tutorials/tutorial_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python for biologists\n",
    "\n",
    "## Tutorial 2 - Read, process, and plot biological data\n",
    "\n",
    "*by Tobias Andermann*\n",
    "\n",
    "In this tutorial you learn how to read data frames with python and how to select, transform, analyze, and plot the data.\n",
    "You can find a data file named `petal_sepal_length.txt` in the tutorial folder. This file contains the measured lengths of petals and sepals of 50 individuals of the plant species `Iris pythonica` (fake data). All individuals are classified as the same species and we want to test if we can find any clustering of these characters or if they are continuously distributed. Some of the following steps are not necessary for this purpose but are meant to provide some useful commands which can be used in many different situations.\n",
    "\n",
    "### 1. Installation\n",
    "\n",
    "For this tutorial we will need several addiitonal packages which you can install from your bash command line like this:\n",
    "\n",
    "```\n",
    "pip install pandas\n",
    "pip install matplotlib\n",
    "pip install sklearn\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Use pandas to read a dataframe\n",
    "Use the `read_csv()` function to read the csv file. You have to specify how the cells in the file are delimited (`,` is the default), which in this case is tab-delimited (`\\t`).\n",
    "\n",
    "Note: the pandas library also has a very hand `read_excel()` function which can be used to read excel files directly (no exporting of data in csv format necessary). Try it out if you have some excel file on your computer that you want to read into python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('../data/petal_sepal_length.txt',sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View the first 10 lines of the dataframe using `head()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>sample_name</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>sepal_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sample1</td>\n",
       "      <td>2.037214</td>\n",
       "      <td>5.172704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sample2</td>\n",
       "      <td>4.915837</td>\n",
       "      <td>6.149615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sample3</td>\n",
       "      <td>1.614314</td>\n",
       "      <td>5.939969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sample4</td>\n",
       "      <td>1.525709</td>\n",
       "      <td>6.603432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sample5</td>\n",
       "      <td>3.729975</td>\n",
       "      <td>6.065105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sample6</td>\n",
       "      <td>0.778760</td>\n",
       "      <td>5.173370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sample7</td>\n",
       "      <td>5.291489</td>\n",
       "      <td>7.381162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sample8</td>\n",
       "      <td>5.486099</td>\n",
       "      <td>6.529424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sample9</td>\n",
       "      <td>1.588885</td>\n",
       "      <td>5.447183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sample10</td>\n",
       "      <td>3.506057</td>\n",
       "      <td>5.821899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sample_name  petal_length  sepal_length\n",
       "0     sample1      2.037214      5.172704\n",
       "1     sample2      4.915837      6.149615\n",
       "2     sample3      1.614314      5.939969\n",
       "3     sample4      1.525709      6.603432\n",
       "4     sample5      3.729975      6.065105\n",
       "5     sample6      0.778760      5.173370\n",
       "6     sample7      5.291489      7.381162\n",
       "7     sample8      5.486099      6.529424\n",
       "8     sample9      1.588885      5.447183\n",
