3 - Pandas - DataFrames.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![rmotr](https://user-images.githubusercontent.com/7065401/52071918-bda15380-2562-11e9-828c-7f95297e4a82.png)\n",
    "<hr style=\"margin-bottom: 40px;\">\n",
    "\n",
    "<img src=\"https://user-images.githubusercontent.com/7065401/75165824-badf4680-5701-11ea-9c5b-5475b0a33abf.png\"\n",
    "    style=\"width:300px; float: right; margin: 0 40px 40px 40px;\"></img>\n",
    "\n",
    "# Pandas - `DataFrame`s\n",
    "\n",
    "Probably the most important data structure of pandas is the `DataFrame`. It's a tabular structure tightly integrated with `Series`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![purple-divider](https://user-images.githubusercontent.com/7065401/52071927-c1cd7100-2562-11e9-908a-dde91ba14e59.png)\n",
    "\n",
    "## Hands on! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll keep our analysis of G7 countries and looking now at DataFrames. As said, a DataFrame looks a lot like a table (as the one you can appreciate [here](https://docs.google.com/spreadsheets/d/1IlorV2-Oh9Da1JAZ7weVw86PQrQydSMp-ydVMH135iI/edit?usp=sharing)):\n",
    "\n",
    "<img width=\"700\" src=\"https://user-images.githubusercontent.com/872296/38153492-72c032ca-3443-11e8-80f4-9de9060a5127.png\" />\n",
    "\n",
    "Creating `DataFrame`s manually can be tedious. 99% of the time you'll be pulling the data from a Database, a csv file or the web. But still, you can create a DataFrame by specifying the columns and values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\n",
    "    'Population': [35.467, 63.951, 80.94 , 60.665, 127.061, 64.511, 318.523],\n",
    "    'GDP': [\n",
    "        1785387,\n",
    "        2833687,\n",
    "        3874437,\n",
    "        2167744,\n",
    "        4602367,\n",
    "        2950039,\n",
    "        17348075\n",
    "    ],\n",
    "    'Surface Area': [\n",
    "        9984670,\n",
    "        640679,\n",
    "        357114,\n",
    "        301336,\n",
    "        377930,\n",
    "        242495,\n",
    "        9525067\n",
    "    ],\n",
    "    'HDI': [\n",
    "        0.913,\n",
    "        0.888,\n",
    "        0.916,\n",
    "        0.873,\n",
    "        0.891,\n",
    "        0.907,\n",
    "        0.915\n",
    "    ],\n",
    "    'Continent': [\n",
    "        'America',\n",
    "        'Europe',\n",
    "        'Europe',\n",
    "        'Europe',\n",
    "        'Asia',\n",
    "        'Europe',\n",
    "        'America'\n",
    "    ]\n",
    "}, columns=['Population', 'GDP', 'Surface Area', 'HDI', 'Continent'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_(The `columns` attribute is optional. I'm using it to keep the same order as in the picture above)_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\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>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.467</td>\n",
       "      <td>1785387</td>\n",
       "      <td>9984670</td>\n",
       "      <td>0.913</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687</td>\n",
       "      <td>640679</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "      <td>357114</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744</td>\n",
       "      <td>301336</td>\n",
       "      <td>0.873</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367</td>\n",
       "      <td>377930</td>\n",
       "      <td>0.891</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>64.511</td>\n",
       "      <td>2950039</td>\n",
       "      <td>242495</td>\n",
       "      <td>0.907</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "      <td>9525067</td>\n",
       "      <td>0.915</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Population       GDP  Surface Area    HDI Continent\n",
       "0      35.467   1785387       9984670  0.913   America\n",
       "1      63.951   2833687        640679  0.888    Europe\n",
       "2      80.940   3874437        357114  0.916    Europe\n",
       "3      60.665   2167744        301336  0.873    Europe\n",
       "4     127.061   4602367        377930  0.891      Asia\n",
       "5      64.511   2950039        242495  0.907    Europe\n",
       "6     318.523  17348075       9525067  0.915   America"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`DataFrame`s also have indexes. As you can see in the \"table\" above, pandas has assigned a numeric, autoincremental index automatically to each \"row\" in our DataFrame. In our case, we know that each row represents a country, so we'll just reassign the index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = [\n",
    "    'Canada',\n",
    "    'France',\n",
    "    'Germany',\n",
    "    'Italy',\n",
    "    'Japan',\n",
    "    'United Kingdom',\n",
    "    'United States',\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>35.467</td>\n",
       "      <td>1785387</td>\n",
       "      <td>9984670</td>\n",
       "      <td>0.913</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687</td>\n",
       "      <td>640679</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "      <td>357114</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744</td>\n",
       "      <td>301336</td>\n",
       "      <td>0.873</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367</td>\n",
       "      <td>377930</td>\n",
       "      <td>0.891</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64.511</td>\n",
       "      <td>2950039</td>\n",
       "      <td>242495</td>\n",
       "      <td>0.907</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "      <td>9525067</td>\n",
       "      <td>0.915</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Population       GDP  Surface Area    HDI Continent\n",
       "Canada              35.467   1785387       9984670  0.913   America\n",
       "France              63.951   2833687        640679  0.888    Europe\n",
       "Germany             80.940   3874437        357114  0.916    Europe\n",
       "Italy               60.665   2167744        301336  0.873    Europe\n",
       "Japan              127.061   4602367        377930  0.891      Asia\n",
       "United Kingdom      64.511   2950039        242495  0.907    Europe\n",
       "United States      318.523  17348075       9525067  0.915   America"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Population', 'GDP', 'Surface Area', 'HDI', 'Continent'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Canada', 'France', 'Germany', 'Italy', 'Japan', 'United Kingdom',\n",
       "       'United States'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 7 entries, Canada to United States\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Population    7 non-null      float64\n",
      " 1   GDP           7 non-null      int64  \n",
      " 2   Surface Area  7 non-null      int64  \n",
      " 3   HDI           7 non-null      float64\n",
      " 4   Continent     7 non-null      object \n",
      "dtypes: float64(2), int64(2), object(1)\n",
      "memory usage: 336.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 5)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>107.302571</td>\n",
       "      <td>5.080248e+06</td>\n",
       "      <td>3.061327e+06</td>\n",
       "      <td>0.900429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>97.249970</td>\n",
       "      <td>5.494020e+06</td>\n",
       "      <td>4.576187e+06</td>\n",
       "      <td>0.016592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>35.467000</td>\n",
       "      <td>1.785387e+06</td>\n",
       "      <td>2.424950e+05</td>\n",
       "      <td>0.873000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>62.308000</td>\n",
       "      <td>2.500716e+06</td>\n",
       "      <td>3.292250e+05</td>\n",
       "      <td>0.889500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>64.511000</td>\n",
       "      <td>2.950039e+06</td>\n",
       "      <td>3.779300e+05</td>\n",
       "      <td>0.907000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>104.000500</td>\n",
       "      <td>4.238402e+06</td>\n",
       "      <td>5.082873e+06</td>\n",
       "      <td>0.914000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>318.523000</td>\n",
       "      <td>1.734808e+07</td>\n",
       "      <td>9.984670e+06</td>\n",
