{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting started with plotting in python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need some biolerplate code, to load the plotting library and to set up your Jupyter notebook for interactive plotting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need a second library, called [pandas](https://pandas.pydata.org), which helps with working with data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding documentation for python functions is easy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "?pd.read_csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we read in the population frequency data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat = pd.read_csv(\"population_frequencies.txt\", delim_whitespace=True, names=[\"nr\", \"pop\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and verify that it worked:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>nr</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>Abkhasian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>Adygei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>Albanian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>Aleut</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Aleut_Tlingit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>Altaian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>Ami</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>Armenian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Atayal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Balkar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>29</td>\n",
       "      <td>Basque</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>25</td>\n",
       "      <td>BedouinA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19</td>\n",
       "      <td>BedouinB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10</td>\n",
       "      <td>Belarusian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6</td>\n",
       "      <td>BolshoyOleniOstrov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>9</td>\n",
       "      <td>Borneo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10</td>\n",
       "      <td>Bulgarian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8</td>\n",
       "      <td>Cambodian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>Canary_Islander</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>ChalmnyVarre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>9</td>\n",
       "      <td>Chechen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>20</td>\n",
       "      <td>Chukchi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>Chukchi1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>10</td>\n",
       "      <td>Chuvash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>10</td>\n",
       "      <td>Croatian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>8</td>\n",
       "      <td>Cypriot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>10</td>\n",
       "      <td>Czech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>10</td>\n",
       "      <td>Dai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>9</td>\n",
       "      <td>Daur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4</td>\n",
       "      <td>Dolgan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>27</td>\n",
       "      <td>Sardinian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>8</td>\n",
       "      <td>Saudi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>4</td>\n",
       "      <td>Scottish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>10</td>\n",
       "      <td>Selkup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>10</td>\n",
       "      <td>Semende</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>10</td>\n",
       "      <td>She</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2</td>\n",
       "      <td>Sherpa.DG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>11</td>\n",
       "      <td>Sicilian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>53</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>5</td>\n",
       "      <td>Spanish_North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>8</td>\n",
       "      <td>Syrian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>8</td>\n",
       "      <td>Tajik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>10</td>\n",
       "      <td>Thai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2</td>\n",
       "      <td>Tibetan.DG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>10</td>\n",
       "      <td>Tu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>22</td>\n",
       "      <td>Tubalar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>10</td>\n",
       "      <td>Tujia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>50</td>\n",
       "      <td>Turkish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>7</td>\n",
       "      <td>Turkmen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>10</td>\n",
       "      <td>Tuvinian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>9</td>\n",
       "      <td>Ukrainian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>25</td>\n",
       "      <td>Ulchi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>10</td>\n",
       "      <td>Uygur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>10</td>\n",
       "      <td>Uzbek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>3</td>\n",
       "      <td>WHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>7</td>\n",
       "      <td>Xibo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>20</td>\n",
       "      <td>Yakut</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>9</td>\n",
       "      <td>Yamnaya_Samara</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>10</td>\n",
       "      <td>Yi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>19</td>\n",