       "9    sample10      3.506057      5.821899"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the .iloc() function to extract a specific cell form the dataframe, e.g. the 2nd cell in the 2nd column (remember that python indexing starts with 0, so the element we want to print has the index 1,1):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.915837058559057"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[1,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the column names of the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sample_name', 'petal_length', 'sepal_length'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract one of the columns by name. You can either do it this way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2.037214\n",
       "1     4.915837\n",
       "2     1.614314\n",
       "3     1.525709\n",
       "4     3.729975\n",
       "5     0.778760\n",
       "6     5.291489\n",
       "7     5.486099\n",
       "8     1.588885\n",
       "9     3.506057\n",
       "10    1.034731\n",
       "11    3.918415\n",
       "12    4.704223\n",
       "13    1.274312\n",
       "14    4.027879\n",
       "15    1.052670\n",
       "16    5.124328\n",
       "17    0.727854\n",
       "18    1.889160\n",
       "19    5.857770\n",
       "20    0.982377\n",
       "21    1.642252\n",
       "22    1.813812\n",
       "23    1.675574\n",
       "24    1.402000\n",
       "25    5.010073\n",
       "26    1.547179\n",
       "27    5.583385\n",
       "28    5.602358\n",
       "29    0.999512\n",
       "30    6.357546\n",
       "31    1.598450\n",
       "32    6.383405\n",
       "33    3.502309\n",
       "34    1.489134\n",
       "35    1.414486\n",
       "36    4.695659\n",
       "37    4.314651\n",
       "38    4.332901\n",
       "39    5.355377\n",
       "40    4.215726\n",
       "41    5.547972\n",
       "42    4.877986\n",
       "43    1.992262\n",
       "44    1.514988\n",
       "45    1.624965\n",
       "46    1.600432\n",
       "47    1.437563\n",
       "48    1.549250\n",
       "49    5.113597\n",
       "Name: petal_length, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.petal_length"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or this way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2.037214\n",
       "1     4.915837\n",
       "2     1.614314\n",
       "3     1.525709\n",
       "4     3.729975\n",
       "5     0.778760\n",
       "6     5.291489\n",
       "7     5.486099\n",
       "8     1.588885\n",
       "9     3.506057\n",
       "10    1.034731\n",
       "11    3.918415\n",
       "12    4.704223\n",
       "13    1.274312\n",
       "14    4.027879\n",
       "15    1.052670\n",
       "16    5.124328\n",
       "17    0.727854\n",
       "18    1.889160\n",
       "19    5.857770\n",
       "20    0.982377\n",
       "21    1.642252\n",
       "22    1.813812\n",
       "23    1.675574\n",
       "24    1.402000\n",
       "25    5.010073\n",
       "26    1.547179\n",
       "27    5.583385\n",
       "28    5.602358\n",
       "29    0.999512\n",
       "30    6.357546\n",
       "31    1.598450\n",
       "32    6.383405\n",
       "33    3.502309\n",
       "34    1.489134\n",
       "35    1.414486\n",
       "36    4.695659\n",
       "37    4.314651\n",
       "38    4.332901\n",
       "39    5.355377\n",
       "40    4.215726\n",
       "41    5.547972\n",
       "42    4.877986\n",
       "43    1.992262\n",
       "44    1.514988\n",
       "45    1.624965\n",
       "46    1.600432\n",
       "47    1.437563\n",
       "48    1.549250\n",
       "49    5.113597\n",
       "Name: petal_length, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['petal_length']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check length of dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Transform and filter data frame:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sort the dataframe by one of the column values, e.g. by the column `petal_length`.\n",
    "\n",
    "In the example command I'm not storing the sorted dataframe, but am simply printing it to the screen. If you want to store the sorted dataframe, you can define the sorted dataframe as a new variable or use the `inplace=True` flag inside the command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "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>sample_name</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>sepal_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>sample33</td>\n",
       "      <td>6.383405</td>\n",