       "      <td>0.916000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Population           GDP  Surface Area       HDI\n",
       "count    7.000000  7.000000e+00  7.000000e+00  7.000000\n",
       "mean   107.302571  5.080248e+06  3.061327e+06  0.900429\n",
       "std     97.249970  5.494020e+06  4.576187e+06  0.016592\n",
       "min     35.467000  1.785387e+06  2.424950e+05  0.873000\n",
       "25%     62.308000  2.500716e+06  3.292250e+05  0.889500\n",
       "50%     64.511000  2.950039e+06  3.779300e+05  0.907000\n",
       "75%    104.000500  4.238402e+06  5.082873e+06  0.914000\n",
       "max    318.523000  1.734808e+07  9.984670e+06  0.916000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Population      float64\n",
       "GDP               int64\n",
       "Surface Area      int64\n",
       "HDI             float64\n",
       "Continent        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float64    2\n",
       "int64      2\n",
       "object     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "## Indexing, Selection and Slicing\n",
    "\n",
    "Individual columns in the DataFrame can be selected with regular indexing. Each column is represented as a `Series`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>35.467</td>\n",
       "      <td>1785387</td>\n",
       "      <td>9984670</td>\n",
       "      <td>0.913</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687</td>\n",
       "      <td>640679</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "      <td>357114</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744</td>\n",
       "      <td>301336</td>\n",
       "      <td>0.873</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367</td>\n",
       "      <td>377930</td>\n",
       "      <td>0.891</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64.511</td>\n",
       "      <td>2950039</td>\n",
       "      <td>242495</td>\n",
       "      <td>0.907</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "      <td>9525067</td>\n",
       "      <td>0.915</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Population       GDP  Surface Area    HDI Continent\n",
       "Canada              35.467   1785387       9984670  0.913   America\n",
       "France              63.951   2833687        640679  0.888    Europe\n",
       "Germany             80.940   3874437        357114  0.916    Europe\n",
       "Italy               60.665   2167744        301336  0.873    Europe\n",
       "Japan              127.061   4602367        377930  0.891      Asia\n",
       "United Kingdom      64.511   2950039        242495  0.907    Europe\n",
       "United States      318.523  17348075       9525067  0.915   America"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Population       35.467\n",
       "GDP             1785387\n",
       "Surface Area    9984670\n",
       "HDI               0.913\n",
       "Continent       America\n",
       "Name: Canada, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Canada']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Population       318.523\n",
       "GDP             17348075\n",
       "Surface Area     9525067\n",
       "HDI                0.915\n",
       "Continent        America\n",
       "Name: United States, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Canada             35.467\n",
       "France             63.951\n",
       "Germany            80.940\n",
       "Italy              60.665\n",
       "Japan             127.061\n",
       "United Kingdom     64.511\n",
       "United States     318.523\n",
       "Name: Population, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Population']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the `index` of the returned Series is the same as the DataFrame one. And its `name` is the name of the column. If you're working on a notebook and want to see a more DataFrame-like format you can use the `to_frame` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>35.467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>63.951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>60.665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>127.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>318.523</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Population\n",
       "Canada              35.467\n",
       "France              63.951\n",
       "Germany             80.940\n",
       "Italy               60.665\n",
       "Japan              127.061\n",
       "United Kingdom      64.511\n",
       "United States      318.523"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Population'].to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multiple columns can also be selected similarly to `numpy` and `Series`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>35.467</td>\n",
       "      <td>1785387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64.511</td>\n",
       "      <td>2950039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Population       GDP\n",
       "Canada              35.467   1785387\n",
       "France              63.951   2833687\n",
       "Germany             80.940   3874437\n",
       "Italy               60.665   2167744\n",
       "Japan              127.061   4602367\n",
       "United Kingdom      64.511   2950039\n",
       "United States      318.523  17348075"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Population', 'GDP']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, the result is another `DataFrame`. Slicing works differently, it acts at \"row level\", and can be counter intuitive:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Row level selection works better with `loc` and `iloc` **which are recommended** over regular \"direct slicing\" (`df[:]`).\n",
    "\n",
    "`loc` selects rows matching the given index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Population       60.665\n",
       "GDP             2167744\n",
       "Surface Area     301336\n",
       "HDI               0.873\n",
       "Continent        Europe\n",
       "Name: Italy, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Italy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687</td>\n",
       "      <td>640679</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "      <td>357114</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744</td>\n",
       "      <td>301336</td>\n",
       "      <td>0.873</td>\n",
       "      <td>Europe</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "         Population      GDP  Surface Area    HDI Continent\n",
       "France       63.951  2833687        640679  0.888    Europe\n",
       "Germany      80.940  3874437        357114  0.916    Europe\n",
       "Italy        60.665  2167744        301336  0.873    Europe"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['France': 'Italy']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a second \"argument\", you can pass the column(s) you'd like to select:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "France     63.951\n",
       "Germany    80.940\n",
       "Italy      60.665\n",
       "Name: Population, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['France': 'Italy', 'Population']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Population      GDP\n",
       "France       63.951  2833687\n",
       "Germany      80.940  3874437\n",
       "Italy        60.665  2167744"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['France': 'Italy', ['Population', 'GDP']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`iloc` works with the (numeric) \"position\" of the index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>35.467</td>\n",
       "      <td>1785387</td>\n",
       "      <td>9984670</td>\n",
       "      <td>0.913</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687</td>\n",
       "      <td>640679</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "      <td>357114</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744</td>\n",
       "      <td>301336</td>\n",
       "      <td>0.873</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367</td>\n",
       "      <td>377930</td>\n",
       "      <td>0.891</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64.511</td>\n",
       "      <td>2950039</td>\n",
