{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CME 193 - Pandas Exercise Supplement\n",
    "\n",
    "In this extended exercise, you'll load and play with CO2 data collected at the Mauna Loa observatory over the last 60 years. \n",
    "\n",
    "* NOAA Website: https://www.esrl.noaa.gov/gmd/ccgg/trends/full.html\n",
    "* NOAA data: https://www.esrl.noaa.gov/gmd/ccgg/trends/data.html\n",
    "\n",
    "The monthly data can be found at this [link](ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_mlo.txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reads the data from the ftp server directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_mlo.txt', \n",
    "                   delim_whitespace=True, \n",
    "                   comment='#',\n",
    "                   names=[\"year\", \"month\", \"decdate\", \"co2\", \"co2interp\", \"trend\", \"days\"],\n",
    "                   index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>decdate</th>\n",
       "      <th>co2</th>\n",
       "      <th>co2interp</th>\n",
       "      <th>trend</th>\n",
       "      <th>days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1958</td>\n",
       "      <td>3</td>\n",
       "      <td>1958.208</td>\n",
       "      <td>315.71</td>\n",
       "      <td>315.71</td>\n",
       "      <td>314.62</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
       "      <td>1958.292</td>\n",
       "      <td>317.45</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.29</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958</td>\n",
       "      <td>5</td>\n",
       "      <td>1958.375</td>\n",
       "      <td>317.50</td>\n",
       "      <td>317.50</td>\n",
       "      <td>314.71</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958</td>\n",
       "      <td>6</td>\n",
       "      <td>1958.458</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>317.10</td>\n",
       "      <td>314.85</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958</td>\n",
       "      <td>7</td>\n",
       "      <td>1958.542</td>\n",
       "      <td>315.86</td>\n",
       "      <td>315.86</td>\n",
       "      <td>314.98</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>2018.708</td>\n",
       "      <td>405.51</td>\n",
       "      <td>405.51</td>\n",
       "      <td>409.08</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "      <td>2018.792</td>\n",
       "      <td>406.00</td>\n",
       "      <td>406.00</td>\n",
       "      <td>409.37</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>2018.875</td>\n",
       "      <td>408.02</td>\n",
       "      <td>408.02</td>\n",
       "      <td>410.03</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>2018.958</td>\n",
       "      <td>409.07</td>\n",
       "      <td>409.07</td>\n",
       "      <td>409.74</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>2019.042</td>\n",
       "      <td>410.81</td>\n",
       "      <td>410.81</td>\n",
       "      <td>410.48</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>731 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  month   decdate     co2  co2interp   trend  days\n",
       "0    1958      3  1958.208  315.71     315.71  314.62    -1\n",
       "1    1958      4  1958.292  317.45     317.45  315.29    -1\n",
       "2    1958      5  1958.375  317.50     317.50  314.71    -1\n",
       "3    1958      6  1958.458  -99.99     317.10  314.85    -1\n",
       "4    1958      7  1958.542  315.86     315.86  314.98    -1\n",
       "..    ...    ...       ...     ...        ...     ...   ...\n",
       "726  2018      9  2018.708  405.51     405.51  409.08    29\n",
       "727  2018     10  2018.792  406.00     406.00  409.37    30\n",
       "728  2018     11  2018.875  408.02     408.02  410.03    24\n",
       "729  2018     12  2018.958  409.07     409.07  409.74    30\n",
       "730  2019      1  2019.042  410.81     410.81  410.48    23\n",
       "\n",
       "[731 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 10)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# copies the original data.\n",
    "orig = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1 - Normalize the Date\n",
    "\n",
    "1. create a new column for the dataframe called 'day' that is set to be 1 in every entry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['day'] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. The dataframe now has columns for 'day', 'month', and 'year'.  Use `pd.to_datetime()` to create a new series of dates \n",
    "\n",
    "`dates = pd.to_datetime(...)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.to_datetime(df[['month', 'day', 'year']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. set a new column of the dataframe to hold this series.  Call the column `'date'`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['date'] = dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. set the index of the dataframe to be the `'date'` column using the `set_index()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.set_index('date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Now let's remove the old columns with date information.  Use the `drop()` method to remove the 'day', 'month', 'year', and 'decdate' columns.  Hint: `df.drop(..., axis=1, inplace=True)`\n",
    "\n",
    "5a. Go ahead and drop the 'days' column as well, since we're not going to use it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>co2</th>\n",
       "      <th>co2interp</th>\n",
       "      <th>trend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1958-03-01</th>\n",
       "      <td>315.71</td>\n",
       "      <td>315.71</td>\n",
       "      <td>314.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-01</th>\n",
       "      <td>317.45</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-05-01</th>\n",