       "      <td>Yukagir</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>116 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     nr                 pop\n",
       "0     9           Abkhasian\n",
       "1    16              Adygei\n",
       "2     6            Albanian\n",
       "3     7               Aleut\n",
       "4     4       Aleut_Tlingit\n",
       "5     7             Altaian\n",
       "6    10                 Ami\n",
       "7    10            Armenian\n",
       "8     9              Atayal\n",
       "9    10              Balkar\n",
       "10   29              Basque\n",
       "11   25            BedouinA\n",
       "12   19            BedouinB\n",
       "13   10          Belarusian\n",
       "14    6  BolshoyOleniOstrov\n",
       "15    9              Borneo\n",
       "16   10           Bulgarian\n",
       "17    8           Cambodian\n",
       "18    2     Canary_Islander\n",
       "19    2        ChalmnyVarre\n",
       "20    9             Chechen\n",
       "21   20             Chukchi\n",
       "22    3            Chukchi1\n",
       "23   10             Chuvash\n",
       "24   10            Croatian\n",
       "25    8             Cypriot\n",
       "26   10               Czech\n",
       "27   10                 Dai\n",
       "28    9                Daur\n",
       "29    4              Dolgan\n",
       "..   ..                 ...\n",
       "86   27           Sardinian\n",
       "87    8               Saudi\n",
       "88    4            Scottish\n",
       "89   10              Selkup\n",
       "90   10             Semende\n",
       "91   10                 She\n",
       "92    2           Sherpa.DG\n",
       "93   11            Sicilian\n",
       "94   53             Spanish\n",
       "95    5       Spanish_North\n",
       "96    8              Syrian\n",
       "97    8               Tajik\n",
       "98   10                Thai\n",
       "99    2          Tibetan.DG\n",
       "100  10                  Tu\n",
       "101  22             Tubalar\n",
       "102  10               Tujia\n",
       "103  50             Turkish\n",
       "104   7             Turkmen\n",
       "105  10            Tuvinian\n",
       "106   9           Ukrainian\n",
       "107  25               Ulchi\n",
       "108  10               Uygur\n",
       "109  10               Uzbek\n",
       "110   3                 WHG\n",
       "111   7                Xibo\n",
       "112  20               Yakut\n",
       "113   9      Yamnaya_Samara\n",
       "114  10                  Yi\n",
       "115  19             Yukagir\n",
       "\n",
       "[116 rows x 2 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, so let's proceed with simple plotting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8e0c1af198>]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dat[\"nr\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not bad, but we'd like to sort the values. For that we use the `sort_values` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "?dat.sort_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat_sorted = dat.sort_values(by=\"nr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>nr</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1</td>\n",
       "      <td>Italian_South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1</td>\n",
       "      <td>JK2065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1</td>\n",
       "      <td>Saami_WGA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2</td>\n",
       "      <td>Levanluhta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>2</td>\n",
       "      <td>Saami.DG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>ChalmnyVarre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>Canary_Islander</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2</td>\n",
       "      <td>Sherpa.DG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2</td>\n",
       "      <td>Tibetan.DG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>Chukchi1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>3</td>\n",
       "      <td>WHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>4</td>\n",
       "      <td>Scottish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4</td>\n",
       "      <td>Dolgan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Aleut_Tlingit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>5</td>\n",
       "      <td>Spanish_North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>6</td>\n",
       "      <td>Jew_Iraqi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>6</td>\n",
       "      <td>Korean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>6</td>\n",
       "      <td>Itelmen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6</td>\n",
       "      <td>BolshoyOleniOstrov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>Albanian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>6</td>\n",
       "      <td>Mongola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6</td>\n",
       "      <td>Jew_Moroccan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>7</td>\n",
       "      <td>Jew_Ashkenazi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>7</td>\n",
       "      <td>Turkmen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>7</td>\n",
       "      <td>Jew_Georgian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>7</td>\n",
       "      <td>Jew_Tunisian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>Aleut</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>Altaian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>7</td>\n",
       "      <td>Xibo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>8</td>\n",
       "      <td>Iranian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>10</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>10</td>\n",
       "      <td>Tuvinian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10</td>\n",
       "      <td>Belarusian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>11</td>\n",