       "      <td>242495</td>\n",
       "      <td>0.907</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "      <td>9525067</td>\n",
       "      <td>0.915</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Population       GDP  Surface Area    HDI Continent\n",
       "Canada              35.467   1785387       9984670  0.913   America\n",
       "France              63.951   2833687        640679  0.888    Europe\n",
       "Germany             80.940   3874437        357114  0.916    Europe\n",
       "Italy               60.665   2167744        301336  0.873    Europe\n",
       "Japan              127.061   4602367        377930  0.891      Asia\n",
       "United Kingdom      64.511   2950039        242495  0.907    Europe\n",
       "United States      318.523  17348075       9525067  0.915   America"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Population       35.467\n",
       "GDP             1785387\n",
       "Surface Area    9984670\n",
       "HDI               0.913\n",
       "Continent       America\n",
       "Name: Canada, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Population       318.523\n",
       "GDP             17348075\n",
       "Surface Area     9525067\n",
       "HDI                0.915\n",
       "Continent        America\n",
       "Name: United States, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>35.467</td>\n",
       "      <td>1785387</td>\n",
       "      <td>9984670</td>\n",
       "      <td>0.913</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687</td>\n",
       "      <td>640679</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "      <td>9525067</td>\n",
       "      <td>0.915</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Population       GDP  Surface Area    HDI Continent\n",
       "Canada             35.467   1785387       9984670  0.913   America\n",
       "France             63.951   2833687        640679  0.888    Europe\n",
       "United States     318.523  17348075       9525067  0.915   America"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[0, 1, -1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687</td>\n",
       "      <td>640679</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "      <td>357114</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Population      GDP  Surface Area    HDI Continent\n",
       "France       63.951  2833687        640679  0.888    Europe\n",
       "Germany      80.940  3874437        357114  0.916    Europe"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "France     0.888\n",
       "Germany    0.916\n",
       "Name: HDI, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>Population</th>\n",
       "      <th>HDI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>63.951</td>\n",
       "      <td>0.888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>0.916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Population    HDI\n",
       "France       63.951  0.888\n",
       "Germany      80.940  0.916"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3, [0, 3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>2833687</td>\n",
       "      <td>640679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>3874437</td>\n",
       "      <td>357114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             GDP  Surface Area\n",
       "France   2833687        640679\n",
       "Germany  3874437        357114"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3, 1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **RECOMMENDED: Always use `loc` and `iloc` to reduce ambiguity, specially with `DataFrame`s with numeric indexes.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "## Conditional selection (boolean arrays)\n",
    "\n",
    "We saw conditional selection applied to `Series` and it'll work in the same way for `DataFrame`s. After all, a `DataFrame` is a collection of `Series`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>35.467</td>\n",
       "      <td>1785387</td>\n",
       "      <td>9984670</td>\n",
       "      <td>0.913</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687</td>\n",
       "      <td>640679</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "      <td>357114</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744</td>\n",
       "      <td>301336</td>\n",
       "      <td>0.873</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367</td>\n",
       "      <td>377930</td>\n",
       "      <td>0.891</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64.511</td>\n",
       "      <td>2950039</td>\n",
       "      <td>242495</td>\n",
       "      <td>0.907</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "      <td>9525067</td>\n",
       "      <td>0.915</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Population       GDP  Surface Area    HDI Continent\n",
       "Canada              35.467   1785387       9984670  0.913   America\n",
       "France              63.951   2833687        640679  0.888    Europe\n",
       "Germany             80.940   3874437        357114  0.916    Europe\n",
       "Italy               60.665   2167744        301336  0.873    Europe\n",
       "Japan              127.061   4602367        377930  0.891      Asia\n",
       "United Kingdom      64.511   2950039        242495  0.907    Europe\n",
       "United States      318.523  17348075       9525067  0.915   America"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Canada            False\n",
       "France            False\n",
       "Germany            True\n",
       "Italy             False\n",
       "Japan              True\n",
       "United Kingdom    False\n",
       "United States      True\n",
       "Name: Population, dtype: bool"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Population'] > 70"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "      <td>357114</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367</td>\n",
       "      <td>377930</td>\n",
       "      <td>0.891</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "      <td>9525067</td>\n",
       "      <td>0.915</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Population       GDP  Surface Area    HDI Continent\n",
       "Germany            80.940   3874437        357114  0.916    Europe\n",
       "Japan             127.061   4602367        377930  0.891      Asia\n",
       "United States     318.523  17348075       9525067  0.915   America"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Population'] > 70]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The boolean matching is done at Index level, so you can filter by any row, as long as it contains the right indexes. Column selection still works as expected:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Germany           80.940\n",
       "Japan            127.061\n",
       "United States    318.523\n",
       "Name: Population, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Population'] > 70, 'Population']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Population</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Population       GDP\n",
       "Germany            80.940   3874437\n",
       "Japan             127.061   4602367\n",
       "United States     318.523  17348075"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Population'] > 70, ['Population', 'GDP']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "## Dropping stuff\n",
    "\n",
    "Opposed to the concept of selection, we have \"dropping\". Instead of pointing out which values you'd like to _select_ you could point which ones you'd like to `drop`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687</td>\n",
       "      <td>640679</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "      <td>357114</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744</td>\n",
       "      <td>301336</td>\n",
       "      <td>0.873</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367</td>\n",
       "      <td>377930</td>\n",
       "      <td>0.891</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64.511</td>\n",
       "      <td>2950039</td>\n",
       "      <td>242495</td>\n",
       "      <td>0.907</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "      <td>9525067</td>\n",
