{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": [
     "remove_input"
    ]
   },
   "outputs": [],
   "source": [
    "path_data = '../../data/'\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overlaid Graphs\n",
    "\n",
    "In this chapter, we have learned how to visualize data by drawing graphs. A common use of such visualizations is to compare two datasets. In this section, we will see how to *overlay* plots, that is, draw them in a single graphic on a common pair of axes.\n",
    "\n",
    "For the overlay to make sense, the graphs that are being overlaid must represent the same variables and be measured in the same units. \n",
    "\n",
    "To draw overlaid graphs, the methods `scatter`, `plot`, and `barh` can all be called in the same way. For `scatter` and `plot`, one column must serve as the common horizontal axis for all the overlaid graphs. For `barh`, one column must serve as the common axis which is the set of categories. The general call looks like:\n",
    "\n",
    "`df.method(column_label_of_common_axis, array_of_labels_of_variables_to_plot)`\n",
    "\n",
    "More commonly, we will first select only the columns needed for our graph, and then call the method by just specifying the variable on the common axis:\n",
    "\n",
    "`df.method(column_label_of_common_axis)`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter Plots\n",
    "\n",
    "[Franics Galton](https://en.wikipedia.org/wiki/Francis_Galton) (1822-1911) was an English polymath who was a pioneer in the analysis of relations between numerical variables. He was particularly interested in the controversial area of eugenics – indeed, he coined that term – which involves understading how physical traits are passed down from one generation to the next. \n",
    "\n",
    "Galton meticulously collected copious amounts of data, some of which we will analyze in this course. Here is a subset of Galton's data on heights of parents and their children. Specifically, the population consists of 179 men who were the first-born in their families. The data are their own heights and the heights of their parents. All heights were measured in inches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>father</th>\n",
       "      <th>mother</th>\n",
       "      <th>son</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>73.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75.5</td>\n",
       "      <td>66.5</td>\n",
       "      <td>73.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>70.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75.0</td>\n",
       "      <td>58.5</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>64.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>70.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>64.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>64.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>64.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>62.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>62.5</td>\n",
       "      <td>63.0</td>\n",
       "      <td>66.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     father  mother   son\n",
       "0      78.5    67.0  73.2\n",
       "1      75.5    66.5  73.5\n",
       "2      75.0    64.0  71.0\n",
       "3      75.0    64.0  70.5\n",
       "4      75.0    58.5  72.0\n",
       "..      ...     ...   ...\n",
       "174    64.0    64.0  70.5\n",
       "175    64.0    63.0  64.5\n",
       "176    64.0    60.0  66.0\n",
       "177    62.0    66.0  64.0\n",
       "178    62.5    63.0  66.5\n",
       "\n",
       "[179 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heights = pd.read_csv(path_data + 'galton_subset.csv')\n",
    "heights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `scatter` method allows us to visualize how the sons' heights are related to the heights of both their parents. In the graph, the sons' heights will form the common horizontal axis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(heights['son'], heights['father'])\n",
    "plt.scatter(heights['son'], heights['mother'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When using matplotlib the simplest way to achieve a scatter plot with two or more 'y' values is to call each plt separately. Having specified the variable (sons' heights) on the common horizontal axis. Python drew two scatter plots: one each for the relation between this variable and the other two.\n",
    "\n",
    "Both the gold and the blue scatter plots slope upwards and show a positive association between the sons' heights and the heights of both their parents. The blue (fathers) plot is in general higher than the gold, because the fathers were in general taller than the mothers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use Pandas to create a scatter plot and whilst there may be a reason for choosing one 'tool' over another, often this is just a matter of personal choice. With the Pandas [Visualization](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html#visualization) library we plot multiple column groups in a single axes by repeating the plot method (again) specifying target ax (shared x-axis)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = heights.plot.scatter(x='son', y='mother', color='DarkBlue', label='Mother');\n",
    "\n",
    "heights.plot.scatter(x='son', y='father', color='DarkGreen', label='Father', ax=ax);\n",
    "\n",
    "plt.ylabel('Parents')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line Plots\n",
    "\n",
    "Our next example involves data on children of more recent times. We will return to the Census data table `us_pop`, created below again for reference. From this, we will extract the counts of all children in each of the age categories 0 through 18 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>AGE</th>\n",
       "      <th>2010</th>\n",
       "      <th>2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3951330</td>\n",
       "      <td>3949775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3957888</td>\n",
       "      <td>3949776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4090862</td>\n",
       "      <td>3959664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4111920</td>\n",
       "      <td>4007079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4077551</td>\n",
       "      <td>4005716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>4064653</td>\n",
       "      <td>4006900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>4073013</td>\n",
       "      <td>4135930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>4043046</td>\n",
       "      <td>4155326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>4025604</td>\n",
       "      <td>4120903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>4125415</td>\n",
       "      <td>4108349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>4187062</td>\n",
       "      <td>4116942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>4115511</td>\n",
       "      <td>4087402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>4113279</td>\n",
       "      <td>4070682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>4119666</td>\n",
       "      <td>4171030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>4145614</td>\n",
       "      <td>4233839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>4231002</td>\n",
       "      <td>4164796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>4313252</td>\n",
       "      <td>4168559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>4376367</td>\n",
       "      <td>4186513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>4491005</td>\n",
