{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lesson preamble\n",
    "\n",
    "### Lecture objectives\n",
    "\n",
    "- Understand which types of figures are suitable to create from raw data.\n",
    "- Learn how to avoid common pitfalls when plotting large data sets.\n",
    "- Learn about tidy data.\n",
    "- Transform data from the long to wide format.\n",
    "\n",
    "### Lecture outline\n",
    "\n",
    "- Visualization tips and tricks\n",
    "    - Choose informative plots for categorical data (35 min)\n",
    "    - Making plots accessible through suitable color choices (10 min)\n",
    "    - Avoiding saturated plots (40 min)\n",
    "- Reshaping with data with `pivot()`, `pivot_table()`, and `melt()` (40 min)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization tips and tricks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>region</th>\n",
       "      <th>sub_region</th>\n",
       "      <th>income_group</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>income</th>\n",
       "      <th>children_per_woman</th>\n",
       "      <th>child_mortality</th>\n",
       "      <th>pop_density</th>\n",
       "      <th>co2_per_capita</th>\n",
       "      <th>years_in_school_men</th>\n",
       "      <th>years_in_school_women</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1800</td>\n",
       "      <td>3280000</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Low</td>\n",
       "      <td>28.2</td>\n",
       "      <td>603</td>\n",
       "      <td>7.0</td>\n",
       "      <td>469.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1801</td>\n",
       "      <td>3280000</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Low</td>\n",
       "      <td>28.2</td>\n",
       "      <td>603</td>\n",
       "      <td>7.0</td>\n",
       "      <td>469.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1802</td>\n",
       "      <td>3280000</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Low</td>\n",
       "      <td>28.2</td>\n",
       "      <td>603</td>\n",
       "      <td>7.0</td>\n",
       "      <td>469.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1803</td>\n",
       "      <td>3280000</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Low</td>\n",
       "      <td>28.2</td>\n",
       "      <td>603</td>\n",
       "      <td>7.0</td>\n",
       "      <td>469.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1804</td>\n",
       "      <td>3280000</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Low</td>\n",
       "      <td>28.2</td>\n",
       "      <td>603</td>\n",
       "      <td>7.0</td>\n",
       "      <td>469.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year  population region     sub_region income_group  \\\n",
       "0  Afghanistan  1800     3280000   Asia  Southern Asia          Low   \n",
       "1  Afghanistan  1801     3280000   Asia  Southern Asia          Low   \n",
       "2  Afghanistan  1802     3280000   Asia  Southern Asia          Low   \n",
       "3  Afghanistan  1803     3280000   Asia  Southern Asia          Low   \n",
       "4  Afghanistan  1804     3280000   Asia  Southern Asia          Low   \n",
       "\n",
       "   life_expectancy  income  children_per_woman  child_mortality  pop_density  \\\n",
       "0             28.2     603                 7.0            469.0          NaN   \n",
       "1             28.2     603                 7.0            469.0          NaN   \n",
       "2             28.2     603                 7.0            469.0          NaN   \n",
       "3             28.2     603                 7.0            469.0          NaN   \n",
       "4             28.2     603                 7.0            469.0          NaN   \n",
       "\n",
       "   co2_per_capita  years_in_school_men  years_in_school_women  \n",
       "0             NaN                  NaN                    NaN  \n",
       "1             NaN                  NaN                    NaN  \n",
       "2             NaN                  NaN                    NaN  \n",
       "3             NaN                  NaN                    NaN  \n",
       "4             NaN                  NaN                    NaN  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup by loading the data set from the previous lecture\n",
    "import pandas as pd\n",
    "\n",
    "world_data = pd.read_csv('../data/world-data-gapminder.csv')\n",
    "# If you don't have the dataset locally \n",
    "# world_data = pd.read_csv('https://raw.githubusercontent.com/UofTCoders/2018-09-10-utoronto/gh-pages/data/world-data-gapminder.csv')\n",
    "\n",
    "world_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choosing informative plots for categorical data\n",
    "\n",
    "When visualizing data it is important to explore different plotting options and reflect on which one best conveys the information within the data. In the following code cells, a sample data set is loaded from the `seaborn` data library in order to illustrate some advantages and disadvantages between categorical plot types. This is the same data as was used in lecture 1 and contains three different species of iris flowers and measurements of their sepals and petals.\n",
    "\n",
    "First let's set the `seaborn` style to something different than last lecture, and to subset the data to only include observations from 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.set(context='notebook', style='darkgrid', palette='muted', font_scale=1.3)\n",
    "\n",
    "world_data_2018 = world_data.loc[world_data['year'] == 2018]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar plots\n",
    "\n",
    "A common visualization when comparing a groups is to create a barplot of the means of each group and plot them next to each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa32e894eb8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='region', y='income', data=world_data_2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This barplot shows the mean and the 95% confidence interval. Since the `seaborn` plotting functions returns a `matplotlib` axes object, these can be used with any `matplotlib` functions. Let's use this to our advantage to create a visualization comparison between four types of distribution or estimate plots. By creating a figure using `subplots()`, the `seaborn` plotting functions can be arranged as subplots in a grid. The syntax is slightly different from doing this with functions that are native to `matplotlib`, and the axes in which the `seaborn` function will plot needs to be specified with the `ax` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fa3295083c8>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x='income', y='population',\n",
    "           label='hey', data=world_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fa3291829b0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(12, 8),\n",
    "                                             sharex=True, sharey=True)\n",
    "ax1.scatter(x='income', y='population',\n",
    "           label='hey', data=world_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(12, 8),\n",
    "                                             sharex=True, sharey=True)\n",
    "fig.suptitle('Visualization comparison', y=1.02) # `y` is used to place the title a little bit higher up\n",
    "\n",
    "sns.barplot(x='region', y='income', data=world_data_2018, ax=ax1)\n",
    "sns.boxplot(x='region', y='income', data=world_data_2018, ax=ax2)\n",
    "sns.violinplot(x='region', y='income', data=world_data_2018, ax=ax3, width=1.4)\n",
    "sns.swarmplot(x='region', y='income', data=world_data_2018, ax=ax4)\n",
    "\n",
    "# Remove the axis labels for region and income where it's not needed\n",
    "# ax1.set_xlabel('')\n",
    "# ax2.set_xlabel('')\n",
    "# ax2.set_ylabel('')\n",
    "# ax3.set_xlabel('')\n",
    "# ax4.set_xlabel('')\n",
    "# ax4.set_ylabel('')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">#### Challenge 3\n",
    ">\n",
    ">1. How many data points and/or distribution statistics are displayed in each of these plots \n",
    ">2. Out of the these plots, which one do you think is the most informative and why? Which is the most true to the underlying data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pros and cons of different graph types\n",
    "\n",
    "We will deepen the discussion around some of these ideas, in the context of the following plot:\n",
    "\n",
    "![*Reproduced with permission from [Dr. Koyama's poster](http://biostat.mc.vanderbilt.edu/wiki/pub/Main/TatsukiKoyama/Poster3.pdf)*](./img/dynamite-bars.png)\n",
    "\n",
    "*Reproduced with permission from [Dr. Koyama's poster](http://biostat.mc.vanderbilt.edu/wiki/pub/Main/TatsukiKoyama/Poster3.pdf)*\n",
    "\n",
    "It is generally advisable to avoid \"decorative\" plot elements that do not convey extra information about the data, *especially* when such elements hide the real data. An early champion of this idea was Edward Tufte, who details how to reduce so called non-data ink and many other things in his book [The visual display of quantitative information](https://www.edwardtufte.com/tufte/books_vdqi). In the bar chart above, the only relevant information is given by the where the rectangles of the bars ends on the y-axis, the rest of it is unnecessary. Instead of using the rectangle's height, a simpler marker (circle, square, etc) could have been used to indicate the height on the y-axis. Note that the body of the rectangle is not representative for where the data lies, there are probably no data points close to 0, and several above the rectangle."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Barplots are especially misleading when used as data summaries, as in the\n",
    "example above. In a summary plot, only two distribution parameters (a measure of\n",
    "central tendency, e.g. the mean, and error, e.g. the standard deviation or a\n",
    "confidence interval) are displayed, instead of showing all the individual data\n",
    "points. This can be highly misleading, since different underlying distributions\n",
    "can give rise to the same summary plot. We also have no idea of how many observations there are in each group. These\n",
    "shortcomings become evident when comparing the barplot to the underlying\n",
    "distributions that were used to create them:\n",
    "\n",
    "![*Reproduced with permission from [Dr. Koyama's poster*](http://biostat.mc.vanderbilt.edu/wiki/pub/Main/TatsukiKoyama/Poster3.pdf)](./img/dynamite-vs-dists.png)\n",
    "\n",
    "*Reproduced with permission from [Dr. Koyama's poster](http://biostat.mc.vanderbilt.edu/wiki/pub/Main/TatsukiKoyama/Poster3.pdf)*\n",
    "\n",
    "Immediately, you can see that conclusions drawn from the barplot, such that A\n",
    "and B have the same outcome, are factually incorrect. The distribution in D is\n",
    "bimodal, so representing that with a mean would be like observing black and\n",
    "white birds and conclude that the average bird color is grey, it's nonsensical.\n",
    "If we would have planned our follow up experiments based on the barplot alone,\n",
    "we would have been setting ourselves up for failure! Always be sceptical when\n",
    "you see a barplot in a published paper, and think of how the underlying\n",
    "distribution might look (note that barplots are more acceptable when used to\n",
    "represents counts, proportion or percentages, where there is only one data point\n",
    "per group in the data set).\n",
    "\n",
    "Boxplots and violin plots are more meaningful data summaries as they represent more than just two distribution parameters (such as mean +/- sd). However, these can still be misleading and it is often the most appropriate to show each individual observation with a dot/hive/swarm plot, possibly combined with a superimposed summary plot or a marker for the mean or median *if* this additional information is useful. One exception, when it is not advisable to show all data points, is when the data set is gigantic and plotting each individual observation would oversaturate the chart. In that case, plot summary statistics or a 2D histogram (more on this later).\n",
    "\n",
    "Here is an example of how a violinplot can be combined together with the individual observations in `seaborn`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "sns.violinplot(x='region', y='income', data=world_data_2018,\n",
    "               color='white', inner=None, ax=ax, width=1.4)\n",
    "sns.swarmplot(x='region', y='income', data=world_data_2018, ax=ax)\n",
    "\n",
