docs/examples/academy/14. Working with DataFrames.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 3\n",
    "\n",
    "## Video 14: Working with DataFrames\n",
    "**Python for the Energy Industry**\n",
    "\n",
    "There are a number of built-in functions for exploring the data in a DataFrame. We'll be working with a larger example DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Population</th>\n",
       "      <th>Oil Production</th>\n",
       "      <th>Proven Reserves</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>North Africa</td>\n",
       "      <td>42228408</td>\n",
       "      <td>1348361.0</td>\n",
       "      <td>1.220000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Southern Africa</td>\n",
       "      <td>30809787</td>\n",
       "      <td>1769615.0</td>\n",
       "      <td>8.423000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>Central Africa</td>\n",
       "      <td>1308975</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gabon</td>\n",
       "      <td>Central Africa</td>\n",
       "      <td>2119275</td>\n",
       "      <td>210820.0</td>\n",
       "      <td>2.000000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Iran</td>\n",
       "      <td>Middle East</td>\n",
       "      <td>81800188</td>\n",
       "      <td>3990956.0</td>\n",
       "      <td>1.575300e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Iraq</td>\n",
       "      <td>Middle East</td>\n",
       "      <td>38433600</td>\n",
       "      <td>4451516.0</td>\n",
       "      <td>1.430690e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Kuwait</td>\n",
       "      <td>Middle East</td>\n",
       "      <td>4137312</td>\n",
       "      <td>2923825.0</td>\n",
       "      <td>1.015000e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Libya</td>\n",
       "      <td>North Africa</td>\n",
       "      <td>6678559</td>\n",
       "      <td>384686.0</td>\n",
       "      <td>4.836300e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>West Africa</td>\n",
       "      <td>195874685</td>\n",
       "      <td>1999885.0</td>\n",
       "      <td>3.707000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Republic of the Congo</td>\n",
       "      <td>Central Africa</td>\n",
       "      <td>5125821</td>\n",
       "      <td>260000.0</td>\n",
       "      <td>1.600000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>Middle East</td>\n",
       "      <td>33702756</td>\n",
       "      <td>10460710.0</td>\n",
       "      <td>2.665780e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>UAE</td>\n",
       "      <td>Middle East</td>\n",
       "      <td>9630959</td>\n",
       "      <td>3106077.0</td>\n",
       "      <td>9.780000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>South America</td>\n",
       "      <td>28887118</td>\n",
       "      <td>2276967.0</td>\n",
       "      <td>2.999530e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Country           Region  Population  Oil Production  \\\n",
       "0                 Algeria     North Africa    42228408       1348361.0   \n",
       "1                  Angola  Southern Africa    30809787       1769615.0   \n",
       "2       Equatorial Guinea   Central Africa     1308975             NaN   \n",
       "3                   Gabon   Central Africa     2119275        210820.0   \n",
       "4                    Iran      Middle East    81800188       3990956.0   \n",
       "5                    Iraq      Middle East    38433600       4451516.0   \n",
       "6                  Kuwait      Middle East     4137312       2923825.0   \n",
       "7                   Libya     North Africa     6678559        384686.0   \n",
       "8                 Nigeria      West Africa   195874685       1999885.0   \n",
       "9   Republic of the Congo   Central Africa     5125821        260000.0   \n",
       "10           Saudi Arabia      Middle East    33702756      10460710.0   \n",
       "11                    UAE      Middle East     9630959       3106077.0   \n",
       "12              Venezuela    South America    28887118       2276967.0   \n",
       "\n",
       "    Proven Reserves  \n",
       "0      1.220000e+10  \n",
       "1      8.423000e+09  \n",
       "2               NaN  \n",
       "3      2.000000e+09  \n",
       "4      1.575300e+11  \n",
       "5      1.430690e+11  \n",
       "6      1.015000e+11  \n",
       "7      4.836300e+10  \n",
       "8      3.707000e+10  \n",
       "9      1.600000e+09  \n",
       "10     2.665780e+11  \n",
       "11     9.780000e+10  \n",
       "12     2.999530e+11  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "OPEC_df = pd.DataFrame({\n",
    "    'Country': ['Algeria','Angola','Equatorial Guinea','Gabon','Iran','Iraq','Kuwait','Libya','Nigeria','Republic of the Congo','Saudi Arabia','UAE','Venezuela'],\n",
    "    'Region': ['North Africa','Southern Africa','Central Africa','Central Africa','Middle East','Middle East','Middle East','North Africa','West Africa','Central Africa','Middle East','Middle East','South America'],\n",
    "    'Population': [42228408,30809787,1308975,2119275,81800188,38433600,4137312,6678559,195874685,5125821,33702756,9630959,28887118],\n",
    "    'Oil Production': [1348361,1769615,np.nan,210820,3990956,4451516,2923825,384686,1999885,260000,10460710,3106077,2276967],\n",