       "      <td>317.50</td>\n",
       "      <td>317.50</td>\n",
       "      <td>314.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-06-01</th>\n",
       "      <td>-99.99</td>\n",
       "      <td>317.10</td>\n",
       "      <td>314.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-07-01</th>\n",
       "      <td>315.86</td>\n",
       "      <td>315.86</td>\n",
       "      <td>314.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-01</th>\n",
       "      <td>405.51</td>\n",
       "      <td>405.51</td>\n",
       "      <td>409.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>406.00</td>\n",
       "      <td>406.00</td>\n",
       "      <td>409.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>408.02</td>\n",
       "      <td>408.02</td>\n",
       "      <td>410.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>409.07</td>\n",
       "      <td>409.07</td>\n",
       "      <td>409.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>410.81</td>\n",
       "      <td>410.81</td>\n",
       "      <td>410.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>731 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               co2  co2interp   trend\n",
       "date                                 \n",
       "1958-03-01  315.71     315.71  314.62\n",
       "1958-04-01  317.45     317.45  315.29\n",
       "1958-05-01  317.50     317.50  314.71\n",
       "1958-06-01  -99.99     317.10  314.85\n",
       "1958-07-01  315.86     315.86  314.98\n",
       "...            ...        ...     ...\n",
       "2018-09-01  405.51     405.51  409.08\n",
       "2018-10-01  406.00     406.00  409.37\n",
       "2018-11-01  408.02     408.02  410.03\n",
       "2018-12-01  409.07     409.07  409.74\n",
       "2019-01-01  410.81     410.81  410.48\n",
       "\n",
       "[731 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop previous date information\n",
    "df.drop(df[['month', 'day', 'year', 'decdate', 'days']], axis=1, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2 - deal with missing values\n",
    "\n",
    "1. First, use the `plot()` method to visualize the contents of your dataframe.  What do you see?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "if you read the header for the file we used to load the dataframe, you'll see that missing values take the value -99.99.\n",
    "\n",
    "2. Set values that are `-99.99` to `None` (this indicates a missing value in Pandas).\n",
    "\n",
    "Hint: use the `applymap()` method, and the lambda function\n",
    "```python\n",
    "lambda x: None if x == -99.99 else x\n",
    "```\n",
    "If you're familiar with [ternary operators](https://en.wikipedia.org/wiki/%3F:), this is the equivalent of\n",
    "```\n",
    "x == -99.99 ? None : x\n",
    "```\n",
    "Note that you may need to make a new assignment e.g., `df = df.applymap(...)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.applymap(lambda x: None if x == -99.99 else x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Plot your dataframe again.  What do you see now?\n",
    "\n",
    "3a. Try plotting just the 'co2' series.  What do you see?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create New DataFrames with rows that meet conditions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Create new dataframe called `recent` that contains all rows of the previous dataframe since 2007.  Plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent = df.loc['2007':,]\n",
    "recent.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Create a new dataframe called `old` that contains all rows of the dataframe before 1990.  Plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "old = df.loc[:'1990',]\n",
    "old.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### At this point, by inspection, you might be convinced there is further analysis to be done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(140.40924595132572, 64.61419947681334)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(old['trend']), np.var(recent['trend'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create some groups\n",
    "\n",
    "Let's go back to the original data that we loaded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>decdate</th>\n",
       "      <th>co2</th>\n",
       "      <th>co2interp</th>\n",
       "      <th>trend</th>\n",
       "      <th>days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1958</td>\n",
       "      <td>3</td>\n",
       "      <td>1958.208</td>\n",
       "      <td>315.71</td>\n",
       "      <td>315.71</td>\n",
       "      <td>314.62</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
       "      <td>1958.292</td>\n",
       "      <td>317.45</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.29</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958</td>\n",
       "      <td>5</td>\n",
       "      <td>1958.375</td>\n",
       "      <td>317.50</td>\n",
       "      <td>317.50</td>\n",
       "      <td>314.71</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958</td>\n",
       "      <td>6</td>\n",
       "      <td>1958.458</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>317.10</td>\n",
       "      <td>314.85</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958</td>\n",
       "      <td>7</td>\n",
       "      <td>1958.542</td>\n",
       "      <td>315.86</td>\n",
       "      <td>315.86</td>\n",
       "      <td>314.98</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>2018.708</td>\n",
       "      <td>405.51</td>\n",
       "      <td>405.51</td>\n",
       "      <td>409.08</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "      <td>2018.792</td>\n",
       "      <td>406.00</td>\n",
       "      <td>406.00</td>\n",
       "      <td>409.37</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>2018.875</td>\n",