       "      <td>0.915</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Population       GDP  Surface Area    HDI Continent\n",
       "France              63.951   2833687        640679  0.888    Europe\n",
       "Germany             80.940   3874437        357114  0.916    Europe\n",
       "Italy               60.665   2167744        301336  0.873    Europe\n",
       "Japan              127.061   4602367        377930  0.891      Asia\n",
       "United Kingdom      64.511   2950039        242495  0.907    Europe\n",
       "United States      318.523  17348075       9525067  0.915   America"
      ]
     },
     "execution_count": 47,
     "metadata": {},
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    "df.drop('Canada')"
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  },
  {
   "cell_type": "code",
   "execution_count": 35,
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       "                Population       GDP  Surface Area    HDI Continent\n",
       "France              63.951   2833687        640679  0.888    Europe\n",
       "Germany             80.940   3874437        357114  0.916    Europe\n",
       "Italy               60.665   2167744        301336  0.873    Europe\n",
       "United Kingdom      64.511   2950039        242495  0.907    Europe\n",
       "United States      318.523  17348075       9525067  0.915   America"
      ]
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     "execution_count": 35,
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   "source": [
    "df.drop(['Canada', 'Japan'])"
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       "                     GDP  Surface Area Continent\n",
       "Canada           1785387       9984670   America\n",
       "France           2833687        640679    Europe\n",
       "Germany          3874437        357114    Europe\n",
       "Italy            2167744        301336    Europe\n",
       "Japan            4602367        377930      Asia\n",
       "United Kingdom   2950039        242495    Europe\n",
       "United States   17348075       9525067   America"
      ]
     },
     "execution_count": 36,
     "metadata": {},
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    "df.drop(columns=['Population', 'HDI'])"
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   "execution_count": 37,
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       "                Population       GDP  Surface Area    HDI Continent\n",
       "France              63.951   2833687        640679  0.888    Europe\n",
       "Germany             80.940   3874437        357114  0.916    Europe\n",
       "Japan              127.061   4602367        377930  0.891      Asia\n",
       "United Kingdom      64.511   2950039        242495  0.907    Europe\n",
       "United States      318.523  17348075       9525067  0.915   America"
      ]
     },
     "execution_count": 37,
     "metadata": {},
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   "source": [
    "df.drop(['Italy', 'Canada'], axis=0)"
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       "                     GDP  Surface Area Continent\n",
       "Canada           1785387       9984670   America\n",
       "France           2833687        640679    Europe\n",
       "Germany          3874437        357114    Europe\n",
       "Italy            2167744        301336    Europe\n",
       "Japan            4602367        377930      Asia\n",
       "United Kingdom   2950039        242495    Europe\n",
       "United States   17348075       9525067   America"
      ]
     },
     "execution_count": 38,
     "metadata": {},
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    }
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    "df.drop(['Population', 'HDI'], axis=1)"
   ]
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  {
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       "                     GDP  Surface Area Continent\n",
       "Canada           1785387       9984670   America\n",
       "France           2833687        640679    Europe\n",
       "Germany          3874437        357114    Europe\n",
       "Italy            2167744        301336    Europe\n",
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       "United Kingdom   2950039        242495    Europe\n",
       "United States   17348075       9525067   America"
      ]
     },
     "execution_count": 39,
     "metadata": {},
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    }
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    "df.drop(['Population', 'HDI'], axis=1)"
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   "execution_count": 40,
   "metadata": {},
   "outputs": [
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       "                     GDP  Surface Area Continent\n",
       "Canada           1785387       9984670   America\n",
       "France           2833687        640679    Europe\n",
       "Germany          3874437        357114    Europe\n",
       "Italy            2167744        301336    Europe\n",
       "Japan            4602367        377930      Asia\n",
       "United Kingdom   2950039        242495    Europe\n",
       "United States   17348075       9525067   America"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.drop(['Population', 'HDI'], axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                Population       GDP  Surface Area    HDI Continent\n",
       "France              63.951   2833687        640679  0.888    Europe\n",
       "Italy               60.665   2167744        301336  0.873    Europe\n",
       "Japan              127.061   4602367        377930  0.891      Asia\n",
       "United Kingdom      64.511   2950039        242495  0.907    Europe\n",
       "United States      318.523  17348075       9525067  0.915   America"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['Canada', 'Germany'], axis='rows')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All these `drop` methods return a new `DataFrame`. If you'd like to modify it \"in place\", you can use the `inplace` attribute (there's an example below)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "## Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Population</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>35.467</td>\n",
       "      <td>1785387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64.511</td>\n",
       "      <td>2950039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Population       GDP\n",
       "Canada              35.467   1785387\n",
       "France              63.951   2833687\n",
       "Germany             80.940   3874437\n",
       "Italy               60.665   2167744\n",
       "Japan              127.061   4602367\n",
       "United Kingdom      64.511   2950039\n",
       "United States      318.523  17348075"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Population', 'GDP']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Population</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>0.35467</td>\n",
       "      <td>17853.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>0.63951</td>\n",
       "      <td>28336.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>0.80940</td>\n",
       "      <td>38744.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>0.60665</td>\n",
       "      <td>21677.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>1.27061</td>\n",
       "      <td>46023.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>0.64511</td>\n",
       "      <td>29500.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>3.18523</td>\n",
       "      <td>173480.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Population        GDP\n",
       "Canada             0.35467   17853.87\n",
       "France             0.63951   28336.87\n",
       "Germany            0.80940   38744.37\n",
       "Italy              0.60665   21677.44\n",
       "Japan              1.27061   46023.67\n",
       "United Kingdom     0.64511   29500.39\n",
       "United States      3.18523  173480.75"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Population', 'GDP']] / 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Operations with Series** work at a column level, broadcasting down the rows (which can be counter intuitive)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GDP   -1000000.0\n",
