04_pandas/04_PandasMatplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lecture 4 Pandas\n",
    "\n",
    "### Table of contents\n",
    "\n",
    "1. [Pandas](#pandas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import pandas as pd\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version 3.10.7 (tags/v3.10.7:6cc6b13, Sep  5 2022, 14:08:36) [MSC v.1933 64 bit (AMD64)]\n",
      "Pandas version 1.4.4\n"
     ]
    }
   ],
   "source": [
    "print(f'Python version {sys.version}')\n",
    "print(f'Pandas version {pd.__version__}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data structures in pandas\n",
    "\n",
    "### Series\n",
    "* 1D labeled array able to hold any data type (int,str,float, Python objects, etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([1,-1,1,-1]).index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`data` (in the example above) can be:\n",
    "    * a dict\n",
    "    * a list\n",
    "    * an ndarray\n",
    "    * a scalar value\n",
    "\n",
    "\n",
    "Examples of from dict and a scalar value below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "aa     1\n",
       "aaa    g\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in case of dictionary\n",
    "pd.Series({'aa':1, 'aaa':'b', 'aaa':'g'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    1\n",
       "c    2\n",
       "d    3\n",
       "e    4\n",
       "dtype: int32"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(np.arange(5), index=['a', 'b', 'c', 'd','e'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* a key difference between Series/pandas and ndarray: operations between Series automatically align the data based on label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A:\n",
      "Vítek      5\n",
      "Martin    10\n",
      "Honza      0\n",
      "dtype: int64\n",
      "B:\n",
      "Martin    20\n",
      "Honza     15\n",
      "Vítek      5\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Honza     15\n",
       "Martin    30\n",
       "Vítek     10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pd.Series({'Vítek':5, 'Martin':10, 'Honza':0})\n",
    "b = pd.Series({'Martin':20,'Honza':15,'Vítek':5})\n",
    "print(f'A:\\n{a}\\nB:\\n{b}')\n",
    "a+ b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([25, 25,  5])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [5,10,0]\n",
    "b = [20,15,5]\n",
    "\n",
    "np.array(a) + np.array(b) # now works as expected"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* looping through (value-by-value) usually not necessary, remember the case of np array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  DataFrame\n",
    "\n",
    "* a 2D labeled data structure with columns of potentially different types\n",
    "* like a spreadsheet or SQL table, or a dict of Series objects\n",
    "* the most frequently used pandas object \n",
    "* can be created:\n",
    "    * typically by reading a csv file\n",
    "    * dict of 1D ndarrays, lists, dicts, Series\n",
    "    * 2D numpy.ndarray\n",
    "    * a Series\n",
    "    * another DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "   var  column2\n",
       "0    1        0\n",
       "1    2        0\n",
       "2    3        0"
      ]
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   "source": [
    "pd.DataFrame({'var':[1,2,3], 'column2':[0,0,0]})"
   ]
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  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
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       "   var  column2\n",
       "0    1        0\n",
       "1    2        0\n",
       "2    3        0"
      ]
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     "execution_count": 13,
     "metadata": {},
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   "source": [
    "pd.DataFrame([{'var':1,'column2':0},{'var':2,'column2':0},{'var':3,'column2':0}])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
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       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
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       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
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       "      <th>e</th>\n",
       "      <td>2.0</td>\n",
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       "   one  two\n",
       "a  1.0  1.0\n",
       "b  NaN  2.0\n",
       "c  3.0  3.0\n",
       "d  NaN  4.0\n",
       "e  2.0  NaN"
      ]
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     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# from dict of Series\n",
    "d = {'one': pd.Series([1., 2., 3.],     index=['a', 'e', 'c']),\n",
    "     'two': pd.Series([2., 1., 3., 4.], index=['b', 'a', 'c', 'd'])}\n",
    "\n",
    "df = pd.DataFrame(d)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our data unzipped, we can load them into Python, specifically using Pandas tools.\n",
    "\n",
    "### Data I/O (in/out)\n",
    "* CSV, JSON, HTML, Excel, HDF5, SQL, pickle, ...\n",
    "    * for specific details, see [documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html)\n",
    "* `pd.read_csv()` is the workhorse for reading text files\n",
    "    * large number of params means great flexibility when getting data in\n",
    "    \n",
    "We will work with the course evaluation data from FSV.\n",
    "  * each observation is a response to the following questionare (... hope it reminds you of something)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cislo_dot</th>\n",
       "      <th>kod_predm</th>\n",
       "      <th>nazev_predm</th>\n",
       "      <th>prednasejici</th>\n",
       "      <th>cvicici</th>\n",
       "      <th>q1</th>\n",
       "      <th>q2</th>\n",
       "      <th>q3</th>\n",
       "      <th>q4</th>\n",
       "      <th>q5</th>\n",
       "      <th>...</th>\n",
       "      <th>q7</th>\n",
       "      <th>q8</th>\n",
       "      <th>q9</th>\n",
       "      <th>q10</th>\n",
       "      <th>q11</th>\n",
       "      <th>q12</th>\n",
       "      <th>q13</th>\n",
       "      <th>t1</th>\n",
       "      <th>t2</th>\n",
       "      <th>katedra_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>JEB003</td>\n",
       "      <td>Ekonomie I</td>\n",
       "      <td>Fanta,N.,Kracík,J.,Švarcová,N.</td>\n",
       "      <td>Fanta,N.,Kracík,J.,Švarcová,N.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>JLB003</td>\n",
       "      <td>Angličtina pro ekonomy I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Poslušná,L.</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cjp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>NMMA701</td>\n",
       "      <td>Matematika 1</td>\n",
       "      <td>Spurný,J.</td>\n",
       "      <td>Rondoš,J.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>JEB023</td>\n",
       "      <td>Úvod do studia práva</td>\n",
       "      <td>Pražák,P.,Wintr,J.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>JEB055</td>\n",
       "      <td>Seminář k aktualitám I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vyhnánek,T.</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cislo_dot kod_predm               nazev_predm  \\\n",
       "0          1    JEB003                Ekonomie I   \n",
       "1          2    JLB003  Angličtina pro ekonomy I   \n",
       "2          3   NMMA701              Matematika 1   \n",
       "3          4    JEB023      Úvod do studia práva   \n",
       "4          5    JEB055    Seminář k aktualitám I   \n",
       "\n",
       "                     prednasejici                         cvicici   q1   q2  \\\n",
       "0  Fanta,N.,Kracík,J.,Švarcová,N.  Fanta,N.,Kracík,J.,Švarcová,N.  3.0  5.0   \n",
       "1                             NaN                     Poslušná,L.  5.0  3.0   \n",
       "2                       Spurný,J.                       Rondoš,J.  3.0  5.0   \n",
       "3              Pražák,P.,Wintr,J.                             NaN  3.0  4.0   \n",
       "4                             NaN                     Vyhnánek,T.  2.0  3.0   \n",
       "\n",
       "    q3   q4   q5  ...   q7   q8   q9  q10  q11  q12  q13   t1   t2  \\\n",
       "0  4.0  5.0  4.0  ...  4.0  3.0  1.0  4.0  1.0  NaN  2.0  NaN  NaN   \n",
       "1  NaN  NaN  NaN  ...  5.0  5.0  1.0  5.0  5.0  5.0  5.0  NaN  NaN   \n",
       "2  3.0  2.0  1.0  ...  4.0  5.0  1.0  3.0  2.0  2.0  1.0  NaN  NaN   \n",
       "3  3.0  3.0  1.0  ...  NaN  NaN  1.0  3.0  2.0  3.0  2.0  NaN  NaN   \n",
       "4  NaN  NaN  NaN  ...  3.0  1.0  1.0  4.0  2.0  2.0  1.0  NaN  NaN   \n",
       "\n",
       "  katedra_code  \n",
       "0          ies  \n",
       "1          cjp  \n",
       "2          ies  \n",
       "3          ies  \n",
       "4          ies  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the \"raw\" data for one particular year\n",
    "df = pd.read_csv('auxiliary/data_2017_zs.csv', sep = ';', on_bad_lines= 'skip')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
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       "    .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>course_code</th>\n",
       "      <th>course_title</th>\n",
       "      <th>teachers</th>\n",
       "      <th>seminar_leaders</th>\n",
       "      <th>q1</th>\n",
       "      <th>q2</th>\n",
       "      <th>q3</th>\n",
       "      <th>q4</th>\n",
       "      <th>q5</th>\n",
       "      <th>q6</th>\n",
       "      <th>q7</th>\n",
       "      <th>q8</th>\n",
       "      <th>q9</th>\n",
       "      <th>q10</th>\n",
       "      <th>q11</th>\n",
       "      <th>q12</th>\n",
       "      <th>q13</th>\n",
       "      <th>c_value</th>\n",
       "      <th>c_improve</th>\n",
       "      <th>department_code</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JEB003</td>\n",
       "      <td>Ekonomie I</td>\n",
       "      <td>Fanta,N.,Kracík,J.,Švarcová,N.</td>\n",
       "      <td>Fanta,N.,Kracík,J.,Švarcová,N.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JLB003</td>\n",
       "      <td>Angličtina pro ekonomy I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Poslušná,L.</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cjp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NMMA701</td>\n",
       "      <td>Matematika 1</td>\n",
       "      <td>Spurný,J.</td>\n",
       "      <td>Rondoš,J.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JEB023</td>\n",
       "      <td>Úvod do studia práva</td>\n",
       "      <td>Pražák,P.,Wintr,J.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>JEB055</td>\n",
       "      <td>Seminář k aktualitám I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vyhnánek,T.</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>JPM314</td>\n",
       "      <td>Theories of International Relations</td>\n",
       "      <td>Ditrych,O.,Plechanovová,B.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>kmv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>JEB998</td>\n",
       "      <td>Úvod do ekonomie</td>\n",
       "      <td>Kameníček,J.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>JEB058</td>\n",
       "      <td>Seminář matematické analýzy I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stráský,J.</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>JPM561</td>\n",
       "      <td>Regional Security Studies</td>\n",
       "      <td>Karásek,T.,Klosek,K.</td>\n",
       "      <td>Karásek,T.,Klosek,K.</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>kbs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>JEB998</td>\n",
       "      <td>Úvod do ekonomie</td>\n",
       "      <td>Kameníček,J.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       course_code                         course_title  \\\n",
       "number                                                    \n",
       "1           JEB003                           Ekonomie I   \n",
       "2           JLB003             Angličtina pro ekonomy I   \n",
       "3          NMMA701                         Matematika 1   \n",
       "4           JEB023                 Úvod do studia práva   \n",
       "5           JEB055               Seminář k aktualitám I   \n",
       "6           JPM314  Theories of International Relations   \n",
       "7           JEB998                     Úvod do ekonomie   \n",
       "8           JEB058        Seminář matematické analýzy I   \n",
       "9           JPM561            Regional Security Studies   \n",
       "10          JEB998                     Úvod do ekonomie   \n",
       "\n",
       "                              teachers                 seminar_leaders   q1  \\\n",
       "number                                                                        \n",
       "1       Fanta,N.,Kracík,J.,Švarcová,N.  Fanta,N.,Kracík,J.,Švarcová,N.  3.0   \n",
       "2                                  NaN                     Poslušná,L.  5.0   \n",
       "3                            Spurný,J.                       Rondoš,J.  3.0   \n",
       "4                   Pražák,P.,Wintr,J.                             NaN  3.0   \n",
       "5                                  NaN                     Vyhnánek,T.  2.0   \n",
       "6           Ditrych,O.,Plechanovová,B.                             NaN  2.0   \n",
       "7                         Kameníček,J.                             NaN  4.0   \n",
       "8                                  NaN                      Stráský,J.  4.0   \n",
       "9                 Karásek,T.,Klosek,K.            Karásek,T.,Klosek,K.  5.0   \n",
       "10                        Kameníček,J.                             NaN  3.0   \n",
       "\n",
       "         q2   q3   q4   q5   q6   q7   q8   q9  q10  q11  q12  q13 c_value  \\\n",
       "number                                                                       \n",
       "1       5.0  4.0  5.0  4.0  2.0  4.0  3.0  1.0  4.0  1.0  NaN  2.0     NaN   \n",
       "2       3.0  NaN  NaN  NaN  5.0  5.0  5.0  1.0  5.0  5.0  5.0  5.0     NaN   \n",
       "3       5.0  3.0  2.0  1.0  4.0  4.0  5.0  1.0  3.0  2.0  2.0  1.0     NaN   \n",
       "4       4.0  3.0  3.0  1.0  NaN  NaN  NaN  1.0  3.0  2.0  3.0  2.0     NaN   \n",
       "5       3.0  NaN  NaN  NaN  2.0  3.0  1.0  1.0  4.0  2.0  2.0  1.0     NaN   \n",
       "6       3.0  2.0  4.0  1.0  NaN  NaN  NaN  2.0  3.0  1.0  3.0  1.0     NaN   \n",
       "7       3.0  2.0  3.0  2.0  NaN  NaN  NaN  1.0  3.0  1.0  2.0  3.0     NaN   \n",
       "8       4.0  NaN  NaN  NaN  4.0  5.0  5.0  1.0  1.0  2.0  3.0  5.0     NaN   \n",
       "9       3.0  5.0  5.0  4.0  5.0  5.0  4.0  1.0  5.0  4.0  5.0  5.0     NaN   \n",
       "10      4.0  2.0  2.0  2.0  NaN  NaN  NaN  1.0  3.0  3.0  3.0  1.0     NaN   \n",
       "\n",
       "       c_improve department_code  \n",
       "number                            \n",
       "1            NaN             ies  \n",
       "2            NaN             cjp  \n",
       "3            NaN             ies  \n",
       "4            NaN             ies  \n",
       "5            NaN             ies  \n",
       "6            NaN             kmv  \n",
       "7            NaN             ies  \n",
       "8            NaN             ies  \n",
       "9            NaN             kbs  \n",
       "10           NaN             ies  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# raw data have column names in czech, let's rename them\n",
    "# if you do not want to reassign, you can provide arg. \"inplace = True\"\n",
    "df = df.rename(columns = {\n",
    "    'cislo_dot' : 'number',\n",
    "    'kod_predm' : 'course_code',\n",
    "    'nazev_predm' : 'course_title',\n",
    "    'prednasejici' : 'teachers',\n",
    "    'cvicici' : 'seminar_leaders',\n",
    "    't1': 'c_value',\n",
    "    't2': 'c_improve', \n",
    "    'katedra_code' : 'department_code'\n",
    "})\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([   1,    2,    3,    4,    5,    6,    7,    8,    9,   10,\n",
       "            ...\n",
       "            6988, 6989, 6990, 6991, 6992, 6993, 6994, 6995, 6996, 6997],\n",
       "           dtype='int64', name='number', length=6995)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# iterative\n",
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set column named \"course_code\" to be an index (or you can use \"inplace\" option again)\n",
    "df.set_index('number', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6995, 20)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "number\n",