       "      <td>5.473322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>sample31</td>\n",
       "      <td>6.357546</td>\n",
       "      <td>6.109699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>sample20</td>\n",
       "      <td>5.857770</td>\n",
       "      <td>4.775423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>sample29</td>\n",
       "      <td>5.602358</td>\n",
       "      <td>5.734823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>sample28</td>\n",
       "      <td>5.583385</td>\n",
       "      <td>6.768361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>sample42</td>\n",
       "      <td>5.547972</td>\n",
       "      <td>6.519929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sample8</td>\n",
       "      <td>5.486099</td>\n",
       "      <td>6.529424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>sample40</td>\n",
       "      <td>5.355377</td>\n",
       "      <td>6.257550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sample7</td>\n",
       "      <td>5.291489</td>\n",
       "      <td>7.381162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>sample17</td>\n",
       "      <td>5.124328</td>\n",
       "      <td>5.799524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>sample50</td>\n",
       "      <td>5.113597</td>\n",
       "      <td>5.029044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>sample26</td>\n",
       "      <td>5.010073</td>\n",
       "      <td>6.460642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sample2</td>\n",
       "      <td>4.915837</td>\n",
       "      <td>6.149615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>sample43</td>\n",
       "      <td>4.877986</td>\n",
       "      <td>5.499600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>sample13</td>\n",
       "      <td>4.704223</td>\n",
       "      <td>6.241321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sample_name  petal_length  sepal_length\n",
       "32    sample33      6.383405      5.473322\n",
       "30    sample31      6.357546      6.109699\n",
       "19    sample20      5.857770      4.775423\n",
       "28    sample29      5.602358      5.734823\n",
       "27    sample28      5.583385      6.768361\n",
       "41    sample42      5.547972      6.519929\n",
       "7      sample8      5.486099      6.529424\n",
       "39    sample40      5.355377      6.257550\n",
       "6      sample7      5.291489      7.381162\n",
       "16    sample17      5.124328      5.799524\n",
       "49    sample50      5.113597      5.029044\n",
       "25    sample26      5.010073      6.460642\n",
       "1      sample2      4.915837      6.149615\n",
       "42    sample43      4.877986      5.499600\n",
       "12    sample13      4.704223      6.241321"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(by=['petal_length'],ascending=False).head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say we want to find those samples, which have a petal length of more than 5. We can do that rather easily with pandas, using some simple indexing. If we use the square brackets `[]` it basically translates into \"extract those elements where condition is fulfilled\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>sample_name</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>sepal_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sample7</td>\n",
       "      <td>5.291489</td>\n",
       "      <td>7.381162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sample8</td>\n",
       "      <td>5.486099</td>\n",
       "      <td>6.529424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>sample17</td>\n",
       "      <td>5.124328</td>\n",
       "      <td>5.799524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>sample20</td>\n",
       "      <td>5.857770</td>\n",
       "      <td>4.775423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>sample26</td>\n",
       "      <td>5.010073</td>\n",
       "      <td>6.460642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>sample28</td>\n",
       "      <td>5.583385</td>\n",
       "      <td>6.768361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>sample29</td>\n",
       "      <td>5.602358</td>\n",
       "      <td>5.734823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>sample31</td>\n",
