||
|
|
" <td>April</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1962-05-01</th>\n",
|
||
|
|
" <td>727</td>\n",
|
||
|
|
" <td>May</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" Production Month\n",
|
||
|
|
"Date \n",
|
||
|
|
"1962-01-01 589 January\n",
|
||
|
|
"1962-02-01 561 February\n",
|
||
|
|
"1962-03-01 640 March\n",
|
||
|
|
"1962-04-01 656 April\n",
|
||
|
|
"1962-05-01 727 May"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 22,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# BONUS SOLUTION:\n",
|
||
|
|
"df['Month']=df.index.strftime('%B')\n",
|
||
|
|
"df.head()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### 6. Create a BoxPlot that groups by the Month field"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 29,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x360 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# DON'T WRITE HERE\n",
|
||
|
|
"df.boxplot(by='Month',figsize=(12,5));"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Great Job!"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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": "3.7.6"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 1
|
||
|
|
}
|