{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___\n",
"\n",
" \n",
"___\n",
"
Copyright Pierian Data \n",
"For more information, visit us at www.pieriandata.com "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pandas Time Series Exercise Set #1 - Solution\n",
"\n",
"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",
"\n",
"IMPORTANT NOTE! Make sure you don't run the cells directly above the example output shown, otherwise you will end up writing over the example output!
"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"168\n",
" Date Production\n",
"0 1962-01 589\n",
"1 1962-02 561\n",
"2 1962-03 640\n",
"3 1962-04 656\n",
"4 1962-05 727\n"
]
}
],
"source": [
"# RUN THIS CELL\n",
"import pandas as pd\n",
"%matplotlib inline\n",
"\n",
"df = pd.read_csv('../Data/monthly_milk_production.csv', encoding='utf8')\n",
"title = \"Monthly milk production: pounds per cow. Jan '62 - Dec '75\"\n",
"\n",
"print(len(df))\n",
"print(df.head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So df has 168 records and 2 columns."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. What is the current data type of the Date column?\n",
"HINT: We show how to list column dtypes in the first set of DataFrame lectures."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Date object\n",
"Production int64\n",
"dtype: object"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# DON'T WRITE HERE\n",
"df.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. Change the Date column to a datetime format"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Date datetime64[ns]\n",
"Production int64\n",
"dtype: object"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# DON'T WRITE HERE\n",
"df['Date']=pd.to_datetime(df['Date'])\n",
"df.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3. Set the Date column to be the new index"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" Production \n",
" \n",
" \n",
" Date \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" 1962-01-01 \n",
" 589 \n",
" \n",
" \n",
" 1962-02-01 \n",
" 561 \n",
" \n",
" \n",
" 1962-03-01 \n",
" 640 \n",
" \n",
" \n",
" 1962-04-01 \n",
" 656 \n",
" \n",
" \n",
" 1962-05-01 \n",
" 727 \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Production\n",
"Date \n",
"1962-01-01 589\n",
"1962-02-01 561\n",
"1962-03-01 640\n",
"1962-04-01 656\n",
"1962-05-01 727"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# DON'T WRITE HERE\n",
"df.set_index('Date',inplace=True)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4. Plot the DataFrame with a simple line plot. What do you notice about the plot?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# DON'T WRITE HERE\n",
"df.plot();\n",
"\n",
"# THE PLOT SHOWS CONSISTENT SEASONALITY, AS WELL AS AN UPWARD TREND"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5. Add a column called 'Month' that takes the month value from the index\n",
"HINT: You have to call df.index as df['Date'] won't work.\n",
"\n",
"BONUS: See if you can obtain the name of the month instead of a number! "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" Production \n",
" Month \n",
" \n",
" \n",
" Date \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" 1962-01-01 \n",
" 589 \n",
" 1 \n",
" \n",
" \n",
" 1962-02-01 \n",
" 561 \n",
" 2 \n",
" \n",
" \n",
" 1962-03-01 \n",
" 640 \n",
" 3 \n",
" \n",
" \n",
" 1962-04-01 \n",
" 656 \n",
" 4 \n",
" \n",
" \n",
" 1962-05-01 \n",
" 727 \n",
" 5 \n",
" \n",
" \n",
"
\n",
"
"
],
"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": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" Production \n",
" Month \n",
" \n",
" \n",
" Date \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" 1962-01-01 \n",
" 589 \n",
" January \n",
" \n",
" \n",
" 1962-02-01 \n",
" 561 \n",
" February \n",
" \n",
" \n",
" 1962-03-01 \n",
" 640 \n",
" March \n",
" \n",
" \n",
" 1962-04-01 \n",
" 656 \n",
" April \n",
" \n",
" \n",
" 1962-05-01 \n",
" 727 \n",
" May \n",
" \n",
" \n",
"
\n",
"
"
],
"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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\n",
"text/plain": [
""
]
},
"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
}