       "1        JEB003\n",
       "2        JLB003\n",
       "3       NMMA701\n",
       "4        JEB023\n",
       "5        JEB055\n",
       "         ...   \n",
       "6993     JJM260\n",
       "6994     JJM264\n",
       "6995     JJM360\n",
       "6996     JJM354\n",
       "6997     JJM340\n",
       "Name: course_code, Length: 6995, dtype: object"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.course_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>course_code</th>\n",
       "      <th>course_title</th>\n",
       "      <th>teachers</th>\n",
       "      <th>seminar_leaders</th>\n",
       "      <th>q1</th>\n",
       "      <th>q2</th>\n",
       "      <th>q3</th>\n",
       "      <th>q4</th>\n",
       "      <th>q5</th>\n",
       "      <th>q6</th>\n",
       "      <th>q7</th>\n",
       "      <th>q8</th>\n",
       "      <th>q9</th>\n",
       "      <th>q10</th>\n",
       "      <th>q11</th>\n",
       "      <th>q12</th>\n",
       "      <th>q13</th>\n",
       "      <th>c_value</th>\n",
       "      <th>c_improve</th>\n",
       "      <th>department_code</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JEB003</td>\n",
       "      <td>Ekonomie I</td>\n",
       "      <td>Fanta,N.,Kracík,J.,Švarcová,N.</td>\n",
       "      <td>Fanta,N.,Kracík,J.,Švarcová,N.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JLB003</td>\n",
       "      <td>Angličtina pro ekonomy I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Poslušná,L.</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cjp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NMMA701</td>\n",
       "      <td>Matematika 1</td>\n",
       "      <td>Spurný,J.</td>\n",
       "      <td>Rondoš,J.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JEB023</td>\n",
       "      <td>Úvod do studia práva</td>\n",
       "      <td>Pražák,P.,Wintr,J.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>JEB055</td>\n",
       "      <td>Seminář k aktualitám I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vyhnánek,T.</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       course_code              course_title                        teachers  \\\n",
       "number                                                                         \n",
       "1           JEB003                Ekonomie I  Fanta,N.,Kracík,J.,Švarcová,N.   \n",
       "2           JLB003  Angličtina pro ekonomy I                             NaN   \n",
       "3          NMMA701              Matematika 1                       Spurný,J.   \n",
       "4           JEB023      Úvod do studia práva              Pražák,P.,Wintr,J.   \n",
       "5           JEB055    Seminář k aktualitám I                             NaN   \n",
       "\n",
       "                       seminar_leaders   q1   q2   q3   q4   q5   q6   q7  \\\n",
       "number                                                                      \n",
       "1       Fanta,N.,Kracík,J.,Švarcová,N.  3.0  5.0  4.0  5.0  4.0  2.0  4.0   \n",
       "2                          Poslušná,L.  5.0  3.0  NaN  NaN  NaN  5.0  5.0   \n",
       "3                            Rondoš,J.  3.0  5.0  3.0  2.0  1.0  4.0  4.0   \n",
       "4                                  NaN  3.0  4.0  3.0  3.0  1.0  NaN  NaN   \n",
       "5                          Vyhnánek,T.  2.0  3.0  NaN  NaN  NaN  2.0  3.0   \n",
       "\n",
       "         q8   q9  q10  q11  q12  q13 c_value c_improve department_code  \n",
       "number                                                                  \n",
       "1       3.0  1.0  4.0  1.0  NaN  2.0     NaN       NaN             ies  \n",
       "2       5.0  1.0  5.0  5.0  5.0  5.0     NaN       NaN             cjp  \n",
       "3       5.0  1.0  3.0  2.0  2.0  1.0     NaN       NaN             ies  \n",
       "4       NaN  1.0  3.0  2.0  3.0  2.0     NaN       NaN             ies  \n",
       "5       1.0  1.0  4.0  2.0  2.0  1.0     NaN       NaN             ies  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the data but refrain from drawing the conclusions\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a copy of original data, so if you mess up, can go back to this\n",
    "# not that smart when you are working with the large data\n",
    "df_copy = df.copy(deep = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* `pd.DataFrame.copy()`:\n",
    "    * deep: modifications to the data or indices of the copy will not be reflected in the original object\n",
    "    * shallow: any changes to the data of the original will be reflected in the shallow copy (and vice versa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6995, 20)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# try to call it as a function\n",
    "# df.shape() # it si an attribute not a function\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>q1</th>\n",
       "      <th>q2</th>\n",
       "      <th>q3</th>\n",
       "      <th>q4</th>\n",
       "      <th>q5</th>\n",
       "      <th>q6</th>\n",
       "      <th>q7</th>\n",
       "      <th>q8</th>\n",
       "      <th>q9</th>\n",
       "      <th>q10</th>\n",
       "      <th>q11</th>\n",
       "      <th>q12</th>\n",
       "      <th>q13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6847.000000</td>\n",
       "      <td>6827.000000</td>\n",
       "      <td>5458.000000</td>\n",
       "      <td>5457.000000</td>\n",
       "      <td>5458.000000</td>\n",
       "      <td>2600.000000</td>\n",
       "      <td>2600.000000</td>\n",
       "      <td>2600.000000</td>\n",
       "      <td>6776.000000</td>\n",
       "      <td>6829.000000</td>\n",
       "      <td>6783.000000</td>\n",
       "      <td>6801.000000</td>\n",
       "      <td>6798.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.108661</td>\n",
       "      <td>3.284166</td>\n",
       "      <td>4.214731</td>\n",
       "      <td>4.359355</td>\n",
       "      <td>3.872664</td>\n",
       "      <td>4.288462</td>\n",
       "      <td>4.494231</td>\n",
       "      <td>4.151923</td>\n",
       "      <td>1.419126</td>\n",
       "      <td>4.020940</td>\n",
       "      <td>3.574672</td>\n",
       "      <td>3.896927</td>\n",
       "      <td>4.12739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.019755</td>\n",
       "      <td>1.056333</td>\n",
       "      <td>1.032399</td>\n",
       "      <td>0.999295</td>\n",
       "      <td>1.267321</td>\n",
       "      <td>0.964880</td>\n",
       "      <td>0.887239</td>\n",
       "      <td>1.142584</td>\n",
       "      <td>0.775423</td>\n",
       "      <td>1.069084</td>\n",
       "      <td>1.274129</td>\n",
       "      <td>1.115977</td>\n",
       "      <td>1.12351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                q1           q2           q3           q4           q5  \\\n",
       "count  6847.000000  6827.000000  5458.000000  5457.000000  5458.000000   \n",
       "mean      4.108661     3.284166     4.214731     4.359355     3.872664   \n",
       "std       1.019755     1.056333     1.032399     0.999295     1.267321   \n",
       "min       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "25%       4.000000     3.000000     4.000000     4.000000     3.000000   \n",
       "50%       4.000000     3.000000     5.000000     5.000000     4.000000   \n",
       "75%       5.000000     4.000000     5.000000     5.000000     5.000000   \n",
       "max       5.000000     5.000000     5.000000     5.000000     5.000000   \n",
       "\n",
       "                q6           q7           q8           q9          q10  \\\n",
       "count  2600.000000  2600.000000  2600.000000  6776.000000  6829.000000   \n",
       "mean      4.288462     4.494231     4.151923     1.419126     4.020940   \n",
       "std       0.964880     0.887239     1.142584     0.775423     1.069084   \n",
       "min       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "25%       4.000000     4.000000     4.000000     1.000000     3.000000   \n",
       "50%       5.000000     5.000000     5.000000     1.000000     4.000000   \n",
       "75%       5.000000     5.000000     5.000000     2.000000     5.000000   \n",
       "max       5.000000     5.000000     5.000000     5.000000     5.000000   \n",
       "\n",
       "               q11          q12         q13  \n",
       "count  6783.000000  6801.000000  6798.00000  \n",
       "mean      3.574672     3.896927     4.12739  \n",
       "std       1.274129     1.115977     1.12351  \n",
       "min       1.000000     1.000000     1.00000  \n",
       "25%       3.000000     3.000000     4.00000  \n",
       "50%       4.000000     4.000000     5.00000  \n",
       "75%       5.000000     5.000000     5.00000  \n",
       "max       5.000000     5.000000     5.00000  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# classical data summarization\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6995 entries, 1 to 6997\n",
      "Data columns (total 20 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   course_code      6995 non-null   object \n",
      " 1   course_title     6995 non-null   object \n",
      " 2   teachers         5434 non-null   object \n",
      " 3   seminar_leaders  2588 non-null   object \n",
      " 4   q1               6847 non-null   float64\n",
      " 5   q2               6827 non-null   float64\n",
      " 6   q3               5458 non-null   float64\n",
      " 7   q4               5457 non-null   float64\n",
      " 8   q5               5458 non-null   float64\n",
      " 9   q6               2600 non-null   float64\n",
      " 10  q7               2600 non-null   float64\n",
      " 11  q8               2600 non-null   float64\n",
      " 12  q9               6776 non-null   float64\n",
      " 13  q10              6829 non-null   float64\n",
      " 14  q11              6783 non-null   float64\n",
      " 15  q12              6801 non-null   float64\n",
      " 16  q13              6798 non-null   float64\n",
      " 17  c_value          2183 non-null   object \n",
      " 18  c_improve        1798 non-null   object \n",
      " 19  department_code  6995 non-null   object \n",
      "dtypes: float64(13), object(7)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# \n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# memory usage of each column in bytes (useful when working with the larger datasets)\n",
    "df.memory_usage()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* you can treat a DataFrame semantically like a dict of like-indexed Series objects\n",
    "    * getting, setting, and deleting columns works with the same syntax as the analogous dict operations\n",
    "\n",
    "## Indexing/Selection\n",
    "\n",
    "| Operation                      | Syntax        |  Result   |\n",
    "|--------------------------------|---------------|-----------|\n",
    "| Select column                  | df[col]       |  Series   |\n",
    "| Select row by label            | df.loc[label] |  Series   |\n",
    "| Select row by integer location | df.iloc[loc]  |  Series   |\n",
    "| Slice rows                     | df[5:10]      | DataFrame |\n",
    "| Select rows by boolean vector  | df[bool_vec]  | DataFrame |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "number\n",
       "1                          Ekonomie I\n",
       "2            Angličtina pro ekonomy I\n",
       "3                        Matematika 1\n",
       "4                Úvod do studia práva\n",
       "5              Seminář k aktualitám I\n",
       "                    ...              \n",
       "6993         Novinářská etika v praxi\n",
       "6994            Diplomový seminář II.\n",
       "6995              Ekonomika v médiích\n",
       "6996           Dějiny populární hudby\n",
       "6997    Tvůrčí dílny – tvůrčí psaní I\n",
       "Name: course_title, Length: 6995, dtype: object"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gives us series\n",
    "df['course_title']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this demonstrates usefullness of proper column naming\n",
    "df.course_title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>course_title</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ekonomie I</td>\n",
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       "      <th>2</th>\n",
       "      <td>Angličtina pro ekonomy I</td>\n",
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       "      <th>3</th>\n",
       "      <td>Matematika 1</td>\n",
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       "      <th>4</th>\n",
       "      <td>Úvod do studia práva</td>\n",
       "    </tr>\n",
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       "      <th>5</th>\n",
       "      <td>Seminář k aktualitám I</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>6993</th>\n",
       "      <td>Novinářská etika v praxi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6994</th>\n",
       "      <td>Diplomový seminář II.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6995</th>\n",
       "      <td>Ekonomika v médiích</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6996</th>\n",
       "      <td>Dějiny populární hudby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6997</th>\n",
       "      <td>Tvůrčí dílny – tvůrčí psaní I</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6995 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         course_title\n",
       "number                               \n",
       "1                          Ekonomie I\n",
       "2            Angličtina pro ekonomy I\n",
       "3                        Matematika 1\n",
       "4                Úvod do studia práva\n",
       "5              Seminář k aktualitám I\n",
       "...                               ...\n",
       "6993         Novinářská etika v praxi\n",
       "6994            Diplomový seminář II.\n",
       "6995              Ekonomika v médiích\n",
       "6996           Dějiny populární hudby\n",
       "6997    Tvůrčí dílny – tvůrčí psaní I\n",
       "\n",
       "[6995 rows x 1 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# multple columns -> gives us dataframe\n",
    "df[['course_title']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# just one column: just convenience (if column name has a space or dot, you are screwed)\n",
    "#naming conventions: no special character, underscore for spaces, no CZECH chars! informative and short\n",
    "df.course_title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subset of columns you want \n",
    "df[['course_title','teachers']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['course_code', 'course_title', 'teachers', 'seminar_leaders', 'q1',\n",
       "       'q2', 'q3', 'q4', 'q5', 'q6', 'q7', 'q8', 'q9', 'q10', 'q11', 'q12',\n",
       "       'q13', 'c_value', 'c_improve', 'department_code'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list of all columns \n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>course_title</th>\n",
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       "      <th>q1</th>\n",
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       "      <th>q4</th>\n",
       "      <th>q5</th>\n",
       "      <th>q6</th>\n",
       "      <th>...</th>\n",
       "      <th>q8</th>\n",
       "      <th>q9</th>\n",
       "      <th>q10</th>\n",
       "      <th>q11</th>\n",
       "      <th>q12</th>\n",
       "      <th>q13</th>\n",
       "      <th>c_value</th>\n",
       "      <th>c_improve</th>\n",
       "      <th>department_code</th>\n",
       "      <th>tmp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JEB003</td>\n",
       "      <td>Ekonomie I</td>\n",
       "      <td>Fanta,N.,Kracík,J.,Švarcová,N.</td>\n",
       "      <td>Fanta,N.,Kracík,J.,Švarcová,N.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JLB003</td>\n",
       "      <td>Angličtina pro ekonomy I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Poslušná,L.</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cjp</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NMMA701</td>\n",
       "      <td>Matematika 1</td>\n",
       "      <td>Spurný,J.</td>\n",
       "      <td>Rondoš,J.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JEB023</td>\n",
       "      <td>Úvod do studia práva</td>\n",
       "      <td>Pražák,P.,Wintr,J.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>JEB055</td>\n",