    "    'Proven Reserves': [12.2e9,8.423e9,np.nan,2e9,157.53e9,143.069e9,101.5e9,48.363e9,37.07e9,1.6e9,266.578e9,97.8e9,299.953e9]\n",
    "})\n",
    "\n",
    "OPEC_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For large DataFrames, rather than printing out large amounts of data, we can take a peek at some data with the 'head' and 'tail' functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Population</th>\n",
       "      <th>Oil Production</th>\n",
       "      <th>Proven Reserves</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>North Africa</td>\n",
       "      <td>42228408</td>\n",
       "      <td>1348361.0</td>\n",
       "      <td>1.220000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Southern Africa</td>\n",
       "      <td>30809787</td>\n",
       "      <td>1769615.0</td>\n",
       "      <td>8.423000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>Central Africa</td>\n",
       "      <td>1308975</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Country           Region  Population  Oil Production  \\\n",
       "0            Algeria     North Africa    42228408       1348361.0   \n",
       "1             Angola  Southern Africa    30809787       1769615.0   \n",
       "2  Equatorial Guinea   Central Africa     1308975             NaN   \n",
       "\n",
       "   Proven Reserves  \n",
       "0     1.220000e+10  \n",
       "1     8.423000e+09  \n",
       "2              NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OPEC_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Population</th>\n",
       "      <th>Oil Production</th>\n",
       "      <th>Proven Reserves</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>Middle East</td>\n",
       "      <td>33702756</td>\n",
       "      <td>10460710.0</td>\n",
       "      <td>2.665780e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>UAE</td>\n",
       "      <td>Middle East</td>\n",
       "      <td>9630959</td>\n",
       "      <td>3106077.0</td>\n",
       "      <td>9.780000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>South America</td>\n",
       "      <td>28887118</td>\n",
       "      <td>2276967.0</td>\n",
       "      <td>2.999530e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country         Region  Population  Oil Production  Proven Reserves\n",
       "10  Saudi Arabia    Middle East    33702756      10460710.0     2.665780e+11\n",
       "11           UAE    Middle East     9630959       3106077.0     9.780000e+10\n",
       "12     Venezuela  South America    28887118       2276967.0     2.999530e+11"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OPEC_df.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also take a look at what columns we have, and what type of data they store, with the 'info' function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13 entries, 0 to 12\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Country          13 non-null     object \n",
      " 1   Region           13 non-null     object \n",
      " 2   Population       13 non-null     int64  \n",
      " 3   Oil Production   12 non-null     float64\n",
      " 4   Proven Reserves  12 non-null     float64\n",
      "dtypes: float64(2), int64(1), object(2)\n",
      "memory usage: 648.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "OPEC_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also get a statistical description of our data. The 'describe' function will return basic stats on all columns with numeric data. There are also functions for each individual statistic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>Population</th>\n",
       "      <th>Oil Production</th>\n",
       "      <th>Proven Reserves</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>1.200000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.697980e+07</td>\n",
       "      <td>2.765285e+06</td>\n",
       "      <td>9.800717e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.295242e+07</td>\n",
       "      <td>2.796082e+06</td>\n",
       "      <td>1.021860e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.308975e+06</td>\n",
       "      <td>2.108200e+05</td>\n",
       "      <td>1.600000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.125821e+06</td>\n",
       "      <td>1.107442e+06</td>\n",
       "      <td>1.125575e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.888712e+07</td>\n",
       "      <td>2.138426e+06</td>\n",
       "      <td>7.308150e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.843360e+07</td>\n",
       "      <td>3.327297e+06</td>\n",
       "      <td>1.466842e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.958747e+08</td>\n",
       "      <td>1.046071e+07</td>\n",
       "      <td>2.999530e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Population  Oil Production  Proven Reserves\n",
       "count  1.300000e+01    1.200000e+01     1.200000e+01\n",
       "mean   3.697980e+07    2.765285e+06     9.800717e+10\n",
       "std    5.295242e+07    2.796082e+06     1.021860e+11\n",
       "min    1.308975e+06    2.108200e+05     1.600000e+09\n",
       "25%    5.125821e+06    1.107442e+06     1.125575e+10\n",
       "50%    2.888712e+07    2.138426e+06     7.308150e+10\n",
       "75%    3.843360e+07    3.327297e+06     1.466842e+11\n",
