{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Background\n",
    "this notebook is focused on documenting the use of [Matplotlib](https://matplotlib.org/) and [Seaborn](https://seaborn.pydata.org/) to plot various data in Python.\n",
    "\n",
    "You can find the docs at:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for plotting in Jupyter, see https://ipython.readthedocs.io/en/stable/interactive/reference.html#plotting-with-matplotlib\n",
    "\n",
    "That documentation states:\n",
    "\n",
    "    If IPython is already running, you can run the `%matplotlib` magic. If no arguments are given, IPython will automatically detect your choice of matplotlib backend. You can also request a specific backend with `%matplotlib backend`, where `backend` must be one of: ‘tk’, ‘qt’, ‘wx’, ‘gtk’, ‘osx’. In the web notebook and Qt console, ‘inline’ is also a valid backend value, which produces static figures inlined inside the application window instead of matplotlib’s interactive figures that live in separate windows.\n",
    "\n",
    "https://github.com/matplotlib/jupyter-matplotlib is the extension for interactive\n",
    "To install ipympl with pip:\n",
    "```\n",
    "pip install ipympl\n",
    "# If using JupyterLab\n",
    "# Install nodejs: https://nodejs.org/en/download/\n",
    "jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
    "jupyter labextension install jupyter-matplotlib\n",
    "```\n",
    "\n",
    "To enable the jupyter-matplotlib backend, simply use the matplotlib Jupyter magic:\n",
    "\n",
    "`%matplotlib widget`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# magic for plotting in notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/seaborn/utils.py:376: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"lxml\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
      "\n",
      "The code that caused this warning is on line 376 of the file /usr/lib/python3/dist-packages/seaborn/utils.py. To get rid of this warning, pass the additional argument 'features=\"lxml\"' to the BeautifulSoup constructor.\n",
      "\n",
      "  gh_list = BeautifulSoup(http)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['anscombe',\n",
       " 'attention',\n",
       " 'brain_networks',\n",
       " 'car_crashes',\n",
       " 'diamonds',\n",
       " 'dots',\n",
       " 'exercise',\n",
       " 'flights',\n",
       " 'fmri',\n",
       " 'gammas',\n",
       " 'geyser',\n",
       " 'iris',\n",
       " 'mpg',\n",
       " 'penguins',\n",
       " 'planets',\n",
       " 'tips',\n",
       " 'titanic']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seaborn has some datasets available\n",
    "sns.get_dataset_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = sns.load_dataset('iris')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "5             5.4          3.9           1.7          0.4     setosa\n",
       "6             4.6          3.4           1.4          0.3     setosa\n",
       "7             5.0          3.4           1.5          0.2     setosa\n",
       "8             4.4          2.9           1.4          0.2     setosa\n",
       "9             4.9          3.1           1.5          0.1     setosa\n",
       "10            5.4          3.7           1.5          0.2     setosa\n",
       "11            4.8          3.4           1.6          0.2     setosa\n",
       "12            4.8          3.0           1.4          0.1     setosa\n",
       "13            4.3          3.0           1.1          0.1     setosa\n",
       "14            5.8          4.0           1.2          0.2     setosa\n",
       "15            5.7          4.4           1.5          0.4     setosa\n",
       "16            5.4          3.9           1.3          0.4     setosa\n",
       "17            5.1          3.5           1.4          0.3     setosa\n",
       "18            5.7          3.8           1.7          0.3     setosa\n",
       "19            5.1          3.8           1.5          0.3     setosa\n",
       "20            5.4          3.4           1.7          0.2     setosa\n",
       "21            5.1          3.7           1.5          0.4     setosa\n",
       "22            4.6          3.6           1.0          0.2     setosa\n",
       "23            5.1          3.3           1.7          0.5     setosa\n",
       "24            4.8          3.4           1.9          0.2     setosa\n",
       "25            5.0          3.0           1.6          0.2     setosa\n",
       "26            5.0          3.4           1.6          0.4     setosa\n",
       "27            5.2          3.5           1.5          0.2     setosa\n",
       "28            5.2          3.4           1.4          0.2     setosa\n",
       "29            4.7          3.2           1.6          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "120           6.9          3.2           5.7          2.3  virginica\n",
       "121           5.6          2.8           4.9          2.0  virginica\n",
       "122           7.7          2.8           6.7          2.0  virginica\n",
       "123           6.3          2.7           4.9          1.8  virginica\n",
       "124           6.7          3.3           5.7          2.1  virginica\n",
       "125           7.2          3.2           6.0          1.8  virginica\n",
       "126           6.2          2.8           4.8          1.8  virginica\n",
       "127           6.1          3.0           4.9          1.8  virginica\n",
       "128           6.4          2.8           5.6          2.1  virginica\n",