       "      <td>Seminář k aktualitám I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vyhnánek,T.</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       course_code              course_title                        teachers  \\\n",
       "number                                                                         \n",
       "1           JEB003                Ekonomie I  Fanta,N.,Kracík,J.,Švarcová,N.   \n",
       "2           JLB003  Angličtina pro ekonomy I                             NaN   \n",
       "3          NMMA701              Matematika 1                       Spurný,J.   \n",
       "4           JEB023      Úvod do studia práva              Pražák,P.,Wintr,J.   \n",
       "5           JEB055    Seminář k aktualitám I                             NaN   \n",
       "\n",
       "                       seminar_leaders   q1   q2   q3   q4   q5   q6  ...  \\\n",
       "number                                                                ...   \n",
       "1       Fanta,N.,Kracík,J.,Švarcová,N.  3.0  5.0  4.0  5.0  4.0  2.0  ...   \n",
       "2                          Poslušná,L.  5.0  3.0  NaN  NaN  NaN  5.0  ...   \n",
       "3                            Rondoš,J.  3.0  5.0  3.0  2.0  1.0  4.0  ...   \n",
       "4                                  NaN  3.0  4.0  3.0  3.0  1.0  NaN  ...   \n",
       "5                          Vyhnánek,T.  2.0  3.0  NaN  NaN  NaN  2.0  ...   \n",
       "\n",
       "         q8   q9  q10  q11  q12  q13  c_value c_improve department_code    tmp  \n",
       "number                                                                          \n",
       "1       3.0  1.0  4.0  1.0  NaN  2.0      NaN       NaN             ies  11/10  \n",
       "2       5.0  1.0  5.0  5.0  5.0  5.0      NaN       NaN             cjp  11/10  \n",
       "3       5.0  1.0  3.0  2.0  2.0  1.0      NaN       NaN             ies  11/10  \n",
       "4       NaN  1.0  3.0  2.0  3.0  2.0      NaN       NaN             ies  11/10  \n",
       "5       1.0  1.0  4.0  2.0  2.0  1.0      NaN       NaN             ies  11/10  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adding columns (first adding, so we have something to drop)\n",
    "df['tmp'] = '11/10'\n",
    "# you can also use assign function, if new column should be a function of original column \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['sumq1q2'] = df.q1+df.q2\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop column (you can also use 'del' (a general python comand for deleting)\n",
    "df.drop('tmp', axis = 1, inplace = True) # axis to specify you want to drop column, inplace operation in this case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>course_code</th>\n",
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       "      <th>...</th>\n",
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       "      <th>q11</th>\n",
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       "      <th>q13</th>\n",
       "      <th>c_value</th>\n",
       "      <th>c_improve</th>\n",
       "      <th>department_code</th>\n",
       "      <th>tmp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Hanus,L.</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Přístup vyučujících a pana Červinky k předmětu...</td>\n",
       "      <td>Možná jasnější prezentace, slidy byly někdy ve...</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Smutná,Š.</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Jako student ekonomické teorii považuji tento ...</td>\n",
       "      <td>Když tento předmět srovnám s výukou matematiky...</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Hanus,L.</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Oceňuji komentáře pana Červinky k našim hodnoc...</td>\n",
       "      <td>Měl bych poznámku ohledně prezentací. U někter...</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Nevrla,M.</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>I když jsem byla jen na první přednášce a prvn...</td>\n",
       "      <td>Možná bych ocenila více tipů na literaturu (na...</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>943</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Hanus,L.</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1418</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Nevrla,M.</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Domácí úkoly byly časově velice náročné, ale m...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1429</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2195</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Smutná,Š.</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2256</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Hanus,L.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Dobře procvičené úlohy ze seminářů</td>\n",
       "      <td>Přednášky byly často přetaženy, příliš mnoho i...</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2464</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Nevrla,M.</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>I guess Mr Červinka's approach is something st...</td>\n",
       "      <td>The home assignments were sometimes a bit too ...</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2835</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3076</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3239</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Hanus,L.</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3667</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3869</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3942</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3958</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Hanus,L.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>The home assignments were very quality and ver...</td>\n",
       "      <td>The lectures were useless for me. They did not...</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4012</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Nevrla,M.</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4141</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Hanus,L.</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>I consider the course to be very useful for my...</td>\n",
       "      <td>The topics covered in the subject are often no...</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4294</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Smutná,Š.</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4388</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Hanus,L.</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4435</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zapojit data do výuky. Názornější vysvětlování...</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4726</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Hanus,L.</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>ústní zkouška byla fajn. Kdybyste to ale neuči...</td>\n",
       "      <td>Úkoly se hodnotili mírněji než minulý rok, ste...</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4758</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Smutná,Š.</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4949</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Smutná,Š.</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Seminars were great</td>\n",
       "      <td>Proofs of theorems were really badly visible, ...</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5103</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Hanus,L.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5237</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Hanus,L.</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Kurz je velmi dobře strukturovaný. Cvičení jso...</td>\n",
       "      <td>Občas jsem měl pocit, že se rigorózní matemati...</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5239</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Smutná,Š.</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5321</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6315</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Smutná,Š.</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6384</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Nevrla,M.</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6718</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Za pozitivní pouvažuji, že jsou prezentace zčá...</td>\n",
       "      <td>Tento předmět se mi hodnotí velice těžko. Pan ...</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6724</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Určitě oceňuji aktivní přístup pana doktora Če...</td>\n",
       "      <td>Upřímně řečeno jsem chtěl kurzu Statistics dát...</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6973</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Smutná,Š.</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Domácí úkoly, ačkoliv systém hodnocení je extr...</td>\n",
       "      <td>Přednášky. Pan Červinka se vše snaží dělat mno...</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       course_code course_title     teachers seminar_leaders   q1   q2   q3  \\\n",
       "number                                                                        \n",
       "36          JEB105   Statistics  Červinka,M.     Červinka,M.  4.0  5.0  5.0   \n",
       "42          JEB105   Statistics  Červinka,M.        Hanus,L.  5.0  5.0  5.0   \n",
       "327         JEB105   Statistics  Červinka,M.       Smutná,Š.  5.0  4.0  4.0   \n",
       "387         JEB105   Statistics  Červinka,M.        Hanus,L.  5.0  4.0  4.0   \n",
       "882         JEB105   Statistics  Červinka,M.       Nevrla,M.  5.0  4.0  5.0   \n",
       "943         JEB105   Statistics  Červinka,M.     Červinka,M.  5.0  5.0  5.0   \n",
       "1003        JEB105   Statistics  Červinka,M.        Hanus,L.  4.0  5.0  4.0   \n",
       "1418        JEB105   Statistics  Červinka,M.       Nevrla,M.  4.0  5.0  4.0   \n",
       "1429        JEB105   Statistics  Červinka,M.     Červinka,M.  5.0  5.0  5.0   \n",
       "2195        JEB105   Statistics  Červinka,M.       Smutná,Š.  5.0  5.0  5.0   \n",
       "2256        JEB105   Statistics  Červinka,M.        Hanus,L.  3.0  4.0  3.0   \n",
       "2464        JEB105   Statistics  Červinka,M.       Nevrla,M.  5.0  5.0  5.0   \n",
       "2835        JEB105   Statistics  Červinka,M.     Červinka,M.  4.0  4.0  5.0   \n",
       "3076        JEB105   Statistics  Červinka,M.     Červinka,M.  4.0  4.0  5.0   \n",
       "3239        JEB105   Statistics  Červinka,M.        Hanus,L.  5.0  5.0  4.0   \n",
       "3667        JEB105   Statistics  Červinka,M.     Červinka,M.  4.0  5.0  5.0   \n",
       "3869        JEB105   Statistics  Červinka,M.     Červinka,M.  5.0  4.0  4.0   \n",
       "3942        JEB105   Statistics  Červinka,M.     Červinka,M.  5.0  4.0  5.0   \n",
       "3958        JEB105   Statistics  Červinka,M.        Hanus,L.  3.0  5.0  1.0   \n",
       "4012        JEB105   Statistics  Červinka,M.       Nevrla,M.  5.0  5.0  3.0   \n",
       "4141        JEB105   Statistics  Červinka,M.        Hanus,L.  5.0  4.0  3.0   \n",
       "4294        JEB105   Statistics  Červinka,M.       Smutná,Š.  4.0  5.0  4.0   \n",
       "4388        JEB105   Statistics  Červinka,M.        Hanus,L.  4.0  5.0  5.0   \n",
       "4435        JEB105   Statistics  Červinka,M.     Červinka,M.  4.0  5.0  3.0   \n",
       "4726        JEB105   Statistics  Červinka,M.        Hanus,L.  4.0  5.0  4.0   \n",
       "4758        JEB105   Statistics  Červinka,M.       Smutná,Š.  5.0  5.0  5.0   \n",
       "4949        JEB105   Statistics  Červinka,M.       Smutná,Š.  4.0  5.0  3.0   \n",
       "5103        JEB105   Statistics  Červinka,M.        Hanus,L.  3.0  4.0  3.0   \n",
       "5237        JEB105   Statistics  Červinka,M.        Hanus,L.  5.0  5.0  5.0   \n",
       "5239        JEB105   Statistics  Červinka,M.       Smutná,Š.  4.0  4.0  4.0   \n",
       "5321        JEB105   Statistics  Červinka,M.     Červinka,M.  3.0  5.0  3.0   \n",
       "6315        JEB105   Statistics  Červinka,M.       Smutná,Š.  4.0  4.0  3.0   \n",
       "6384        JEB105   Statistics  Červinka,M.       Nevrla,M.  5.0  5.0  5.0   \n",
       "6718        JEB105   Statistics  Červinka,M.     Červinka,M.  3.0  5.0  3.0   \n",
       "6724        JEB105   Statistics  Červinka,M.     Červinka,M.  3.0  5.0  3.0   \n",
       "6973        JEB105   Statistics  Červinka,M.       Smutná,Š.  2.0  4.0  1.0   \n",
       "\n",
       "         q4   q5   q6  ...   q8   q9  q10  q11  q12  q13  \\\n",
       "number                 ...                                 \n",
       "36      4.0  3.0  5.0  ...  5.0  1.0  3.0  4.0  2.0  3.0   \n",
       "42      5.0  5.0  5.0  ...  5.0  2.0  5.0  5.0  5.0  5.0   \n",
       "327     5.0  4.0  4.0  ...  5.0  1.0  5.0  5.0  4.0  5.0   \n",
       "387     5.0  4.0  4.0  ...  5.0  1.0  5.0  4.0  4.0  4.0   \n",
       "882     5.0  3.0  3.0  ...  3.0  1.0  5.0  5.0  5.0  5.0   \n",
       "943     5.0  5.0  5.0  ...  5.0  1.0  5.0  5.0  4.0  5.0   \n",
       "1003    3.0  5.0  4.0  ...  5.0  1.0  5.0  5.0  5.0  4.0   \n",
       "1418    5.0  5.0  5.0  ...  5.0  1.0  NaN  NaN  NaN  NaN   \n",
       "1429    5.0  5.0  5.0  ...  5.0  1.0  5.0  5.0  5.0  5.0   \n",
       "2195    5.0  5.0  5.0  ...  5.0  1.0  5.0  4.0  4.0  5.0   \n",
       "2256    3.0  2.0  3.0  ...  5.0  1.0  4.0  4.0  3.0  4.0   \n",
       "2464    5.0  5.0  4.0  ...  5.0  1.0  5.0  5.0  5.0  5.0   \n",
       "2835    4.0  4.0  3.0  ...  3.0  1.0  4.0  3.0  2.0  5.0   \n",
       "3076    4.0  4.0  5.0  ...  5.0  1.0  5.0  5.0  5.0  5.0   \n",
       "3239    4.0  3.0  4.0  ...  5.0  1.0  5.0  3.0  4.0  NaN   \n",
       "3667    5.0  5.0  5.0  ...  5.0  2.0  4.0  4.0  4.0  5.0   \n",
       "3869    5.0  4.0  5.0  ...  5.0  1.0  5.0  5.0  4.0  5.0   \n",
       "3942    5.0  5.0  5.0  ...  5.0  NaN  5.0  5.0  5.0  5.0   \n",
       "3958    5.0  1.0  3.0  ...  3.0  1.0  5.0  5.0  5.0  4.0   \n",
       "4012    4.0  4.0  3.0  ...  5.0  1.0  4.0  4.0  5.0  4.0   \n",
       "4141    5.0  3.0  5.0  ...  5.0  1.0  5.0  5.0  5.0  5.0   \n",
       "4294    4.0  5.0  4.0  ...  5.0  1.0  4.0  4.0  4.0  4.0   \n",
       "4388    5.0  5.0  5.0  ...  5.0  1.0  4.0  3.0  3.0  5.0   \n",
       "4435    3.0  3.0  4.0  ...  4.0  1.0  4.0  4.0  5.0  4.0   \n",
       "4726    5.0  5.0  4.0  ...  4.0  1.0  5.0  4.0  4.0  4.0   \n",
       "4758    5.0  5.0  5.0  ...  5.0  1.0  5.0  5.0  5.0  5.0   \n",
       "4949    5.0  5.0  5.0  ...  5.0  1.0  4.0  4.0  4.0  4.0   \n",
       "5103    5.0  3.0  2.0  ...  4.0  1.0  4.0  4.0  3.0  3.0   \n",
       "5237    5.0  4.0  4.0  ...  5.0  1.0  5.0  3.0  5.0  5.0   \n",
       "5239    4.0  5.0  4.0  ...  4.0  1.0  4.0  4.0  4.0  4.0   \n",
       "5321    3.0  4.0  4.0  ...  4.0  1.0  4.0  3.0  2.0  3.0   \n",
       "6315    4.0  2.0  3.0  ...  4.0  1.0  4.0  3.0  4.0  5.0   \n",
       "6384    4.0  5.0  4.0  ...  4.0  1.0  5.0  5.0  5.0  4.0   \n",
       "6718    5.0  2.0  3.0  ...  3.0  1.0  3.0  2.0  3.0  3.0   \n",
       "6724    5.0  2.0  4.0  ...  5.0  1.0  4.0  3.0  4.0  NaN   \n",
       "6973    2.0  2.0  5.0  ...  5.0  1.0  4.0  4.0  3.0  1.0   \n",
       "\n",
       "                                                  c_value  \\\n",
       "number                                                      \n",
       "36                                                    NaN   \n",
       "42      Přístup vyučujících a pana Červinky k předmětu...   \n",
       "327     Jako student ekonomické teorii považuji tento ...   \n",
       "387     Oceňuji komentáře pana Červinky k našim hodnoc...   \n",
       "882     I když jsem byla jen na první přednášce a prvn...   \n",
       "943                                                   NaN   \n",
       "1003                                                  NaN   \n",
       "1418    Domácí úkoly byly časově velice náročné, ale m...   \n",
       "1429                                                  NaN   \n",
       "2195                                                  NaN   \n",
       "2256                   Dobře procvičené úlohy ze seminářů   \n",
       "2464    I guess Mr Červinka's approach is something st...   \n",
       "2835                                                  NaN   \n",
       "3076                                                  NaN   \n",
       "3239                                                  NaN   \n",
