{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Processing change over time with pandas\n",
    "\n",
    "Sometimes data is easy to graph and communicate, and sometimes it takes a few extra steps. Let's take a look at `visas-by-continent.csv` in the `data/` folder.\n",
    "\n",
    "**Open it without moving it.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>year</th>\n",
       "      <th>Africa</th>\n",
       "      <th>Asia</th>\n",
       "      <th>Europe</th>\n",
       "      <th>North America</th>\n",
       "      <th>Oceania</th>\n",
       "      <th>South America</th>\n",
       "      <th>Unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1997</td>\n",
       "      <td>534420.0</td>\n",
       "      <td>4779368.0</td>\n",
       "      <td>2290328.0</td>\n",
       "      <td>1934950.0</td>\n",
       "      <td>92818.0</td>\n",
       "      <td>2159310.0</td>\n",
       "      <td>6034989.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1998</td>\n",
       "      <td>568142.0</td>\n",
       "      <td>4095252.0</td>\n",
       "      <td>2198228.0</td>\n",
       "      <td>2455302.0</td>\n",
       "      <td>94106.0</td>\n",
       "      <td>2170502.0</td>\n",
       "      <td>5860927.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1999</td>\n",
       "      <td>599452.0</td>\n",
       "      <td>4254634.0</td>\n",
       "      <td>2163808.0</td>\n",
       "      <td>3332712.0</td>\n",
       "      <td>105130.0</td>\n",
       "      <td>1883214.0</td>\n",
       "      <td>6238484.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>655136.0</td>\n",
       "      <td>4912918.0</td>\n",
       "      <td>2190592.0</td>\n",
       "      <td>4723946.0</td>\n",
       "      <td>105670.0</td>\n",
       "      <td>1652472.0</td>\n",
       "      <td>7184174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001</td>\n",
       "      <td>697870.0</td>\n",
       "      <td>4883414.0</td>\n",
       "      <td>2130078.0</td>\n",
       "      <td>5589696.0</td>\n",
       "      <td>111022.0</td>\n",
       "      <td>1730232.0</td>\n",
       "      <td>7624022.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year    Africa       Asia     Europe  North America   Oceania  \\\n",
       "0  1997  534420.0  4779368.0  2290328.0      1934950.0   92818.0   \n",
       "1  1998  568142.0  4095252.0  2198228.0      2455302.0   94106.0   \n",
       "2  1999  599452.0  4254634.0  2163808.0      3332712.0  105130.0   \n",
       "3  2000  655136.0  4912918.0  2190592.0      4723946.0  105670.0   \n",
       "4  2001  697870.0  4883414.0  2130078.0      5589696.0  111022.0   \n",
       "\n",
       "   South America    Unknown  \n",
       "0      2159310.0  6034989.0  \n",
       "1      2170502.0  5860927.0  \n",
       "2      1883214.0  6238484.0  \n",
       "3      1652472.0  7184174.0  \n",
       "4      1730232.0  7624022.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/visas-by-continent.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking our data\n",
    "\n",
    "- How many columns and rows does it have?\n",
    "- What is the data type of each column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year               int64\n",
       "Africa           float64\n",
       "Asia             float64\n",
       "Europe           float64\n",
       "North America    float64\n",
       "Oceania          float64\n",
       "South America    float64\n",
       "Unknown          float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>year</th>\n",
       "      <th>Africa</th>\n",
       "      <th>Asia</th>\n",
       "      <th>Europe</th>\n",
       "      <th>North America</th>\n",
       "      <th>Oceania</th>\n",
       "      <th>South America</th>\n",
       "      <th>Unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1997-01-01</td>\n",
       "      <td>534420.0</td>\n",
       "      <td>4779368.0</td>\n",
       "      <td>2290328.0</td>\n",
       "      <td>1934950.0</td>\n",
       "      <td>92818.0</td>\n",
       "      <td>2159310.0</td>\n",
       "      <td>6034989.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1998-01-01</td>\n",
       "      <td>568142.0</td>\n",
       "      <td>4095252.0</td>\n",
       "      <td>2198228.0</td>\n",
       "      <td>2455302.0</td>\n",
       "      <td>94106.0</td>\n",
       "      <td>2170502.0</td>\n",
       "      <td>5860927.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1999-01-01</td>\n",
       "      <td>599452.0</td>\n",
       "      <td>4254634.0</td>\n",
       "      <td>2163808.0</td>\n",
       "      <td>3332712.0</td>\n",
       "      <td>105130.0</td>\n",
       "      <td>1883214.0</td>\n",
       "      <td>6238484.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>655136.0</td>\n",
       "      <td>4912918.0</td>\n",
       "      <td>2190592.0</td>\n",
       "      <td>4723946.0</td>\n",
       "      <td>105670.0</td>\n",
       "      <td>1652472.0</td>\n",
       "      <td>7184174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>697870.0</td>\n",
       "      <td>4883414.0</td>\n",
       "      <td>2130078.0</td>\n",
       "      <td>5589696.0</td>\n",
       "      <td>111022.0</td>\n",
       "      <td>1730232.0</td>\n",
       "      <td>7624022.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        year    Africa       Asia     Europe  North America   Oceania  \\\n",
       "0 1997-01-01  534420.0  4779368.0  2290328.0      1934950.0   92818.0   \n",
       "1 1998-01-01  568142.0  4095252.0  2198228.0      2455302.0   94106.0   \n",
       "2 1999-01-01  599452.0  4254634.0  2163808.0      3332712.0  105130.0   \n",
       "3 2000-01-01  655136.0  4912918.0  2190592.0      4723946.0  105670.0   \n",
       "4 2001-01-01  697870.0  4883414.0  2130078.0      5589696.0  111022.0   \n",
       "\n",
       "   South America    Unknown  \n",
       "0      2159310.0  6034989.0  \n",
       "1      2170502.0  5860927.0  \n",
       "2      1883214.0  6238484.0  \n",
       "3      1652472.0  7184174.0  \n",