       "HDI         -0.3\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crisis = pd.Series([-1_000_000, -0.3], index=['GDP', 'HDI'])\n",
    "crisis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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",
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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>GDP</th>\n",
       "      <th>HDI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>1785387</td>\n",
       "      <td>0.913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>2833687</td>\n",
       "      <td>0.888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>3874437</td>\n",
       "      <td>0.916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2167744</td>\n",
       "      <td>0.873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>4602367</td>\n",
       "      <td>0.891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>2950039</td>\n",
       "      <td>0.907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>17348075</td>\n",
       "      <td>0.915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     GDP    HDI\n",
       "Canada           1785387  0.913\n",
       "France           2833687  0.888\n",
       "Germany          3874437  0.916\n",
       "Italy            2167744  0.873\n",
       "Japan            4602367  0.891\n",
       "United Kingdom   2950039  0.907\n",
       "United States   17348075  0.915"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['GDP', 'HDI']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>GDP</th>\n",
       "      <th>HDI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>785387.0</td>\n",
       "      <td>0.613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>1833687.0</td>\n",
       "      <td>0.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>2874437.0</td>\n",
       "      <td>0.616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>1167744.0</td>\n",
       "      <td>0.573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>3602367.0</td>\n",
       "      <td>0.591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>1950039.0</td>\n",
       "      <td>0.607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>16348075.0</td>\n",
       "      <td>0.615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       GDP    HDI\n",
       "Canada            785387.0  0.613\n",
       "France           1833687.0  0.588\n",
       "Germany          2874437.0  0.616\n",
       "Italy            1167744.0  0.573\n",
       "Japan            3602367.0  0.591\n",
       "United Kingdom   1950039.0  0.607\n",
       "United States   16348075.0  0.615"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['GDP', 'HDI']] + crisis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "## Modifying DataFrames\n",
    "\n",
    "It's simple and intuitive, You can add columns, or replace values for columns without issues:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding a new column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "langs = pd.Series(\n",
    "    ['French', 'German', 'Italian'],\n",
    "    index=['France', 'Germany', 'Italy'],\n",
    "    name='Language'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "France      French\n",
       "Germany     German\n",
       "Italy      Italian\n",
       "Name: Language, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "langs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Language'] = langs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>35.467</td>\n",
       "      <td>1785387</td>\n",
       "      <td>9984670</td>\n",
       "      <td>0.913</td>\n",
       "      <td>America</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687</td>\n",
       "      <td>640679</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Europe</td>\n",
       "      <td>French</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "      <td>357114</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "      <td>German</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744</td>\n",
       "      <td>301336</td>\n",
       "      <td>0.873</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Italian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367</td>\n",
       "      <td>377930</td>\n",
       "      <td>0.891</td>\n",
       "      <td>Asia</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64.511</td>\n",
       "      <td>2950039</td>\n",
       "      <td>242495</td>\n",
       "      <td>0.907</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "      <td>9525067</td>\n",
       "      <td>0.915</td>\n",
       "      <td>America</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Population       GDP  Surface Area    HDI Continent Language\n",
       "Canada              35.467   1785387       9984670  0.913   America      NaN\n",
       "France              63.951   2833687        640679  0.888    Europe   French\n",
       "Germany             80.940   3874437        357114  0.916    Europe   German\n",
       "Italy               60.665   2167744        301336  0.873    Europe  Italian\n",
       "Japan              127.061   4602367        377930  0.891      Asia      NaN\n",
       "United Kingdom      64.511   2950039        242495  0.907    Europe      NaN\n",
       "United States      318.523  17348075       9525067  0.915   America      NaN"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Replacing values per column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Language'] = 'English'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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",
       "    .dataframe thead th {\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>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>35.467</td>\n",
       "      <td>1785387</td>\n",
       "      <td>9984670</td>\n",
       "      <td>0.913</td>\n",
       "      <td>America</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687</td>\n",
       "      <td>640679</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Europe</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "      <td>357114</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744</td>\n",
       "      <td>301336</td>\n",
       "      <td>0.873</td>\n",
       "      <td>Europe</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367</td>\n",
       "      <td>377930</td>\n",
       "      <td>0.891</td>\n",
       "      <td>Asia</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64.511</td>\n",
       "      <td>2950039</td>\n",
       "      <td>242495</td>\n",
       "      <td>0.907</td>\n",
       "      <td>Europe</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "      <td>9525067</td>\n",
       "      <td>0.915</td>\n",
       "      <td>America</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Population       GDP  Surface Area    HDI Continent Language\n",
       "Canada              35.467   1785387       9984670  0.913   America  English\n",
       "France              63.951   2833687        640679  0.888    Europe  English\n",
       "Germany             80.940   3874437        357114  0.916    Europe  English\n",
       "Italy               60.665   2167744        301336  0.873    Europe  English\n",
       "Japan              127.061   4602367        377930  0.891      Asia  English\n",
       "United Kingdom      64.511   2950039        242495  0.907    Europe  English\n",
       "United States      318.523  17348075       9525067  0.915   America  English"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Renaming Columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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",
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       "    }\n",
       "\n",
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       "        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>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>Human Development Index</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>35.467</td>\n",
       "      <td>1785387</td>\n",
       "      <td>9984670</td>\n",
       "      <td>0.913</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687</td>\n",