    "ax.set_ylabel('Sepal Length')\n",
    "ax.set_xlabel('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting elements have a default order in which they appear. This can be changed by explicitly via the `zorder` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "sns.violinplot(x='region', y='income', data=world_data_2018,\n",
    "               color='white', inner=None, ax=ax, width=1.4)\n",
    "sns.swarmplot(x='region', y='income', data=world_data_2018,\n",
    "              ax=ax, zorder=0)\n",
    "\n",
    "ax.set_ylabel('Sepal Length')\n",
    "ax.set_xlabel('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is not very helpful in this particular case, but it is good to be aware of the `zorder` parameter if the need arises to combine plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">#### Challenge 4\n",
    ">\n",
    ">1. Combine a `stripplot()` with a `boxplot()`. Set the `jitter` parameter to distribute the dots so that they are not all on one line."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making plots accessible through suitable color choices\n",
    "\n",
    "Colour blindness is common in the population, and red-green colour blindness in particular affects 8% of men and 0.5% of women. Guidelines for making your visualizations more accessible to people affected by colour blindness, will in many cases also improve the interpretability of your graphs for people who have standard color vision. Here are a couple of examples:\n",
    "\n",
    "Don't use jet rainbow-coloured heatmaps. Jet colourmaps are often the default heatmap used in many visualization packages (you've probably seen them before). \n",
    "\n",
    "![](./img/heatmap.png)\n",
    "\n",
    "Colour blind viewers are going to have a difficult time distinguishing the meaning of this heat map if some of the colours blend together.\n",
    "\n",
    "![](./img/colourblind.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The jet colormap should be avoided for other reasons, including that the sharp transitions between colors introduces visual threshold levels that do not represent the underlying continuous data. Another issue is luminance, or brightness. For example, your eye is drawn to the yellow and cyan regions, because the luminance is higher. This can have the unfortunate effect of highlighting features in your data that don't actually exist, misleading your viewers! It also means that your graph is not going to translate well to greyscale in publication format.\n",
    "\n",
    "More details about jet can be found in [this blog post](https://jakevdp.github.io/blog/2014/10/16/how-bad-is-your-colormap/) and [this series of posts](https://mycarta.wordpress.com/2012/05/12/the-rainbow-is-dead-long-live-the-rainbow-part-1/). In general, when presenting continuous data, a perceptually uniform colormap is often the most suitable choice. This type of colormap ensures that equal steps in data are perceived as equal steps in color space. The human brain perceives changes in lightness as changes in the data much better than, for example, changes in hue. Therefore, colormaps which have monotonically increasing lightness through the colormap will be better interpreted by the viewer. More details and examples of such colormaps are available in the [`matplotlib` documentation](http://matplotlib.org/users/colormaps.html), and many of the core design principles are outlined in [this entertaining talk](https://www.youtube.com/watch?v=xAoljeRJ3lU).\n",
    "\n",
    "The default colormap in `matplotlib` is `viridis` which to have monotonically increasing lightness throughout. There is also `cividis`, which is designed to look the same for common colorblindess as for people without colorblindness. Heatmaps is a good example on where color matters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a correlation matrix, the diagonal is a column's correlaiton with itself, so it is always perfect (1). The same values are mirrored above and below the diagonal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another approach to improve visualization clarity is to use different symbols for the groups and to change the color palette to one specifically designed to work well for common colorblindness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fa32c588f28>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 470.98x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To see all available palettes, set it to an empty string and view the error message\n",
    "sns.relplot(x='income', y='pop_density', hue='region', style='region', \n",
    "            data=world_data_2018, palette='colorblind')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">#### Challenge 5 (optional)\n",
    ">\n",
    ">1. Take one of the figures created previously and upload it to [this website](http://www.color-blindness.com/coblis-color-blindness-simulator/) to see how it  looks in the color blindness simulator."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Avoiding saturated plots\n",
    "\n",
    "Summary plots (especially bar plots) were previously mentioned to potentially be misleading, and it is often most appropriate to show every individual observation with a dot plot or the like, perhaps combined with summary markers where appropriate. But, what if the data set is too big to visualize every single observations? In large data sets, it is often the case that plotting each individual observation would oversaturate the chart.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When plotting a data frame, `matplotlib` plotting functions can be made aware of the structure of the data by specifying the `data` parameter and the `x` and `y` parameters can then be specified just by passing the name of a column in the data frame as a string."
   ]
  },
  {
   "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",
       "    }\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>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     x     y     z\n",
       "1   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "2   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "3   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "4   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "5   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds = pd.read_csv('https://vincentarelbundock.github.io/Rdatasets/csv/ggplot2/diamonds.csv', index_col=0)\n",
    "diamonds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fa32c6506a0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter('carat', 'price', data=diamonds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because this is a dataset with 33,288 observations, visualizing it in two dimensions creates a graph that is incredibly oversaturated. Oversaturated graphs make it *far more* difficult to glean information from the visualization. Maybe adjusting the size of each observation could help?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fa33a926780>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter('carat', 'price', data=diamonds, s=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's a bit better. Reducing the transparency might help further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fa32a2617f0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter('carat', 'price', data=diamonds, s=1, alpha=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is clearer than initially, but does still not reveal the full structure of the underlying data. Before proceeding, add axis labels and remove the axis lines (spines) on the top and the right."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is still not satisfactory, which illustrates that a scatter plot is simply not a good choice with huge data sets. A more suitable plot type for this data, is a so called `hexbin` plot, which essentially is a two dimensional histogram, where the color of each hexagonal bin represents the amount of observations in that bin (analogous to the height in a one dimensional histogram). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x7fa32a23a080>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.hexbin('carat', 'price', data=diamonds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks ugly because the bins with zero observations are still colored. This can be avoided by setting the minimum count of observations to color a bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x7fa32c47a8d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.hexbin('carat', 'price', data=diamonds, mincnt=1)\n",
    "# ax.hexbin('income', 'life_expectancy', data=survey, mincnt=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of the data is not more akin to that of the scatter plot. To know what the different colors represent, a colorbar needs to be added to this plot. The space for the colorbar will be taken from a plot in the current figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "# Assign to a variable to reuse with the colorbar\n",
    "hex_plot = ax.hexbin('carat', 'price', data=diamonds, mincnt=1)\n",
    "# Create the colorbar from the hexbin plot axis\n",
    "cax = fig.colorbar(hex_plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the overall figure is the same size, and the axes that contains the hexbin plot shrank to make room for the colorbar. To remind ourselves what is plotted, axis labels can be added like previously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "hex_plot = ax.hexbin('carat', 'price', data=diamonds, mincnt=1, gridsize=50)\n",
    "sns.despine()\n",
    "cax = fig.colorbar(hex_plot)\n",
    "\n",
    "ax.set_title('Diamond prices')\n",
    "ax.set_xlabel('Carat')\n",
    "ax.set_ylabel('Price')\n",
    "cax.set_label('Number of observations')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is now clear that the yellow area represents over 2000 observations!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Price')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diamonds_subset = diamonds.loc[(diamonds['carat'] < 1.3) & (diamonds['price'] < 2500)]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "hexbin = ax.hexbin('carat', 'price', data=diamonds_subset, mincnt=1)\n",
    "sns.despine()\n",
    "cax = fig.colorbar(hexbin)\n",
    "\n",
    "cax.set_label('Observation density')\n",
    "ax.set_title('Diamond prices')\n",
    "ax.set_xlabel('Carat')\n",
    "ax.set_ylabel('Price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although this hexbin plot is a great way of visualizing the distributions, it could be valuable to compare it to the histograms for each the plotted variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))\n",
    "fig.suptitle('Distribution plots', y=1.05)\n",
    "sns.despine()\n",
    "\n",
    "ax1.hist('carat', bins=30, data=diamonds) \n",
    "ax1.set_title('Diamond weight')\n",
    "ax1.set_xlabel('Carat')\n",
    "\n",
    "ax2.hist('price', bins=30, data=diamonds) \n",
    "ax2.set_title('Diamond price')\n",
    "ax2.set_xlabel('USD')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since visualizing two individual 1D distribution together with their joint 2D distribution is a common operation, `seaborn` has a built-in function to create a hexbin plot with histograms on the marginal axes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7fa32a0f2550>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='carat', y='price', data=diamonds, kind='hex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can be customized to appear more like the previous hexbin plots. Since `joinplot()` deals with both the hexbin and the histogram, the parameter names must be separated so that it is clear which plot they are referring to. This is done by passing them as dictionaries to the `joint_kws` and `marginal_kws` parameters (\"kws\" stands for \"keywords\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7fa329beb5c0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='carat', y='price', data=diamonds, kind='hex', \n",
    "              joint_kws={'cmap':'viridis', 'mincnt':1},\n",
    "              marginal_kws={'color': 'indigo'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/joel/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:960: UserWarning: The following kwargs were not used by contour: 'mincnt'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7fa329a26b00>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='carat', y='price', data=diamonds, kind='kde', \n",
    "              joint_kws={'cmap':'viridis', 'mincnt':1},\n",
    "              marginal_kws={'color': 'indigo'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshaping data between long and wide formats\n",