       "4      1730232.0  7624022.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['year'] = pd.to_datetime(df['year'], format=\"%Y\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year             datetime64[ns]\n",
       "Africa                  float64\n",
       "Asia                    float64\n",
       "Europe                  float64\n",
       "North America           float64\n",
       "Oceania                 float64\n",
       "South America           float64\n",
       "Unknown                 float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.set_index('year', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Africa</th>\n",
       "      <th>Asia</th>\n",
       "      <th>Europe</th>\n",
       "      <th>North America</th>\n",
       "      <th>Oceania</th>\n",
       "      <th>South America</th>\n",
       "      <th>Unknown</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1997-01-01</th>\n",
       "      <td>534420.0</td>\n",
       "      <td>4779368.0</td>\n",
       "      <td>2290328.0</td>\n",
       "      <td>1934950.0</td>\n",
       "      <td>92818.0</td>\n",
       "      <td>2159310.0</td>\n",
       "      <td>6034989.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-01</th>\n",
       "      <td>568142.0</td>\n",
       "      <td>4095252.0</td>\n",
       "      <td>2198228.0</td>\n",
       "      <td>2455302.0</td>\n",
       "      <td>94106.0</td>\n",
       "      <td>2170502.0</td>\n",
       "      <td>5860927.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-01</th>\n",
       "      <td>599452.0</td>\n",
       "      <td>4254634.0</td>\n",
       "      <td>2163808.0</td>\n",
       "      <td>3332712.0</td>\n",
       "      <td>105130.0</td>\n",
       "      <td>1883214.0</td>\n",
       "      <td>6238484.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>655136.0</td>\n",
       "      <td>4912918.0</td>\n",
       "      <td>2190592.0</td>\n",
       "      <td>4723946.0</td>\n",
       "      <td>105670.0</td>\n",
       "      <td>1652472.0</td>\n",
       "      <td>7184174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>697870.0</td>\n",
       "      <td>4883414.0</td>\n",
       "      <td>2130078.0</td>\n",
       "      <td>5589696.0</td>\n",
       "      <td>111022.0</td>\n",
       "      <td>1730232.0</td>\n",
       "      <td>7624022.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Africa       Asia     Europe  North America   Oceania  \\\n",
       "year                                                                  \n",
       "1997-01-01  534420.0  4779368.0  2290328.0      1934950.0   92818.0   \n",
       "1998-01-01  568142.0  4095252.0  2198228.0      2455302.0   94106.0   \n",
       "1999-01-01  599452.0  4254634.0  2163808.0      3332712.0  105130.0   \n",
       "2000-01-01  655136.0  4912918.0  2190592.0      4723946.0  105670.0   \n",
       "2001-01-01  697870.0  4883414.0  2130078.0      5589696.0  111022.0   \n",
       "\n",
       "            South America    Unknown  \n",
       "year                                  \n",
       "1997-01-01      2159310.0  6034989.0  \n",
       "1998-01-01      2170502.0  5860927.0  \n",
       "1999-01-01      1883214.0  6238484.0  \n",
       "2000-01-01      1652472.0  7184174.0  \n",
       "2001-01-01      1730232.0  7624022.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making a simple sample plot\n",
    "\n",
    "I worked on this data a bit for you, so it's **almost** very easy to graph. Just take the dataframe and `.plot()` it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10835ada0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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78m2oK6tLO5xq4f3EOZnDjwfZlpubi759+6J79+5Yu3attMOptGsx1zDiyAhc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\nGk0K8/8Bb8AZjBavdWMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108341940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That x axis looks terrible, and \"year\" isn't really a country. Let's fix that.\n",
    "\n",
    "## Improving our data to improve our plot\n",
    "\n",
    "We can improve the x axis and get rid of the 'year' column if we **switch to a datetime index.** This requires the following steps:\n",
    "\n",
    "- Convert the year into a **datetime column**,\n",
    "- Set it as our index,\n",
    "- and Graph it again\n",
    "\n",
    "Be sure to specify the **format** when converting to a date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How can we compare the change in visas between different continents when their populations are so much different?\n",
    "\n",
    "## Tough comparisons made easy with percent change\n",
    "\n",
    "If we're looking at one set of values that's in the billions and another in the thousands, it's **very tough to see on a graphic.** \n",
    "\n",
    "Instead of looking at raw values, a better way of thinking about this is **percent change.** If one thing goes up 10% and the other goes up 50%, we can make a simple comparison!\n",
    "\n",
    "You can do this in pandas by selecting a column and asking for `.pct_change()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1997-01-01          NaN\n",
       "1998-01-01     1.387662\n",
       "1999-01-01    11.714450\n",
       "2000-01-01     0.513650\n",
       "2001-01-01     5.064824\n",
       "2002-01-01   -15.431176\n",
       "2003-01-01    -2.867185\n",
       "2004-01-01     7.973859\n",
       "2005-01-01    -3.170509\n",
       "2006-01-01     1.843772\n",
       "2007-01-01     2.986427\n",
       "2008-01-01     6.171630\n",
       "2009-01-01   -12.906628\n",
       "2010-01-01     3.324178\n",
       "2011-01-01     2.945117\n",
       "2012-01-01     0.919054\n",
       "2013-01-01     6.650817\n",
       "2014-01-01     6.919938\n",
       "2015-01-01     7.708889\n",
       "2016-01-01    13.681102\n",