       "3667                                                  NaN   \n",
       "3869                                                  NaN   \n",
       "3942                                                  NaN   \n",
       "3958    The home assignments were very quality and ver...   \n",
       "4012                                                  NaN   \n",
       "4141    I consider the course to be very useful for my...   \n",
       "4294                                                  NaN   \n",
       "4388                                                  NaN   \n",
       "4435                                                  NaN   \n",
       "4726    ústní zkouška byla fajn. Kdybyste to ale neuči...   \n",
       "4758                                                  NaN   \n",
       "4949                                  Seminars were great   \n",
       "5103                                                  NaN   \n",
       "5237    Kurz je velmi dobře strukturovaný. Cvičení jso...   \n",
       "5239                                                  NaN   \n",
       "5321                                                  NaN   \n",
       "6315                                                  NaN   \n",
       "6384                                                  NaN   \n",
       "6718    Za pozitivní pouvažuji, že jsou prezentace zčá...   \n",
       "6724    Určitě oceňuji aktivní přístup pana doktora Če...   \n",
       "6973    Domácí úkoly, ačkoliv systém hodnocení je extr...   \n",
       "\n",
       "                                                c_improve department_code  \\\n",
       "number                                                                      \n",
       "36                                                    NaN             ies   \n",
       "42      Možná jasnější prezentace, slidy byly někdy ve...             ies   \n",
       "327     Když tento předmět srovnám s výukou matematiky...             ies   \n",
       "387     Měl bych poznámku ohledně prezentací. U někter...             ies   \n",
       "882     Možná bych ocenila více tipů na literaturu (na...             ies   \n",
       "943                                                   NaN             ies   \n",
       "1003                                                  NaN             ies   \n",
       "1418                                                  NaN             ies   \n",
       "1429                                                  NaN             ies   \n",
       "2195                                                  NaN             ies   \n",
       "2256    Přednášky byly často přetaženy, příliš mnoho i...             ies   \n",
       "2464    The home assignments were sometimes a bit too ...             ies   \n",
       "2835                                                  NaN             ies   \n",
       "3076                                                  NaN             ies   \n",
       "3239                                                  NaN             ies   \n",
       "3667                                                  NaN             ies   \n",
       "3869                                                  NaN             ies   \n",
       "3942                                                  NaN             ies   \n",
       "3958    The lectures were useless for me. They did not...             ies   \n",
       "4012                                                  NaN             ies   \n",
       "4141    The topics covered in the subject are often no...             ies   \n",
       "4294                                                  NaN             ies   \n",
       "4388                                                  NaN             ies   \n",
       "4435    Zapojit data do výuky. Názornější vysvětlování...             ies   \n",
       "4726    Úkoly se hodnotili mírněji než minulý rok, ste...             ies   \n",
       "4758                                                  NaN             ies   \n",
       "4949    Proofs of theorems were really badly visible, ...             ies   \n",
       "5103                                                  NaN             ies   \n",
       "5237    Občas jsem měl pocit, že se rigorózní matemati...             ies   \n",
       "5239                                                  NaN             ies   \n",
       "5321                                                  NaN             ies   \n",
       "6315                                                  NaN             ies   \n",
       "6384                                                  NaN             ies   \n",
       "6718    Tento předmět se mi hodnotí velice těžko. Pan ...             ies   \n",
       "6724    Upřímně řečeno jsem chtěl kurzu Statistics dát...             ies   \n",
       "6973    Přednášky. Pan Červinka se vše snaží dělat mno...             ies   \n",
       "\n",
       "          tmp  \n",
       "number         \n",
       "36      11/10  \n",
       "42      11/10  \n",
       "327     11/10  \n",
       "387     11/10  \n",
       "882     11/10  \n",
       "943     11/10  \n",
       "1003    11/10  \n",
       "1418    11/10  \n",
       "1429    11/10  \n",
       "2195    11/10  \n",
       "2256    11/10  \n",
       "2464    11/10  \n",
       "2835    11/10  \n",
       "3076    11/10  \n",
       "3239    11/10  \n",
       "3667    11/10  \n",
       "3869    11/10  \n",
       "3942    11/10  \n",
       "3958    11/10  \n",
       "4012    11/10  \n",
       "4141    11/10  \n",
       "4294    11/10  \n",
       "4388    11/10  \n",
       "4435    11/10  \n",
       "4726    11/10  \n",
       "4758    11/10  \n",
       "4949    11/10  \n",
       "5103    11/10  \n",
       "5237    11/10  \n",
       "5239    11/10  \n",
       "5321    11/10  \n",
       "6315    11/10  \n",
       "6384    11/10  \n",
       "6718    11/10  \n",
       "6724    11/10  \n",
       "6973    11/10  \n",
       "\n",
       "[36 rows x 21 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.department_code == 'ies') & (df.teachers.str.contains('Červinka'))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>course_code</th>\n",
       "      <th>course_title</th>\n",
       "      <th>teachers</th>\n",
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       "      <th>tmp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JEB003</td>\n",
       "      <td>Ekonomie I</td>\n",
       "      <td>Fanta,N.,Kracík,J.,Švarcová,N.</td>\n",
       "      <td>Fanta,N.,Kracík,J.,Švarcová,N.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NMMA701</td>\n",
       "      <td>Matematika 1</td>\n",
       "      <td>Spurný,J.</td>\n",
       "      <td>Rondoš,J.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JEB023</td>\n",
       "      <td>Úvod do studia práva</td>\n",
       "      <td>Pražák,P.,Wintr,J.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>JEB055</td>\n",
       "      <td>Seminář k aktualitám I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vyhnánek,T.</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>JEB998</td>\n",
       "      <td>Úvod do ekonomie</td>\n",
       "      <td>Kameníček,J.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6973</th>\n",
       "      <td>JEB105</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>Červinka,M.</td>\n",
       "      <td>Smutná,Š.</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Domácí úkoly, ačkoliv systém hodnocení je extr...</td>\n",
       "      <td>Přednášky. Pan Červinka se vše snaží dělat mno...</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6976</th>\n",
       "      <td>NMMA703</td>\n",
       "      <td>Matematika 3</td>\n",
       "      <td>Zelený,M.</td>\n",
       "      <td>Zelený,M.</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6977</th>\n",
       "      <td>JEB114</td>\n",
       "      <td>Macroeconomics I</td>\n",
       "      <td>Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.</td>\n",
       "      <td>Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6978</th>\n",
       "      <td>JEB108</td>\n",
       "      <td>Microeconomics II</td>\n",
       "      <td>Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.</td>\n",
       "      <td>Chytilová,J.,Jonášová,J.,Smutná,Š.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6979</th>\n",
       "      <td>JEB111</td>\n",
       "      <td>Advanced Data Analysis in MS Excel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Poláková,N.,Polák,P.</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1103 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       course_code                        course_title  \\\n",
       "number                                                   \n",
       "1           JEB003                          Ekonomie I   \n",
       "3          NMMA701                        Matematika 1   \n",
       "4           JEB023                Úvod do studia práva   \n",
       "5           JEB055              Seminář k aktualitám I   \n",
       "7           JEB998                    Úvod do ekonomie   \n",
       "...            ...                                 ...   \n",
       "6973        JEB105                          Statistics   \n",
       "6976       NMMA703                        Matematika 3   \n",
       "6977        JEB114                    Macroeconomics I   \n",
       "6978        JEB108                   Microeconomics II   \n",
       "6979        JEB111  Advanced Data Analysis in MS Excel   \n",
       "\n",
       "                                            teachers  \\\n",
       "number                                                 \n",
       "1                     Fanta,N.,Kracík,J.,Švarcová,N.   \n",
       "3                                          Spurný,J.   \n",
       "4                                 Pražák,P.,Wintr,J.   \n",
       "5                                                NaN   \n",
       "7                                       Kameníček,J.   \n",
       "...                                              ...   \n",
       "6973                                     Červinka,M.   \n",
       "6976                                       Zelený,M.   \n",
       "6977       Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.   \n",
       "6978    Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.   \n",
       "6979                                             NaN   \n",
       "\n",
       "                                  seminar_leaders   q1   q2   q3   q4   q5  \\\n",
       "number                                                                       \n",
       "1                  Fanta,N.,Kracík,J.,Švarcová,N.  3.0  5.0  4.0  5.0  4.0   \n",
       "3                                       Rondoš,J.  3.0  5.0  3.0  2.0  1.0   \n",
       "4                                             NaN  3.0  4.0  3.0  3.0  1.0   \n",
       "5                                     Vyhnánek,T.  2.0  3.0  NaN  NaN  NaN   \n",
       "7                                             NaN  4.0  3.0  2.0  3.0  2.0   \n",
       "...                                           ...  ...  ...  ...  ...  ...   \n",
       "6973                                    Smutná,Š.  2.0  4.0  1.0  2.0  2.0   \n",
       "6976                                    Zelený,M.  5.0  5.0  5.0  5.0  5.0   \n",
       "6977    Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.  1.0  1.0  1.0  1.0  1.0   \n",
       "6978           Chytilová,J.,Jonášová,J.,Smutná,Š.  3.0  3.0  5.0  5.0  4.0   \n",
       "6979                         Poláková,N.,Polák,P.  5.0  4.0  NaN  NaN  NaN   \n",
       "\n",
       "         q6  ...   q8   q9  q10  q11  q12  q13  \\\n",
       "number       ...                                 \n",
       "1       2.0  ...  3.0  1.0  4.0  1.0  NaN  2.0   \n",
       "3       4.0  ...  5.0  1.0  3.0  2.0  2.0  1.0   \n",
       "4       NaN  ...  NaN  1.0  3.0  2.0  3.0  2.0   \n",
       "5       2.0  ...  1.0  1.0  4.0  2.0  2.0  1.0   \n",
       "7       NaN  ...  NaN  1.0  3.0  1.0  2.0  3.0   \n",
       "...     ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "6973    5.0  ...  5.0  1.0  4.0  4.0  3.0  1.0   \n",
       "6976    5.0  ...  5.0  1.0  5.0  5.0  5.0  5.0   \n",
       "6977    1.0  ...  1.0  3.0  2.0  2.0  2.0  1.0   \n",
       "6978    3.0  ...  4.0  1.0  2.0  1.0  3.0  3.0   \n",
       "6979    5.0  ...  5.0  1.0  3.0  4.0  2.0  4.0   \n",
       "\n",
       "                                                  c_value  \\\n",
       "number                                                      \n",
       "1                                                     NaN   \n",
       "3                                                     NaN   \n",
       "4                                                     NaN   \n",
       "5                                                     NaN   \n",
       "7                                                     NaN   \n",
       "...                                                   ...   \n",
       "6973    Domácí úkoly, ačkoliv systém hodnocení je extr...   \n",
       "6976                                                  NaN   \n",
       "6977                                                  NaN   \n",
       "6978                                                  NaN   \n",
       "6979                                                  NaN   \n",
       "\n",
       "                                                c_improve department_code  \\\n",
       "number                                                                      \n",
       "1                                                     NaN             ies   \n",
       "3                                                     NaN             ies   \n",
       "4                                                     NaN             ies   \n",
       "5                                                     NaN             ies   \n",
       "7                                                     NaN             ies   \n",
       "...                                                   ...             ...   \n",
       "6973    Přednášky. Pan Červinka se vše snaží dělat mno...             ies   \n",
       "6976                                                  NaN             ies   \n",
       "6977                                                  NaN             ies   \n",
       "6978                                                  NaN             ies   \n",
       "6979                                                  NaN             ies   \n",
       "\n",
       "          tmp  \n",
       "number         \n",
       "1       11/10  \n",
       "3       11/10  \n",
       "4       11/10  \n",
       "5       11/10  \n",
       "7       11/10  \n",
       "...       ...  \n",
       "6973    11/10  \n",
       "6976    11/10  \n",
       "6977    11/10  \n",
       "6978    11/10  \n",
       "6979    11/10  \n",
       "\n",
       "[1103 rows x 21 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.department_code == 'ies']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### loc and Idioms\n",
    "* `.loc` selects data by the label of the rows and columns (as opposed to the `.iloc`) integer location\n",
    "* we can also use `.loc` for subsetting based on condition(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>department_code</th>\n",
       "      <th>teachers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ies</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ies</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>cjp</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ks</td>\n",
       "      <td>Hendl,J.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ks</td>\n",
       "      <td>Bureš,J.,Numerato,D.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>kvsp</td>\n",
       "      <td>Vlk,A.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ks</td>\n",
       "      <td>Soukup,P.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       department_code              teachers\n",
       "number                                      \n",
       "5                  ies                   NaN\n",
       "8                  ies                   NaN\n",
       "11                 cjp                   NaN\n",
       "14                  ks              Hendl,J.\n",
       "17                  ks  Bureš,J.,Numerato,D.\n",
       "20                kvsp                Vlk,A.\n",
       "23                  ks             Soukup,P."
