udemy-ML/03-Pandas/.ipynb_checkpoints/08-Time-Series-with-Pandas-Project-Exercise-SET-TWO-Solutions-checkpoint.ipynb

{
 "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": {
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W/7XDwloZd3/hcVfYVPPoy/M1rsdGKGi2WEooMrrjEdupygmnyhY8qVgYa+XK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\nTkSuQmCC\n",
      "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": 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OSsMAAMC2cJgoAABAhzSDAAAAHdIMAgAAdEgzCAAA0CEnkIFt5EQuAADsjLwzCAAA0CHN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\nRU5ErkJggg==\n",
      "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": {
      "image/png": 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UuKyM6JFJvS9MvzwHx/XJ6dAp9wajOK0mfXEnQM/shCgvybTpmfKWeXJIFuUB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\n8UsKDY3Y376W8T3srNnT1Kn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gD3dLfKVQLYsYl+GmSenXIBwIrjSZ8o46VbrtZhwWI6PT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\nUG6ttgn2MuW1RUZQb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Tru57d2MK8ij5buEFoby8w3tAfZ0Tm0ImeT2UPc6TDuA4oDHk47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\n",
      "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
}