       "Name: Oceania, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Oceania'].pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1086f63c8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1nl8M/NV7fhYww3veElgBNAda+M/j/v3ifoR1fb2fP4n79ym1RwHX\n91jgdOBO4KaU8zQAlgOdgcbAXODEuH+/uB9hXV/vtfeAlkHKYzX/EDnnNjrn5nrPt6O9oTpw6GC4\nId7zIWj3V5xzbwLNReQ44CJggnNum3NuKzABGFy0X6REhXh9IXNX40TL4/pe6u3zoXNuFrA/7VT9\ngWXOudVO2/DGcOBvklghXl/Qz2+g+G3BPyIi0gXoC8xAu8OmDoZr4+3WHlibctg6b1v69vXeNuMp\n8PqmXsemIvKWd1ud+MCUrp7rW+dgTrJ/ro0n4PUF7TgzXkRmisi1hZTBJlKLgIgcBfwB+K5zbruI\nZGtVT699+r2hij4ArpwEuL5w4Dp2cs5t9HqcTRaRec65lVGUt9zkcX2zniLDNvv8ekK4vgDnep/f\nY4GJIrLYOTc9nxNYzT9k3sykfwB+65zzxy1s8tMNItIWHdwGWiPqmHJ4B+B9b3unDNsTL6Tr69ew\n8AJ+NXAaJt/rm419frMI6fqmfn4/BP6EptryYsE/fE8Ci5xzP0vZ5g+Gw/v3pZTtVwGIyNnAVu/2\nbzwwSESai0hLYJC3zYRwfUWkhYg08ba3Bs4FFkVf9LJQ3/XNNhgztbY/E+ghIp296zzUO4cJ4fqK\nyBHe3QMiciTwOWBB3iWJuwW8kh7AAKAG7d0wB5iNNtS2AiahrfsTgRYpxzyC9ox4B+iXsv1qYBmw\nFLgq7t+tFB4hXN/TvG3nAPO8c7yDDjqM/feL+5Hv9QWOQ3P7W4HN6EDPo7zXBnv7LwNui/t3K4VH\nWNcX6JpyjvmFXl8b5GWMMQlkaR9jjEkgC/7GGJNAFvyNMSaBLPgbY0wCWfA3xpgEsuBvjDEJZMHf\nGGMSyIK/MUUiIvb/zZQM+zAak4GI/MhfbMP7+S4RuUFEbvZmA50rIsNTXv+TN8PifBH595Tt/xCR\nkSLyBnB2kX8NY7Ky4G9MZk+g86wgIoLOT7MR6Omc649OBHeGiJzn7f8N59yZwJnAd705mQCOBOY5\n585xzr1e1N/AmDrYlM7GZOCcWy0iH4lIH3QFptnozImDRGQ2OtHWkUBPYDpwo4hc6h3ewdv+FroQ\nx9hil9+Y+ljwNya73wDfQIP/k8CFwD3OuV+n7iQiA4ELgLOcc3tEZApwmPfybmcTaJkSZGkfY7J7\nEZ118Qx0Su3xwDe9aXQRkXbeYhrNgS1e4D+Rg3P7tlykKUlW8zcmC+fcPq8Wv8WrvU/0gvsb2gzA\nP4Argb8D14nIXHRa3jdST1PkYhuTE5vS2ZgsvK6Zs4DLnHMr4i6PMWGytI8xGYhIb3QhkokW+E0l\nspq/McYkkNX8jTEmgSz4G2NMAlnwN8aYBLLgb4wxCWTB3xhjEsiCvzHGJND/A7ceLDKUXD9eAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10874d908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Oceania'].pct_change().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's missing the first value because it wasn't changing from anything before! You can also easily **plot the results.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x108901668>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GHiydnDpMa5ssvQa0tgrU0h2yTSVZKnTaBSeQqFBKQT98C4cLLkNFwSWROTJ9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\nvBO/+tWvQrmcaJzq7McUnQJqWWKtboo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ZAqlUCqfTiY0bN2LhwoXQ6XQhGc5gMBiM4Akp\nvFNVVYVly5YBAJYvX47KyspR50gkEkilwneLy+UK5XIMBoPBCJGQVvq9vb2DK3adTgeLxeLzvK6u\nLjz++OMwmUy4/fbb2SqfwWAwosS4Tv+RRx5Bb2/v4GNKKQghuPXWWwO+SGZmJp588kn09PTgiSee\nwKJFi5CamjoxixkMBoMxYcZ1+ps3b/Z7TKfToaenZ/D/tLS0McfS6XQoLCzEiRMncMUVV4w6XldX\nh7q6usHH69evR35+/ngmxh1arTbaJiQ0bH7DD5vj8CLG/G7btm3wZ4PBAIPBACDEmP6CBQuwb98+\nAMC+fftQUVEx6hyz2TwYy7darTh16pRfR24wGLB+/frBf0ONDoVYGudnP/uZCJbE1nuKJVvY/IZ/\nHDHmOJbeUyzZAog3v0N96YDDB0KM6a9btw7PPPMM9u7di6ysLGzcuBEA0NjYiF27duHuu+9Gc3Mz\n/vKXv4AQAkopbrnlFkyaNCmg8YcaGgqxNE52drYIlsTWe4olW9j8hn8cMeY4lt5TLNkChH9+CaWU\nhnwFRsAMfAMzwgOb3/DD5ji8hHt+WUVuhBFrNcDwDZvf8MPmOLyEe37ZSp/BYDCSCLbSZzAYjCSC\nOX0Gg8FIIkLK3mEIdHV14fnnn0dPTw84jsOqVatwww03jClI96c//f/27i0kqq6P4/h3z6RNTsk0\nlhRJmWgU0XiTeSGOZRGkQQcw8cYsIqiRoAOB3UR0QkKFoiSrSTHqIsiw7qKLKCswbJsRZpZYImk6\nmacxPKz3YnRefZ98eHycnOmd/+dKNu7FXj83f/cs9vyXE13XmT17Ng6Hg+joaMDz6mtFRQUAO3fu\n9La5CGbTzffgwYMsX74cgMzMTKKjo1FKsWDBAo4fP+7PqQWEqebb2trKlStXaGpqIisri61bt3rH\n0nWd0tJSlFJs2LCB7du3+3FmgcGX+TocDsLCwtA0DaPRyPnz56d+QUpM2/fv31VTU5NSSim3260O\nHTqkWlpaVHl5ubp//75SSqmKigp169YtpZRSNTU16ty5c0oppRoaGtSJEyeUUkr19PSo3Nxc1dfX\np3p7e70/Bztf5auUUtnZ2TN78X+Aqeb748cP9fHjR3Xnzh314MED7zjDw8MqNzdXtbe3q8HBQXXs\n2DHV0tIy4/MJNL7KVymlHA6H6unpmdb1yPKOD1gsFu+TuslkYsmSJXR2dv6lId2rV68AqK6u9h6P\ni4ujv7+frq4uamtrsdlshIWFYTabsdls6LrulzkFEl/lC542ImKif5rvWEPF8PBwYmJiMBqNE8Zp\nbGxk8eLFLFy4kFmzZpGUlPTLJozBxlf5guf+ne49LMs7Ptbe3k5zczMrVqz4S0O6sR5GLpeLiIgI\n7zlWqxWXyzXpcfFf08nXYrEwODhIXl4eRqORbdu2kZCQ4Jd5BKq/y3eyhopjfpV7Y2Pjb73eP810\n8gXQNI2zZ8+iaRobN25k06ZNU74GKfo+NDAwQGFhITk5OZhMpimdO/aNZTG56eYLUFxcjMViob29\nnVOnTrFs2TIiIyN/x+X+caaT72TGche+yffMmTPefxCnT58mKiqKlStXTmkMWd7xkeHhYQoKCrDb\n7d6nx7FGdMCEhnRWq5XOzk7vuZ2dncyfP5+IiAg6OjomHLdarTM4i8Dli3zHzgGIjIxk9erVNDU1\nzeQ0AtZU8p2M1WqdcP+6XC5v7sHOF/mOnQOeJaB169b9q09SUvR9pLi4mKioKNLS0rzHJmtIt3bt\nWp48eQJAQ0MDZrMZi8VCfHw8dXV19Pf309vbS11dHfHx8TM+l0Dki3z7+voYGhoCoLu7m/fv3xMV\nFTWzEwlQU8l3vPGfTmNjY/n69Svfvn1jaGiIqqqqX54TjHyR78+fPxkYGAA8nxrevHnzj/uYjSff\nyPWB+vp6Tp48ydKlS9E0DU3TyMrKIjY2lqKiIjo6OrwN6cxmMwA3btxA13VMJhMHDhwgJiYG8Pzx\n7927h6Zp8srmqOnmO/bKZkNDAyUlJRgMBpRSpKens379ev9OLgBMNd+uri7y8vJwu91omobJZKKo\nqAiTyYSu69y8eROlFKmpqfLKJr7Lt7u7mwsXLqBpGsPDwyQnJ/+rfKXoCyFEEJHlHSGECCJS9IUQ\nIohI0RdCiCAiRV8IIYKIFH0hhAgiUvSFECKISNEXQoggIkVfCCGCiBR9IWbIyMiIvy9BCOmyKQRA\nZWUlHz584OjRo95jTqcTo9FIRkYGZWVl6LqOwWAgJSWFzMxMNE2jra2Nq1ev0tzcjKZp2Gw29u3b\n593By+FwsHnzZp49e0Zrayvl5eUYDPKsJfxH7j4hALvdTm1tLf39/YDnqfzFixfY7XYuX75MSEgI\nly5dIj8/n7q6Oh4/fgx4GmLt2LGDkpISioqKcLlc3L17d8LYz58/Jy8vj9LSUin4wu/kDhQCT8va\nVatW8fLlSwBev35NeHg4VqsVXdfZvXs3oaGhhIeHk5aWRlVVFQCLFi1izZo1GI1G5s2bR3p6Ou/e\nvZsw9pYtW7BarYSEhMz4vIT4X7K8I8SolJQUHj16RGpqKk+fPiU5OdnbJnj//v3e31Ojm6qDp0Wz\n0+mkvr6egYEBRkZGmDt37oRxx+8mJYS/SdEXYlRCQgLXr1/ny5cv1NTUkJ2djcFgIDQ0FKfT+ctd\noG7fvo3BYKCgoACz2Ux1dTVOp3PC78juUSKQyPKOEKNCQkJITEzk4sWLxMXFYbVasVgs2Gw2ysrK\ncLvdKKVoa2vzLuG43W5MJhNz5szB5XJRWVnp51kI8fek6AsxTkpKCp8/f8Zut3uP5ebmMjQ0xJEj\nR9i7dy+FhYXebe4yMjL49OkTe/bsIT8/n8TExAnjyVO+CDSyiYoQ43R0dHD48GGuXbvms83BhQgk\n8qQvxKiRkREePnxIUlKSFHzxf0uKvhB4Np3Oycnh7du37Nq1y9+XI8RvI8s7QggRRORJXwghgogU\nfSGECCJS9IUQIohI0RdCiCAiRV8IIYKIFH0hhAgi/wFAplDQu5N7WgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1088f9710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Oceania'].pct_change().plot()\n",
    "df['Asia'].pct_change().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Africa</th>\n",
       "      <th>Asia</th>\n",
       "      <th>Europe</th>\n",
       "      <th>North America</th>\n",
       "      <th>Oceania</th>\n",
       "      <th>South America</th>\n",
       "      <th>Unknown</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1997-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-01</th>\n",
       "      <td>0.063100</td>\n",
       "      <td>-0.143139</td>\n",
       "      <td>-0.040213</td>\n",
       "      <td>0.268923</td>\n",
       "      <td>0.013877</td>\n",
       "      <td>0.005183</td>\n",
       "      <td>-0.028842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-01</th>\n",
       "      <td>0.055109</td>\n",