       "      <td>6.357546</td>\n",
       "      <td>6.109699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>sample33</td>\n",
       "      <td>6.383405</td>\n",
       "      <td>5.473322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>sample40</td>\n",
       "      <td>5.355377</td>\n",
       "      <td>6.257550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>sample42</td>\n",
       "      <td>5.547972</td>\n",
       "      <td>6.519929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>sample50</td>\n",
       "      <td>5.113597</td>\n",
       "      <td>5.029044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sample_name  petal_length  sepal_length\n",
       "6      sample7      5.291489      7.381162\n",
       "7      sample8      5.486099      6.529424\n",
       "16    sample17      5.124328      5.799524\n",
       "19    sample20      5.857770      4.775423\n",
       "25    sample26      5.010073      6.460642\n",
       "27    sample28      5.583385      6.768361\n",
       "28    sample29      5.602358      5.734823\n",
       "30    sample31      6.357546      6.109699\n",
       "32    sample33      6.383405      5.473322\n",
       "39    sample40      5.355377      6.257550\n",
       "41    sample42      5.547972      6.519929\n",
       "49    sample50      5.113597      5.029044"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.petal_length>5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's store the data from the dataframe into lists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "petal_list = list(data.petal_length)\n",
    "sepal_list = list(data.sepal_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Plot the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to plot in python you need to import the library `matplotlib.pyplot`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the matplotlib.pyplot library and activate the plotting option in the jupyter notebook (activation not required if you are working in spyder)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now plot the data as a scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1221cf990>"
      ]
     },
     "execution_count": 19,
     "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.scatter(petal_list,sepal_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can modify the plot by adding labels to the axis. In python the plot is being modified in \"layers\", which means, every modification/addition is stated in a new line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Iris data')"
      ]
     },
     "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": [
    "plt.scatter(petal_list,sepal_list)\n",
    "plt.xlabel('petal length')\n",
    "plt.ylabel('sepal length')\n",
    "plt.title('Iris data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can change the color of the plot like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Iris data')"
      ]
     },
     "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": [
    "plt.scatter(petal_list,sepal_list,color='green')\n",
    "plt.xlabel('petal length')\n",
    "plt.ylabel('sepal length')\n",
    "plt.title('Iris data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or change the shape of the plotted points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Iris data')"
      ]
     },
     "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": [
    "plt.scatter(petal_list,sepal_list,color='green',marker='^')\n",
    "plt.xlabel('petal length')\n",
    "plt.ylabel('sepal length')\n",
    "plt.title('Iris data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to save a plot as a figure, you first need to define a figure object and after all your plotting commands you can use the function `savefig()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig = plt.figure()\n",
    "plt.scatter(petal_list,sepal_list,color='green',marker='^')\n",
    "plt.xlabel('petal length')\n",
    "plt.ylabel('sepal length')\n",
    "plt.title('Iris data')\n",