       "      <td>640679</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "      <td>357114</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744</td>\n",
       "      <td>301336</td>\n",
       "      <td>0.873</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367</td>\n",
       "      <td>377930</td>\n",
       "      <td>0.891</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>64.511</td>\n",
       "      <td>2950039</td>\n",
       "      <td>242495</td>\n",
       "      <td>0.907</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "      <td>9525067</td>\n",
       "      <td>0.915</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Population       GDP  Surface Area  Human Development Index Continent\n",
       "Canada       35.467   1785387       9984670                    0.913   America\n",
       "France       63.951   2833687        640679                    0.888    Europe\n",
       "Germany      80.940   3874437        357114                    0.916    Europe\n",
       "Italy        60.665   2167744        301336                    0.873    Europe\n",
       "Japan       127.061   4602367        377930                    0.891      Asia\n",
       "UK           64.511   2950039        242495                    0.907    Europe\n",
       "USA         318.523  17348075       9525067                    0.915   America"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(\n",
    "    columns={\n",
    "        'HDI': 'Human Development Index',\n",
    "        'Anual Popcorn Consumption': 'APC'\n",
    "    }, index={\n",
    "        'United States': 'USA',\n",
    "        'United Kingdom': 'UK',\n",
    "        'Argentina': 'AR'\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "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>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CANADA</th>\n",
       "      <td>35.467</td>\n",
       "      <td>1785387</td>\n",
       "      <td>9984670</td>\n",
       "      <td>0.913</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRANCE</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687</td>\n",
       "      <td>640679</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GERMANY</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "      <td>357114</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ITALY</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744</td>\n",
       "      <td>301336</td>\n",
       "      <td>0.873</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JAPAN</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367</td>\n",
       "      <td>377930</td>\n",
       "      <td>0.891</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNITED KINGDOM</th>\n",
       "      <td>64.511</td>\n",
       "      <td>2950039</td>\n",
       "      <td>242495</td>\n",
       "      <td>0.907</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNITED STATES</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "      <td>9525067</td>\n",
       "      <td>0.915</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Population       GDP  Surface Area    HDI Continent\n",
       "CANADA              35.467   1785387       9984670  0.913   America\n",
       "FRANCE              63.951   2833687        640679  0.888    Europe\n",
       "GERMANY             80.940   3874437        357114  0.916    Europe\n",
       "ITALY               60.665   2167744        301336  0.873    Europe\n",
       "JAPAN              127.061   4602367        377930  0.891      Asia\n",
       "UNITED KINGDOM      64.511   2950039        242495  0.907    Europe\n",
       "UNITED STATES      318.523  17348075       9525067  0.915   America"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(index=str.upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>canada</th>\n",
       "      <td>35.467</td>\n",
       "      <td>1785387</td>\n",
       "      <td>9984670</td>\n",
       "      <td>0.913</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>france</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687</td>\n",
       "      <td>640679</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "      <td>357114</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>italy</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744</td>\n",
       "      <td>301336</td>\n",
       "      <td>0.873</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>japan</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367</td>\n",
       "      <td>377930</td>\n",
       "      <td>0.891</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>united kingdom</th>\n",
       "      <td>64.511</td>\n",
       "      <td>2950039</td>\n",
       "      <td>242495</td>\n",
       "      <td>0.907</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>united states</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "      <td>9525067</td>\n",
       "      <td>0.915</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Population       GDP  Surface Area    HDI Continent\n",
       "canada              35.467   1785387       9984670  0.913   America\n",
       "france              63.951   2833687        640679  0.888    Europe\n",
       "germany             80.940   3874437        357114  0.916    Europe\n",
       "italy               60.665   2167744        301336  0.873    Europe\n",
       "japan              127.061   4602367        377930  0.891      Asia\n",
       "united kingdom      64.511   2950039        242495  0.907    Europe\n",
       "united states      318.523  17348075       9525067  0.915   America"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(index=lambda x: x.lower())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Dropping columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns='Language', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Adding values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>35.467</td>\n",
       "      <td>1785387.0</td>\n",
       "      <td>9984670.0</td>\n",
       "      <td>0.913</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687.0</td>\n",
       "      <td>640679.0</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437.0</td>\n",
       "      <td>357114.0</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744.0</td>\n",
       "      <td>301336.0</td>\n",
       "      <td>0.873</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367.0</td>\n",
       "      <td>377930.0</td>\n",
       "      <td>0.891</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64.511</td>\n",
       "      <td>2950039.0</td>\n",
       "      <td>242495.0</td>\n",
       "      <td>0.907</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075.0</td>\n",
       "      <td>9525067.0</td>\n",
       "      <td>0.915</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>3.000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Population         GDP  Surface Area    HDI Continent\n",
       "Canada              35.467   1785387.0     9984670.0  0.913   America\n",
       "France              63.951   2833687.0      640679.0  0.888    Europe\n",
       "Germany             80.940   3874437.0      357114.0  0.916    Europe\n",
       "Italy               60.665   2167744.0      301336.0  0.873    Europe\n",
       "Japan              127.061   4602367.0      377930.0  0.891      Asia\n",
       "United Kingdom      64.511   2950039.0      242495.0  0.907    Europe\n",
       "United States      318.523  17348075.0     9525067.0  0.915   America\n",
       "China                3.000         5.0           NaN    NaN       NaN"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.append(pd.Series({\n",
    "    'Population': 3,\n",
    "    'GDP': 5\n",
    "}, name='China'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Append returns a new `DataFrame`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>GDP</th>\n",
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       "      <th>HDI</th>\n",
       "      <th>Continent</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>35.467</td>\n",
       "      <td>1785387</td>\n",
       "      <td>9984670</td>\n",
       "      <td>0.913</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687</td>\n",
       "      <td>640679</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "      <td>357114</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744</td>\n",