       "      <td>0.038919</td>\n",
       "      <td>-0.015658</td>\n",
       "      <td>0.357353</td>\n",
       "      <td>0.117144</td>\n",
       "      <td>-0.132360</td>\n",
       "      <td>0.064419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>0.092892</td>\n",
       "      <td>0.154722</td>\n",
       "      <td>0.012378</td>\n",
       "      <td>0.417448</td>\n",
       "      <td>0.005136</td>\n",
       "      <td>-0.122526</td>\n",
       "      <td>0.151590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>0.065229</td>\n",
       "      <td>-0.006005</td>\n",
       "      <td>-0.027624</td>\n",
       "      <td>0.183268</td>\n",
       "      <td>0.050648</td>\n",
       "      <td>0.047057</td>\n",
       "      <td>0.061225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Africa      Asia    Europe  North America   Oceania  \\\n",
       "year                                                                \n",
       "1997-01-01       NaN       NaN       NaN            NaN       NaN   \n",
       "1998-01-01  0.063100 -0.143139 -0.040213       0.268923  0.013877   \n",
       "1999-01-01  0.055109  0.038919 -0.015658       0.357353  0.117144   \n",
       "2000-01-01  0.092892  0.154722  0.012378       0.417448  0.005136   \n",
       "2001-01-01  0.065229 -0.006005 -0.027624       0.183268  0.050648   \n",
       "\n",
       "            South America   Unknown  \n",
       "year                                 \n",
       "1997-01-01            NaN       NaN  \n",
       "1998-01-01       0.005183 -0.028842  \n",
       "1999-01-01      -0.132360  0.064419  \n",
       "2000-01-01      -0.122526  0.151590  \n",
       "2001-01-01       0.047057  0.061225  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pct_change().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1090a99e8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jjygmJsbsSPBChYWFKisrMzsGAACA16rtLlEWdxPdUmxojrKi79TFrgNmRwHg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u/Adjqk8EiJIRrDEA53C0OKqpGwWkjYbAQL8tPRQqfIz20gSE2DbevRXn0G/B2IS65C3Pq1YxZB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\n2B5/hIQGClVELreOK693YDJfoJ/XtxmzRWHabAsNM8x0Hcmxt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D8fkgR2UMgx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\n+n0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LJvc7aMJVhJx3Ek7/KAg1BIJj1qexRKigdLGBvdtbWLHWMiDD6OGmuSHO8YMhylaa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\nInIBPDw86N27N7179+bEiRPs27ePTZs2UVZWZ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6tjmiZvvfUWN9xwg/aSERERERERERFx\nYmfdo2jhwoUUFRXZX5umiWEYzJw585we8J///IcBAwYQGBh44VGKiIiIiIiIiEizO2ui6KGHHjrt\nNX9/fwoLC+3/9fPzq1cnLS2N1NRU1q5dS3l5OTabDQ8PD6699tp6dffs2cOePXvsr2fMmEFYWNi5\n9kXEKemEPhFxJhqTRMTZaFwSEWfTXsal5cuX279OSEggISEBaOTSs8TERDZs2ADAhg0bGDhwYL06\nd9xxBy+88ALPP/88N9xwA6NGjWowSfRDYDNmzLD/+2nQbZ362jY98sgjjg6hRbSnn6n62ja1l762\nlzEJ2s/PFNTXtqi99BM0LrVV7aWv7aWf0L762l7GpeXLl9fJv/yQJIJGJoqmTp3K7t27ufPOO9m9\nezdTp04FTm5evXTp0sZFDXUCbevU17YpODjY0SG0iPb0M1Vf26b20tf2MiZB+/mZgvraFrWXfoLG\npbaqvfS1vfQT2ldf28u4dKafqWFqh2mRZvNDllZExBloTBIRZ6NxSUScjcalRs4oEpEza0+ZdxFx\nfhqTRMTZaFwSEWejcUkzikRERERERERE5BTNKBIREREREREREUCJIhEREREREREROcXF0QGItCZ5\neXk8//zzFBYWYrFYuOSSS5gwYQIlJSU8/fTT5OTkEBISwrx58/Dy8gLg9ddfZ+fOnbi7u3P77bfT\nvXt3ADZs2MCqVasAuOqqqxg9erSjuiUirVRjx6Tf/e53REZGAnDNNdfQvXt3TNMkKCiIe++915Fd\nE5FW6nzHpczMTF588UUOHjzIrFmzmDhxor2tnTt38sYbb2CaJmPHjrWfsCwicj6acly6/fbb8fLy\nwjAMrFYrjz32mAN71nyUKBI5D1arlRtvvJHu3btTUVHBfffdR79+/Vi/fj19+vRhypQprF69mlWr\nVnHdddexY8cOsrOzefbZZ9m3bx+vvPIKjz76KCUlJXzwwQcsWrQI0zT54x//yKBBg+y/yImInIvG\njkmvvvoqjz76KAAeHh4sWrTIwT0SkdbufMclb29vfvOb37Bly5Y67dTW1vLaa6/x8MMPExAQwIIF\nCxg0aBDh4eEO6pmItFZNNS4BGIbBI488gre3twN60nK09EzkPPj7+9tnBHl4eBAeHk5eXh7btm2z\nzwgaM2YM27ZtA2Dr1q328p49e1JWVkZhYSHffvstffv2xcvLiw4dOtC3b1927tzpkD6JSOvVVGMS\ngM62EJGmcK7j0tatWwHw9fUlKioKq9Vap5309HQ6d+5McHAwLi4uDB8+3H6PiMj5aKpxCU5+XmoP\nn5k0o0jkAh0/fpzDhw8TExNDUVER/v7+wMmBqKioCID8/Hw6duxovycwMJD8/PzTlouIXKjGjEn+\n/v5UV1ezYMECrFYrU6ZMYdCgQQ7ph4i0HWcal4qLi894b0PjVXp6erPGKyJtX2PGJTg5o+jRRx/F\nMAwuueQSLr300uYO2SGUKBK5ABUVFTz11FPcdNNNeHh4nNe9hmG0iyy0iLScxo5JAC+99BL+/v4c\nP36cP/3pT3Tr1o2QkJDmCFdE2oHGjEun88N4JSJyIZpiXPrLX/5iTyotXLiQiIgIevXq1cSROp6W\nnomcJ5vNxt/+9jdGjRpl/4u7v7+/fflGYWEhfn5+wMm/fuXl5dnvzcvLIyAggI4dO5Kbm1unPDAw\nsAV7ISJtRVOMST/cAxASEkJCQgIHDx5syW6ISBtyPuPS6QQGBtb5rJSfn28fr0REzldTjEs/3AMn\nl6cNHjy4zc50VKJI5Dy99NJLREREMGHCBHtZYmIiGzZsAE6eZjZw4EAABg4cyMaNGwFIS0ujQ4cO\n+Pv7069fP3bv3k1ZWRklJSXs3r2bfv36tXhfRKT1a4oxqbS0lJqaGgCKi4tJTU0lIiKiZTsiIm3G\n+YxLP/XTGdfR0dFkZWWRk5NDTU0NX375ZYP3iIici6YYlyorK6moqABOzk7atWsXXbp0ad7AHcQw\ntQZG5Jzt3buXRx55hK5du2IYBoZhMGvWLKKjo1myZAm5ubkEBQVx11130aFDBwBee+01du7ciYeH\nB7fddhtRUVHAycHoww8/xDAMrrrqKvtGaiIi56qxY9Lvfvc7IiMjSUtL4+9//zsWiwXTNLnyyisZ\nM2aMYzsnIq3S+Y5LhYWFLFiwgPLycgzDwMPDgyVLluDh4cHOnTv5xz/+gWmajBs3jqlTpzq6eyLS\nCjXVuFRcXMzixYsxDAObzcbIkSPb7LikRJGIiIiIiIiIiABaeiYiIiIiIiIiIqcoUSQiIiIiIiIi\nIoASRSIiIiIiIiIicooSRSIiIiIiIiIiAihRJCIiIiIiIiIipyhRJCIiIiIiIiIigBJFIiIiIiIi\nIiJyihJFIiIiIiIiIiICKFEkIiIi4hC1tbWODkFERESkHhdHByAiIiLibD766CP27dvH3XffbS97\n/fXXsVqtXH311bz55pvs3LkTi8XC6NGjueaaazAMg+zsbJYuXcrhw4cxDIO+ffsye/ZsvLy8ALj9\n9tu5/PLL2bRpE5mZmbz11ltYLPq7nYiIiDgPfTIRERER+ZlRo0bx7bffUlZWBpyc/bN582ZGjRrF\nCy+8gKurK8899xyLFi1i9+7drFu3DgDTNJk2bRp///vfWbJkCfn5+axYsaJO21999RULFizgjTfe\nUJJIREREnI4+nYiIiIj8jL+/P3FxcXz99dcA7NixA19f3//fvt2yxKJFYQB+HZlhxA9kjsE/YNQm\nE0QHbGrW6FcwWfRfGDRYlQGLP0BsRjEJYhAxGTSJImIZhWHmFBG818vlhKvCfZ642Xuxdn1ZK5VK\nJefn51lYWEipVEpfX1+mp6dzcnKSJBkcHMzw8HA6OzvT29ubmZmZXF5efqg9NTWVSqWSYrH45f8C\nAPg3Vs8AAD5Rq9VydHSUycnJHB8fZ3x8PPf392k2m1lZWXm/1263MzAwkCR5fn5OvV7P1dVVXl5e\n0mq10tPT86Hur1+/vvQfAAB/QlAEAPCJ0dHR7O7u5vb2NmdnZ5mfn0+hUEipVEq9Xk9HR8ff3uzv\n76dQKGRzczPd3d05PT1NvV7/cOezdwAAP4XVMwCATxSLxVSr1Wxvb2doaCiVSiX9/f0ZGRnJ3t5e\nGo1G2u127u7u3tfLGo1GyuVyurq68vj4mIODg2/+BQDAnxEUAQD8g1qtlpubm0xMTLyfra6uptls\nZn19PcvLy9na2srT01OSZHZ2NtfX11laWsrGxkaq1eqHeqaJAICfrqPdbre/uwkAgJ/o4eEha2tr\n2dnZSblc/u52AAD+cyaKAAA+0Wq1cnh4mLGxMSERAPC/ISgCAPiL19fXLC4u5uLiInNzc9/dDgDA\nl7F6BgAAAEASE0UAAAAAvBEUAQAAAJBEUAQAAADAG0ERAAAAAEkERQAAAAC8ERQBAAAAkCT5DXlQ\n+tWRFgb6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10901c278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.pct_change().plot(figsize=(20, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Friendly, friendly data\n",