       "      <td>Norwegian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>11</td>\n",
       "      <td>Nganasan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>11</td>\n",
       "      <td>Sicilian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>12</td>\n",
       "      <td>Icelandic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>13</td>\n",
       "      <td>Orcadian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>14</td>\n",
       "      <td>LBK_EN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>Adygei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>19</td>\n",
       "      <td>Yukagir</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19</td>\n",
       "      <td>BedouinB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>20</td>\n",
       "      <td>Chukchi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>20</td>\n",
       "      <td>Yakut</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>20</td>\n",
       "      <td>Italian_North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>20</td>\n",
       "      <td>Hungarian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>20</td>\n",
       "      <td>Greek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>22</td>\n",
       "      <td>Tubalar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>22</td>\n",
       "      <td>Russian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>25</td>\n",
       "      <td>Ulchi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>25</td>\n",
       "      <td>BedouinA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>27</td>\n",
       "      <td>Sardinian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>29</td>\n",
       "      <td>Basque</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>29</td>\n",
       "      <td>Japanese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>32</td>\n",
       "      <td>French</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>38</td>\n",
       "      <td>Palestinian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>39</td>\n",
       "      <td>Druze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>43</td>\n",
       "      <td>Han</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>50</td>\n",
       "      <td>Turkish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>53</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>116 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     nr                 pop\n",
       "44    1       Italian_South\n",
       "56    1              JK2065\n",
       "85    1           Saami_WGA\n",
       "67    2          Levanluhta\n",
       "84    2            Saami.DG\n",
       "19    2        ChalmnyVarre\n",
       "18    2     Canary_Islander\n",
       "92    2           Sherpa.DG\n",
       "99    2          Tibetan.DG\n",
       "22    3            Chukchi1\n",
       "110   3                 WHG\n",
       "88    4            Scottish\n",
       "29    4              Dolgan\n",
       "4     4       Aleut_Tlingit\n",
       "95    5       Spanish_North\n",
       "50    6           Jew_Iraqi\n",
       "60    6              Korean\n",
       "45    6             Itelmen\n",
       "14    6  BolshoyOleniOstrov\n",
       "2     6            Albanian\n",
       "73    6             Mongola\n",
       "52    6        Jew_Moroccan\n",
       "47    7       Jew_Ashkenazi\n",
       "104   7             Turkmen\n",
       "48    7        Jew_Georgian\n",
       "53    7        Jew_Tunisian\n",
       "3     7               Aleut\n",
       "5     7             Altaian\n",
       "111   7                Xibo\n",
       "42    8             Iranian\n",
       "..   ..                 ...\n",
       "31   10             English\n",
       "105  10            Tuvinian\n",
       "13   10          Belarusian\n",
       "78   11           Norwegian\n",
       "76   11            Nganasan\n",
       "93   11            Sicilian\n",
       "41   12           Icelandic\n",
       "79   13            Orcadian\n",
       "65   14              LBK_EN\n",
       "1    16              Adygei\n",
       "115  19             Yukagir\n",
       "12   19            BedouinB\n",
       "21   20             Chukchi\n",
       "112  20               Yakut\n",
       "43   20       Italian_North\n",
       "40   20           Hungarian\n",
       "37   20               Greek\n",
       "101  22             Tubalar\n",
       "83   22             Russian\n",
       "107  25               Ulchi\n",
       "11   25            BedouinA\n",
       "86   27           Sardinian\n",
       "10   29              Basque\n",
       "46   29            Japanese\n",
       "35   32              French\n",
       "82   38         Palestinian\n",
       "30   39               Druze\n",
       "38   43                 Han\n",
       "103  50             Turkish\n",
       "94   53             Spanish\n",
       "\n",
       "[116 rows x 2 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8e0bff15c0>]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(len(dat_sorted))\n",
    "y = dat_sorted[\"nr\"]\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we just need to add tick labels and change the size of the plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dat_sorted = dat.sort_values(by=\"nr\")\n",
    "y = dat_sorted[\"nr\"]\n",
    "x = range(len(y))\n",
    "xticks = dat_sorted[\"pop\"]\n",
    "plt.figure(figsize=(20,8))\n",
    "plt.plot(x, y)\n",
    "plt.xticks(x, xticks, rotation=\"vertical\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, this was a very short introduction to python and plotting. Clearly there is a lot more to learn, but hopefully this may serve as a teaser for learning more about it. The [matplotlib-](https://matplotlib.org) and [pandas-](https://pandas.pydata.org)libraries are both well documented, check out the linked websites to find out more."
   ]
  }
 ],
 "metadata": {
  "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": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}