    "fig.savefig('./scatter_plot.pdf',bbox_inches='tight', dpi = 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can adjust the figure size using the `figsize=()` argument inside the `plt.figure()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GVM3jP3rYaYvUOXL4AItqHs+SFcKYqnn8Rw87bZE6Rw4fYFEdu/pY+gf6XreRZ89Wdw978fOxtLTUhw8fHl0GQJITnaPLDlyWu+6+KxfsuyC3vOSWXTuGZevvsmm3/04wWlXd2N1Lp1qnEwZwHyxS58jhAzBbQhjAeVq0E08cPgCzZWI+wHk6U+do9CTu82GyPMyWThjAeRrROVqkMzFhr9MJAzhPIzpH83TdO+C+0QkD2CXM8ILFMtUQVlUPq6o3VdUHq+oDVfXEk9ZXVR2oqg9V1U1VdcU06wHYzRbpTExg+p2wVyZ5W3d/cZLHJfnASeufkeTRk9tVSV495Xo4D45BgfEW7UxMYIohrKoemuTJSV6XJN291t23n7TZ1yd5fZ/w9iQPq6pHTKsmzs8iTQOH3coML1g80+yEXZbktiQrVfWuqnptVT34pG0emeQjWx4fmyz7LFV1VVUdrqrDt9122/Qq5l4cgwLzwQwvWDzTPDtyX5Irkry4u99RVa9M8rIkL9+yTZ3i5+51HaXuvjbJtcmJyxZNoVZO41THoDgjC2bPDC9YPNPshB1Lcqy73zF5/KacCGUnb/OFWx5fkuRjU6yJc+AYFACYnqmFsO6+NclHquoxk0VfneT9J212MMm3T86S/Mokd3T36rRq4tw4BgUApmfaZ0e+OMkbq+qmJJcn+eGqekFVvWCy/i1JbknyoSQ/neSFU66Hc+AYFACYnureXYdYLS0t9eHDh0eXwWmsHl/NlStX5obn35CLL7x4dDkAMFRV3djdS6daZ2I+O8o4CwDYHiGMHWOcBQBsnxDGjnFJFQDYPiGMHWGcBQCcGyGMHWGcBQCcGyGMHWGcBQCcm2letog9xCVVgJ1m5A2LTicMgLlk5A2LTggDYO4YecNeIIQBMHeMvGEvEMIAmCtG3rBXCGEAzBUjb9grhDAA5oqRN+wVRlQAMFeMvGGv0AkDABhACAMAGEAIA/ac1eOr2X9gv7PtgKGEMGDPMYkdmAdCGLCnmMQOzAshDNhTTGJnJ9ilzU4QwoA9wyR2dopd2uwEIQzYM0xiZyfYpc1OOWsIq6onVdVvVdUfVdUtVfU/quqWWRQHsJNMYmcn2KXNTqnuPvMGVR9M8r8nuTHJ+uby7v7EdEs7taWlpT58+PCIlwZgj1s9vprLDlyWu+6+655lF+y7ILe85JZcfOHFAytjXlXVjd29dKp129kdeUd3v7W7P97dn9i87XCNADD37NJmJ502hFXVFVV1RZLfqar/s6qeuLlsshwA9hS7tNlJZ7qA938+6fHWVlonedrOlwMA88vFxdlJpw1h3f3UJKmqy7r7sw7Er6rLpl0YAMAi284xYW86xbJf2elCAICdZ7Ds/DptJ6yqvjjJY5N8TlX9ky2rHprkgdMuDAC477YOlr3mmdeMLoctztQJe0ySZyV5WJKv23K7Ism/nH5pe4NvKABMi8Gy8+20Iay7r+vu5yV5Vnc/b8vte7r7v8+wxoXm0hcATIvBsvNtO8NaX5UTZ0NudUeSw90983NyF2lY69ahf4b9AbCTDJadD/d1WOvfTHJ5kj+e3P5+ks9N8s+r6sd3rMo9yDcUAKbFYNn5t50Q9neSPK27X9Xdr0ryNUn+5yTfmOTp0yxukW3up98c+re2vmZ/PQA7xmDZ+XemYa2bHpnkwTmxCzKT+1/Q3etV9empVbbgzvQNxdkrANxXBsvOv+10wv5TkiNVtVJVP5vkXUl+tKoenOT/nWZxi8w3FADY2856YH6SVNUjkjwhSSX5g+7+2LQLO51FOjAfAFhs9/XA/M3tbkvy50n+TlU9eaeKAwDYi856TFhV/UiSf5rkfUk2D2LqJIemWBcAwELbzoH535DkMd3tIPwdsnp8NVeuXJkbnn+DWS0AsEdtZ3fkLUnuP+1C9hJT8gGA7YSwT+XE2ZE/VVUHNm/TLmxRLfJ1vFwHEwC2bzsh7GCS5ST/PcmNW26ch0Wekq/DBwDbt90RFRckeVR33zz9ks5sN4+oWOTreLkOJgDc230aUVFVX5fkSJK3TR5fXlUHd7bEvWGRr+O1fGg56xvrSZK7N+5eiN8JAKZpO7sjX5ETg1pvT5LuPpLkb0+xpoW1qFPyN49z+8zGZ5Ikn9n4zMId7wYAO207Iyru7u47qmrrsrPvw+ReFvU6Xlu7YJs2u2GugwkAp7adTth7q+pbktyvqh5dVa/KiYP0IcmJDt9mF2zTZzY+s+s7fAAwTdsJYS9O8tgkn07yC0k+meSl0yyK3eUP/+Uf5oH7HvhZyy7Yd0EOX7U7T6AAgFk4awjr7k9197/r7i/v7qXJ/bvO9nPsHYt8wgEATMtpjwmrql/PGY796u5nT6Uidp0znXDgmDAAOLUzHZj/ozOrgl1tUU84AIBpOm0I6+7rZ1kIAMBesp0D8wEA2GFCGADAAEIYAMAAzo4EABjA2ZEAAAM4OxIAYICzHhM2uV7km6rq/VV1y+ZtO09eVUer6j1VdaSq7nUNm6p6SlXdMVl/pKq+/3x+CQA4ldXjq9l/YH9uvfPW0aXAvWznwPyVJK9OcneSpyZ5fZL/+xxe46ndfXl3L51m/e9N1l/e3T90Ds/LQNv5YPPhB4y2fGg5R28/6jJqzKXthLALuvu/Janu/nB3vyLJ06ZbFvNuOx9sPvyAkVaPr2blyEo2eiMrR1Z8IWTubCeE3VVVfyPJH1fVv6qqb0zyt7b5/J3kN6vqxqq66jTbPLGq3l1Vb62qx55qg6q6qqoOV9Xh2267bZsvzbRs54PNhx8w2vKh5Wz0RpJkvdd9IWTubCeEvTTJg5J8T5LHJ/lnSb5jm8//pO6+Iskzkryoqp580vp3Jvmi7n5cklcl+bVTPUl3X9vdS929dNFFF23zpZmW7Xyw+fADRtr8Iri2vpYkWVtf84WQzzIPh8ycNYR19x92951JPpnke7r7n3T327fz5N39scmfH0/y5iRPOGn9JyfPne5+S5L7V9XDz/F3YIa288Hmww8YbesXwU2+ELLVPBwys52zI5eq6j1Jbkrynsmuw8dv4+ceXFUP2byf5OlJ3nvSNhdXVU3uP2FSzyfO/dfgZNNK+Nv5YPPhB4x28OaD93wR3LS2vpbrbr5uUEXMk3k5ZGY7uyN/JskLu/vS7r40yYty4ozJs/n8JL9fVe9O8gdJ/mt3v62qXlBVL5hs85wk751scyDJc7v7tFP62b5pJfztfLD58ANGO3b1sfQP9L1ux64+Nro05sC8HDJTZ8s8VXVDdz/pbMtmZWlpqQ8fvtfIMbZYPb6ayw5clrvuvisX7Lsgt7zkllx84cVTf80rV67MDc+/YeqvBQDna+v/kZum+X9lVd14ujFd2+mE/UFV/dRksOpXVdVPJvndqrqiqq7Y2VLZCSMS/jzsWweAs5mnQ2a20wn7nTOs7u6e6cwwnbAzm3XCP/k1Z9V5A4DzccmPXZKPHv/ovZY/8iGPnMru6jN1ws50Ae8kSXc/dccrYmrOlPCveeY1U3/Nab8WANwX83Rc4HbOjvz8qnpdVb118vhLquqfT780zsesD4o3jgIAzs9ZO2FJfjYnzob8d5PHf5Tkl5K8bko1cR/MOuGP6LwBwCLYzoH5D+/uX06ykSTdfXeS9alWxa5hHAUAnJ/tdML+sqo+LyeuA5mq+sokd0y1KnaNedq3vpWRGQDMu+10wq5OcjDJ/qq6Icnrk7x4qlXBfWRkBgDzbjvXjnxnkq9K8g+SfFeSx3b3TdMuDM7XvFyOAgDOZDtnR/5vSS7o7vcl+YYkv2RIK/NsXi5HAQBnsp3dkS/v7uNVdWWS/yXJzyV59XTLgvNjZAYAu8V2QtjmmZDPTPLq7r4uyQOmVxKcv3m6HAUAnMl2QthHq+qnknxTkrdU1d/c5s/BzBmZAcBusZ0RFd+U5B8n+dHuvr2qHpHk30y3LDg/8zoyAwBOtp1rR34qyX/Z8ng1yeo0iwIAWHR2KwIADCCEAcyB1eOr2X9gvzN5YQ8RwgDmgKs8wN4jhAEM5ioPsDcJYQCDucoD7E1CGMBArvIAe5cQxmdxcDDMlqs8wN4lhPFZHBwMs+UqD7B3VXePruGcLC0t9eHDh0eXsZBWj6/msgOX5a6778oF+y7ILS+5JRdfePHosgBg16qqG7t76VTrdMK4h4ODAWB2hDCSODgYAGZNCCOJg4MBYNaEMJI4OBgAZm3f6AKYD8euPja6BADYU3TC2DYzxABg5whhbJsZYgCwc4QwtsUFhgFgZwlhbIsZYgCws4QwzsoMMQDYeUIYZ2WGGADsPCGMszJDDAB2njlh27B6fDVXrlyZG55/w568oLUZYgCw83TCtsFoht3PjDMA5o0QdhZGMywGQRqAeSOEnYXRDLufIA3APBLCzsBohsUgSAMwj4SwMzCaYfcTpAGYV0LYGRjNsPsJ0gDMKyMqzsBoht3vTEH6mmdeM6gqABDCWHCCNADzyu7IUzBTCgCYNiHsFMyUAgCmTQg7iZlSAMAsCGEnMVNq/tldDMAiEMK2MFNqd7C7GM6fLzEwP4SwLcyUmn92F8N940sMzA8hbAvDWeef3cVw/nyJgfliTtgWZkrNt9PtLn75V708F1948eDqYP6d6kuMocUwjk4Yu4bdxXD+HPMK80cIY9ewuxjOny8xMH/sjmTXsLsYzp/rqML8EcIA9gBfYnbO6vHVXLlyZW54/g2OR+U+sTsSAM6BMR/sFCEMALbJmA92khAGANtkViE7aaohrKqOVtV7qupIVR0+xfqqqgNV9aGquqmqrphmPQBwvoz5YKfNohP21O6+vLuXTrHuGUkePbldleTVM6gHAM6ZMR/stNG7I78+yev7hLcneVhVPWJwTQBwL2YVstOmPaKik/xmVXWSn+rua09a/8gkH9ny+Nhk2erWjarqqpzolOVRj3rU9KoFgNMw5oOdNu1O2JO6+4qc2O34oqp68knr6xQ/0/da0H1tdy9199JFF100jToBAGZqqiGsuz82+fPjSd6c5AknbXIsyRdueXxJko9NsyYAgHkwtRBWVQ+uqods3k/y9CTvPWmzg0m+fXKW5FcmuaO7VwMAsOCmeUzY5yd5c1Vtvs7Pd/fbquoFSdLdr0nyliRfm+RDST6V5HlTrAcAYG5MLYR19y1JHneK5a/Zcr+TvGhaNQAAzKvRIyoAAPYkIQwAYAAhDABgACEMAGAAIQwAYAAhDABgACEMAGAAIQwAYAAhDABgACEMAGAAIQwAYAAhDABgACEMAGAAIQwAYAAhDABgACEMAGAAIQwAYAAhDABgACEMAGAAIQwAYAAhDABgACEMAGAAIQwAYAAhDAA4L6vHV7P/wP7ceueto0vZlYQwAOC8LB9aztHbj2b5+uXRpexKQhgAcM5Wj69m5chKNnojK0dWdMPOgxAGAJyz5UPL2eiNJMl6r+uGnQchDAA4J5tdsLX1tSTJ2vqabth5EMIAgHOytQu2STfs3AlhAMA5OXjzwXu6YJvW1tdy3c3XDapod9o3ugAAYHc5dvWx0SUsBJ0wAIABhDAAgAGEMACAAYQwAIABhDAAgAGEMACAAYQwAIABhDAAgAGEMACAAYQwAIABhDAAgAGEMACAAYQwAIABhDDm0urx1ew/sD+33nnr6FIAYCqEMObS8qHlHL39aJavXx5dCgBMhRDG3Fk9vpqVIyvZ6I2sHFnRDQNgIQlhzJ3lQ8vZ6I0kyXqv64YBsJCEMObKZhdsbX0tSbK2vqYbBsBCEsKYK1u7YJt0wwBYREIYc+XgzQfv6YJtWltfy3U3XzeoIgCYjn2jC9hLVo+v5sqVK3PD82/IxRdePLqcuXTs6mOjSwCAmdAJmyFjFwCATULYjBi7AABsJYTNiLELAMBWQtgMGLsAAJxMCJsBYxcAgJMJYTNg7AIAcDIjKmbA2AUA4GQ6YQAAAwhh7LjV46vZf2C/Ew8A4AymHsKq6n5V9a6q+o1TrHtKVd1RVUcmt++fdj1Mn6G0AHB2s+iEvSTJB86w/ve6+/LJ7YdmUA9TZCgtAGzPVENYVV2S5JlJXjvN12F+GEoLANsz7U7Yjyf53iQbZ9jmiVX17qp6a1U99lQbVNVVVXW4qg7fdtttUymU+48IIpYAAAtKSURBVM5QWgDYvqmFsKp6VpKPd/eNZ9jsnUm+qLsfl+RVSX7tVBt197XdvdTdSxdddNEUqmUnGEoLANs3zU7Yk5I8u6qOJvnFJE+rqjds3aC7P9ndd07uvyXJ/avq4VOsiSkylBYAtm9qw1q7+/uSfF9y4izIJP+6u79t6zZVdXGSP+3urqon5EQo/MS0amK6DKUFgO2b+cT8qnpBknT3a5I8J8l3V9XdSf4qyXO7u2ddEwDArNVuyzxLS0t9+PDh0WUAAJxVVd3Y3UunWmdiPgDAAEIYAMAAQhgAwABCGADAAEIYAMAAQhgAwABCGADAAEIYAMAAQhgAwABCGADAAEIYAMAAQhgAwABCGADAAEIYAMAAQhgAwABCGADAAEIYAMAAQhgAwABCGADAAEIYAMAAQhgAwABCGADAAEIYAMAAQhgAwABCGADAAEIYAMAAQhgAwABCGADAAEIYAMAAQhgAwABCGADAAEIYSZLV46vZf2B/br3z1tGlAMCeIISRJFk+tJyjtx/N8vXLo0sBgD1BCCOrx1ezcmQlG72RlSMrumEAMANCGFk+tJyN3kiSrPe6bhgAzIAQtsdtdsHW1teSJGvra7phADADQtget7ULtkk3DACmTwjb4w7efPCeLtimtfW1XHfzdYMqAoC9Yd/oAhjr2NXHRpcAAHuSThgAwABCGADAAEIYAMAAQhgAwABCGADAAEIYAMAAQhgAwABCGADAAEIYAMAAQhgAwABCGADAAEIYAMAAQhgAwABCGADAAEIYAMAAQhhZPb6a/Qf259Y7bx1dCgDsGUIYWT60nKO3H83y9cujSwGAPUMI2+NWj69m5chKNnojK0dWdMMAYEaEsD1u+dByNnojSbLe67phADAjQtgettkFW1tfS5Ksra/phgHAjAhhe9jWLtgm3TAAmI2ph7Cqul9VvauqfuMU66qqDlTVh6rqpqq6Ytr18NcO3nzwni7YprX1tVx383WDKgKAvWPfDF7jJUk+kOShp1j3jCSPnty+IsmrJ38yA8euPja6BADYs6baCauqS5I8M8lrT7PJ1yd5fZ/w9iQPq6pHTLMmAIB5MO3dkT+e5HuTbJxm/SOTfGTL42OTZZ+lqq6qqsNVdfi2227b+SoBAGZsaiGsqp6V5OPdfeOZNjvFsr7Xgu5ru3upu5cuuuiiHasRAGCUaXbCnpTk2VV1NMkvJnlaVb3hpG2OJfnCLY8vSfKxKdYEADAXphbCuvv7uvuS7r40yXOT/HZ3f9tJmx1M8u2TsyS/Mskd3b06rZoAAObFLM6O/CxV9YIk6e7XJHlLkq9N8qEkn0ryvFnXAwAwwkxCWHf/bpLfndx/zZblneRFs6gBAGCemJgPADCAEAYAMIAQBgAwgBAGADCAEAYAMIAQBgAwgBAGADCAEAYAMIAQBgAwgBAGADCAEAYAMIAQBgAwgBAGADCAEAYAMIAQBgAwgBAGADCAEAYAMIAQxnlZPb6a/Qf259Y7bx1dCgDsSkIY52X50HKO3n40y9cvjy4FAHYlIYxztnp8NStHVrLRG1k5sqIbBgDnQQjjnC0fWs5GbyRJ1ntdNwwAzoMQxjnZ7IKtra8lSdbW13TDAOA8CGGck61dsE26YQBw7oQwzsnBmw/e0wXbtLa+lutuvm5QRQCwO+0bXQC7y7Grj40uAQAWgk4YAMAAQhgAwABCGADAAEIYAMAAQhgAwABCGADAAEIYAMAAQhgAwABCGADAAEIYAMAAQhgAwABCGADAAEIYAMAAQhgAwABCGADAAEIYAMAAQhgAwABCGADAAEIYAMAAQhgAwADV3aNrOCdVdVuSD4+uYwc8PMmfjS5iD/A+T5/3eDa8z7PhfZ6+vfYef1F3X3SqFbsuhC2Kqjrc3Uuj61h03ufp8x7Phvd5NrzP0+c9/mt2RwIADCCEAQAMIISNc+3oAvYI7/P0eY9nw/s8G97n6fMeTzgmDABgAJ0wAIABhDAAgAGEsBmrqp+pqo9X1XtH17KoquoLq+p3quoDVfW+qnrJ6JoWUVU9sKr+oKrePXmff3B0TYuqqu5XVe+qqt8YXcuiqqqjVfWeqjpSVYdH17OoquphVfWmqvrg5DP6iaNrGskxYTNWVU9OcmeS13f3l46uZxFV1SOSPKK731lVD0lyY5Jv6O73Dy5toVRVJXlwd99ZVfdP8vtJXtLdbx9c2sKpqquTLCV5aHc/a3Q9i6iqjiZZ6u69NER05qrq55L8Xne/tqoekORB3X376LpG0Qmbse4+lOTPR9exyLp7tbvfObl/PMkHkjxybFWLp0+4c/Lw/pObb3U7rKouSfLMJK8dXQvcF1X10CRPTvK6JOnutb0cwBIhjAVXVZcm+bIk7xhbyWKa7CY7kuTjSX6ru73PO+/Hk3xvko3RhSy4TvKbVXVjVV01upgFdVmS25KsTHavv7aqHjy6qJGEMBZWVV2Y5FeTvLS7Pzm6nkXU3evdfXmSS5I8oarsYt9BVfWsJB/v7htH17IHPKm7r0jyjCQvmhw6ws7al+SKJK/u7i9L8pdJXja2pLGEMBbS5BilX03yxu7+L6PrWXSTXQq/m+QfDy5l0TwpybMnxyv9YpKnVdUbxpa0mLr7Y5M/P57kzUmeMLaihXQsybEtHfM35UQo27OEMBbO5IDx1yX5QHf/2Oh6FlVVXVRVD5vcvyDJ1yT54NiqFkt3f193X9LdlyZ5bpLf7u5vG1zWwqmqB09O4slk99jTkziDfYd1961JPlJVj5ks+uoke/qEqX2jC9hrquoXkjwlycOr6liSH+ju142tauE8Kck/S/KeyfFKSfJvu/stA2taRI9I8nNVdb+c+EL3y91thAK70ecnefOJ72/Zl+Tnu/ttY0taWC9O8sbJmZG3JHne4HqGMqICAGAAuyMBAAYQwgAABhDCAAAGEMIAAAYQwgAABhDCgF2vqr6zqr5gG9v9bFU9Z7vLd6Cuf7vl/qVVZfYUcA8hDFgE35nkrCFsgH979k2AvUoIA+bKpGP0war6uaq6qareVFUPmqx7fFVdP7nI8v9TVY+YdLCWcmIA5JGquqCqvr+q/rCq3ltV106uorDd17/Xa0yW/25V/UhV/UFV/VFV/cPJ8gdV1S9Pav2lqnpHVS1V1X9McsGkpjdOnv5+VfXTVfW+qvrNyZUGgD1KCAPm0WOSXNvdfz/JJ5O8cHI90FcleU53Pz7JzyT5D939piSHk3xrd1/e3X+V5Ce6+8u7+0uTXJDkWdt50dO9xpZN9nX3E5K8NMkPTJa9MMlfTGpdTvL4JOnulyX5q0lN3zrZ9tFJrunuxya5Pcn/eu5vDbAoXLYImEcf6e4bJvffkOR7krwtyZcm+a1JY+t+SVZP8/NPrarvTfKgJJ+b5H1Jfn0br/uYs7zG5sXgb0xy6eT+lUlemSTd/d6quukMz/8/unvzUlpbnwPYg4QwYB6dfD21TlJJ3tfdTzzTD1bVA5P8ZJKl7v5IVb0iyQO3+bpne41PT/5cz19/fm57V+eWn998DrsjYQ+zOxKYR4+qqs0g9M1Jfj/JzUku2lxeVfevqsdOtjme5CGT+5uB68+q6sIk53LW45le43R+P8k3Tbb/kiR/b8u6z0x2cQLcixAGzKMPJPmOya69z03y6u5ey4lA9SNV9e4kR5L8g8n2P5vkNVV1JCe6TT+d5D1Jfi3JH273Rc/yGqfzkzkR3G5K8n8kuSnJHZN11ya5acuB+QD3qO6Tu/4A41TVpUl+Y3JQ/dyrqvsluX9331VV+5P8tyR/dxLoAE7LMWEA982DkvzOZLdjJfluAQzYDp0wAIABHBMGADCAEAYAMIAQBgAwgBAGADCAEAYAMMD/D4FR93Nl2hc5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,10))\n",
    "plt.scatter(petal_list,sepal_list,color='green',marker='^')\n",
    "plt.xlabel('petal length')\n",
    "plt.ylabel('sepal length')\n",
    "plt.title('Iris data')\n",
    "fig.savefig('../output_files/scatter_plot.pdf',bbox_inches='tight', dpi = 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Identify clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see in the plots above that the data points seem to cluster into two separate clusters (maybe representing separate populations or cryptic species. We can use the kmeans algorithm to identify clusters and automatically assign the data points belonging to each cluster. For this we use the `sklearn` python library, which contains many useful machine learning algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we can use the `KMeans()` function of this library we need to slightly transform our data. This function want the data in tuple format, which means every datapoint should be contained in a seaprate tuple, which looks like this `(x_value1, y_value1), (x_value2, y_value2), ...`.\n",
    "\n",
    "Currently our data is in list format, looking like this `[x_value1,x_value2,...]` and `[y_value1,y_value2,...]`. To transform these lists into the desired format we can use the `zip()` function. The zip function returns an object which we have to transform into a list again to make it visible, which we can do with the `list()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(2.037213545574565, 5.172704440755738), (4.915837058559057, 6.149615365303662), (1.6143144404422318, 5.939969222731516), (1.5257094629553694, 6.603431558181938), (3.7299754141452683, 6.065105089620714), (0.7787600224579809, 5.173369526730656), (5.291488606290831, 7.381162369515891), (5.486099357579179, 6.529423906221606), (1.5888847511006476, 5.447182516437644), (3.506056546470821, 5.821898711988933), (1.0347308723897142, 5.7191141594954535), (3.9184146064300767, 5.32433750535842), (4.704222691916959, 6.241320619454233), (1.2743118663460602, 5.460195145790823), (4.027879380970228, 6.018648753254772), (1.0526702377383088, 6.1491582372113704), (5.124327526301326, 5.799524450012601), (0.7278537859299755, 5.1110545058489665), (1.889159752975209, 5.09323743199745), (5.8577698329937276, 4.775423113784206), (0.9823765564976252, 4.5701405527312895), (1.642251547933402, 5.496335497225293), (1.8138118759285808, 4.62066074530137), (1.6755744474602847, 6.098174133374835), (1.4019996878544254, 4.044936687505984), (5.010073235092442, 6.460642131711677), (1.5471793166899546, 3.81078255673585), (5.583384761827779, 6.768361361219995), (5.602358230386057, 5.734823297994679), (0.999512446749096, 5.644809160886201), (6.357546048970175, 6.1096992728792765), (1.5984496681847908, 