    "\n",
    "If we look at our data, **every column is a category we'd like to calculate percent change on**. We can actually use `df.pct_change()` to do it for every single column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's graph it, and use `figsize` to make it big enough that the legend doesn't get in the way of everything."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Long vs. wide data\n",
    "\n",
    "Using `pct_change` is easy when we have our set of data as a column. This makes a lot of other calculations more difficult, though. **Different analysis and graphing approaches require different formats of data**.\n",
    "\n",
    "The current format - where a column is a list of observations - is called **wide format.** The more subjects we have, the more columns we have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Africa</th>\n",
       "      <th>Asia</th>\n",
       "      <th>Europe</th>\n",
       "      <th>North America</th>\n",
       "      <th>Oceania</th>\n",
       "      <th>South America</th>\n",
       "      <th>Unknown</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>534420.0</td>\n",
       "      <td>4779368.0</td>\n",
       "      <td>2290328.0</td>\n",
       "      <td>1934950.0</td>\n",
       "      <td>92818.0</td>\n",
       "      <td>2159310.0</td>\n",
       "      <td>6034989.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>568142.0</td>\n",
       "      <td>4095252.0</td>\n",
       "      <td>2198228.0</td>\n",
       "      <td>2455302.0</td>\n",
       "      <td>94106.0</td>\n",
       "      <td>2170502.0</td>\n",
       "      <td>5860927.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>599452.0</td>\n",
       "      <td>4254634.0</td>\n",
       "      <td>2163808.0</td>\n",
       "      <td>3332712.0</td>\n",
       "      <td>105130.0</td>\n",
       "      <td>1883214.0</td>\n",
       "      <td>6238484.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>655136.0</td>\n",
       "      <td>4912918.0</td>\n",
       "      <td>2190592.0</td>\n",
       "      <td>4723946.0</td>\n",
       "      <td>105670.0</td>\n",
       "      <td>1652472.0</td>\n",
       "      <td>7184174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>697870.0</td>\n",
       "      <td>4883414.0</td>\n",
       "      <td>2130078.0</td>\n",
       "      <td>5589696.0</td>\n",
       "      <td>111022.0</td>\n",
       "      <td>1730232.0</td>\n",
       "      <td>7624022.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Africa       Asia     Europe  North America   Oceania  South America  \\\n",
       "year                                                                           \n",
       "1997  534420.0  4779368.0  2290328.0      1934950.0   92818.0      2159310.0   \n",
       "1998  568142.0  4095252.0  2198228.0      2455302.0   94106.0      2170502.0   \n",
       "1999  599452.0  4254634.0  2163808.0      3332712.0  105130.0      1883214.0   \n",
       "2000  655136.0  4912918.0  2190592.0      4723946.0  105670.0      1652472.0   \n",
       "2001  697870.0  4883414.0  2130078.0      5589696.0  111022.0      1730232.0   \n",
       "\n",
       "        Unknown  \n",
       "year             \n",
       "1997  6034989.0  \n",
       "1998  5860927.0  \n",
       "1999  6238484.0  \n",
       "2000  7184174.0  \n",
       "2001  7624022.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wide_df = pd.read_csv(\"data/visas-by-continent.csv\")\n",
    "wide_df.set_index('year', inplace=True)\n",
    "wide_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1997-01-01    17826183.00\n",
       "1998-01-01    17442459.00\n",
       "1999-01-01    18577434.00\n",
       "2000-01-01    21424908.00\n",
       "2001-01-01    22766334.00\n",
       "2002-01-01    17308311.00\n",
       "2003-01-01    14644902.00\n",
       "2004-01-01    15147297.00\n",
       "2005-01-01    16166853.00\n",
       "2006-01-01    17510190.00\n",
       "2007-01-01    19332855.00\n",
       "2008-01-01    19809228.00\n",
       "2009-01-01    17412546.00\n",
       "2010-01-01    19268253.00\n",
       "2011-01-01    22523817.00\n",
       "2012-01-01    26781270.00\n",
       "2013-01-01    27493047.00\n",
       "2014-01-01    29797440.00\n",
       "2015-01-01    32675235.72\n",
       "2016-01-01    31144473.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The other option is called **long data**, where every row is an observation with a category variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>continent</th>\n",
       "      <th>visas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1997</td>\n",
       "      <td>Africa</td>\n",
       "      <td>534420.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1998</td>\n",
       "      <td>Africa</td>\n",
       "      <td>568142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1999</td>\n",
       "      <td>Africa</td>\n",
       "      <td>599452.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>Africa</td>\n",
       "      <td>655136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001</td>\n",
       "      <td>Africa</td>\n",
       "      <td>697870.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year continent     visas\n",
       "0  1997    Africa  534420.0\n",
       "1  1998    Africa  568142.0\n",
       "2  1999    Africa  599452.0\n",
       "3  2000    Africa  655136.0\n",
       "4  2001    Africa  697870.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_df = pd.read_csv(\"data/visas-by-continent-long.csv\")\n",
    "long_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting long data to wide data\n",
    "\n",
    "You can convert from long data to wide data using `.pivot`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>continent</th>\n",
       "      <th>visas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1997</td>\n",
       "      <td>Africa</td>\n",
       "      <td>534420.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1998</td>\n",
       "      <td>Africa</td>\n",
       "      <td>568142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1999</td>\n",
       "      <td>Africa</td>\n",
       "      <td>599452.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>Africa</td>\n",
       "      <td>655136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001</td>\n",
       "      <td>Africa</td>\n",
       "      <td>697870.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year continent     visas\n",
       "0  1997    Africa  534420.0\n",
       "1  1998    Africa  568142.0\n",
       "2  1999    Africa  599452.0\n",
       "3  2000    Africa  655136.0\n",
       "4  2001    Africa  697870.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reminder of what long data looks like\n",
    "long_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>continent</th>\n",
       "      <th>Africa</th>\n",
       "      <th>Asia</th>\n",
       "      <th>Europe</th>\n",
       "      <th>North America</th>\n",
       "      <th>Oceania</th>\n",
       "      <th>South America</th>\n",
       "      <th>Unknown</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>534420.0</td>\n",
       "      <td>4779368.0</td>\n",
       "      <td>2290328.0</td>\n",
       "      <td>1934950.0</td>\n",
       "      <td>92818.0</td>\n",
       "      <td>2159310.0</td>\n",
       "      <td>6034989.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>568142.0</td>\n",
       "      <td>4095252.0</td>\n",
       "      <td>2198228.0</td>\n",
       "      <td>2455302.0</td>\n",
       "      <td>94106.0</td>\n",
       "      <td>2170502.0</td>\n",
       "      <td>5860927.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>599452.0</td>\n",
       "      <td>4254634.0</td>\n",
       "      <td>2163808.0</td>\n",
       "      <td>3332712.0</td>\n",
       "      <td>105130.0</td>\n",
       "      <td>1883214.0</td>\n",
       "      <td>6238484.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>655136.0</td>\n",
       "      <td>4912918.0</td>\n",
       "      <td>2190592.0</td>\n",
       "      <td>4723946.0</td>\n",
       "      <td>105670.0</td>\n",
       "      <td>1652472.0</td>\n",
       "      <td>7184174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>697870.0</td>\n",
       "      <td>4883414.0</td>\n",
       "      <td>2130078.0</td>\n",
       "      <td>5589696.0</td>\n",
       "      <td>111022.0</td>\n",
       "      <td>1730232.0</td>\n",
       "      <td>7624022.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "continent    Africa       Asia     Europe  North America   Oceania  \\\n",
       "year                                                                 \n",
       "1997       534420.0  4779368.0  2290328.0      1934950.0   92818.0   \n",
       "1998       568142.0  4095252.0  2198228.0      2455302.0   94106.0   \n",
       "1999       599452.0  4254634.0  2163808.0      3332712.0  105130.0   \n",
       "2000       655136.0  4912918.0  2190592.0      4723946.0  105670.0   \n",
       "2001       697870.0  4883414.0  2130078.0      5589696.0  111022.0   \n",
       "\n",
       "continent  South America    Unknown  \n",
       "year                                 \n",
       "1997           2159310.0  6034989.0  \n",
       "1998           2170502.0  5860927.0  \n",
       "1999           1883214.0  6238484.0  \n",
       "2000           1652472.0  7184174.0  \n",
       "2001           1730232.0  7624022.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn the year into an index\n",
    "# And make columns out of all of the values in 'continent'\n",
    "# and turn the visas column into the cells\n",
    "long_df.pivot(index='year', columns='continent', values='visas').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting wide data to long data\n",
    "\n",
    "You can convert from wide data to long data using `.melt`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\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>Africa</th>\n",
       "      <th>Asia</th>\n",
       "      <th>Europe</th>\n",
       "      <th>North America</th>\n",
       "      <th>Oceania</th>\n",
       "      <th>South America</th>\n",
       "      <th>Unknown</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>534420.0</td>\n",
       "      <td>4779368.0</td>\n",
       "      <td>2290328.0</td>\n",
       "      <td>1934950.0</td>\n",
       "      <td>92818.0</td>\n",
       "      <td>2159310.0</td>\n",
       "      <td>6034989.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>568142.0</td>\n",
       "      <td>4095252.0</td>\n",
       "      <td>2198228.0</td>\n",
       "      <td>2455302.0</td>\n",
       "      <td>94106.0</td>\n",
       "      <td>2170502.0</td>\n",
       "      <td>5860927.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Africa       Asia     Europe  North America  Oceania  South America  \\\n",
       "year                                                                          \n",
       "1997  534420.0  4779368.0  2290328.0      1934950.0  92818.0      2159310.0   \n",
       "1998  568142.0  4095252.0  2198228.0      2455302.0  94106.0      2170502.0   \n",
       "\n",
       "        Unknown  \n",
       "year             \n",
       "1997  6034989.0  \n",
       "1998  5860927.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's remind ourselves what it looks like\n",
    "wide_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr 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>year</th>\n",
       "      <th>Africa</th>\n",
       "      <th>Asia</th>\n",
       "      <th>Europe</th>\n",
       "      <th>North America</th>\n",
       "      <th>Oceania</th>\n",
       "      <th>South America</th>\n",
       "      <th>Unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1997</td>\n",
       "      <td>534420.0</td>\n",
       "      <td>4779368.0</td>\n",
       "      <td>2290328.0</td>\n",