    "\n",
    "Data is often presented in a so-called wide format, e.g. with one column per measurement:\n",
    "\n",
    "|person|weight|height|age|\n",
    "|------|------|------|---|\n",
    "|A|70|170|32|\n",
    "|B|85|179|28|\n",
    "\n",
    "This can be a great way to display data so that it is easily interpretable by humans and is often used for summary statistics (commonly referred to as pivot tables). However, many data analysis functions in `pandas`, `seaborn` and other packages are optimized to work with the tidy data format. Tidy data is a long format where each row is a single observation and each column contains a single variable:\n",
    "\n",
    "|person|measure|value|\n",
    "|------|-----------|-----|\n",
    "|     A|     weight|   70|\n",
    "|     A|     height|  170|\n",
    "|     A|        age|   32|\n",
    "|     B|     weight|   85|\n",
    "|     B|     height|  179|\n",
    "|     B|        age|   28|\n",
    "\n",
    "`pandas` enables a wide range of manipulations of the structure of data, including alternating between the long and wide format. The survey data presented here is in a tidy format. To facilitate visual comparisons of the relationships between measurements across columns, it would be beneficial to display this data in the wide format. For example, what is the relationship between mean weights of different species caught at the same plot type?\n",
    "\n",
    "### Subset data\n",
    "\n",
    "To facilitate the visualization of the the transformations between wide and tidy data,  it is beneficial to create a subset of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>region</th>\n",
       "      <th>sub_region</th>\n",
       "      <th>income_group</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>income</th>\n",
       "      <th>children_per_woman</th>\n",
       "      <th>child_mortality</th>\n",
       "      <th>pop_density</th>\n",
       "      <th>co2_per_capita</th>\n",
       "      <th>years_in_school_men</th>\n",
       "      <th>years_in_school_women</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>32800000</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Low</td>\n",
       "      <td>57.8</td>\n",
       "      <td>1780</td>\n",
       "      <td>4.98</td>\n",
       "      <td>76.1</td>\n",
       "      <td>50.2</td>\n",
       "      <td>0.299</td>\n",
       "      <td>4.04</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2014</td>\n",
       "      <td>2920000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>77.4</td>\n",
       "      <td>10700</td>\n",
       "      <td>1.71</td>\n",
       "      <td>14.4</td>\n",
       "      <td>107.0</td>\n",
       "      <td>1.960</td>\n",
       "      <td>11.80</td>\n",
       "      <td>12.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2014</td>\n",
       "      <td>39100000</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>77.1</td>\n",
       "      <td>13500</td>\n",
       "      <td>2.89</td>\n",
       "      <td>25.6</td>\n",
       "      <td>16.4</td>\n",
       "      <td>3.720</td>\n",
       "      <td>8.38</td>\n",
       "      <td>7.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2014</td>\n",
       "      <td>26900000</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Lower middle</td>\n",
       "      <td>63.3</td>\n",
       "      <td>6260</td>\n",
       "      <td>5.84</td>\n",
       "      <td>91.2</td>\n",
       "      <td>21.6</td>\n",
       "      <td>1.290</td>\n",
       "      <td>7.11</td>\n",
       "      <td>5.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1090</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>2014</td>\n",
       "      <td>98900</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Latin America and the Caribbean</td>\n",
       "      <td>High</td>\n",
       "      <td>77.1</td>\n",
       "      <td>19500</td>\n",
       "      <td>2.08</td>\n",
       "      <td>9.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>5.380</td>\n",
       "      <td>13.10</td>\n",
       "      <td>14.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  country  year  population    region  \\\n",
       "214           Afghanistan  2014    32800000      Asia   \n",
       "433               Albania  2014     2920000    Europe   \n",
       "652               Algeria  2014    39100000    Africa   \n",
       "871                Angola  2014    26900000    Africa   \n",
       "1090  Antigua and Barbuda  2014       98900  Americas   \n",
       "\n",
       "                           sub_region  income_group  life_expectancy  income  \\\n",
       "214                     Southern Asia           Low             57.8    1780   \n",
       "433                   Southern Europe  Upper middle             77.4   10700   \n",
       "652                   Northern Africa  Upper middle             77.1   13500   \n",
       "871                Sub-Saharan Africa  Lower middle             63.3    6260   \n",
       "1090  Latin America and the Caribbean          High             77.1   19500   \n",
       "\n",
       "      children_per_woman  child_mortality  pop_density  co2_per_capita  \\\n",
       "214                 4.98             76.1         50.2           0.299   \n",
       "433                 1.71             14.4        107.0           1.960   \n",
       "652                 2.89             25.6         16.4           3.720   \n",
       "871                 5.84             91.2         21.6           1.290   \n",
       "1090                2.08              9.0        225.0           5.380   \n",
       "\n",
       "      years_in_school_men  years_in_school_women  \n",
       "214                  4.04                   0.95  \n",
       "433                 11.80                  12.10  \n",
       "652                  8.38                   7.58  \n",
       "871                  7.11                   5.18  \n",
       "1090                13.10                  14.30  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_data_2014 = world_data.loc[world_data['year'].isin(['2014'])]\n",
    "world_data_2014.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 178 entries, 214 to 38977\n",
      "Data columns (total 14 columns):\n",
      "country                  178 non-null object\n",
      "year                     178 non-null int64\n",
      "population               178 non-null int64\n",
      "region                   178 non-null object\n",
      "sub_region               178 non-null object\n",
      "income_group             178 non-null object\n",
      "life_expectancy          178 non-null float64\n",
      "income                   178 non-null int64\n",
      "children_per_woman       178 non-null float64\n",
      "child_mortality          178 non-null float64\n",
      "pop_density              178 non-null float64\n",
      "co2_per_capita           178 non-null float64\n",
      "years_in_school_men      178 non-null float64\n",
      "years_in_school_women    178 non-null float64\n",
      "dtypes: float64(7), int64(3), object(4)\n",
      "memory usage: 20.9+ KB\n"
     ]
    }
   ],
   "source": [
    "world_data_2014.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the average CO2 emissions across reions and income_group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .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>region</th>\n",
       "      <th>income_group</th>\n",
       "      <th>co2_per_capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>High</td>\n",
       "      <td>5.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Low</td>\n",
       "      <td>0.206270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Lower middle</td>\n",
       "      <td>0.930375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>4.696250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Americas</td>\n",
       "      <td>High</td>\n",
       "      <td>9.565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Americas</td>\n",
       "      <td>Low</td>\n",
       "      <td>0.271000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Americas</td>\n",
       "      <td>Lower middle</td>\n",
       "      <td>1.204750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Americas</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>2.519375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Asia</td>\n",
       "      <td>High</td>\n",
       "      <td>17.914545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Low</td>\n",
       "      <td>0.879667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Lower middle</td>\n",
       "      <td>1.564824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>6.210769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Europe</td>\n",
       "      <td>High</td>\n",
       "      <td>6.921429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Lower middle</td>\n",
       "      <td>3.135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>5.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>High</td>\n",
       "      <td>11.495000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>Lower middle</td>\n",
       "      <td>0.581000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>1.163333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      region  income_group  co2_per_capita\n",
       "0     Africa          High        5.310000\n",
       "1     Africa           Low        0.206270\n",
       "2     Africa  Lower middle        0.930375\n",
       "3     Africa  Upper middle        4.696250\n",
       "4   Americas          High        9.565000\n",
       "5   Americas           Low        0.271000\n",
       "6   Americas  Lower middle        1.204750\n",
       "7   Americas  Upper middle        2.519375\n",
       "8       Asia          High       17.914545\n",
       "9       Asia           Low        0.879667\n",
       "10      Asia  Lower middle        1.564824\n",
       "11      Asia  Upper middle        6.210769\n",
       "12    Europe          High        6.921429\n",
       "13    Europe  Lower middle        3.135000\n",
       "14    Europe  Upper middle        5.280000\n",
       "15   Oceania          High       11.495000\n",
       "16   Oceania  Lower middle        0.581000\n",
       "17   Oceania  Upper middle        1.163333"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_data_2014_co2avg = (\n",
    "    world_data_2014\n",
    "        .groupby(['region','income_group'])['co2_per_capita']\n",
    "        .mean()\n",
    "        .reset_index()\n",
    ")\n",
    "world_data_2014_co2avg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Long to wide with `pivot()` and `pivot_table()`\n",
    "\n",
    "The data we created is a long or tidy format. A long to wide transformation would be suitable to effectively visualize the relationship between the average co2 emission of the countries based on their region and income. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To remove the repeating information for `region` and `income_group`, this table can be pivoted into a wide formatted using the `pivot()` method. The arguments passed to `pivot()` includes the rows (the index), the columns, and which values should populate the table. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>income_group</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Lower middle</th>\n",
       "      <th>Upper middle</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>5.310000</td>\n",
       "      <td>0.206270</td>\n",
       "      <td>0.930375</td>\n",
       "      <td>4.696250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>9.565000</td>\n",
       "      <td>0.271000</td>\n",
       "      <td>1.204750</td>\n",
       "      <td>2.519375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>17.914545</td>\n",
       "      <td>0.879667</td>\n",
       "      <td>1.564824</td>\n",
       "      <td>6.210769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>6.921429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.135000</td>\n",
       "      <td>5.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>11.495000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.581000</td>\n",
       "      <td>1.163333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "income_group       High       Low  Lower middle  Upper middle\n",
       "region                                                       \n",
       "Africa         5.310000  0.206270      0.930375      4.696250\n",
       "Americas       9.565000  0.271000      1.204750      2.519375\n",
