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udemy-ML/03-Pandas/.ipynb_checkpoints/08-Time-Series-with-Pandas-...

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___\n",
"\n",
"<a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo.png'/></a>\n",
"___\n",
"<center><em>Copyright Pierian Data</em></center>\n",
"<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Time Series with Pandas Project Exercise\n",
"\n",
"For this exercise, answer the questions below given the dataset: https://fred.stlouisfed.org/series/UMTMVS\n",
"\n",
"This dataset is the Value of Manufacturers' Shipments for All Manufacturing Industries."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Import any necessary libraries.**"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Read in the data UMTMVS.csv file from the Data folder**"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.read_csv('../Data/UMTMVS.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Check the head of the data**"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 47,
"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>DATE</th>\n",
" <th>UMTMVS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1992-01-01</td>\n",
" <td>209438.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1992-02-01</td>\n",
" <td>232679.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1992-03-01</td>\n",
" <td>249673.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1992-04-01</td>\n",
" <td>239666.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1992-05-01</td>\n",
" <td>243231.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DATE UMTMVS\n",
"0 1992-01-01 209438.0\n",
"1 1992-02-01 232679.0\n",
"2 1992-03-01 249673.0\n",
"3 1992-04-01 239666.0\n",
"4 1992-05-01 243231.0"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Set the DATE column as the index.**"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = df.set_index('DATE')"
]
},
{
"cell_type": "code",
"execution_count": 50,
"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>UMTMVS</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DATE</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1992-01-01</th>\n",
" <td>209438.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1992-02-01</th>\n",
" <td>232679.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1992-03-01</th>\n",
" <td>249673.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1992-04-01</th>\n",
" <td>239666.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1992-05-01</th>\n",
" <td>243231.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" UMTMVS\n",
"DATE \n",
"1992-01-01 209438.0\n",
"1992-02-01 232679.0\n",
"1992-03-01 249673.0\n",
"1992-04-01 239666.0\n",
"1992-05-01 243231.0"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Check the data type of the index.**"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['1992-01-01', '1992-02-01', '1992-03-01', '1992-04-01', '1992-05-01',\n",
" '1992-06-01', '1992-07-01', '1992-08-01', '1992-09-01', '1992-10-01',\n",
" ...\n",
" '2018-04-01', '2018-05-01', '2018-06-01', '2018-07-01', '2018-08-01',\n",
" '2018-09-01', '2018-10-01', '2018-11-01', '2018-12-01', '2019-01-01'],\n",
" dtype='object', name='DATE', length=325)"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.index"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Convert the index to be a datetime index. Note, there are many, many correct ways to do this!**"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df.index = pd.to_datetime(df.index)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatetimeIndex(['1992-01-01', '1992-02-01', '1992-03-01', '1992-04-01',\n",
" '1992-05-01', '1992-06-01', '1992-07-01', '1992-08-01',\n",
" '1992-09-01', '1992-10-01',\n",
" ...\n",
" '2018-04-01', '2018-05-01', '2018-06-01', '2018-07-01',\n",
" '2018-08-01', '2018-09-01', '2018-10-01', '2018-11-01',\n",
" '2018-12-01', '2019-01-01'],\n",
" dtype='datetime64[ns]', name='DATE', length=325, freq=None)"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.index"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Plot out the data, choose a reasonable figure size**"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1d10ba9bcc0>"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA1UAAAHjCAYAAADL8zWHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngSioOnJlHbnS1pTTEBI
"text/plain": [
"<Figure size 1008x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot(figsize=(14,8))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**What was the percent increase in value from Jan 2009 to Jan 2019?**"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"UMTMVS 38.472149\n",
"dtype: float64"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"100 * (df.loc['2019-01-01'] - df.loc['2009-01-01']) / df.loc['2009-01-01']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**What was the percent decrease from Jan 2008 to Jan 2009?**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"UMTMVS -22.022775\n",
"dtype: float64"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"100 * (df.loc['2009-01-01'] - df.loc['2008-01-01']) / df.loc['2008-01-01']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**What is the month with the least value after 2005?** [HINT](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.idxmin.html)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"UMTMVS 2009-01-01\n",
"dtype: datetime64[ns]"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc['2005-01-01':].idxmin()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**What 6 months have the highest value?**"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 80,
"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>UMTMVS</th>\n",
" <th>Yearly Mean</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>2018-08-01</th>\n",
" <td>529157.0</td>\n",
" <td>490453.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-10-01</th>\n",
" <td>527031.0</td>\n",
" <td>496482.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-01</th>\n",
" <td>525660.0</td>\n",
" <td>483611.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-03-01</th>\n",
" <td>518285.0</td>\n",
" <td>474351.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-09-01</th>\n",
" <td>516992.0</td>\n",
" <td>493075.583333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" UMTMVS Yearly Mean\n",
"DATE \n",
"2018-08-01 529157.0 490453.500000\n",
"2018-10-01 527031.0 496482.333333\n",
"2018-06-01 525660.0 483611.000000\n",
"2018-03-01 518285.0 474351.250000\n",