       "      <td>1934950.0</td>\n",
       "      <td>92818.0</td>\n",
       "      <td>2159310.0</td>\n",
       "      <td>6034989.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1998</td>\n",
       "      <td>568142.0</td>\n",
       "      <td>4095252.0</td>\n",
       "      <td>2198228.0</td>\n",
       "      <td>2455302.0</td>\n",
       "      <td>94106.0</td>\n",
       "      <td>2170502.0</td>\n",
       "      <td>5860927.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year    Africa       Asia     Europe  North America  Oceania  \\\n",
       "0  1997  534420.0  4779368.0  2290328.0      1934950.0  92818.0   \n",
       "1  1998  568142.0  4095252.0  2198228.0      2455302.0  94106.0   \n",
       "\n",
       "   South America    Unknown  \n",
       "0      2159310.0  6034989.0  \n",
       "1      2170502.0  5860927.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bring the year out as a real column again\n",
    "wide_df.head(2).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr 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>year</th>\n",
       "      <th>continent</th>\n",
       "      <th>visas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1997</td>\n",
       "      <td>Africa</td>\n",
       "      <td>534420.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1998</td>\n",
       "      <td>Africa</td>\n",
       "      <td>568142.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1999</td>\n",
       "      <td>Africa</td>\n",
       "      <td>599452.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>Africa</td>\n",
       "      <td>655136.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001</td>\n",
       "      <td>Africa</td>\n",
       "      <td>697870.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2002</td>\n",
       "      <td>Africa</td>\n",
       "      <td>498682.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2003</td>\n",
       "      <td>Africa</td>\n",
       "      <td>478112.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2004</td>\n",
       "      <td>Africa</td>\n",
       "      <td>501824.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2005</td>\n",
       "      <td>Africa</td>\n",
       "      <td>505656.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2006</td>\n",
       "      <td>Africa</td>\n",
       "      <td>509844.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2007</td>\n",
       "      <td>Africa</td>\n",
       "      <td>553236.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2008</td>\n",
       "      <td>Africa</td>\n",
       "      <td>624568.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2009</td>\n",
       "      <td>Africa</td>\n",
       "      <td>582636.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2010</td>\n",
       "      <td>Africa</td>\n",
       "      <td>607702.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2011</td>\n",
       "      <td>Africa</td>\n",
       "      <td>651340.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2012</td>\n",
       "      <td>Africa</td>\n",
       "      <td>774964.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2013</td>\n",
       "      <td>Africa</td>\n",
       "      <td>807014.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014</td>\n",
       "      <td>Africa</td>\n",
       "      <td>939464.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2015</td>\n",
       "      <td>Africa</td>\n",
       "      <td>987590.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2016</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1102308.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1997</td>\n",
       "      <td>Asia</td>\n",
       "      <td>4779368.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1998</td>\n",
       "      <td>Asia</td>\n",
       "      <td>4095252.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1999</td>\n",
       "      <td>Asia</td>\n",
       "      <td>4254634.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2000</td>\n",
       "      <td>Asia</td>\n",
       "      <td>4912918.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2001</td>\n",
       "      <td>Asia</td>\n",
       "      <td>4883414.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2002</td>\n",
       "      <td>Asia</td>\n",
       "      <td>3709890.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2003</td>\n",
       "      <td>Asia</td>\n",
       "      <td>3409702.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2004</td>\n",
       "      <td>Asia</td>\n",
       "      <td>3679070.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2005</td>\n",
       "      <td>Asia</td>\n",
       "      <td>4059790.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2006</td>\n",
       "      <td>Asia</td>\n",
       "      <td>4491056.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>2007</td>\n",
       "      <td>South America</td>\n",
       "      <td>1972496.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>2008</td>\n",
       "      <td>South America</td>\n",
       "      <td>2205672.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>2009</td>\n",
       "      <td>South America</td>\n",
       "      <td>2312754.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>2010</td>\n",
       "      <td>South America</td>\n",
       "      <td>2515038.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>2011</td>\n",
       "      <td>South America</td>\n",
       "      <td>3397106.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>2012</td>\n",
       "      <td>South America</td>\n",
       "      <td>4353292.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>2013</td>\n",
       "      <td>South America</td>\n",
       "      <td>4541802.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>2014</td>\n",
       "      <td>South America</td>\n",
       "      <td>4686358.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>2015</td>\n",
       "      <td>South America</td>\n",
       "      <td>4345696.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>2016</td>\n",