       "Asia          17.914545  0.879667      1.564824      6.210769\n",
       "Europe         6.921429       NaN      3.135000      5.280000\n",
       "Oceania       11.495000       NaN      0.581000      1.163333"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_data_2014_pvt = world_data_2014_co2avg.pivot(\n",
    "    index='region', columns='income_group', values='co2_per_capita')\n",
    "world_data_2014_pvt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare how this table is displayed with the table in the previous cell. It is certainly easier to spot differences between the species and plot types in this wide format.\n",
    "\n",
    "Since presenting summary statistics in a wide format is such a common operation, `pandas` has a dedicated method, `pivot_table()`, that performs both the data aggregation and pivoting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
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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>income_group</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Lower middle</th>\n",
       "      <th>Upper middle</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</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>Africa</th>\n",
       "      <td>5.310000</td>\n",
       "      <td>0.206270</td>\n",
       "      <td>0.930375</td>\n",
       "      <td>4.696250</td>\n",
       "      <td>1.217987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>9.565000</td>\n",
       "      <td>0.271000</td>\n",
       "      <td>1.204750</td>\n",
       "      <td>2.519375</td>\n",
       "      <td>4.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>17.914545</td>\n",
       "      <td>0.879667</td>\n",
       "      <td>1.564824</td>\n",
       "      <td>6.210769</td>\n",
       "      <td>6.588936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>6.921429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.135000</td>\n",
       "      <td>5.280000</td>\n",
       "      <td>6.348462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>11.495000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.581000</td>\n",
       "      <td>1.163333</td>\n",
       "      <td>3.200444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>9.900192</td>\n",
       "      <td>0.327009</td>\n",
       "      <td>1.276767</td>\n",
       "      <td>4.278163</td>\n",
       "      <td>4.440783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "income_group       High       Low  Lower middle  Upper middle       All\n",
       "region                                                                 \n",
       "Africa         5.310000  0.206270      0.930375      4.696250  1.217987\n",
       "Americas       9.565000  0.271000      1.204750      2.519375  4.550000\n",
       "Asia          17.914545  0.879667      1.564824      6.210769  6.588936\n",
       "Europe         6.921429       NaN      3.135000      5.280000  6.348462\n",
       "Oceania       11.495000       NaN      0.581000      1.163333  3.200444\n",
       "All            9.900192  0.327009      1.276767      4.278163  4.440783"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_data_2014.pivot_table(\n",
    "    index='region', columns='income_group',\n",
    "    values='co2_per_capita', margins=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With `pivot_table()` it is also possible to change the aggregation function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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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>income_group</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Lower middle</th>\n",
       "      <th>Upper middle</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</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>Africa</th>\n",
       "      <td>5.310</td>\n",
       "      <td>0.1470</td>\n",
       "      <td>0.6510</td>\n",
       "      <td>3.540</td>\n",
       "      <td>0.339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>5.065</td>\n",
       "      <td>0.2710</td>\n",
       "      <td>1.0400</td>\n",
       "      <td>2.435</td>\n",
       "      <td>2.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>15.400</td>\n",
       "      <td>0.7425</td>\n",
       "      <td>1.0600</td>\n",
       "      <td>4.620</td>\n",
       "      <td>3.270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>6.010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.1350</td>\n",
       "      <td>4.240</td>\n",
       "      <td>5.870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>11.495</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5795</td>\n",
       "      <td>1.140</td>\n",
       "      <td>1.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>6.630</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>0.8920</td>\n",
       "      <td>3.500</td>\n",
       "      <td>2.435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "income_group    High     Low  Lower middle  Upper middle    All\n",
       "region                                                         \n",
       "Africa         5.310  0.1470        0.6510         3.540  0.339\n",
       "Americas       5.065  0.2710        1.0400         2.435  2.590\n",
       "Asia          15.400  0.7425        1.0600         4.620  3.270\n",
       "Europe         6.010     NaN        3.1350         4.240  5.870\n",
       "Oceania       11.495     NaN        0.5795         1.140  1.030\n",
       "All            6.630  0.1940        0.8920         3.500  2.435"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_data_2014.pivot_table(\n",
    "    index='region', columns='income_group', \n",
    "    values='co2_per_capita', margins=True, aggfunc='median'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although `pivot_table()` is the most convenient way to aggregate *and* pivot data, `pivot()` is still useful to reshape a data frame from wide to long *without* performing aggregation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The columns and rows can be swapped in the call to `pivot_table()`. This is useful both to present the table differently and to perform computations on a different axis (dimension) of the data frame (this result can also be obtained by calling the `transpose()` method)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>region</th>\n",
       "      <th>Africa</th>\n",
       "      <th>Americas</th>\n",
       "      <th>Asia</th>\n",
       "      <th>Europe</th>\n",
       "      <th>Oceania</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_group</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>High</th>\n",
       "      <td>5.310000</td>\n",
       "      <td>9.565000</td>\n",
       "      <td>17.914545</td>\n",
       "      <td>6.921429</td>\n",
       "      <td>11.495000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low</th>\n",
       "      <td>0.206270</td>\n",
       "      <td>0.271000</td>\n",
       "      <td>0.879667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lower middle</th>\n",
       "      <td>0.930375</td>\n",
       "      <td>1.204750</td>\n",
       "      <td>1.564824</td>\n",
       "      <td>3.135000</td>\n",
       "      <td>0.581000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper middle</th>\n",
       "      <td>4.696250</td>\n",
       "      <td>2.519375</td>\n",
       "      <td>6.210769</td>\n",
       "      <td>5.280000</td>\n",
       "      <td>1.163333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "region          Africa  Americas       Asia    Europe    Oceania\n",
       "income_group                                                    \n",
       "High          5.310000  9.565000  17.914545  6.921429  11.495000\n",
       "Low           0.206270  0.271000   0.879667       NaN        NaN\n",
       "Lower middle  0.930375  1.204750   1.564824  3.135000   0.581000\n",
       "Upper middle  4.696250  2.519375   6.210769  5.280000   1.163333"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_data_2014.pivot_table(index='income_group', columns='region', values='co2_per_capita')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wide to long with `melt()`\n",
    "\n",
    "It is also a common operation to reshape data from the wide to the long format, e.g. when getting the data into the most suitable format for analysis. For this transformation, the `melt()` method can be used to sweep up a set of columns into one key-value pair.\n",
    "\n",
    "To prepare the data frame, the `plot_type` index name can be moved to a column name with the `reset_index()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>income_group</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Lower middle</th>\n",
       "      <th>Upper middle</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>5.310000</td>\n",
       "      <td>0.206270</td>\n",
       "      <td>0.930375</td>\n",
       "      <td>4.696250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>9.565000</td>\n",
       "      <td>0.271000</td>\n",
       "      <td>1.204750</td>\n",
       "      <td>2.519375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>17.914545</td>\n",
       "      <td>0.879667</td>\n",
       "      <td>1.564824</td>\n",
       "      <td>6.210769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>6.921429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.135000</td>\n",
       "      <td>5.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>11.495000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.581000</td>\n",
       "      <td>1.163333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "income_group       High       Low  Lower middle  Upper middle\n",
       "region                                                       \n",
       "Africa         5.310000  0.206270      0.930375      4.696250\n",
       "Americas       9.565000  0.271000      1.204750      2.519375\n",
       "Asia          17.914545  0.879667      1.564824      6.210769\n",
       "Europe         6.921429       NaN      3.135000      5.280000\n",
       "Oceania       11.495000       NaN      0.581000      1.163333"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_data_2014_pvt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>income_group</th>\n",
       "      <th>region</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Lower middle</th>\n",
       "      <th>Upper middle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>5.310000</td>\n",
       "      <td>0.206270</td>\n",
       "      <td>0.930375</td>\n",
       "      <td>4.696250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Americas</td>\n",
       "      <td>9.565000</td>\n",
       "      <td>0.271000</td>\n",
       "      <td>1.204750</td>\n",
       "      <td>2.519375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Asia</td>\n",
       "      <td>17.914545</td>\n",
       "      <td>0.879667</td>\n",
       "      <td>1.564824</td>\n",
       "      <td>6.210769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Europe</td>\n",
       "      <td>6.921429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.135000</td>\n",
       "      <td>5.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>11.495000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.581000</td>\n",
       "      <td>1.163333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "income_group    region       High       Low  Lower middle  Upper middle\n",
       "0               Africa   5.310000  0.206270      0.930375      4.696250\n",
       "1             Americas   9.565000  0.271000      1.204750      2.519375\n",
       "2                 Asia  17.914545  0.879667      1.564824      6.210769\n",
       "3               Europe   6.921429       NaN      3.135000      5.280000\n",
       "4              Oceania  11.495000       NaN      0.581000      1.163333"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_data_2014_res = world_data_2014_pvt.reset_index()\n",
    "world_data_2014_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At a minimum, `melt()` only requires the name of the column that should be kept intact. All remaining columns will have their values in the `value` column and their name in the `variable` column (here, our columns already has a name \"income_group\", so this will be used automatically instead of \"variable\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>income_group</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>High</td>\n",
       "      <td>5.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Americas</td>\n",
       "      <td>High</td>\n",