       "      <td>301336</td>\n",
       "      <td>0.873</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367</td>\n",
       "      <td>377930</td>\n",
       "      <td>0.891</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64.511</td>\n",
       "      <td>2950039</td>\n",
       "      <td>242495</td>\n",
       "      <td>0.907</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "      <td>9525067</td>\n",
       "      <td>0.915</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Population       GDP  Surface Area    HDI Continent\n",
       "Canada              35.467   1785387       9984670  0.913   America\n",
       "France              63.951   2833687        640679  0.888    Europe\n",
       "Germany             80.940   3874437        357114  0.916    Europe\n",
       "Italy               60.665   2167744        301336  0.873    Europe\n",
       "Japan              127.061   4602367        377930  0.891      Asia\n",
       "United Kingdom      64.511   2950039        242495  0.907    Europe\n",
       "United States      318.523  17348075       9525067  0.915   America"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can directly set the new index and values to the `DataFrame`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc['China'] = pd.Series({'Population': 1_400_000_000, 'Continent': 'Asia'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use `drop` to just remove a row by index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('China', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### More radical index changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>index</th>\n",
       "      <th>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Canada</td>\n",
       "      <td>35.467</td>\n",
       "      <td>1785387</td>\n",
       "      <td>9984670</td>\n",
       "      <td>0.913</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687</td>\n",
       "      <td>640679</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Germany</td>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "      <td>357114</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Italy</td>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744</td>\n",
       "      <td>301336</td>\n",
       "      <td>0.873</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Japan</td>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367</td>\n",
       "      <td>377930</td>\n",
       "      <td>0.891</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>64.511</td>\n",
       "      <td>2950039</td>\n",
       "      <td>242495</td>\n",
       "      <td>0.907</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>United States</td>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "      <td>9525067</td>\n",
       "      <td>0.915</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            index  Population       GDP  Surface Area    HDI Continent\n",
       "0          Canada      35.467   1785387       9984670  0.913   America\n",
       "1          France      63.951   2833687        640679  0.888    Europe\n",
       "2         Germany      80.940   3874437        357114  0.916    Europe\n",
       "3           Italy      60.665   2167744        301336  0.873    Europe\n",
       "4           Japan     127.061   4602367        377930  0.891      Asia\n",
       "5  United Kingdom      64.511   2950039        242495  0.907    Europe\n",
       "6   United States     318.523  17348075       9525067  0.915   America"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Population</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35.467</th>\n",
       "      <td>1785387</td>\n",
       "      <td>9984670</td>\n",
       "      <td>0.913</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63.951</th>\n",
       "      <td>2833687</td>\n",
       "      <td>640679</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80.940</th>\n",
       "      <td>3874437</td>\n",
       "      <td>357114</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60.665</th>\n",
       "      <td>2167744</td>\n",
       "      <td>301336</td>\n",
       "      <td>0.873</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127.061</th>\n",
       "      <td>4602367</td>\n",
       "      <td>377930</td>\n",
       "      <td>0.891</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64.511</th>\n",
       "      <td>2950039</td>\n",
       "      <td>242495</td>\n",
       "      <td>0.907</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318.523</th>\n",
       "      <td>17348075</td>\n",
       "      <td>9525067</td>\n",
       "      <td>0.915</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 GDP  Surface Area    HDI Continent\n",
       "Population                                         \n",
       "35.467       1785387       9984670  0.913   America\n",
       "63.951       2833687        640679  0.888    Europe\n",
       "80.940       3874437        357114  0.916    Europe\n",
       "60.665       2167744        301336  0.873    Europe\n",
       "127.061      4602367        377930  0.891      Asia\n",
       "64.511       2950039        242495  0.907    Europe\n",
       "318.523     17348075       9525067  0.915   America"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('Population')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "## Creating columns from other columns\n",
    "\n",
    "Altering a DataFrame often involves combining different columns into another. For example, in our Countries analysis, we could try to calculate the \"GDP per capita\", which is just, `GDP / Population`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "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",
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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>Population</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>35.467</td>\n",
       "      <td>1785387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64.511</td>\n",
       "      <td>2950039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Population       GDP\n",
       "Canada              35.467   1785387\n",
       "France              63.951   2833687\n",
       "Germany             80.940   3874437\n",
       "Italy               60.665   2167744\n",
       "Japan              127.061   4602367\n",
       "United Kingdom      64.511   2950039\n",
       "United States      318.523  17348075"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Population', 'GDP']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The regular pandas way of expressing that, is just dividing each series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Canada            50339.385908\n",
       "France            44310.284437\n",
       "Germany           47868.013343\n",
       "Italy             35733.025633\n",
       "Japan             36221.712406\n",
       "United Kingdom    45729.239975\n",
       "United States     54464.120330\n",
       "China                      NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['GDP'] / df['Population']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result of that operation is just another series that you can add to the original `DataFrame`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['GDP Per Capita'] = df['GDP'] / df['Population']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Language</th>\n",
       "      <th>GDP Per Capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>3.546700e+01</td>\n",
       "      <td>1785387.0</td>\n",
       "      <td>9984670.0</td>\n",
       "      <td>0.913</td>\n",
       "      <td>America</td>\n",
       "      <td>English</td>\n",
       "      <td>50339.385908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>6.395100e+01</td>\n",
       "      <td>2833687.0</td>\n",
       "      <td>640679.0</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Europe</td>\n",
       "      <td>English</td>\n",
       "      <td>44310.284437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>8.094000e+01</td>\n",
       "      <td>3874437.0</td>\n",
       "      <td>357114.0</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "      <td>English</td>\n",