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[5:25:3,['department_code','teachers']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Subset using a mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select only observations for IES only\n",
    "df_ies = df.loc[df['department_code'] == 'ies']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JEB003</td>\n",
       "      <td>Ekonomie I</td>\n",
       "      <td>Fanta,N.,Kracík,J.,Švarcová,N.</td>\n",
       "      <td>Fanta,N.,Kracík,J.,Švarcová,N.</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NMMA701</td>\n",
       "      <td>Matematika 1</td>\n",
       "      <td>Spurný,J.</td>\n",
       "      <td>Rondoš,J.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JEB023</td>\n",
       "      <td>Úvod do studia práva</td>\n",
       "      <td>Pražák,P.,Wintr,J.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>JEB055</td>\n",
       "      <td>Seminář k aktualitám I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vyhnánek,T.</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>JEB998</td>\n",
       "      <td>Úvod do ekonomie</td>\n",
       "      <td>Kameníček,J.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       course_code            course_title                        teachers  \\\n",
       "number                                                                       \n",
       "1           JEB003              Ekonomie I  Fanta,N.,Kracík,J.,Švarcová,N.   \n",
       "3          NMMA701            Matematika 1                       Spurný,J.   \n",
       "4           JEB023    Úvod do studia práva              Pražák,P.,Wintr,J.   \n",
       "5           JEB055  Seminář k aktualitám I                             NaN   \n",
       "7           JEB998        Úvod do ekonomie                    Kameníček,J.   \n",
       "\n",
       "                       seminar_leaders   q1   q2   q3   q4   q5   q6  ...  \\\n",
       "number                                                                ...   \n",
       "1       Fanta,N.,Kracík,J.,Švarcová,N.  3.0  5.0  4.0  5.0  4.0  2.0  ...   \n",
       "3                            Rondoš,J.  3.0  5.0  3.0  2.0  1.0  4.0  ...   \n",
       "4                                  NaN  3.0  4.0  3.0  3.0  1.0  NaN  ...   \n",
       "5                          Vyhnánek,T.  2.0  3.0  NaN  NaN  NaN  2.0  ...   \n",
       "7                                  NaN  4.0  3.0  2.0  3.0  2.0  NaN  ...   \n",
       "\n",
       "         q8   q9  q10  q11  q12  q13  c_value c_improve department_code    tmp  \n",
       "number                                                                          \n",
       "1       3.0  1.0  4.0  1.0  NaN  2.0      NaN       NaN             ies  11/10  \n",
       "3       5.0  1.0  3.0  2.0  2.0  1.0      NaN       NaN             ies  11/10  \n",
       "4       NaN  1.0  3.0  2.0  3.0  2.0      NaN       NaN             ies  11/10  \n",
       "5       1.0  1.0  4.0  2.0  2.0  1.0      NaN       NaN             ies  11/10  \n",
       "7       NaN  1.0  3.0  1.0  2.0  3.0      NaN       NaN             ies  11/10  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
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       "      <th>89</th>\n",
       "      <td>JEM005</td>\n",
       "      <td>Advanced Econometrics</td>\n",
       "      <td>Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevr...</td>\n",
       "      <td>Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mr.Barunik is very sexy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
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       "      <th>973</th>\n",
       "      <td>JEM005</td>\n",
       "      <td>Advanced Econometrics</td>\n",
       "      <td>Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevr...</td>\n",
       "      <td>Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       course_code           course_title  \\\n",
       "number                                      \n",
       "89          JEM005  Advanced Econometrics   \n",
       "973         JEM005  Advanced Econometrics   \n",
       "\n",
       "                                                 teachers  \\\n",
       "number                                                      \n",
       "89      Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevr...   \n",
       "973     Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevr...   \n",
       "\n",
       "                                 seminar_leaders   q1   q2   q3   q4   q5  \\\n",
       "number                                                                      \n",
       "89      Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.  5.0  5.0  5.0  5.0  4.0   \n",
       "973     Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.  4.0  4.0  5.0  5.0  5.0   \n",
       "\n",
       "         q6  ...   q8   q9  q10  q11  q12  q13                  c_value  \\\n",
       "number       ...                                                          \n",
       "89      5.0  ...  5.0  1.0  5.0  5.0  5.0  1.0  Mr.Barunik is very sexy   \n",
       "973     2.0  ...  4.0  1.0  3.0  5.0  4.0  4.0                      NaN   \n",
       "\n",
       "       c_improve department_code    tmp  \n",
       "number                                   \n",
       "89           NaN             ies  11/10  \n",
       "973          NaN             ies  11/10  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select only observations for Advanced Econometrics\n",
    "df.loc[df['course_title'] == 'Advanced Econometrics'].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1138</th>\n",
       "      <td>JEM005</td>\n",
       "      <td>Advanced Econometrics</td>\n",
       "      <td>Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevr...</td>\n",
       "      <td>Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>The lecturers teaching method.</td>\n",
       "      <td>More practical exercises and involvement of st...</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1870</th>\n",
       "      <td>JEM005</td>\n",
       "      <td>Advanced Econometrics</td>\n",
       "      <td>Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevr...</td>\n",
       "      <td>Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Positive atmosphere the lecturer provided</td>\n",
       "      <td>It was too theoretical and abstract. Since it ...</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       course_code           course_title  \\\n",
       "number                                      \n",
       "1138        JEM005  Advanced Econometrics   \n",
       "1870        JEM005  Advanced Econometrics   \n",
       "\n",
       "                                                 teachers  \\\n",
       "number                                                      \n",
       "1138    Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevr...   \n",
       "1870    Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevr...   \n",
       "\n",
       "                                 seminar_leaders   q1   q2   q3   q4   q5  \\\n",
       "number                                                                      \n",
       "1138    Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.  NaN  5.0  4.0  4.0  4.0   \n",
       "1870    Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.  3.0  5.0  5.0  5.0  5.0   \n",
       "\n",
       "         q6  ...   q8   q9  q10  q11  q12  q13  \\\n",
       "number       ...                                 \n",
       "1138    3.0  ...  3.0  NaN  2.0  5.0  5.0  5.0   \n",
       "1870    3.0  ...  3.0  1.0  3.0  3.0  2.0  3.0   \n",
       "\n",
       "                                          c_value  \\\n",
       "number                                              \n",
       "1138               The lecturers teaching method.   \n",
       "1870    Positive atmosphere the lecturer provided   \n",
       "\n",
       "                                                c_improve department_code  \\\n",
       "number                                                                      \n",
       "1138    More practical exercises and involvement of st...             ies   \n",
       "1870    It was too theoretical and abstract. Since it ...             ies   \n",
       "\n",
       "          tmp  \n",
       "number         \n",
       "1138    11/10  \n",
       "1870    11/10  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# subsetting based on multiple conditions: AE and non-missing comment on what to improve\n",
    "df.loc[(df['course_title'] == 'Advanced Econometrics') & (~df['c_improve'].isnull())].head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Sometimes, we don't have a clear list of columns to be selected ready, e.g. how to select columns from q1 to q13? \n",
    "    * using actual list of column names :(\n",
    "        * be lazy!\n",
    "    * or ... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list comprehension\n",
    "print([x for x in df.columns if 'q' in x]) #by substring\n",
    "print([x for x in df.columns if (len(x) == 2) | (len(x) == 3)]) #by length\n",
    "print([x for x in df.columns if x.startswith('q')]) #by first letter\n",
    "# by regular expression is the safest - q and then at most 2 digit number -> later in course"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['q1',\n",
       " 'q2',\n",
       " 'q3',\n",
       " 'q4',\n",
       " 'q5',\n",
       " 'q6',\n",
       " 'q7',\n",
       " 'q8',\n",
       " 'q9',\n",
       " 'q10',\n",
       " 'q11',\n",
       " 'q12',\n",
       " 'q13']"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x for x in df.columns if 'q' in x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>q1</th>\n",
       "      <th>q2</th>\n",
       "      <th>q3</th>\n",
       "      <th>q4</th>\n",
       "      <th>q5</th>\n",