       "      <td>9.565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Asia</td>\n",
       "      <td>High</td>\n",
       "      <td>17.914545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Europe</td>\n",
       "      <td>High</td>\n",
       "      <td>6.921429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>High</td>\n",
       "      <td>11.495000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Low</td>\n",
       "      <td>0.206270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Americas</td>\n",
       "      <td>Low</td>\n",
       "      <td>0.271000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Low</td>\n",
       "      <td>0.879667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Low</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>Low</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Lower middle</td>\n",
       "      <td>0.930375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Americas</td>\n",
       "      <td>Lower middle</td>\n",
       "      <td>1.204750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Lower middle</td>\n",
       "      <td>1.564824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Lower middle</td>\n",
       "      <td>3.135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>Lower middle</td>\n",
       "      <td>0.581000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>4.696250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Americas</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>2.519375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>6.210769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>5.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>1.163333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      region  income_group      value\n",
       "0     Africa          High   5.310000\n",
       "1   Americas          High   9.565000\n",
       "2       Asia          High  17.914545\n",
       "3     Europe          High   6.921429\n",
       "4    Oceania          High  11.495000\n",
       "5     Africa           Low   0.206270\n",
       "6   Americas           Low   0.271000\n",
       "7       Asia           Low   0.879667\n",
       "8     Europe           Low        NaN\n",
       "9    Oceania           Low        NaN\n",
       "10    Africa  Lower middle   0.930375\n",
       "11  Americas  Lower middle   1.204750\n",
       "12      Asia  Lower middle   1.564824\n",
       "13    Europe  Lower middle   3.135000\n",
       "14   Oceania  Lower middle   0.581000\n",
       "15    Africa  Upper middle   4.696250\n",
       "16  Americas  Upper middle   2.519375\n",
       "17      Asia  Upper middle   6.210769\n",
       "18    Europe  Upper middle   5.280000\n",
       "19   Oceania  Upper middle   1.163333"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_data_2014_res.melt(id_vars='region')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be more explicit, all the arguments to `melt()` can be specified. This way it is also possible to exclude some columns, e.g. the income group 'Lower middle'."
   ]
  },
  {
   "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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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>income_group</th>\n",
       "      <th>co2_per_capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>High</td>\n",
       "      <td>5.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Americas</td>\n",
       "      <td>High</td>\n",
       "      <td>9.565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Asia</td>\n",
       "      <td>High</td>\n",
       "      <td>17.914545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Europe</td>\n",
       "      <td>High</td>\n",
       "      <td>6.921429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>High</td>\n",
       "      <td>11.495000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Low</td>\n",
       "      <td>0.206270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Americas</td>\n",
       "      <td>Low</td>\n",
       "      <td>0.271000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Low</td>\n",
       "      <td>0.879667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Low</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>Low</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Lower middle</td>\n",
       "      <td>0.930375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Americas</td>\n",
       "      <td>Lower middle</td>\n",
       "      <td>1.204750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Lower middle</td>\n",
       "      <td>1.564824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Lower middle</td>\n",
       "      <td>3.135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>Lower middle</td>\n",
       "      <td>0.581000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>4.696250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Americas</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>2.519375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>6.210769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>5.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>1.163333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      region  income_group  co2_per_capita\n",
       "0     Africa          High        5.310000\n",
       "1   Americas          High        9.565000\n",
       "2       Asia          High       17.914545\n",
       "3     Europe          High        6.921429\n",
       "4    Oceania          High       11.495000\n",
       "5     Africa           Low        0.206270\n",
       "6   Americas           Low        0.271000\n",
       "7       Asia           Low        0.879667\n",
       "8     Europe           Low             NaN\n",
       "9    Oceania           Low             NaN\n",
       "10    Africa  Lower middle        0.930375\n",
       "11  Americas  Lower middle        1.204750\n",
       "12      Asia  Lower middle        1.564824\n",
       "13    Europe  Lower middle        3.135000\n",
       "14   Oceania  Lower middle        0.581000\n",
       "15    Africa  Upper middle        4.696250\n",
       "16  Americas  Upper middle        2.519375\n",
       "17      Asia  Upper middle        6.210769\n",
       "18    Europe  Upper middle        5.280000\n",
       "19   Oceania  Upper middle        1.163333"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_data_2014_res.melt(id_vars='region', value_vars=['High','Low','Lower middle','Upper middle'], \n",
    "                     var_name='income_group', value_name='co2_per_capita')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">#### Challenge 1\n",
    ">\n",
    "> 1. Subset the data to contain only the year 1950 and the region Southern Europe.\n",
    "> 2. Reset the index of this data frame and assign it to a new variable name\n",
    "> 3. Create a tidy data frame with country as the id column, and `pop_density` and `co2_per_capita` as values in the variable column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>country</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>pop_density</td>\n",
       "      <td>46.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>pop_density</td>\n",
       "      <td>52.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>pop_density</td>\n",
       "      <td>68.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Greece</td>\n",
       "      <td>pop_density</td>\n",
       "      <td>59.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Italy</td>\n",
       "      <td>pop_density</td>\n",
       "      <td>158.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Macedonia, FYR</td>\n",
       "      <td>pop_density</td>\n",
       "      <td>49.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Malta</td>\n",
       "      <td>pop_density</td>\n",
       "      <td>975.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Montenegro</td>\n",
       "      <td>pop_density</td>\n",
       "      <td>29.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>pop_density</td>\n",
       "      <td>91.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Serbia</td>\n",
       "      <td>pop_density</td>\n",
       "      <td>77.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Slovenia</td>\n",
       "      <td>pop_density</td>\n",
       "      <td>73.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Spain</td>\n",
       "      <td>pop_density</td>\n",
       "      <td>56.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Albania</td>\n",
       "      <td>co2_per_capita</td>\n",
       "      <td>0.235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Greece</td>\n",
       "      <td>co2_per_capita</td>\n",
       "      <td>0.539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Italy</td>\n",
       "      <td>co2_per_capita</td>\n",
       "      <td>0.890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Malta</td>\n",
       "      <td>co2_per_capita</td>\n",
       "      <td>0.787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>co2_per_capita</td>\n",
       "      <td>0.668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Spain</td>\n",
       "      <td>co2_per_capita</td>\n",
       "      <td>1.180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   country        variable    value\n",
       "0                  Albania     pop_density   46.100\n",
       "1   Bosnia and Herzegovina     pop_density   52.200\n",
       "2                  Croatia     pop_density   68.800\n",
       "3                   Greece     pop_density   59.500\n",
       "4                    Italy     pop_density  158.000\n",
       "5           Macedonia, FYR     pop_density   49.700\n",
       "6                    Malta     pop_density  975.000\n",
       "7               Montenegro     pop_density   29.300\n",
       "8                 Portugal     pop_density   91.900\n",
       "9                   Serbia     pop_density   77.000\n",
       "10                Slovenia     pop_density   73.100\n",
       "11                   Spain     pop_density   56.300\n",
       "12                 Albania  co2_per_capita    0.235\n",
       "15                  Greece  co2_per_capita    0.539\n",
       "16                   Italy  co2_per_capita    0.890\n",
       "18                   Malta  co2_per_capita    0.787\n",
       "20                Portugal  co2_per_capita    0.668\n",
       "23                   Spain  co2_per_capita    1.180"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Challenge solution\n",
    "# 1.\n",
    "world_data_1950_se = world_data.loc[world_data['year'].isin(['1950']) & world_data['sub_region'].isin(['Southern Europe'])]\n",
    "\n",
    "# 2.\n",
    "world_data_1950_se_res = world_data_1950_se.reset_index()[['country','pop_density','co2_per_capita']]\n",
    "\n",
    "# 3.\n",
    "world_data_1950_tidy = world_data_1950_se_res.melt(id_vars='country')\n",
    "# if we wanted to drop the NaN values in the previous format we had to index a 2D dataframe\n",
    "# but in a tidy data format it's easier to drop the NaN values:\n",
    "world_data_1950_tidy.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning data (time permitting)\n",
    "\n",
    "`pandas` has many helpful methods for cleaning data, [an overview can be found in the documentation](https://pandas.pydata.org/pandas-docs/stable/missing_data.html). We will explore the most commonly used methods here. First, let's load a sample data frame with some dirty raw data that needs cleaning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>region</th>\n",
       "      <th>income_group</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>co2_per_capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>2011</td>\n",
       "      <td>188000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>71.5</td>\n",
       "      <td>1.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>samoa</td>\n",
       "      <td>2012</td>\n",
       "      <td>189000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>no value</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sammoa</td>\n",
       "      <td>2013</td>\n",
       "      <td>191000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>71.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>missing</td>\n",
       "      <td>1.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Samia</td>\n",
       "      <td>2015</td>\n",
       "      <td>194000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>71.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>2016</td>\n",