       "      <td>4227920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    AGE     2010     2014\n",
       "0     0  3951330  3949775\n",
       "1     1  3957888  3949776\n",
       "2     2  4090862  3959664\n",
       "3     3  4111920  4007079\n",
       "4     4  4077551  4005716\n",
       "5     5  4064653  4006900\n",
       "6     6  4073013  4135930\n",
       "7     7  4043046  4155326\n",
       "8     8  4025604  4120903\n",
       "9     9  4125415  4108349\n",
       "10   10  4187062  4116942\n",
       "11   11  4115511  4087402\n",
       "12   12  4113279  4070682\n",
       "13   13  4119666  4171030\n",
       "14   14  4145614  4233839\n",
       "15   15  4231002  4164796\n",
       "16   16  4313252  4168559\n",
       "17   17  4376367  4186513\n",
       "18   18  4491005  4227920"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the full Census table\n",
    "census_url = 'http://www2.census.gov/programs-surveys/popest/datasets/2010-2015/national/asrh/nc-est2015-agesex-res.csv'\n",
    "\n",
    "full_census_table = pd.read_csv(census_url)\n",
    "\n",
    "# Select columns from the full table and relabel some of them\n",
    "partial_census_table = full_census_table[['SEX', 'AGE', 'POPESTIMATE2010', 'POPESTIMATE2014']]\n",
    "\n",
    "us_pop = partial_census_table.rename(columns={'POPESTIMATE2010':'2010', 'POPESTIMATE2014':'2014'})\n",
    "\n",
    "# Access the rows corresponding to all children, ages 0-18\n",
    "children = us_pop[(us_pop['SEX'] == 0) & (us_pop['AGE'] < 19)].drop(columns=['SEX'])\n",
    "\n",
    "children"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now draw two overlaid line plots, showing the numbers of children in the different age groups for each of the years 2010 and 2014. The method call is analogous to the `scatter` call in the previous example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "children.plot('AGE')\n",
    "\n",
    "plt.ylim(3900000, 4500000)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On this scale, it's important to remember that we only have data at ages 0, 1, 2, and so on; the graphs \"join the dots\" in between.\n",
    "\n",
    "The graphs cross each other in a few places: for example, there were more 4-year-olds in 2010 than in 2014, and there were more 14-year-olds in 2014 than in 2010.\n",
    "\n",
    "Of course, the 14-year-olds in 2014 mostly consist of the 10-year-olds in 2010. To see this, look at the red graph at `AGE` 14 and the blue graph at `AGE` 10. Indeed, you will notice that the entire red graph (2014) looks like the blue graph (2010) slid over to the right by 4 years. The slide is accompanied by a slight rise due to the net effect of children who entered the country between 2010 and 2014 outnumbering those who left. Fortunately at these ages there is not much loss of life."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar Charts\n",
    "\n",
    "For our final example of this section, we look at distributions of ethnicities of adults and children in California as well as in the entire United States.\n",
    "\n",
    "The Kaiser Family Foundation has complied Census data on the distribution of race and ethnicity in the U.S. The Foundation's website provides compilations of data for [the entire U.S. population](http://kff.org/other/state-indicator/distribution-by-raceethnicity/) in 2014, as well as for [U.S. children](http://kff.org/other/state-indicator/children-by-raceethnicity/) who were younger than 18 years old that year.\n",
    "\n",
    "Here is a table adapted from their data for the United States and California. The columns represent everyone in the U.S.A., everyone in California, children in the U.S.A., and children in California. The body of the table contains proportions in the different categories. Each column shows the distribution of ethnicities in the group of people corresponding to that column. So in each column, the entries add up to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>USA All</th>\n",
       "      <th>CA All</th>\n",
       "      <th>USA Children</th>\n",
       "      <th>CA Children</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ethnicity</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>0.12</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>0.62</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>0.08</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           USA All  CA All  USA Children  CA Children\n",
       "Ethnicity                                            \n",
       "Black         0.12    0.05          0.14         0.05\n",
       "Hispanic      0.18    0.38          0.24         0.50\n",
       "White         0.62    0.39          0.52         0.29\n",
       "Other         0.08    0.18          0.10         0.16"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usa_ca = pd.read_csv(path_data + 'usa_ca_2014.csv')\n",
    "\n",
    "usa_ca.set_index('Ethnicity')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is natural to want to compare these distributions. It makes sense to compare the columns directly, because all the entries are proportions and are therefore on the same scale.\n",
    "\n",
    "The method `barh` allows us to visualize the comparisons by drawing multiple bar charts on the same axes. The call is analogous to those for `scatter` and `plot`: we have to specify the common axis of categories. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "usa_ca.plot.barh('Ethnicity')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While drawing the overlaid bar charts is straightforward, there is a bit too much information on this graph for us to be able to sort out similarities and differences between populations. It seems clear that the distributions of ethnicities for everyone in the U.S. and for children in the U.S. are more similar to each other than any other pair, but it's much easier to compare the populations one pair at a time. \n",
    "\n",
    "Let's start by comparing the entire populations of the U.S.A. and California. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "usa_ca1 = usa_ca[['Ethnicity', 'USA All', 'CA All']].plot.barh('Ethnicity', width=0.8, color=('goldenrod', 'navy'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two distributions are quite different. California has higher proportions in the `Hispanic` and `Other` categories, and correspondingly lower proportions of `Black` and `White`. The differences are largely due to California's geographical location and patterns of immigration, both historically and in more recent decades. For example, the `Other` category in California includes a significant proportion of Asians and Pacific Islanders.\n",
    "\n",
    "As you can see from the graph, almost 40% of the Californian population in 2014 was `Hispanic`. A comparison with the population of children in the state indicates that the `Hispanic` proportion is likely to be greater in future years. Among Californian children, 50% are in the `Hispanic` category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "usa_ca1 = usa_ca[['Ethnicity', 'CA All', 'CA Children']].plot.barh('Ethnicity', width=0.8, color=('goldenrod', 'navy'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More complex datasets naturally give rise to varied and interesting visualizations, including overlaid graphs of different kinds. To analyze such data, it helps to have some more skills in data manipulation, so that we can get the data into a form that allows us to use methods like those in this section. In the next chapter we will develop some of these skills."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}