       "      <th>q6</th>\n",
       "      <th>q7</th>\n",
       "      <th>q8</th>\n",
       "      <th>q9</th>\n",
       "      <th>q10</th>\n",
       "      <th>q11</th>\n",
       "      <th>q12</th>\n",
       "      <th>q13</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         q1   q2   q3   q4   q5   q6   q7   q8   q9  q10  q11  q12  q13\n",
       "number                                                                 \n",
       "1       3.0  5.0  4.0  5.0  4.0  2.0  4.0  3.0  1.0  4.0  1.0  NaN  2.0\n",
       "2       5.0  3.0  NaN  NaN  NaN  5.0  5.0  5.0  1.0  5.0  5.0  5.0  5.0\n",
       "3       3.0  5.0  3.0  2.0  1.0  4.0  4.0  5.0  1.0  3.0  2.0  2.0  1.0\n",
       "4       3.0  4.0  3.0  3.0  1.0  NaN  NaN  NaN  1.0  3.0  2.0  3.0  2.0\n",
       "5       2.0  3.0  NaN  NaN  NaN  2.0  3.0  1.0  1.0  4.0  2.0  2.0  1.0"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_q = df[[x for x in df.columns if 'q' in x]]\n",
    "df_q.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using functions on pandas objects\n",
    "\n",
    "| Operation          | Function              |\n",
    "|--------------------|-----------------------|\n",
    "| Row or Column-wise | `apply()`             |\n",
    "| Aggregation        | `agg() / transform()` |\n",
    "| Elementwise        | `applymap()`          |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tablewise**\n",
    "* DFs and Series can be arguments of the functions\n",
    "* if multiple functions need to be called in a sequence, use `pipe()` method, also called the method chaining\n",
    "    * often used in the data science setting\n",
    "    * inspired by unix pipes and dplyr (%>%) operator in R "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Row or Column-wise Function Application**\n",
    "* `apply()` is extremely powerful, when used with some brainpower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_q.apply(np.mean, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using lambda - anonymous function\n",
    "#standardization to unit variance\n",
    "df_q.apply(lambda x: (x - np.mean(x)) / np.std(x), axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using custom function, with arguments (could have also be done with lambda)\n",
    "def add_and_substract(df, sub = 1, add = 1):\n",
    "    return df - sub + add\n",
    "df_q.apply(add_and_substract, args = (0,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A little bit more sophisticated:  show the longest  comment\n",
    "df.loc[df['c_value'].apply(lambda x: len(str(x))).idxmax(),'c_value']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Aggregation**\n",
    "* *`aggregate()`* and *`transform()`*\n",
    "* aggregation allows multiple aggregation operations in a single concise way\n",
    "* `transform()` method returns an object that is indexed the same as the original\n",
    "   * allows multiple operations at the same time, instead of one-by-one as `aggregate()` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# aggregating simple function is the same as apply\n",
    "df_q.agg(np.mean, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# aggregating more functions more interesting (you could do your own describe function easily! )\n",
    "df_q.aggregate([np.mean, np.std, np.min, np.max], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# aggregating using dictionary, i.e. column specific aggregation \n",
    "df_q.agg({'q1' : [np.mean], 'q2': np.std, 'q3': [np.mean, np.std, np.var]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using single function, the same as with apply\n",
    "df_q.transform(lambda x: np.power(x,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using multiple functions (can also be done using dictionary as in the case of aggregate)\n",
    "df_q.transform([np.abs, lambda x: x + 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "number\n",
       "1        True\n",
       "2       False\n",
       "3        True\n",
       "4        True\n",
       "5       False\n",
       "        ...  \n",
       "6993     True\n",
       "6994    False\n",
       "6995     True\n",
       "6996     True\n",
       "6997     True\n",
       "Name: teachers, Length: 6995, dtype: bool"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.teachers.notnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>course_code</th>\n",
       "      <th>course_title</th>\n",
       "      <th>teachers</th>\n",
       "      <th>seminar_leaders</th>\n",
       "      <th>q1</th>\n",
       "      <th>q2</th>\n",
       "      <th>q3</th>\n",
       "      <th>q4</th>\n",
       "      <th>q5</th>\n",
       "      <th>q6</th>\n",
       "      <th>...</th>\n",
       "      <th>q8</th>\n",
       "      <th>q9</th>\n",
       "      <th>q10</th>\n",
       "      <th>q11</th>\n",
       "      <th>q12</th>\n",
       "      <th>q13</th>\n",
       "      <th>c_value</th>\n",
       "      <th>c_improve</th>\n",
       "      <th>department_code</th>\n",
       "      <th>tmp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JEB003</td>\n",
       "      <td>Ekonomie I</td>\n",
       "      <td>Fanta,N.,Kracík,J.,Švarcová,N.</td>\n",
       "      <td>Fanta,N.,Kracík,J.,Švarcová,N.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NMMA701</td>\n",
       "      <td>Matematika 1</td>\n",
       "      <td>Spurný,J.</td>\n",
       "      <td>Rondoš,J.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JEB023</td>\n",
       "      <td>Úvod do studia práva</td>\n",
       "      <td>Pražák,P.,Wintr,J.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>JPM314</td>\n",
       "      <td>Theories of International Relations</td>\n",
       "      <td>Ditrych,O.,Plechanovová,B.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>kmv</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>JEB998</td>\n",
       "      <td>Úvod do ekonomie</td>\n",
       "      <td>Kameníček,J.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ies</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6991</th>\n",
       "      <td>JPM620</td>\n",
       "      <td>Geopolitical Thought</td>\n",
       "      <td>Kofroň,J.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>kp</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6993</th>\n",
       "      <td>JJM260</td>\n",
       "      <td>Novinářská etika v praxi</td>\n",
       "      <td>Moravec,V.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Tlak ze strany vyucujicicho - eseje, pestovani...</td>\n",
       "      <td>Vetsi ucebnu?</td>\n",
       "      <td>kz</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6995</th>\n",
       "      <td>JJM360</td>\n",
       "      <td>Ekonomika v médiích</td>\n",
       "      <td>Klimeš,D.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Hosty a jejich prednasky.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>kz</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6996</th>\n",
       "      <td>JJM354</td>\n",
       "      <td>Dějiny populární hudby</td>\n",
       "      <td>Halada,A.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Kurz mel velky potencial, zabavne a prinosne p...</td>\n",
       "      <td>Aby prednasky probihaly.</td>\n",
       "      <td>kz</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6997</th>\n",
       "      <td>JJM340</td>\n",
       "      <td>Tvůrčí dílny – tvůrčí psaní I</td>\n",
       "      <td>Novotný,D.</td>\n",
       "      <td>Novotný,D.</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Ja kurz absolvovala s panem docentem Malym - j...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>kz</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5434 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       course_code                         course_title  \\\n",
       "number                                                    \n",
       "1           JEB003                           Ekonomie I   \n",
       "3          NMMA701                         Matematika 1   \n",
       "4           JEB023                 Úvod do studia práva   \n",
       "6           JPM314  Theories of International Relations   \n",
       "7           JEB998                     Úvod do ekonomie   \n",
       "...            ...                                  ...   \n",
       "6991        JPM620                 Geopolitical Thought   \n",
       "6993        JJM260             Novinářská etika v praxi   \n",
       "6995        JJM360                  Ekonomika v médiích   \n",
       "6996        JJM354               Dějiny populární hudby   \n",
       "6997        JJM340        Tvůrčí dílny – tvůrčí psaní I   \n",
       "\n",
       "                              teachers                 seminar_leaders   q1  \\\n",
       "number                                                                        \n",
       "1       Fanta,N.,Kracík,J.,Švarcová,N.  Fanta,N.,Kracík,J.,Švarcová,N.  3.0   \n",
       "3                            Spurný,J.                       Rondoš,J.  3.0   \n",
       "4                   Pražák,P.,Wintr,J.                             NaN  3.0   \n",
       "6           Ditrych,O.,Plechanovová,B.                             NaN  2.0   \n",
       "7                         Kameníček,J.                             NaN  4.0   \n",
       "...                                ...                             ...  ...   \n",
       "6991                         Kofroň,J.                             NaN  3.0   \n",
       "6993                        Moravec,V.                             NaN  5.0   \n",
       "6995                         Klimeš,D.                             NaN  5.0   \n",
       "6996                         Halada,A.                             NaN  3.0   \n",
       "6997                        Novotný,D.                      Novotný,D.  5.0   \n",
       "\n",