       "      <td>195000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>72.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2011</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2013</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.1</td>\n",
       "      <td>1.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Tonnga</td>\n",
       "      <td>2014</td>\n",
       "      <td>106000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.2</td>\n",
       "      <td>1.140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2015</td>\n",
       "      <td>106000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>not given</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2016</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year  population             region  income_group life_expectancy  \\\n",
       "0    Samoa  2011    188000.0  Oceania.Polynesia  Upper middle            71.5   \n",
       "1    samoa  2012    189000.0  Oceania.Polynesia  Upper middle        no value   \n",
       "2   Sammoa  2013    191000.0  Oceania.Polynesia  Upper middle            71.6   \n",
       "3    Samoa  2014         NaN  Oceania.Polynesia  Upper middle         missing   \n",
       "4    Samia  2015    194000.0  Oceania.Polynesia  Upper middle            71.7   \n",
       "5    Samoa  2016    195000.0  Oceania.Polynesia  Upper middle            72.0   \n",
       "6    Tonga  2011    105000.0  Oceania.Polynesia  Upper middle            70.0   \n",
       "7    Tonga  2012         NaN  Oceania.Polynesia  Upper middle            70.0   \n",
       "8    Tonga  2013    105000.0  Oceania.Polynesia  Upper middle            70.1   \n",
       "9   Tonnga  2014    106000.0  Oceania.Polynesia  Upper middle            70.2   \n",
       "10   Tonga  2015    106000.0  Oceania.Polynesia  Upper middle       not given   \n",
       "11   Tonga  2016    107000.0  Oceania.Polynesia  Upper middle            70.4   \n",
       "\n",
       "    co2_per_capita  \n",
       "0            1.070  \n",
       "1              NaN  \n",
       "2              NaN  \n",
       "3            1.030  \n",
       "4              NaN  \n",
       "5              NaN  \n",
       "6            0.982  \n",
       "7            1.010  \n",
       "8            1.080  \n",
       "9            1.140  \n",
       "10             NaN  \n",
       "11             NaN  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv('https://raw.githubusercontent.com/UofTCoders/2018-09-10-utoronto/gh-pages/data/raw_dirty_data.csv')\n",
    "clean_df = raw_data.copy() # To ensure the original df is not modified\n",
    "clean_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A few rows in this data frame contain missing values. As mentioned earlier, there are several ways to handle missing values. A robust option is to remove any rows with missing values, which can be done wth the `dropna()` method of the data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>region</th>\n",
       "      <th>income_group</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>co2_per_capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>2011</td>\n",
       "      <td>188000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>71.5</td>\n",
       "      <td>1.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2011</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2013</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.1</td>\n",
       "      <td>1.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Tonnga</td>\n",
       "      <td>2014</td>\n",
       "      <td>106000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.2</td>\n",
       "      <td>1.140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country  year  population             region  income_group life_expectancy  \\\n",
       "0   Samoa  2011    188000.0  Oceania.Polynesia  Upper middle            71.5   \n",
       "6   Tonga  2011    105000.0  Oceania.Polynesia  Upper middle            70.0   \n",
       "8   Tonga  2013    105000.0  Oceania.Polynesia  Upper middle            70.1   \n",
       "9  Tonnga  2014    106000.0  Oceania.Polynesia  Upper middle            70.2   \n",
       "\n",
       "   co2_per_capita  \n",
       "0           1.070  \n",
       "6           0.982  \n",
       "8           1.080  \n",
       "9           1.140  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default all columns are considered. However, if the purpose is to study the population changes over time, it is not desirable to drop rows with valid population values just because they are missing a co2 measurement. `dropna()` can therefore be adjusted to only consider specific columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>region</th>\n",
       "      <th>income_group</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>co2_per_capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>2011</td>\n",
       "      <td>188000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>71.5</td>\n",
       "      <td>1.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>samoa</td>\n",
       "      <td>2012</td>\n",
       "      <td>189000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>no value</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sammoa</td>\n",
       "      <td>2013</td>\n",
       "      <td>191000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>71.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Samia</td>\n",
       "      <td>2015</td>\n",
       "      <td>194000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>71.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>2016</td>\n",
       "      <td>195000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>72.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2011</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2013</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.1</td>\n",
       "      <td>1.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Tonnga</td>\n",
       "      <td>2014</td>\n",
       "      <td>106000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.2</td>\n",
       "      <td>1.140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2015</td>\n",
       "      <td>106000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>not given</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2016</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year  population             region  income_group life_expectancy  \\\n",
       "0    Samoa  2011    188000.0  Oceania.Polynesia  Upper middle            71.5   \n",
       "1    samoa  2012    189000.0  Oceania.Polynesia  Upper middle        no value   \n",
       "2   Sammoa  2013    191000.0  Oceania.Polynesia  Upper middle            71.6   \n",
       "4    Samia  2015    194000.0  Oceania.Polynesia  Upper middle            71.7   \n",
       "5    Samoa  2016    195000.0  Oceania.Polynesia  Upper middle            72.0   \n",
       "6    Tonga  2011    105000.0  Oceania.Polynesia  Upper middle            70.0   \n",
       "8    Tonga  2013    105000.0  Oceania.Polynesia  Upper middle            70.1   \n",
       "9   Tonnga  2014    106000.0  Oceania.Polynesia  Upper middle            70.2   \n",
       "10   Tonga  2015    106000.0  Oceania.Polynesia  Upper middle       not given   \n",
       "11   Tonga  2016    107000.0  Oceania.Polynesia  Upper middle            70.4   \n",
       "\n",
       "    co2_per_capita  \n",
       "0            1.070  \n",
       "1              NaN  \n",
       "2              NaN  \n",
       "4              NaN  \n",
       "5              NaN  \n",
       "6            0.982  \n",
       "8            1.080  \n",
       "9            1.140  \n",
       "10             NaN  \n",
       "11             NaN  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df.dropna(subset=['population'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "A common alternative to removing rows containing `NA` values is to fill out the values with e.g. the mean of all observations or the previous non-NA value. This can be done with the `fillna()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>2011</td>\n",
       "      <td>188000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>71.5</td>\n",
       "      <td>1.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>samoa</td>\n",
       "      <td>2012</td>\n",
       "      <td>189000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>no value</td>\n",
       "      <td>1.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sammoa</td>\n",
       "      <td>2013</td>\n",
       "      <td>191000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>71.6</td>\n",
       "      <td>1.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>2014</td>\n",
       "      <td>148600.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>missing</td>\n",
       "      <td>1.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Samia</td>\n",
       "      <td>2015</td>\n",
       "      <td>194000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>71.7</td>\n",
       "      <td>1.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>2016</td>\n",
       "      <td>195000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2011</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2012</td>\n",
       "      <td>148600.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2013</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.1</td>\n",
       "      <td>1.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Tonnga</td>\n",
       "      <td>2014</td>\n",
       "      <td>106000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.2</td>\n",
       "      <td>1.140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2015</td>\n",
       "      <td>106000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>not given</td>\n",
       "      <td>1.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2016</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.4</td>\n",
       "      <td>1.052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year  population             region  income_group life_expectancy  \\\n",
       "0    Samoa  2011    188000.0  Oceania.Polynesia  Upper middle            71.5   \n",
       "1    samoa  2012    189000.0  Oceania.Polynesia  Upper middle        no value   \n",
       "2   Sammoa  2013    191000.0  Oceania.Polynesia  Upper middle            71.6   \n",
       "3    Samoa  2014    148600.0  Oceania.Polynesia  Upper middle         missing   \n",
       "4    Samia  2015    194000.0  Oceania.Polynesia  Upper middle            71.7   \n",
       "5    Samoa  2016    195000.0  Oceania.Polynesia  Upper middle            72.0   \n",
       "6    Tonga  2011    105000.0  Oceania.Polynesia  Upper middle            70.0   \n",
       "7    Tonga  2012    148600.0  Oceania.Polynesia  Upper middle            70.0   \n",
       "8    Tonga  2013    105000.0  Oceania.Polynesia  Upper middle            70.1   \n",
       "9   Tonnga  2014    106000.0  Oceania.Polynesia  Upper middle            70.2   \n",
       "10   Tonga  2015    106000.0  Oceania.Polynesia  Upper middle       not given   \n",
       "11   Tonga  2016    107000.0  Oceania.Polynesia  Upper middle            70.4   \n",
       "\n",
       "    co2_per_capita  \n",
       "0            1.070  \n",
       "1            1.052  \n",
       "2            1.052  \n",
       "3            1.030  \n",
       "4            1.052  \n",
       "5            1.052  \n",
       "6            0.982  \n",
       "7            1.010  \n",
       "8            1.080  \n",
       "9            1.140  \n",
       "10           1.052  \n",
       "11           1.052  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill missing values with mean value for that column\n",
    "raw_data.fillna(raw_data.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, it would have been better to calculate different values for the different countries.\n",
    "\n",
    "Another way of filling values is to copy the previous or next value. This is especially relevant in time series where the values are ordered chronologically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
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       "      <td>Samoa</td>\n",
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       "      <td>Oceania.Polynesia</td>\n",
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       "      <td>71.5</td>\n",
       "      <td>1.070</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>samoa</td>\n",
       "      <td>2012</td>\n",
       "      <td>189000.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>Sammoa</td>\n",