       "max    1.958747e+08    1.046071e+07     2.999530e+11"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OPEC_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2765284.8333333335"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the mean of a single column\n",
    "OPEC_df['Oil Production'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading & Writing to File\n",
    "\n",
    "Pandas can also be used to write a DataFrame into an excel/csv format, or read in a DataFrame from a file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write OPEC_df to an excel spreadsheet\n",
    "OPEC_df.to_csv('OPEC_df.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Population</th>\n",
       "      <th>Oil Production</th>\n",
       "      <th>Proven Reserves</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>North Africa</td>\n",
       "      <td>42228408</td>\n",
       "      <td>1348361.0</td>\n",
       "      <td>1.220000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Southern Africa</td>\n",
       "      <td>30809787</td>\n",
       "      <td>1769615.0</td>\n",
       "      <td>8.423000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>Central Africa</td>\n",
       "      <td>1308975</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gabon</td>\n",
       "      <td>Central Africa</td>\n",
       "      <td>2119275</td>\n",
       "      <td>210820.0</td>\n",
       "      <td>2.000000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Iran</td>\n",
       "      <td>Middle East</td>\n",
       "      <td>81800188</td>\n",
       "      <td>3990956.0</td>\n",
       "      <td>1.575300e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Iraq</td>\n",
       "      <td>Middle East</td>\n",
       "      <td>38433600</td>\n",
       "      <td>4451516.0</td>\n",
       "      <td>1.430690e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Kuwait</td>\n",
       "      <td>Middle East</td>\n",
       "      <td>4137312</td>\n",
       "      <td>2923825.0</td>\n",
       "      <td>1.015000e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Libya</td>\n",
       "      <td>North Africa</td>\n",
       "      <td>6678559</td>\n",
       "      <td>384686.0</td>\n",
       "      <td>4.836300e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>West Africa</td>\n",
       "      <td>195874685</td>\n",
       "      <td>1999885.0</td>\n",
       "      <td>3.707000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Republic of the Congo</td>\n",
       "      <td>Central Africa</td>\n",
       "      <td>5125821</td>\n",
       "      <td>260000.0</td>\n",
       "      <td>1.600000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>Middle East</td>\n",
       "      <td>33702756</td>\n",
       "      <td>10460710.0</td>\n",
       "      <td>2.665780e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>UAE</td>\n",
       "      <td>Middle East</td>\n",
       "      <td>9630959</td>\n",
       "      <td>3106077.0</td>\n",
       "      <td>9.780000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>South America</td>\n",
       "      <td>28887118</td>\n",
       "      <td>2276967.0</td>\n",
       "      <td>2.999530e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Country           Region  Population  Oil Production  \\\n",
       "0                 Algeria     North Africa    42228408       1348361.0   \n",
       "1                  Angola  Southern Africa    30809787       1769615.0   \n",
       "2       Equatorial Guinea   Central Africa     1308975             NaN   \n",
       "3                   Gabon   Central Africa     2119275        210820.0   \n",
       "4                    Iran      Middle East    81800188       3990956.0   \n",
       "5                    Iraq      Middle East    38433600       4451516.0   \n",
       "6                  Kuwait      Middle East     4137312       2923825.0   \n",
       "7                   Libya     North Africa     6678559        384686.0   \n",
       "8                 Nigeria      West Africa   195874685       1999885.0   \n",
       "9   Republic of the Congo   Central Africa     5125821        260000.0   \n",
       "10           Saudi Arabia      Middle East    33702756      10460710.0   \n",
       "11                    UAE      Middle East     9630959       3106077.0   \n",
       "12              Venezuela    South America    28887118       2276967.0   \n",
       "\n",
       "    Proven Reserves  \n",
       "0      1.220000e+10  \n",
       "1      8.423000e+09  \n",
       "2               NaN  \n",
       "3      2.000000e+09  \n",
       "4      1.575300e+11  \n",
       "5      1.430690e+11  \n",
       "6      1.015000e+11  \n",
       "7      4.836300e+10  \n",
       "8      3.707000e+10  \n",
       "9      1.600000e+09  \n",
       "10     2.665780e+11  \n",
       "11     9.780000e+10  \n",
       "12     2.999530e+11  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a new DataFrame from OPEC_df.csv\n",
    "OPEC_df_copy = pd.read_csv('OPEC_df.csv')\n",
    "OPEC_df_copy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "\n",
    "Obtain a spreadsheet with some data you would like to explore. You can use the example 'countries.csv' on the course page.\n",
    "\n",
    "Make sure the spreadsheet is in the sample folder as this notebook. Read the file in as a DataFrame, and use the above functions to explore the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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