"2018-09-01 516992.0 493075.583333"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.sort_values(by='UMTMVS',ascending=False).head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**How many millions of dollars in value was lost in 2008? (Another way of posing this question is what was the value difference between Jan 2008 and Jan 2009)**"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"UMTMVS 95206.0\n",
"dtype: float64"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc['2008-01-01'] - df.loc['2009-01-01']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Create a bar plot showing the average value in millions of dollars per year**"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1d10a074588>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA4MAAAJDCAYAAACxCMH6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPCqzdwr3rKr11ylcneS
"text/plain": [
"<Figure size 1080x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.resample('Y').mean().plot.bar(figsize=(15,8))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**What year had the biggest increase in mean value from the previous year's mean value? (Lots of ways to get this answer!)**\n",
"\n",
"[HINT for a useful method](https://pandas.pydata.org/pandas-docs/version/0.21/generated/pandas.DataFrame.idxmax.html)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"yearly_data = df.resample('Y').mean()\n",
"yearly_data_shift = yearly_data.shift(1)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"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>UMTMVS</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DATE</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1992-12-31</th>\n",
" <td>242002.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1993-12-31</th>\n",
" <td>251708.083333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1994-12-31</th>\n",
" <td>269842.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1995-12-31</th>\n",
" <td>289973.083333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996-12-31</th>\n",
" <td>299765.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" UMTMVS\n",
"DATE \n",
"1992-12-31 242002.000000\n",
"1993-12-31 251708.083333\n",
"1994-12-31 269842.666667\n",
"1995-12-31 289973.083333\n",
"1996-12-31 299765.666667"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yearly_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"change = yearly_data - yearly_data_shift "
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2011-12-31 00:00:00', freq='A-DEC')"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"change['UMTMVS'].idxmax()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Plot out the yearly rolling mean on top of the original data. Recall that this is monthly data and there are 12 months in a year!**"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 864x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df['Yearly Mean'] = df['UMTMVS'].rolling(window=12).mean()\n",
"df[['UMTMVS','Yearly Mean']].plot(figsize=(12,5)).autoscale(axis='x',tight=True);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**BONUS QUESTION (HARD).**\n",
"\n",
"**Some month in 2008 the value peaked for that year. How many months did it take to surpass that 2008 peak? (Since it crashed immediately after this peak) There are many ways to get this answer. NOTE: I get 70 months as my answer, you may get 69 or 68, depending on whether or not you count the start and end months. Refer to the video solutions for full explanation on this.**"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.read_csv('../Data/UMTMVS.csv',index_col='DATE',parse_dates=True)"
]
},
{
"cell_type": "code",
"execution_count": 98,
"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>UMTMVS</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DATE</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1992-01-01</th>\n",
" <td>209438.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1992-02-01</th>\n",
" <td>232679.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1992-03-01</th>\n",
" <td>249673.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1992-04-01</th>\n",
" <td>239666.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1992-05-01</th>\n",
" <td>243231.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" UMTMVS\n",
"DATE \n",
"1992-01-01 209438.0\n",
"1992-02-01 232679.0\n",
"1992-03-01 249673.0\n",
"1992-04-01 239666.0\n",
"1992-05-01 243231.0"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df2008 = df.loc['2008-01-01':'2009-01-01']"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"UMTMVS 2008-06-01\n",
"dtype: datetime64[ns]"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2008.idxmax()"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"UMTMVS 510081.0\n",
"dtype: float64"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2008.max()"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_post_peak = df.loc['2008-06-01':]"
]
},
{
"cell_type": "code",
"execution_count": 106,
"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>UMTMVS</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DATE</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2008-06-01</th>\n",
" <td>510081.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-03-01</th>\n",
" <td>513700.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-06-01</th>\n",
" <td>516935.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-09-01</th>\n",
" <td>512988.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-03-01</th>\n",
" <td>518285.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-05-01</th>\n",
" <td>515105.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-01</th>\n",
" <td>525660.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-08-01</th>\n",
" <td>529157.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-09-01</th>\n",
" <td>516992.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-10-01</th>\n",
" <td>527031.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" UMTMVS\n",
"DATE \n",
"2008-06-01 510081.0\n",
"2014-03-01 513700.0\n",
"2014-06-01 516935.0\n",
"2014-09-01 512988.0\n",
"2018-03-01 518285.0\n",
"2018-05-01 515105.0\n",
"2018-06-01 525660.0\n",
"2018-08-01 529157.0\n",
"2018-09-01 516992.0\n",
"2018-10-01 527031.0"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_post_peak[df_post_peak>=510081].dropna()"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"70"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(df.loc['2008-06-01':'2014-03-01'])"
]
},
{
"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": 2
}