5.633241635573474), (6.383405004182108, 5.473321810175103), (3.502308512726084, 6.4668535112453975), (1.4891339583350467, 5.992310194737472), (1.4144863580569658, 4.2119740358904725), (4.695659246296348, 5.8898347763694945), (4.3146508860279305, 6.078577681601839), (4.332901307434329, 6.525604264626423), (5.355376889210996, 6.257550125001438), (4.215725527898536, 5.083585059284203), (5.547972099580166, 6.519929044689162), (4.877986170333111, 5.499600054451635), (1.9922623575693024, 5.392825131125007), (1.514988105180589, 5.229604218306541), (1.6249645875023282, 4.50732836329002), (1.6004321884727593, 5.609623442827154), (1.4375633938613075, 5.288216939487398), (1.5492498365689618, 5.496505964948804), (5.1135973948012285, 5.029043605683267)]\n"
     ]
    }
   ],
   "source": [
    "tuple_data = list(zip(petal_list, sepal_list))\n",
    "print(tuple_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to run the KMeans() function on our transformed tuple data. We have to provide how many clusters we believe there are in the data. Let's go with `2` for now (`n_clusters=2`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kmeans = KMeans(n_clusters=2).fit(tuple_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output of the `KMeans()` command is stored as a variable which we named `kmeans`. From this variable we can extract the labels, which is the assignment of our data points to cluster 0 or cluster 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 1], dtype=int32)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use that label array to color the points accordingly, using the `c=` flag in the `scatter()` function. Let's save the label array as a new variable `labels` which we then parse into the `scatter()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '50 Iris samples')"
      ]
     },
     "execution_count": 29,
     "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": [
    "labels = kmeans.labels_\n",
    "\n",
    "plt.scatter(petal_list,sepal_list,c=labels,cmap='plasma')\n",
    "plt.xlabel('sepal length')\n",
    "plt.ylabel('petal length')\n",
    "plt.title('50 Iris samples')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the kmeans output you can also export the center points of each cluster. These are stored as the values `cluster_centers_` in the kmeans output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.45414789 5.289111  ]\n",
      "[4.89395901 6.00016191]\n"
     ]
    }
   ],
   "source": [
    "center0,center1 = kmeans.cluster_centers_\n",
    "print(center0)\n",
    "print(center1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can plot a point (or a list of points) using the `plot()` function. The `plot()` function needs the input data as x and y coordinates (`plot(x_coord,y_coord)`). Since our variables contain the x and y coordinates for each modpoint, we can plot each of these points by typing `plt.plot(center0[0],center0[1])` and `plt.plot(center1[0],center1[1]` respectively:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '50 Iris samples')"
      ]
     },
     "execution_count": 31,
     "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.scatter(petal_list,sepal_list,c=labels,cmap='plasma')\n",
    "plt.plot(center0[0],center0[1],marker='^',color='black',markersize=20)\n",
    "plt.plot(center1[0],center1[1],marker='^',color='black',markersize=20)\n",
    "\n",
    "plt.xlabel('sepal length')\n",
    "plt.ylabel('petal length')\n",
    "plt.title('50 Iris samples')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "**Task:** Run the kmeans algorithm for 4 clusters (`n_clusters=4`), plot all four clusters including the center points, and save the plot as a pdf file.\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output should look like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Rib32U9slnw4bDJKkV3z7VE07d5qe/tXT8th1yJsPVqI5oXmz7lCxp7j1uGQmqRmfnCGL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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121c68d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's move on to [tutorial 3](https://github.com/tobiashofmann88/python_for_biologists/blob/master/tutorials/tutorial_3.ipynb)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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