       "      <td>South America</td>\n",
       "      <td>3515940.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>1997</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>6034989.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>1998</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>5860927.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>1999</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>6238484.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>2000</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>7184174.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2001</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>7624022.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>2002</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>5796777.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>2003</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>4897012.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>2004</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>5057073.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2005</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>5395153.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2006</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>5842172.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2007</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>6450295.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2008</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>6609864.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2009</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>5809512.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>2010</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>6428425.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>2011</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>7514001.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>2012</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>8933756.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>2013</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>9172705.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>2014</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>9941932.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>2015</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>10898813.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>2016</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>10388767.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year      continent        visas\n",
       "0    1997         Africa    534420.00\n",
       "1    1998         Africa    568142.00\n",
       "2    1999         Africa    599452.00\n",
       "3    2000         Africa    655136.00\n",
       "4    2001         Africa    697870.00\n",
       "5    2002         Africa    498682.00\n",
       "6    2003         Africa    478112.00\n",
       "7    2004         Africa    501824.00\n",
       "8    2005         Africa    505656.00\n",
       "9    2006         Africa    509844.00\n",
       "10   2007         Africa    553236.00\n",
       "11   2008         Africa    624568.00\n",
       "12   2009         Africa    582636.00\n",
       "13   2010         Africa    607702.00\n",
       "14   2011         Africa    651340.00\n",
       "15   2012         Africa    774964.00\n",
       "16   2013         Africa    807014.00\n",
       "17   2014         Africa    939464.00\n",
       "18   2015         Africa    987590.00\n",
       "19   2016         Africa   1102308.00\n",
       "20   1997           Asia   4779368.00\n",
       "21   1998           Asia   4095252.00\n",
       "22   1999           Asia   4254634.00\n",
       "23   2000           Asia   4912918.00\n",
       "24   2001           Asia   4883414.00\n",
       "25   2002           Asia   3709890.00\n",
       "26   2003           Asia   3409702.00\n",
       "27   2004           Asia   3679070.00\n",
       "28   2005           Asia   4059790.00\n",
       "29   2006           Asia   4491056.00\n",
       "..    ...            ...          ...\n",
       "110  2007  South America   1972496.00\n",
       "111  2008  South America   2205672.00\n",
       "112  2009  South America   2312754.00\n",
       "113  2010  South America   2515038.00\n",
       "114  2011  South America   3397106.00\n",
       "115  2012  South America   4353292.00\n",
       "116  2013  South America   4541802.00\n",
       "117  2014  South America   4686358.00\n",
       "118  2015  South America   4345696.00\n",
       "119  2016  South America   3515940.00\n",
       "120  1997        Unknown   6034989.00\n",
       "121  1998        Unknown   5860927.00\n",
       "122  1999        Unknown   6238484.00\n",
       "123  2000        Unknown   7184174.00\n",
       "124  2001        Unknown   7624022.00\n",
       "125  2002        Unknown   5796777.00\n",
       "126  2003        Unknown   4897012.00\n",
       "127  2004        Unknown   5057073.00\n",
       "128  2005        Unknown   5395153.00\n",
       "129  2006        Unknown   5842172.00\n",
       "130  2007        Unknown   6450295.00\n",
       "131  2008        Unknown   6609864.00\n",
       "132  2009        Unknown   5809512.00\n",
       "133  2010        Unknown   6428425.00\n",
       "134  2011        Unknown   7514001.00\n",
       "135  2012        Unknown   8933756.00\n",
       "136  2013        Unknown   9172705.00\n",
       "137  2014        Unknown   9941932.00\n",
       "138  2015        Unknown  10898813.24\n",
       "139  2016        Unknown  10388767.00\n",
       "\n",
       "[140 rows x 3 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert the column into the variable \"CONTINENT\"\n",
    "# and the value in the cell to the column \"VISAS\"\n",
    "# and separate them based on the \"year\"\n",
    "wide_df.reset_index().melt(var_name='continent', value_name='visas', id_vars=['year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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