       "129           7.2          3.0           5.8          1.6  virginica\n",
       "130           7.4          2.8           6.1          1.9  virginica\n",
       "131           7.9          3.8           6.4          2.0  virginica\n",
       "132           6.4          2.8           5.6          2.2  virginica\n",
       "133           6.3          2.8           5.1          1.5  virginica\n",
       "134           6.1          2.6           5.6          1.4  virginica\n",
       "135           7.7          3.0           6.1          2.3  virginica\n",
       "136           6.3          3.4           5.6          2.4  virginica\n",
       "137           6.4          3.1           5.5          1.8  virginica\n",
       "138           6.0          3.0           4.8          1.8  virginica\n",
       "139           6.9          3.1           5.4          2.1  virginica\n",
       "140           6.7          3.1           5.6          2.4  virginica\n",
       "141           6.9          3.1           5.1          2.3  virginica\n",
       "142           5.8          2.7           5.1          1.9  virginica\n",
       "143           6.8          3.2           5.9          2.3  virginica\n",
       "144           6.7          3.3           5.7          2.5  virginica\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# datasets are pandas dataframes\n",
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Colors\n",
    "seaborns's color palettes is documented at https://seaborn.pydata.org/tutorial/color_palettes.html#palette-tutorial\n",
    "\n",
    "`color_palette()` will accept the name of any seaborn palette or matplotlib colormap (except `jet`, which you should never use). It can also take a list of colors specified in any valid matplotlib format (RGB tuples, hex color codes, or HTML color names). The return value is always a list of RGB tuples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),\n",
       " (1.0, 0.4980392156862745, 0.054901960784313725),\n",
       " (0.17254901960784313, 0.6274509803921569, 0.17254901960784313),\n",
       " (0.8392156862745098, 0.15294117647058825, 0.1568627450980392),\n",
       " (0.5803921568627451, 0.403921568627451, 0.7411764705882353),\n",
       " (0.5490196078431373, 0.33725490196078434, 0.29411764705882354),\n",
       " (0.8901960784313725, 0.4666666666666667, 0.7607843137254902),\n",
       " (0.4980392156862745, 0.4980392156862745, 0.4980392156862745),\n",
       " (0.7372549019607844, 0.7411764705882353, 0.13333333333333333),\n",
       " (0.09019607843137255, 0.7450980392156863, 0.8117647058823529)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get default color palette tuples\n",
    "sns.color_palette()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# or to look at them\n",
    "sns.palplot(sns.color_palette())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# predefined palettes `Blues`\n",
    "sns.palplot(sns.color_palette(\"Blues\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# predefined palettes dark version\n",
    "sns.palplot(sns.color_palette(\"Blues_d\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set size of palette\n",
    "sns.palplot(sns.color_palette(\"Blues\", 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# predefined palettes\n",
    "sns.palplot(sns.color_palette(\"Greens\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scatter\n",
    "seaborns's `scatterplot()` is documented at https://seaborn.pydata.org/generated/seaborn.scatterplot.html#seaborn.scatterplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc2350fce10>"
      ]
     },
     "execution_count": 21,
     "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": [
    "# start simple\n",
    "sns.scatterplot(x=\"petal_length\", y=\"petal_width\", data=iris[iris.species == \"setosa\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc23511b898>"
      ]
     },
     "execution_count": 20,
     "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": [
    "# can separate another column by color using `hue`\n",
    "sns.scatterplot(x=\"petal_length\", y=\"petal_width\", data=iris, hue=\"species\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc234ea2be0>"
      ]
     },
     "execution_count": 30,
     "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": [
    "# can choose colors redefining the `palette`\n",
    "sns.scatterplot(x=\"petal_length\", y=\"petal_width\", data=iris, hue=\"species\", palette=[\"r\", \"g\", \"b\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc2350764a8>"
      ]
     },
     "execution_count": 22,
     "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": [
    "# can also separate another column by size using `size`\n",
    "sns.scatterplot(x=\"petal_length\", y=\"petal_width\", data=iris, size=\"species\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc234c566a0>"
      ]
     },
     "execution_count": 33,
     "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": [
    "# can also separate another column by markers using `style`\n",
    "sns.scatterplot(x=\"petal_length\", y=\"petal_width\", data=iris, style=\"species\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc234bd9390>"
      ]
     },
     "execution_count": 34,
     "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": [
    "# can mix combinations\n",
    "sns.scatterplot(x=\"petal_length\", y=\"petal_width\", data=iris, hue=\"species\", style=\"species\")"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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