       "      <td>47868.013343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>6.066500e+01</td>\n",
       "      <td>2167744.0</td>\n",
       "      <td>301336.0</td>\n",
       "      <td>0.873</td>\n",
       "      <td>Europe</td>\n",
       "      <td>English</td>\n",
       "      <td>35733.025633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>1.270610e+02</td>\n",
       "      <td>4602367.0</td>\n",
       "      <td>377930.0</td>\n",
       "      <td>0.891</td>\n",
       "      <td>Asia</td>\n",
       "      <td>English</td>\n",
       "      <td>36221.712406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>6.451100e+01</td>\n",
       "      <td>2950039.0</td>\n",
       "      <td>242495.0</td>\n",
       "      <td>0.907</td>\n",
       "      <td>Europe</td>\n",
       "      <td>English</td>\n",
       "      <td>45729.239975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>3.185230e+02</td>\n",
       "      <td>17348075.0</td>\n",
       "      <td>9525067.0</td>\n",
       "      <td>0.915</td>\n",
       "      <td>America</td>\n",
       "      <td>English</td>\n",
       "      <td>54464.120330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1.400000e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Asia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Population         GDP  Surface Area    HDI Continent  \\\n",
       "Canada          3.546700e+01   1785387.0     9984670.0  0.913   America   \n",
       "France          6.395100e+01   2833687.0      640679.0  0.888    Europe   \n",
       "Germany         8.094000e+01   3874437.0      357114.0  0.916    Europe   \n",
       "Italy           6.066500e+01   2167744.0      301336.0  0.873    Europe   \n",
       "Japan           1.270610e+02   4602367.0      377930.0  0.891      Asia   \n",
       "United Kingdom  6.451100e+01   2950039.0      242495.0  0.907    Europe   \n",
       "United States   3.185230e+02  17348075.0     9525067.0  0.915   America   \n",
       "China           1.400000e+09         NaN           NaN    NaN      Asia   \n",
       "\n",
       "               Language  GDP Per Capita  \n",
       "Canada          English    50339.385908  \n",
       "France          English    44310.284437  \n",
       "Germany         English    47868.013343  \n",
       "Italy           English    35733.025633  \n",
       "Japan           English    36221.712406  \n",
       "United Kingdom  English    45729.239975  \n",
       "United States   English    54464.120330  \n",
       "China               NaN             NaN  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "## Statistical info\n",
    "\n",
    "You've already seen the `describe` method, which gives you a good \"summary\" of the `DataFrame`. Let's explore other methods in more detail:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "        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>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Language</th>\n",
       "      <th>GDP Per Capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>35.467</td>\n",
       "      <td>1785387.0</td>\n",
       "      <td>9984670.0</td>\n",
       "      <td>0.913</td>\n",
       "      <td>America</td>\n",
       "      <td>English</td>\n",
       "      <td>50339.385908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687.0</td>\n",
       "      <td>640679.0</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Europe</td>\n",
       "      <td>English</td>\n",
       "      <td>44310.284437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437.0</td>\n",
       "      <td>357114.0</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "      <td>English</td>\n",
       "      <td>47868.013343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744.0</td>\n",
       "      <td>301336.0</td>\n",
       "      <td>0.873</td>\n",
       "      <td>Europe</td>\n",
       "      <td>English</td>\n",
       "      <td>35733.025633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367.0</td>\n",
       "      <td>377930.0</td>\n",
       "      <td>0.891</td>\n",
       "      <td>Asia</td>\n",
       "      <td>English</td>\n",
       "      <td>36221.712406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Population        GDP  Surface Area    HDI Continent Language  \\\n",
       "Canada       35.467  1785387.0     9984670.0  0.913   America  English   \n",
       "France       63.951  2833687.0      640679.0  0.888    Europe  English   \n",
       "Germany      80.940  3874437.0      357114.0  0.916    Europe  English   \n",
       "Italy        60.665  2167744.0      301336.0  0.873    Europe  English   \n",
       "Japan       127.061  4602367.0      377930.0  0.891      Asia  English   \n",
       "\n",
       "         GDP Per Capita  \n",
       "Canada     50339.385908  \n",
       "France     44310.284437  \n",
       "Germany    47868.013343  \n",
       "Italy      35733.025633  \n",
       "Japan      36221.712406  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "      <th>GDP Per Capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.750001e+08</td>\n",
       "      <td>5.080248e+06</td>\n",
       "      <td>3.061327e+06</td>\n",
       "      <td>0.900429</td>\n",
       "      <td>44952.254576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.949747e+08</td>\n",
       "      <td>5.494020e+06</td>\n",
       "      <td>4.576187e+06</td>\n",
       "      <td>0.016592</td>\n",
       "      <td>6954.983875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.546700e+01</td>\n",
       "      <td>1.785387e+06</td>\n",
       "      <td>2.424950e+05</td>\n",
       "      <td>0.873000</td>\n",
       "      <td>35733.025633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.312950e+01</td>\n",
       "      <td>2.500716e+06</td>\n",
       "      <td>3.292250e+05</td>\n",
       "      <td>0.889500</td>\n",
       "      <td>40265.998421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.272550e+01</td>\n",
       "      <td>2.950039e+06</td>\n",
       "      <td>3.779300e+05</td>\n",
       "      <td>0.907000</td>\n",
       "      <td>45729.239975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.749265e+02</td>\n",
       "      <td>4.238402e+06</td>\n",
       "      <td>5.082873e+06</td>\n",
       "      <td>0.914000</td>\n",
       "      <td>49103.699626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.400000e+09</td>\n",
       "      <td>1.734808e+07</td>\n",
       "      <td>9.984670e+06</td>\n",
       "      <td>0.916000</td>\n",
       "      <td>54464.120330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Population           GDP  Surface Area       HDI  GDP Per Capita\n",
       "count  8.000000e+00  7.000000e+00  7.000000e+00  7.000000        7.000000\n",
       "mean   1.750001e+08  5.080248e+06  3.061327e+06  0.900429    44952.254576\n",
       "std    4.949747e+08  5.494020e+06  4.576187e+06  0.016592     6954.983875\n",
       "min    3.546700e+01  1.785387e+06  2.424950e+05  0.873000    35733.025633\n",
       "25%    6.312950e+01  2.500716e+06  3.292250e+05  0.889500    40265.998421\n",
       "50%    7.272550e+01  2.950039e+06  3.779300e+05  0.907000    45729.239975\n",
       "75%    1.749265e+02  4.238402e+06  5.082873e+06  0.914000    49103.699626\n",
       "max    1.400000e+09  1.734808e+07  9.984670e+06  0.916000    54464.120330"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "population = df['Population']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "population.min(), population.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "population.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "population.sum() / len(population)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "population.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "population.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "population.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "population.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "population.quantile(.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "population.quantile([.2, .4, .6, .8, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![purple-divider](https://user-images.githubusercontent.com/7065401/52071927-c1cd7100-2562-11e9-908a-dde91ba14e59.png)\n"
   ]
  }
 ],
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