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       "      <td>Upper middle</td>\n",
       "      <td>71.6</td>\n",
       "      <td>1.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Samoa</td>\n",
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       "      <td>191000.0</td>\n",
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       "      <td>Upper middle</td>\n",
       "      <td>missing</td>\n",
       "      <td>1.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Samia</td>\n",
       "      <td>2015</td>\n",
       "      <td>194000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>71.7</td>\n",
       "      <td>1.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>2016</td>\n",
       "      <td>195000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2011</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2012</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2013</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.1</td>\n",
       "      <td>1.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Tonnga</td>\n",
       "      <td>2014</td>\n",
       "      <td>106000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.2</td>\n",
       "      <td>1.140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2015</td>\n",
       "      <td>106000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>not given</td>\n",
       "      <td>1.140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2016</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.4</td>\n",
       "      <td>1.140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year  population             region  income_group life_expectancy  \\\n",
       "0    Samoa  2011    188000.0  Oceania.Polynesia  Upper middle            71.5   \n",
       "1    samoa  2012    189000.0  Oceania.Polynesia  Upper middle        no value   \n",
       "2   Sammoa  2013    191000.0  Oceania.Polynesia  Upper middle            71.6   \n",
       "3    Samoa  2014    191000.0  Oceania.Polynesia  Upper middle         missing   \n",
       "4    Samia  2015    194000.0  Oceania.Polynesia  Upper middle            71.7   \n",
       "5    Samoa  2016    195000.0  Oceania.Polynesia  Upper middle            72.0   \n",
       "6    Tonga  2011    105000.0  Oceania.Polynesia  Upper middle            70.0   \n",
       "7    Tonga  2012    105000.0  Oceania.Polynesia  Upper middle            70.0   \n",
       "8    Tonga  2013    105000.0  Oceania.Polynesia  Upper middle            70.1   \n",
       "9   Tonnga  2014    106000.0  Oceania.Polynesia  Upper middle            70.2   \n",
       "10   Tonga  2015    106000.0  Oceania.Polynesia  Upper middle       not given   \n",
       "11   Tonga  2016    107000.0  Oceania.Polynesia  Upper middle            70.4   \n",
       "\n",
       "    co2_per_capita  \n",
       "0            1.070  \n",
       "1            1.070  \n",
       "2            1.070  \n",
       "3            1.030  \n",
       "4            1.030  \n",
       "5            1.030  \n",
       "6            0.982  \n",
       "7            1.010  \n",
       "8            1.080  \n",
       "9            1.140  \n",
       "10           1.140  \n",
       "11           1.140  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill with previews non-null value\n",
    "raw_data.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For NA values that are surrounded by two measurements, the most appropriate method could be to interpolate the missing values. The default interpolation method is to linearly estimate the values, but there are many more options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>region</th>\n",
       "      <th>income_group</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>co2_per_capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>2011</td>\n",
       "      <td>188000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>71.5</td>\n",
       "      <td>1.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>samoa</td>\n",
       "      <td>2012</td>\n",
       "      <td>189000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>no value</td>\n",
       "      <td>1.056667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sammoa</td>\n",
       "      <td>2013</td>\n",
       "      <td>191000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>71.6</td>\n",
       "      <td>1.043333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>2014</td>\n",
       "      <td>192500.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>missing</td>\n",
       "      <td>1.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Samia</td>\n",
       "      <td>2015</td>\n",
       "      <td>194000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>71.7</td>\n",
       "      <td>1.014000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>2016</td>\n",
       "      <td>195000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.998000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2011</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.982000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2012</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2013</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.1</td>\n",
       "      <td>1.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Tonnga</td>\n",
       "      <td>2014</td>\n",
       "      <td>106000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.2</td>\n",
       "      <td>1.140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2015</td>\n",
       "      <td>106000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>not given</td>\n",
       "      <td>1.140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2016</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>Upper middle</td>\n",
       "      <td>70.4</td>\n",
       "      <td>1.140000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year  population             region  income_group life_expectancy  \\\n",
       "0    Samoa  2011    188000.0  Oceania.Polynesia  Upper middle            71.5   \n",
       "1    samoa  2012    189000.0  Oceania.Polynesia  Upper middle        no value   \n",
       "2   Sammoa  2013    191000.0  Oceania.Polynesia  Upper middle            71.6   \n",
       "3    Samoa  2014    192500.0  Oceania.Polynesia  Upper middle         missing   \n",
       "4    Samia  2015    194000.0  Oceania.Polynesia  Upper middle            71.7   \n",
       "5    Samoa  2016    195000.0  Oceania.Polynesia  Upper middle            72.0   \n",
       "6    Tonga  2011    105000.0  Oceania.Polynesia  Upper middle            70.0   \n",
       "7    Tonga  2012    105000.0  Oceania.Polynesia  Upper middle            70.0   \n",
       "8    Tonga  2013    105000.0  Oceania.Polynesia  Upper middle            70.1   \n",
       "9   Tonnga  2014    106000.0  Oceania.Polynesia  Upper middle            70.2   \n",
       "10   Tonga  2015    106000.0  Oceania.Polynesia  Upper middle       not given   \n",
       "11   Tonga  2016    107000.0  Oceania.Polynesia  Upper middle            70.4   \n",
       "\n",
       "    co2_per_capita  \n",
       "0         1.070000  \n",
       "1         1.056667  \n",
       "2         1.043333  \n",
       "3         1.030000  \n",
       "4         1.014000  \n",
       "5         0.998000  \n",
       "6         0.982000  \n",
       "7         1.010000  \n",
       "8         1.080000  \n",
       "9         1.140000  \n",
       "10        1.140000  \n",
       "11        1.140000  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df.interpolate(limit_direction='both')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whether to use `dropna()`, `fillna()`, or `interpolate()` depends on the data set and the purpose of the analysis.\n",
    "\n",
    "Data frames have plenty of built-in `str` [(string) methods](https://pandas.pydata.org/pandas-docs/stable/api.html#string-handling) and many of these are helpful when handling typos and text formatting. Say for examples that it is desired to have the values of the `income_group` column as lower case characters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     upper middle\n",
       "1     upper middle\n",
       "2     upper middle\n",
       "3     upper middle\n",
       "4     upper middle\n",
       "5     upper middle\n",
       "6     upper middle\n",
       "7     upper middle\n",
       "8     upper middle\n",
       "9     upper middle\n",
       "10    upper middle\n",
       "11    upper middle\n",
       "Name: income_group, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df['income_group'].str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The space can easily be replaced with an underscore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     upper-middle\n",
       "1     upper-middle\n",
       "2     upper-middle\n",
       "3     upper-middle\n",
       "4     upper-middle\n",
       "5     upper-middle\n",
       "6     upper-middle\n",
       "7     upper-middle\n",
       "8     upper-middle\n",
       "9     upper-middle\n",
       "10    upper-middle\n",
       "11    upper-middle\n",
       "Name: income_group, dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df['income_group'].str.lower().str.replace(' ', '-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can then be assigned back to the original data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>region</th>\n",
       "      <th>income_group</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>co2_per_capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>2011</td>\n",
       "      <td>188000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>71.5</td>\n",
       "      <td>1.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>samoa</td>\n",
       "      <td>2012</td>\n",
       "      <td>189000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>no value</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sammoa</td>\n",
       "      <td>2013</td>\n",
       "      <td>191000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>71.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>missing</td>\n",
       "      <td>1.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Samia</td>\n",
       "      <td>2015</td>\n",
       "      <td>194000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>71.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>2016</td>\n",
       "      <td>195000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>72.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2011</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2013</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>70.1</td>\n",
       "      <td>1.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Tonnga</td>\n",
       "      <td>2014</td>\n",
       "      <td>106000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>70.2</td>\n",
       "      <td>1.140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2015</td>\n",
       "      <td>106000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>not given</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2016</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>Oceania.Polynesia</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>70.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year  population             region  income_group life_expectancy  \\\n",
       "0    Samoa  2011    188000.0  Oceania.Polynesia  upper-middle            71.5   \n",
       "1    samoa  2012    189000.0  Oceania.Polynesia  upper-middle        no value   \n",
       "2   Sammoa  2013    191000.0  Oceania.Polynesia  upper-middle            71.6   \n",
       "3    Samoa  2014         NaN  Oceania.Polynesia  upper-middle         missing   \n",
       "4    Samia  2015    194000.0  Oceania.Polynesia  upper-middle            71.7   \n",
       "5    Samoa  2016    195000.0  Oceania.Polynesia  upper-middle            72.0   \n",
       "6    Tonga  2011    105000.0  Oceania.Polynesia  upper-middle            70.0   \n",
       "7    Tonga  2012         NaN  Oceania.Polynesia  upper-middle            70.0   \n",
       "8    Tonga  2013    105000.0  Oceania.Polynesia  upper-middle            70.1   \n",
       "9   Tonnga  2014    106000.0  Oceania.Polynesia  upper-middle            70.2   \n",
       "10   Tonga  2015    106000.0  Oceania.Polynesia  upper-middle       not given   \n",
       "11   Tonga  2016    107000.0  Oceania.Polynesia  upper-middle            70.4   \n",
       "\n",
       "    co2_per_capita  \n",
       "0            1.070  \n",