       "      <td>408.02</td>\n",
       "      <td>408.02</td>\n",
       "      <td>410.03</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>2018.958</td>\n",
       "      <td>409.07</td>\n",
       "      <td>409.07</td>\n",
       "      <td>409.74</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>2019.042</td>\n",
       "      <td>410.81</td>\n",
       "      <td>410.81</td>\n",
       "      <td>410.48</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>731 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  month   decdate     co2  co2interp   trend  days\n",
       "0    1958      3  1958.208  315.71     315.71  314.62    -1\n",
       "1    1958      4  1958.292  317.45     317.45  315.29    -1\n",
       "2    1958      5  1958.375  317.50     317.50  314.71    -1\n",
       "3    1958      6  1958.458  -99.99     317.10  314.85    -1\n",
       "4    1958      7  1958.542  315.86     315.86  314.98    -1\n",
       "..    ...    ...       ...     ...        ...     ...   ...\n",
       "726  2018      9  2018.708  405.51     405.51  409.08    29\n",
       "727  2018     10  2018.792  406.00     406.00  409.37    30\n",
       "728  2018     11  2018.875  408.02     408.02  410.03    24\n",
       "729  2018     12  2018.958  409.07     409.07  409.74    30\n",
       "730  2019      1  2019.042  410.81     410.81  410.48    23\n",
       "\n",
       "[731 rows x 7 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = orig\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose that we want to look at co2 averages by year instead of by month.\n",
    "\n",
    "1. drop rows with missing values\n",
    "\n",
    "1a. apply the map that sends -99.99 to none\n",
    "\n",
    "1b. use the `dropna()` method to remove rows with missing values: `df = df.dropna()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>decdate</th>\n",
       "      <th>co2</th>\n",
       "      <th>co2interp</th>\n",
       "      <th>trend</th>\n",
       "      <th>days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1958</td>\n",
       "      <td>3</td>\n",
       "      <td>1958.208</td>\n",
       "      <td>315.71</td>\n",
       "      <td>315.71</td>\n",
       "      <td>314.62</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
       "      <td>1958.292</td>\n",
       "      <td>317.45</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.29</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958</td>\n",
       "      <td>5</td>\n",
       "      <td>1958.375</td>\n",
       "      <td>317.50</td>\n",
       "      <td>317.50</td>\n",
       "      <td>314.71</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958</td>\n",
       "      <td>7</td>\n",
       "      <td>1958.542</td>\n",
       "      <td>315.86</td>\n",
       "      <td>315.86</td>\n",
       "      <td>314.98</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1958</td>\n",
       "      <td>8</td>\n",
       "      <td>1958.625</td>\n",
       "      <td>314.93</td>\n",
       "      <td>314.93</td>\n",
       "      <td>315.94</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>2018.708</td>\n",
       "      <td>405.51</td>\n",
       "      <td>405.51</td>\n",
       "      <td>409.08</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "      <td>2018.792</td>\n",
       "      <td>406.00</td>\n",
       "      <td>406.00</td>\n",
       "      <td>409.37</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>2018.875</td>\n",
       "      <td>408.02</td>\n",
       "      <td>408.02</td>\n",
       "      <td>410.03</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>2018.958</td>\n",
       "      <td>409.07</td>\n",
       "      <td>409.07</td>\n",
       "      <td>409.74</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>2019.042</td>\n",
       "      <td>410.81</td>\n",
       "      <td>410.81</td>\n",
       "      <td>410.48</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>724 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  month   decdate     co2  co2interp   trend  days\n",
       "0    1958      3  1958.208  315.71     315.71  314.62    -1\n",
       "1    1958      4  1958.292  317.45     317.45  315.29    -1\n",
       "2    1958      5  1958.375  317.50     317.50  314.71    -1\n",
       "4    1958      7  1958.542  315.86     315.86  314.98    -1\n",
       "5    1958      8  1958.625  314.93     314.93  315.94    -1\n",
       "..    ...    ...       ...     ...        ...     ...   ...\n",
       "726  2018      9  2018.708  405.51     405.51  409.08    29\n",
       "727  2018     10  2018.792  406.00     406.00  409.37    30\n",
       "728  2018     11  2018.875  408.02     408.02  410.03    24\n",
       "729  2018     12  2018.958  409.07     409.07  409.74    30\n",
       "730  2019      1  2019.042  410.81     410.81  410.48    23\n",
       "\n",
       "[724 rows x 7 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.applymap(lambda x: None if x == -99.99 else x)\n",
    "df = df.dropna()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Create a group for each year (use key 'year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "groups = df.groupby('year')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Aggregate the groups into a new dataframe, `df2`, using `np.mean`\n",
    "\n",
    "3a. you can drop all the columns except `'co2'` if you'd like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = groups.aggregate(np.mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. make a plot of the `'co2'` series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f36f0b0e0b8>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2['co2'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (3.6-cme193)",
   "language": "python",
   "name": "cme193"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}