       "1              NaN  \n",
       "2              NaN  \n",
       "3            1.030  \n",
       "4              NaN  \n",
       "5              NaN  \n",
       "6            0.982  \n",
       "7            1.010  \n",
       "8            1.080  \n",
       "9            1.140  \n",
       "10             NaN  \n",
       "11             NaN  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df['income_group'] = clean_df['income_group'].str.lower().str.replace(' ', '-')\n",
    "clean_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the NA values are still around because the original data frame was never overwritten with modified one without NA values.\n",
    "\n",
    "To find spelling mistakes the `unique()` method is useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Samoa', 'samoa', 'Sammoa', 'Samia', 'Tonga', 'Tonnga'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df['country'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `replace()` method can be used here again, this time replacing several spelling mistakes simultaneously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Samoa', 'Sammoa', 'Tonga'], dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(clean_df['country']\n",
    "     .str.replace('samoa|Samia', 'Samoa')\n",
    "     .str.replace('Tonnga', 'Tonga')\n",
    "     .unique()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `|` bar means `or`, similar to how we saw it used previously with `loc[]`. Using a `|` in a string like this work because the `str.replace()` method supports \"regular expressions\". This is a powerful way of using strings as search patterns, such as with `|`, rather than interpreting the literally.\n",
    "\n",
    "A more powerful \"regular expression\" to replace everything starting with `S` or `s` with `Samoa` and every word starting with `T` with `Tongo` would look like this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Samoa', 'Tonga'], dtype=object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(clean_df['country']\n",
    "     .str.replace('[S,s].*', 'Samoa') # .* means \"any sequence of characters\n",
    "     .str.replace('T.*', 'Tonga')\n",
    "     .unique()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Entire books have been written on regular expressions and covering them fully here is outside the scope of this tutorial, but it is very useful to know about `|` (and to a lesser extent `[]` and `.*`) when replacing misspelled words."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another common data cleaning operation is to split one column into two in order to have one measurement per column. This can be done via `str.split()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     [Oceania, Polynesia]\n",
       "1     [Oceania, Polynesia]\n",
       "2     [Oceania, Polynesia]\n",
       "3     [Oceania, Polynesia]\n",
       "4     [Oceania, Polynesia]\n",
       "5     [Oceania, Polynesia]\n",
       "6     [Oceania, Polynesia]\n",
       "7     [Oceania, Polynesia]\n",
       "8     [Oceania, Polynesia]\n",
       "9     [Oceania, Polynesia]\n",
       "10    [Oceania, Polynesia]\n",
       "11    [Oceania, Polynesia]\n",
       "Name: region, dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df['region'].str.split('.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To assign the results to two different columns The returned object is a series where each row is a list of two values. This cannot be assigned to to different columns in the data frame, since there is only one column in the output. To get around this, we can append `str` to the output, which allows assigning the first item of each list to the first specified column and the second item to the second specified column."
   ]
  },
  {
   "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>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>region</th>\n",
       "      <th>income_group</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>co2_per_capita</th>\n",
       "      <th>sub_region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>2011</td>\n",
       "      <td>188000.0</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>71.5</td>\n",
       "      <td>1.070</td>\n",
       "      <td>Polynesia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>samoa</td>\n",
       "      <td>2012</td>\n",
       "      <td>189000.0</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>no value</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Polynesia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sammoa</td>\n",
       "      <td>2013</td>\n",
       "      <td>191000.0</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>71.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Polynesia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>missing</td>\n",
       "      <td>1.030</td>\n",
       "      <td>Polynesia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Samia</td>\n",
       "      <td>2015</td>\n",
       "      <td>194000.0</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>71.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Polynesia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>2016</td>\n",
       "      <td>195000.0</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>72.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Polynesia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2011</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.982</td>\n",
       "      <td>Polynesia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>Polynesia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2013</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>70.1</td>\n",
       "      <td>1.080</td>\n",
       "      <td>Polynesia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Tonnga</td>\n",
       "      <td>2014</td>\n",
       "      <td>106000.0</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>70.2</td>\n",
       "      <td>1.140</td>\n",
       "      <td>Polynesia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2015</td>\n",
       "      <td>106000.0</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>not given</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Polynesia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>2016</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>70.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Polynesia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year  population   region  income_group life_expectancy  \\\n",
       "0    Samoa  2011    188000.0  Oceania  upper-middle            71.5   \n",
       "1    samoa  2012    189000.0  Oceania  upper-middle        no value   \n",
       "2   Sammoa  2013    191000.0  Oceania  upper-middle            71.6   \n",
       "3    Samoa  2014         NaN  Oceania  upper-middle         missing   \n",
       "4    Samia  2015    194000.0  Oceania  upper-middle            71.7   \n",
       "5    Samoa  2016    195000.0  Oceania  upper-middle            72.0   \n",
       "6    Tonga  2011    105000.0  Oceania  upper-middle            70.0   \n",
       "7    Tonga  2012         NaN  Oceania  upper-middle            70.0   \n",
       "8    Tonga  2013    105000.0  Oceania  upper-middle            70.1   \n",
       "9   Tonnga  2014    106000.0  Oceania  upper-middle            70.2   \n",
       "10   Tonga  2015    106000.0  Oceania  upper-middle       not given   \n",
       "11   Tonga  2016    107000.0  Oceania  upper-middle            70.4   \n",
       "\n",
       "    co2_per_capita sub_region  \n",
       "0            1.070  Polynesia  \n",
       "1              NaN  Polynesia  \n",
       "2              NaN  Polynesia  \n",
       "3            1.030  Polynesia  \n",
       "4              NaN  Polynesia  \n",
       "5              NaN  Polynesia  \n",
       "6            0.982  Polynesia  \n",
       "7            1.010  Polynesia  \n",
       "8            1.080  Polynesia  \n",
       "9            1.140  Polynesia  \n",
       "10             NaN  Polynesia  \n",
       "11             NaN  Polynesia  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df['region'], clean_df['sub_region'] = clean_df['region'].str.split('.').str\n",
    "clean_df\n",
    "# To get only one of the list items, use indexing\n",
    "# clean_df['region'], clean_df['sub_region'] = clean_df['region'].str.split('.').str[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get rid of certain rows or columns, the `drop()` method can be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>income_group</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>co2_per_capita</th>\n",
       "      <th>sub_region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>188000.0</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>71.5</td>\n",
       "      <td>1.070</td>\n",
       "      <td>Polynesia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>191000.0</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>71.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Polynesia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>missing</td>\n",
       "      <td>1.030</td>\n",
       "      <td>Polynesia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>105000.0</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.982</td>\n",
       "      <td>Polynesia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>Polynesia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>105000.0</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>70.1</td>\n",
       "      <td>1.080</td>\n",
       "      <td>Polynesia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>106000.0</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>70.2</td>\n",
       "      <td>1.140</td>\n",
       "      <td>Polynesia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>106000.0</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>not given</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Polynesia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>107000.0</td>\n",
       "      <td>upper-middle</td>\n",
       "      <td>70.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Polynesia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    population  income_group life_expectancy  co2_per_capita sub_region\n",
       "0     188000.0  upper-middle            71.5           1.070  Polynesia\n",
       "2     191000.0  upper-middle            71.6             NaN  Polynesia\n",
       "3          NaN  upper-middle         missing           1.030  Polynesia\n",
       "6     105000.0  upper-middle            70.0           0.982  Polynesia\n",
       "7          NaN  upper-middle            70.0           1.010  Polynesia\n",
       "8     105000.0  upper-middle            70.1           1.080  Polynesia\n",
       "9     106000.0  upper-middle            70.2           1.140  Polynesia\n",
       "10    106000.0  upper-middle       not given             NaN  Polynesia\n",
       "11    107000.0  upper-middle            70.4             NaN  Polynesia"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df.drop(index=[1, 4, 5], columns=['region', 'year', 'country'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> # Challenge\n",
    ">\n",
    "> 1. Create a new data frame called `world_data_co2` from the `world_data` data that contains only the `country`, `year`, `population` and `co2_pet_capita` columns and no NA values.\n",
    "> 2. Create a new column in `clean_df_co2` called `total_co2` containing the total co2 emissions of the country using the per capita value in `co2_per_capita` and the `population` column.\n",
    "> 3. Retrieve all observations that have a `total_co2` value greater than 10,000,000."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More general resources on plotting\n",
    "\n",
    "- [Ten Simple Rules for Better Figures](http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003833)\n",
    "- [Finding the Right Color Palettes for Data Visualizations](https://blog.graphiq.com/finding-the-right-color-palettes-for-data-visualizations-fcd4e707a283)\n",
    "- [Examples of bad graphs](https://www.biostat.wisc.edu/~kbroman/topten_worstgraphs/)\n",
    "- [More examples of bad graphs and how to improve them](https://www.stat.auckland.ac.nz/~ihaka/120/Lectures/lecture03.pdf)\n",
    "- [Wikipedia has a great article on misleading graphs](https://en.wikipedia.org/wiki/Misleading_graph)\n",
    "- [Usability article about how to design for people with color blindness](http://blog.usabilla.com/how-to-design-for-color-blindness/)"
   ]
  }
 ],
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