       "         q2   q3   q4   q5   q6  ...   q8   q9  q10  q11  q12  q13  \\\n",
       "number                           ...                                 \n",
       "1       5.0  4.0  5.0  4.0  2.0  ...  3.0  1.0  4.0  1.0  NaN  2.0   \n",
       "3       5.0  3.0  2.0  1.0  4.0  ...  5.0  1.0  3.0  2.0  2.0  1.0   \n",
       "4       4.0  3.0  3.0  1.0  NaN  ...  NaN  1.0  3.0  2.0  3.0  2.0   \n",
       "6       3.0  2.0  4.0  1.0  NaN  ...  NaN  2.0  3.0  1.0  3.0  1.0   \n",
       "7       3.0  2.0  3.0  2.0  NaN  ...  NaN  1.0  3.0  1.0  2.0  3.0   \n",
       "...     ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "6991    4.0  3.0  3.0  3.0  NaN  ...  NaN  1.0  4.0  3.0  4.0  3.0   \n",
       "6993    5.0  5.0  5.0  5.0  NaN  ...  NaN  1.0  5.0  4.0  5.0  5.0   \n",
       "6995    4.0  5.0  5.0  4.0  NaN  ...  NaN  2.0  5.0  3.0  5.0  5.0   \n",
       "6996    3.0  5.0  5.0  1.0  NaN  ...  NaN  5.0  1.0  3.0  3.0  5.0   \n",
       "6997    5.0  5.0  5.0  5.0  5.0  ...  5.0  1.0  5.0  5.0  4.0  5.0   \n",
       "\n",
       "                                                  c_value  \\\n",
       "number                                                      \n",
       "1                                                     NaN   \n",
       "3                                                     NaN   \n",
       "4                                                     NaN   \n",
       "6                                                     NaN   \n",
       "7                                                     NaN   \n",
       "...                                                   ...   \n",
       "6991                                                  NaN   \n",
       "6993    Tlak ze strany vyucujicicho - eseje, pestovani...   \n",
       "6995                            Hosty a jejich prednasky.   \n",
       "6996    Kurz mel velky potencial, zabavne a prinosne p...   \n",
       "6997    Ja kurz absolvovala s panem docentem Malym - j...   \n",
       "\n",
       "                       c_improve department_code    tmp  \n",
       "number                                                   \n",
       "1                            NaN             ies  11/10  \n",
       "3                            NaN             ies  11/10  \n",
       "4                            NaN             ies  11/10  \n",
       "6                            NaN             kmv  11/10  \n",
       "7                            NaN             ies  11/10  \n",
       "...                          ...             ...    ...  \n",
       "6991                         NaN              kp  11/10  \n",
       "6993               Vetsi ucebnu?              kz  11/10  \n",
       "6995                         NaN              kz  11/10  \n",
       "6996    Aby prednasky probihaly.              kz  11/10  \n",
       "6997                         NaN              kz  11/10  \n",
       "\n",
       "[5434 rows x 21 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.teachers.notnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "% of missing observations for specific column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['q1'].isnull().sum() / df['q1'].isnull().count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "% of missing observations for all columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df.isnull().sum()/df.shape[0]).sort_values().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EJBXDCBEREUnFMEJERERSMYwQERGRVAwjREREJBXDCBEREUnFMEJERERSMYwQERGRVAwjREREJBXDCBEREUnFMEJERERSMYwQERGRVAwjREREJBXDCBEREUnFMEJERERSMYwQERGRVAwjREREJBXDCBEREUnFMEJERERSMYwQERGRVAwjREREJBXDCBEREUnFMEJERERSMYwQERGRVAwjREREJBXDCBEREUnFMEJERERSMYwQERGRVAwjREREJBXDCBEREUnFMEJERERSMYwQERGRVAwjREREJBXDCBEREUnFMEJERERSMYwQERGRVAwjREREJBXDCBEREUnFMEJERERSMYwQERGRVAwjREREJJVRYeTrr7+GRqOBo6MjAgMDcezYsSK9b926dVCpVOjevbsxqyUiIiIrVOwwsn79eoSFhWHChAmIjY1F48aN0aFDB9y5c+eZ77t69SpGjx6N119/3ehmiYiIyPoUO4zMnTsXgwYNQmhoKOrVq4fFixfD2dkZK1asKPQ9ubm56Nu3LyZNmoTq1aub1DARERFZF9viLJydnY0TJ04gPDxcN6ZWq9G2bVscOXKk0Pd9/vnn8Pb2xoABA3Dw4MHnricrKwtZWVm652lpaQAArVYLrVb73PfnL1OUZYtLydpK17fk3pWuz96ts74l9650fXPWdrARhmNqofffgtZtbH0la1tSfUvovajrVAkhDCsW4tatW/Dx8cHhw4cRFBSkGx87diz279+Po0ePGrzn0KFDeO+993Dy5El4eXmhf//+SE1NxdatWwtdz8SJEzFp0iSD8bVr18LZ2bmo7RIREZFEmZmZ6NOnDx48eAB3d/dClyvWnpHievjwIfr164elS5fCy8uryO8LDw9HWFiY7nlaWhp8fX3Rvn37Z34x+bRaLfbs2YN27drBzs7OqN5l1Fa6viX3rnR99m6d9S25d6Xrm7N2g4m7DMYc1AKTA/IQcVyNrDyV3mtnJ3Ywqb6StS2pviX0nn9k43mKFUa8vLxgY2OD5ORkvfHk5GRUrFjRYPn4+HhcvXoVXbp00Y3l5eU9XrGtLS5cuICXXnrJ4H0ODg5wcHAwGLezsyvWP5riLl8cStZWur4l9650ffZunfUtuXel65ujdlauqvDX8lQGrxd3fYXVV7K2JdS3hN6Lus5incBqb28Pf39/REVF6cby8vIQFRWld9gmX506dXDmzBmcPHlS9+jatStat26NkydPwtfXtzirJyIiIitU7MM0YWFhCAkJQUBAAJo1a4Z58+YhIyMDoaGhAIDg4GD4+Phg2rRpcHR0RIMGDfTe7+npCQAG40RERPRiKnYY6dWrF+7evYvIyEgkJSWhSZMm2LlzJypUqAAASExMhFrNiV2JiIioaIw6gXXYsGEYNmxYga9FR0c/872rVq0yZpVERERkpbgLg4iIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpbGU3QEREytKM324w5mAjMLMZ0GDiLmTlqvReuzr9zZJqjQgAwwgRWQmlf+GWpvoMC2RteJiGiIiIpGIYISIiIqkYRoiIiEgqo8LI119/DY1GA0dHRwQGBuLYsWOFLrt06VK8/vrrKFOmDMqUKYO2bds+c3kiIiJ6sRQ7jKxfvx5hYWGYMGECYmNj0bhxY3To0AF37twpcPno6Gj07t0b+/btw5EjR+Dr64v27dvj5s2bJjdPRERElq/YV9PMnTsXgwYNQmhoKABg8eLF2L59O1asWIHx48cbLL9mzRq958uWLcOmTZsQFRWF4ODgAteRlZWFrKws3fO0tDQAgFarhVarfW6P+csUZdniUrK20vUtuXel67N3y6/vYCMMx9RC778FrdcS61ty7+aob8m9m7O+JfRe1HWqhBCGFQuRnZ0NZ2dnbNy4Ed27d9eNh4SEIDU1Fdu2bXtujYcPH8Lb2xsbNmzAW2+9VeAyEydOxKRJkwzG165dC2dn56K2S0RERBJlZmaiT58+ePDgAdzd3Qtdrlh7Ru7du4fc3FxUqFBBb7xChQo4f/58kWqMGzcOlStXRtu2bQtdJjw8HGFhYbrnaWlpusM7z/pi8mm1WuzZswft2rWDnZ1dkfoqKiVrK13fkntXuj57t/z6DSbuMhhzUAtMDshDxHE1svL05wE5O7GDxda35N7NUd+SezdnfUvoPf/IxvOU6KRn06dPx7p16xAdHQ1HR8dCl3NwcICDg4PBuJ2dXbE+rIq7fHEoWVvp+pbcu9L12bvl1n960jG91/JUBq8Xd12lqb4l927O+pbcuznqW0LvRV1nscKIl5cXbGxskJycrDeenJyMihUrPvO9s2fPxvTp0/Hbb7+hUaNGxVktERERWbFiXU1jb28Pf39/REVF6cby8vIQFRWFoKCgQt83c+ZMTJ48GTt37kRAQIDx3RIREZHVKfZhmrCwMISEhCAgIADNmjXDvHnzkJGRobu6Jjg4GD4+Ppg2bRoAYMaMGYiMjMTatWuh0WiQlJQEAHB1dYWrq6sZvxQiIiKyRMUOI7169cLdu3cRGRmJpKQkNGnSBDt37tSd1JqYmAi1+n87XBYtWoTs7Gy88847enUmTJiAiRMnmtY9ERERWTyjTmAdNmwYhg0bVuBr0dHRes+vXr1qzCqIiIjoBcF70xAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERSWUruwEiKj0047cbjDnYCMxsBjSYuAtZuSq9165Of7NU1Sciy8QwQmRmJf0Lnb/MicjS8TANERERScU9I1QqKbl3gYcKiIhKF4YRMgp/oRMRkbnwMA0RERFJxTBCREREUjGMEBERkVRGhZGvv/4aGo0Gjo6OCAwMxLFjx565/IYNG1CnTh04OjqiYcOG2LFjh1HNEhERkfUpdhhZv349wsLCMGHCBMTGxqJx48bo0KED7ty5U+Dyhw8fRu/evTFgwADExcWhe/fu6N69O86ePWty80RERGT5in01zdy5czFo0CCEhoYCABYvXozt27djxYoVGD9+vMHy8+fPR8eOHTFmzBgAwOTJk7Fnzx589dVXWLx4cYHryMrKQlZWlu75gwcPAAApKSnQarXP7VGr1SIzMxP379+HnZ1dcb9EabWVrm/O2rY5GYZjeQKZmXmw1aqRm6d/Nc39+/dLTX1L7r2g+pbcuznrW3Lvxa1vyb2bo74l927O+pbQ+8OHDwEAQohnr0wUQ1ZWlrCxsRFbtmzRGw8ODhZdu3Yt8D2+vr7iyy+/1BuLjIwUjRo1KnQ9EyZMEAD44IMPPvjggw8reFy/fv2Z+aJYe0bu3buH3NxcVKhQQW+8QoUKOH/+fIHvSUpKKnD5pKSkQtcTHh6OsLAw3fO8vDykpKSgXLlyUKlUhb4vX1paGnx9fXH9+nW4u7s/d/niULK20vUtuXel67N366xvyb0rXZ+9W2f90ta7EAIPHz5E5cqVn7lcqZz0zMHBAQ4ODnpjnp6exa7j7u6uyDdD6dpK17fk3pWuz96ts74l9650ffZunfVLU+8eHh7PXaZYJ7B6eXnBxsYGycnJeuPJycmoWLFige+pWLFisZYnIiKiF0uxwoi9vT38/f0RFRWlG8vLy0NUVBSCgoIKfE9QUJDe8gCwZ8+eQpcnIiKiF0uxD9OEhYUhJCQEAQEBaNasGebNm4eMjAzd1TXBwcHw8fHBtGnTAAAjRoxAy5YtMWfOHLz55ptYt24djh8/jiVLlpj3K3mCg4MDJkyYYHCop7TXVrq+JfeudH32bp31Lbl3peuzd+usb6m9q4R43vU2hr766ivMmjULSUlJaNKkCRYsWIDAwEAAQKtWraDRaLBq1Srd8hs2bMBnn32Gq1evombNmpg5cyY6d+5sti+CiIiILJdRYYSIiIjIXHhvGiIiIpKKYYSIiIikYhghIiIiqRhGiIiISCqGESIiIpLK6sLIo0ePFKudnZ2NCxcuICcnR5H6Qojn39nQCAcPHsT777+PoKAg3Lx5EwDw/fff49ChQ2ZflzkdO3YM8+fPR3h4OMLDwzF//nwcO3bMbPVv3LiB9PR0g3GtVosDBw6YXD8vL6/Q8cTERKPrCiFw5coV3c9hdnY21q9fj9WrV+PevXtG1y2Kv//+G6tXr1Z0Haa4f/8+9u3bh5SUFACP76c1Y8YMfP755zh37pxZ1yWEwL59+7B06VL8+uuvRbqjeHFVr14dly5dMlu92NhYnDlzRvd827Zt6N69Oz755BNkZ2ebbT3mNGfOHFy7dk12GyYpie3+/fffo0WLFqhcubJue82bNw/btm2ziPrFumtvaZWbmys+//xzUblyZWFjYyPi4+OFEEJ89tlnYtmyZSbXz8jIEB988IGwsbHRqz9s2DAxbdo0k+svW7ZM1K9fX9jb2wt7e3tRv359sXTpUpPrCiHExo0bhZOTkxg4cKBwcHDQ9b5w4ULRqVMns6yjIElJSWLSpElGvTc5OVm89tprQqVSiWrVqolmzZqJZs2aiWrVqgmVSiVee+01kZycbHRvt27dEq+88opQq9XCxsZG9OvXTzx8+FCvd7VabXT9Bw8eiH/961/C0dFReHt7i4iICJGTk2OW+ufPnxfVqlUTarVa1KhRQyQkJAh/f3/h4uIinJ2dhZeXl7h48aLRvT/PyZMnTdo2mZmZYvny5SI0NFR07NhRdO7cWQwbNkz89ttvJvd29OhR4eHhIVQqlShTpow4fvy48PPzEzVr1hQvvfSScHJyEidOnDC6fqdOnURqaqoQQoj79++LwMBAoVKpRPny5YVarRZ16tQRd+7cMar2/PnzC3zY2NiI8PBw3XNTBQQEiI0bNwohhIiPjxeOjo6id+/eokaNGmLEiBEm1f7ll19ERESEOHTokBBCiKioKNGpUyfRoUMH8e233xpdV6VSCRsbG9G2bVuxbt06kZWVZVKfT7t+/bq4e/eu7vmBAwdEnz59xGuvvSb69u0rDh8+bPI6lNzuQgjxzTffCC8vLzFlyhTh5OSk+5xfuXKlaNWqVamvL8Tjv8Qt3qRJk0T16tXFDz/8oLeh1q1bJ1599VWT6w8fPlz4+/uLgwcPChcXF139rVu3iiZNmphUOyIiQri4uIjx48eLbdu2iW3btonx48cLV1dXERERYXLvTZo0Ed99950QQghXV1dd77GxsaJChQom1y+MKb+03n77bREUFCTOnz9v8Nr58+dF8+bNxTvvvGN0b8HBwSIwMFD88ccfYs+ePcLf318EBASIlJQUIcTjsKBSqYyuP3z4cFGrVi2xYcMGsXTpUlGtWjXx5ptv6j5ETanfrVs30bVrV3H69Gnx8ccfi7p164pu3bqJ7Oxs8ejRI9GlSxfx/vvvG937gwcPnvk4ePCg0d/XS5cuiWrVqglvb2/h6+srVCqVePPNN0VgYKCwsbER//rXv4RWqzW697Zt24qBAweKtLQ0MWvWLFGlShUxcOBA3euhoaGie/fuRtdXqVS6EDxkyBBRr149kZCQIIR4/AvN399ffPjhh0bXrlKlitBoNHoPlUolfHx8hEajEX5+fkb3ns/d3V1cvnxZCCHE9OnTRfv27YUQQhw6dEhUqVLF6LqLFy8Wtra2wt/fX7i7u4vvv/9euLm5iYEDB4rBgwcLJycnMW/ePKNqq1QqsXLlStGtWzdhZ2cnypUrJ0aMGCHOnDljdL9Patasmfjll1+EEI8/09VqtejatasYN26c6NGjh7Czs9O9biyltnu+unXrii1btggh9D/nz5w5I8qVK1fq6wthJWHkpZde0v1l9eSGOnfunPD09DS5ftWqVcWRI0cM6l+6dEm4ubmZVNvLy0usXbvWYHzt2rVm+SY7OTmJK1euCCH0e4+PjxcODg5G1z116tQzH+vXrzf6l5arq6uIjY0t9PXjx48LV1dXY1sXlStXFkePHtU9z/8l3qRJE3H//n2T94xUrVpV7Nu3T/f87t27olmzZqJ9+/bi0aNHJtUvX768iIuLE0IIkZ6eLlQqlTh48KDu9ZiYGFG1alWje1epVEKtVhf6yH/dGJ06dRKDBw8WeXl5QojHH8r5e+cuXrwoNBqNmDBhgtG9lylTRvz1119CCCGys7OFWq3W+z6fOHFC+Pj4GF3/yTBSu3ZtsW3bNr3Xf/vtN6MDw+DBg0WTJk10/eeztbUVf/75p3ENF8DNzU2356xt27a6gHDt2jXh6OhodN169eqJJUuWCCGE2Lt3r3B0dBRff/217vWVK1eKunXrGlX7ye2enJwsZsyYIerUqSPUarV45ZVXxJIlS0RaWprRvbu4uOhCZWBgoJg+fbre6wsXLhRNmzY1ur4Qym33fI6OjuLq1atCCP3P+YsXL1pEfSGsJIwUtqH+/PNP4eLiYnL9J/e2PFn/5MmTwt3d3aTaHh4eBe5Wv3DhgvDw8DCpthBC+Pn5iT179ggh9Hv/7rvvjP5wEOJ/v7RUKpXBw9RfWuXKlRPR0dGFvr5v3z6TgpqLi4vBNtdqtaJ79+6iUaNG4vTp0yaFEScnJ92HW760tDQRFBQk2rRpIxISEoyu7+TkJK5du6Z77urqqvuLSwghEhMTTQqZ7u7uYsaMGSI6OrrAx9KlS43u3dnZWW+7Z2VlCTs7O3Hv3j0hxOO/SjUajdG9u7i46IK3EPo/70KY/sGvUql0h2G8vb3F2bNn9V6/evWqSdt+8+bNwtfXVyxcuFA3Zu4w0rp1axEcHCxWr14t7OzsxKVLl4QQQkRHR4tq1aoZXffpn0s7Ozu9PRdXrlwRzs7ORtV+Mow86cCBAyIkJES4uLiY9Dnv4eEhTp06JYR4/H3N//98ly9fNrr3fEpt93x169YVW7duFULo/9wvWLDA5CBVEvWFEMIqTmCtV68eDh48aDC+ceNGNG3a1OT6AQEB2L59u+65SqUCACxbtszkuw/369cPixYtMhhfsmQJ+vbta1JtABg0aBBGjBiBo0ePQqVS4datW1izZg1Gjx6NIUOGGF23bNmyWLp0Ka5cuWLwSEhIwK+//mp07V69eiEkJARbtmxBWlqabjwtLQ1btmxBaGgoevfubXT96tWr4/Tp03pjtra22LBhA6pXr4633nrL6NoAULVqVYOTJd3c3LB79278888/6NGjh9G1K1eurHfy68yZM+Ht7a17fvfuXZQpU8bo+i+//DIAoGXLlgU+XnnlFaNPsvb09MTDhw91zzMzM5GTkwN7e3sAQKNGjXD79m2je/f19UVCQoLu+bp161CpUiXd89u3b8PLy8vo+gDQv39/9OzZE1qtFleuXNF7LSkpCZ6enkbX7tGjB44cOYItW7agU6dOSEpKMqnXgsybNw+xsbEYNmwYPv30U9SoUQPA48/K5s2bG123XLlyupMab926hZycHL2f02vXrqFs2bJG1c7/vH3a66+/jlWrVuHWrVv48ssvjaoNPP5Z//HHHwEATZs2RXR0tN7r+/btg4+Pj9H1AeW2e76wsDAMHToU69evhxACx44dwxdffIHw8HCMHTu21NcHYB0nsG7dulV4eHiI6dOnC2dnZzFr1iwxcOBAYW9vL3bv3m1y/YMHDwpXV1fx4YcfCkdHRzFixAjRrl074eLiIo4fP25S7WHDhgl3d3dRv359MWDAADFgwADRoEED4e7uLoYNGyZGjhypexgjLy9PTJkyRbi4uOj2XDg6OorPPvvMpL7bt28vJk+eXOjrJ0+eNPq8iEePHokPP/xQ2NvbC7VaLRwdHYWjo6NQq9XC3t5eDBkyRDx69MjY1sXYsWN1x2yfptVqRdeuXU06Z+Q///lPoee0pKWlicDAQKP3LgwePPiZJzdPmzZNdO7c2ajaQgixZMmSZ54omZSUJCZOnGhU7ZCQENGyZUtx7tw5kZCQIHr16qX3V1V0dLTw9fU1qrYQQkycOFH8+OOPhb7+ySefiJ49expdv3///nqP9evX670+ZswY0aFDB6Pr58vLyxNTp04VFStWFDY2NmbbM5KTkyP279+vOzfqSf/884/Izs42uvbQoUNFzZo1xZQpU0SzZs1ESEiIqFOnjvjvf/8rdu7cKRo2bCg++OADo2oXtmfEXP766y9Rrlw5ERwcLCZPnixcXV3F+++/L7744gsRHBwsHBwcxMqVK42ur+R2f9IPP/wgatSoofuc9/HxMcsFHCVV32pulHfw4EF8/vnnOHXqFNLT0/Hyyy8jMjIS7du3N0v9+Ph4TJ8+Xa/+uHHj0LBhQ5Pqtm7dukjLqVQq7N271+j1ZGdn4/Lly0hPT0e9evXg6upqdC0A2LJlCzIyMvD+++8X+Prff/+Nn3/+GSEhIUavIy0tDSdOnND9hVixYkX4+/vD3d3d6JoAkJOTg8zMzELr5OTk4ObNm6hWrZpR9f/++2/cunUL9evXL/D1hw8fIjY2Fi1btjSq/rNcuXIFjo6OensESos7d+6gW7duOHr0KIDHe5A2b96s2xuzceNG3L59G//5z38UWX9mZiZsbGwUu7V6RkYGbGxs4OjoaJZ6sbGxOHjwIIKDg03a2/UkR0dHnDt3Dn5+fmaply8jIwMjR47EkSNH0Lx5cyxcuBALFizAp59+Cq1Wi5YtW2L9+vV6e/FKk/j4eHz66afYsWOH7nJ/W1tbvPLKKxgzZgy6d+9uUn2ltntBMjMzkZ6erti2Vqq+1YQRouIKCwsr8rJz585VsJMXy6VLl5CVlYU6derA1tbW7PWV/r4qWV/p3gMCAjBjxgy88cYbxX6vMR49egStVgs3NzeTa5XEv1chBO7cuYO8vDx4eXnBzs7OqDpPK+ntbm5TpkxB3759FQ1T5v8ksBJPnqvwPKb+pW5uPXv2LPKymzdvNmldMn6hJycn49tvv0VkZKRJdeLi4hAXFwetVovatWsDAC5evAgbGxvdX+tA4cesjWWu/ku6trnqF3SOVGGM+ZlR+vsaFxeH2NhY5OTkmL2+0r1PmTIFo0ePxuTJk+Hv7w8XFxe91039LFPy80DpbaNk70pvdz8/v2d+3U+eS2WMDRs2YMKECQgMDMT777+Pd9991+Tzr55msXtGypQpU+QfuvzZGItDrVYXuX5ubm6x6+d79OgRFi5ciH379ukS+ZNiY2OLXTM0NLTIy65cubLY9Z/UunXrIn9AmHKY6UmnTp3Cyy+/bNJ2Bx5/oERHR+O7777T7Qb/+++/ERoaitdffx2jRo0yR7sGzNV/Sdc2V32lf2aU/r4qWV/p3tXq/12z8OTnmxACKpXK5J+b1q1bFzmoFfd7q/S2UbJ3pbf7/Pnz9Z5rtVrExcVh586dGDNmDMaPH29SfQD4888/sWbNGqxbtw43btxAu3bt0LdvX3Tv3h3Ozs4m17fYMPLdd9/p/v/+/fuYMmUKOnTooLu65ciRI9i1axciIiIwcuTIYtffv3+/7v+vXr2K8ePHo3///nr1v/vuO0ybNs2k8yL69u2L3bt345133kGFChUMAtCECROMrl0SlPiAePpKl6edP38evXv3NvkfsI+PD3bv3m1wbsfZs2fRvn173Lp1y6i6Svav9LYpiW2v9C8Vpb6vJVFf6d6f/FwriKnnMSn5vVV62yjZu9LbvTBff/01jh8/bvIfnU+LiYnB2rVrsWHDBjx69KhYRxIKZbZTYSXq2bOn3rX5+RYuXCi6detmcv02bdoUODHZmjVrRMuWLU2q7e7urps+WQmtW7cWf//9t8H4gwcPROvWrU2uX7lyZYP5FoR4PDNfpUqVjKqp5BwmT3J1ddWbnCzf3r17TZpUTcn+ld42JbHtlfiZeZJS39eSqK9070pT8nur9LZR+udShvj4eJMn5ixIXFycGDVqlPDx8THbpGdWMc/Irl270LFjR4Pxjh074rfffjO5/pEjRxAQEGAwHhAQYPKN23x8fMxycldhoqOjC7wR06NHjwqcm6W40tLScPfuXYPxu3fv6s0pURxKzmHypB49eiA0NBSbN2/GjRs3cOPGDWzatAkDBgwo1nk3Jdm/0tumJLa9Ej8zT1Lq+1oS9ZXuHfjfjTObN29u9htnKvm9VXrbKP1zqeR2L8zGjRuNnt/laVeuXMEXX3yB+vXrIyAgAHFxcZg0aZL55sMxS6SRrGrVqmL27NkG47NnzzZpaux8tWrVEmPGjDEYHzNmjKhVq5ZJtXfs2CE6duyom0HWXPKnZVepVGLfvn16U7XHxsaKqVOnmmXmv379+gmNRiM2bdokrl+/Lq5fvy42btwo/Pz8RHBwsFE1lZzD5EkZGRliyJAhwsHBQTfdef48Junp6UbXVbJ/pbdNSWx7JX5mnqTU97Uk6ivdu9I3zlTye6v0tlGyd6W3e5MmTUTTpk11jyZNmujmqTHlJoX58udGatKkiZg1a5a4ceOGyTWfZhVhZOXKlcLGxka89dZbYvLkyWLy5MnirbfeEra2tiZNVpNv+/btwtHRUTRo0EA3MVnDhg2Fo6Oj2L59u0m179y5I1q1aiXUarVwdXUVZcqU0XsY68l7jBS0y93Z2VksX77cpN6FUOYDYvPmzeL7778v9PWUlBSxatUqY1s2kJ6ergtq5vhQU7J/pbdNSWx7pX+p5DP397Uk6ytVW+kbZ5bE91apbaNk70pv94kTJ+o9Pv/8c7Fo0SJx7tw5k2sL8XjCQHPelqAgFnsC69OOHj2KBQsW6Kbhrlu3LoYPH47AwECz1L9x4wa++eYbnD9/Xlf/ww8/hK+vr0l127Zti8TERAwYMKDAE1iNPTn22rVrEEKgevXqOHbsGMqXL697zd7eHt7e3rCxsTGp9ydlZGQgPj4eAPDSSy8ZXLpG9DT+zJQ8Z2dn/PXXX9BoNHBzc8OpU6dQvXp1JCQkoF69enj06JFZ1mPJ31slei+p7V4S8iODuac8sJp5RgIDA7FmzRrF6lepUgVTp041e93Dhw/jyJEjaNy4sVnr5s8e+vSlwkpxcXFBo0aNzFrT0icls+TJsUpi2yvxM0PPVrFiRVy+fBkajUZv/NChQ6hevbrZ1mPJ31sleldiu5f0XFirV6/GrFmzcOnSJQBArVq1MGbMGPTr18/k2oAVhZHc3Fxs3bpVt2ekfv366Nq1q9n++k9NTcXy5cv16n/wwQfw8PAwqW6dOnXwzz//mKNFnZ9//hmdOnWCnZ0dfv7552cu27VrV7Ou25xkTUpmLkr2XxITe1nytqeC5d84c8WKFbobZx45cgSjR49GRESE7PaslhLb3dPT87n//oSZ5jGZO3cuIiIiMGzYMLRo0QLA4yD14Ycf4t69e0ZNn1FQsxbv0qVLolatWsLZ2Vl3Ao+zs7OoXbu23u3VjfXHH3+IsmXLCh8fH9GjRw/Ro0cPUaVKFVGuXDlx4sQJk2rv2rVLNG/eXOzbt0/cu3dPPHjwQO9hjCdvLFXQ+SJPXqpZms2ZM0d06dJF7wZTKSkpolu3bgWesFzaKNm/0tvG0rc9FUypG2fSsymx3aOjo4v8MJVGo9Gd8/KkVatWCY1GY3J9IazkBNZOnTqJjh07ivv37+vG7t27Jzp27GjSHUzzvfbaa6J///5Cq9XqxrRarQgJCRGvv/66SbWfDAZPPiwhLCjN0q/7V7J/pbeNpW97erasrCzx559/iqNHj4qHDx/KbueFYanb3cHBQVy6dMlg/OLFi8LBwcEs67CKwzT79+/H77//rnc9dbly5TB9+nTdLiVTHD9+HEuXLtW7qZetrS3Gjh1b4PwjxbFy5Ur4+voaHE7Ky8tDYmKiSbWBx8f5evXqZXCn0uzsbKxbtw7BwcEmr0MpSl/3rzQl+1d621j6tqdns7e3R7169WS38cJRertnZmYiMTHRYG4pU8+BqVGjBn766Sd88skneuPr169HzZo1TaqdzyrCiIODQ4EfkOnp6bC3tze5vru7OxITE1GnTh298evXr5s8YdkHH3yA27dvG9yO+f79+2jbtq1JU80Dj+9T07FjR4P6Dx8+RGhoaKkOI/mTHM2ZMwfNmjUD8PiqqTFjxphtAiglKdm/0tvG0rc9FSwjIwPTp09HVFRUgffCMvWGavQ/JXnD0rt37yI0NBT//e9/C3zd1HNGJk2ahF69euHAgQO6P/BjYmIQFRWFn376yaTa+awijLz11lv497//jeXLl+t9cH744YdmOUGzV69eGDBgAGbPno3mzZsDePyNGDNmDHr37m1SbfH/TzB6Wnp6OhwdHU2q/az6N27cMPnkW6UtXrwYo0ePRp8+faDVagE83iM1YMAAzJo1S3J3z6dk/0pvG0vf9lSwgQMHYv/+/ejXrx8qVarEE5AV9OTnqxACW7ZsgYeHh25v+okTJ5CammqWcP/xxx8jNTUVR48eRatWrbBlyxYkJydjypQpmDNnjsn13377bRw7dgxz587F1q1bATye3uLYsWNo2rSpyfUBC75R3pNSU1MREhKCX375BXZ2dgCAnJwcdO3aFatWrTL5l252djbGjBmDxYsXIycnBwBgZ2eHIUOGYPr06QaHQIoi/9LJ+fPnY9CgQXp3PczNzcXRo0dhY2ODmJgYo3pu2rQpVCoVTp06hfr16+sdYsrNzcWVK1fQsWNHs6VaJVnynAWAsv0rvW0sfduTPk9PT2zfvt0sh6+p6MaNG4eUlBQsXrxYd0g+NzcXH330Edzd3U0O+JUqVcK2bdvQrFkzuLu74/jx46hVqxZ+/vlnzJw506Qp57VaLQYPHoyIiAj4+fmZ1OezWEUYyXf58mW9Sc9q1Khh1vqZmZl6H8ym3Da5devWAB6f7xIUFKR3OMne3h4ajQajR482+njcpEmTdP8dNWoUXF1dDeq//fbbZjmMRUSWwc/PDzt27EDdunVlt/JCKV++PA4dOqS7TD7fhQsX0Lx5c9y/f9+k+u7u7jh9+jQ0Gg2qVauGtWvXokWLFrhy5Qrq16+PzMxMk+p7eHjg5MmTioYRqzhMk69GjRpmDyAA8ODBA+Tm5qJs2bJo2LChbjwlJQW2trZGTSizb98+AI/P6Zg/f75ZJqV50oQJEwAAGo0GvXr1eu4hnx9//BFdu3blX75EVmzy5MmIjIzEd999Z9IfU1Q8OTk5OH/+vEEYOX/+vFkmpqxduzYuXLgAjUaDxo0b49tvv4VGo8HixYtRqVIlk+t3794dW7duNc98IoWwij0jb7/9Npo1a4Zx48bpjc+cORN//PEHNmzYYFL9Tp06oUuXLvjoo4/0xhcvXoyff/4ZO3bsMKl+aeDu7o6TJ0+adRZGIpIv/5BtvsuXL0MIAY1GozusnS82Nrak23shhIWFYfXq1fjkk0/0zmucPn06+vXrZ/IM0j/88ANycnLQv39/nDhxAh07dkRKSgrs7e2xatUq9OrVy6T6+eeevPHGG/D39zf4o3X48OEm1QesJIyUL18ee/fu1dtrAQBnzpxB27ZtkZycbFL9smXLIiYmxmDX5vnz59GiRQuTd7GVBk/eL4GIrEf+IduiyN+jSuaVl5eH2bNnY/78+bh9+zaAx+d5jBgxAqNGjTLrfcKAx6cUnD9/HlWrVoWXl5fJ9Z51eEalUpnlKiyrCCNOTk44efJkgbvAmjZtavJ06y4uLvj9998LDDuBgYEmH48rDRhGiIiUl39PGXMemj906BBee+01s9WTQS27AXNo2LAh1q9fbzC+bt06s0ww06xZMyxZssRgfPHixfD39ze5PhFRSfjjjz9w9OhRg/GjR4/i+PHjEjp68bi7u5v9HME2bdrAz88Pn3zyCf766y+z1n6aeDxzu9nrWsUJrBEREejZsyfi4+PRpk0bAEBUVBR+/PFHk88XAR4fL2vbti1OnTqFN954Q1f/jz/+wO7du02uT0RUEoYOHYqxY8ciMDBQb/zmzZuYMWNGgUGFzGPjxo346aefCpwh1dRzdW7duoV169bhxx9/xPTp09GoUSP07dsXvXv3RpUqVUyqnW/58uX48ssvdXftrVmzJj7++GMMHDjQLPWt4t40Qgjx66+/iubNmwtnZ2dRrlw50bp1a7PcIChfXFyc6NOnj6hXr57w9/cXoaGh4uLFi2arL5urq6uIj4+X3QYRKcjFxaXAf+cJCQnC1dVVQkcvhvnz5wtXV1cxbNgwYW9vLwYPHizatm0rPDw8xCeffGLWdSUkJIgpU6aI+vXrCxsbG9G6dWuTa0ZERAgXFxcxfvx4sW3bNrFt2zYxfvx44erqKiIiIszQtZXcKK+o1q5dK9LT0xWrP23aNPH3338rVl9J9evXF4mJibLbICIFlS1bVhw+fNhgPCYmRnh6ekro6MVQu3ZtsXbtWiGE/h9+ERERYujQoWZfX05Ojvjll19EkyZNzHLDVS8vL13/T1q7dq0oV66cyfWFEMIqzhkpqsGDB5t8Zc2zTJ06FSkpKYrVN1ZqaiqWLVuG8PBwXX+xsbG4efOmbpmzZ8/C19dXVotEVALat2+P8PBwPHjwQDeWmpqKTz75BO3atZPYmXVLTEzU3UrEyclJdy+1fv364ccffzTbemJiYvDRRx+hUqVK6NOnDxo0aIDt27ebXFer1RZ4U1h/f3/drOSmeqHCiFD4wiGl6xvj9OnTqFWrFmbMmIHZs2cjNTUVwOMbM4WHh8ttjohK1OzZs3H9+nVUq1YNrVu3RuvWreHn54ekpCSz3MOEClaxYkXdH4JVq1bF77//DgC4cuWKWX5vhIeHw8/PD23atEFiYiLmz5+PpKQkfP/99+jYsaPJ9fv164dFixYZjC9ZsgR9+/Y1uT5gJSewUuHCwsLQv39/zJw5U+8Ow507d0afPn0kdkZEJc3HxwenT5/GmjVrcOrUKTg5OSE0NBS9e/c2mACNzKdNmzb4+eef0bRpU4SGhmLkyJHYuHEjjh8/bpYb5R04cABjxozBu+++a5Z5RQqyfPly7N69G6+++iqAx1dgJSYmIjg4WHevNQBGT+BmFfOMFJXSc2mUxrk6PDw8EBsbi5deekmvv2vXrqF27dp49OiR7BaJiKxaXl4e8vLydDcsXbduHQ4fPoyaNWti8ODBJXaPsDfffBPLli0r9hTx+fdSex6VSoW9e/ca0xr3jFg7BwcH3SQ7T7p48SLKly8voSMiku2vv/4q8BLTrl27SurIuqnVaqjV/zsr4r333sN7771X4n0cOHDAqElA8++lpiSGESvXtWtXfP755/jpp58APE6uiYmJGDduHN5++23J3RFRSUpISECPHj1w5swZqFQq3fkK+feuyc3NldmeVTt48CC+/fZbxMfHY+PGjfDx8cH3338PPz8/i5891RxeqBNYq1Wrpuhx0ddffx1OTk6K1TfGnDlzkJ6eDm9vb/zzzz9o2bIlatSoATc3N3zxxRey2yOiEjRixAj4+fnhzp07cHZ2xp9//okDBw4gICAA0dHRstuzWps2bUKHDh3g5OSEuLg4ZGVlAXh8R/ipU6dK7u75Hj16hFmzZqFz584ICAjAyy+/rPcwB6s5ZyQ1NRUbN25EfHw8xowZg7JlyyI2NhYVKlSAj4+PSbVtbGxw+/ZteHt7643fv38f3t7eFvHXRExMDE6dOoX09HS8/PLLaNu2reyWiKiEeXl5Ye/evWjUqBE8PDxw7Ngx1K5dG3v37sWoUaMQFxcnu0Wr1LRpU4wcORLBwcF65+7FxcWhU6dOSEpKKpE+jD2vsW/fvti9ezfeeecdVKhQQe8u0IB5brBoFYdpTp8+jbZt28LDwwNXr17FoEGDULZsWWzevBmJiYlYvXq1SfULy2tZWVklduKRqVq0aIEWLVoAgO7yXiJ6seTm5uquqvPy8sKtW7dQu3ZtVKtWDRcuXJDcnfW6cOEC/u///s9g3MPDwyI+j3/99Vfs2LFD9ztECVYRRpS6fHXBggUAHh9PXbZsGVxdXXWv5ebm4sCBA6hTp47xjZeAGTNmQKPRoFevXgCAd999F5s2bULFihWxY8cONG7cWHKHRFRSGjRogFOnTsHPzw+BgYGYOXMm7O3tsWTJklJ1FaC1qVixIi5fvgyNRqM3fujQIYvY7j4+Pnq/WxVhlnlcJXN3dxeXL18WQuhPtXv16lXh4OBgdF2NRiM0Go1QqVTC19dX91yj0YhatWqJ9u3bi99//90sX4NSNBqNiImJEUIIsXv3buHp6Sl27dolBgwYINq1aye5OyIqSTt37hSbNm0SQghx6dIlUbt2baFSqYSXl5eIioqS3J31mjp1qqhXr574/fffhZubmzh48KD44YcfRPny5cWCBQvMUn/58uUG48uXLxfTp0/XW86YW5bs2LFDdOzYUVy9etWUNp/JKsJI+fLlRWxsrBBCP4zs3r1bVKlSxeT6rVq1EikpKSbXkcHR0VF3z5nhw4eLf//730IIIS5cuMB7URCRuH//vsjLy5PdhlXLy8sTU6ZMES4uLkKlUgmVSiUcHR3FZ599Zpb61apV0/3R+aTff/9daDQak+vfuXNHtGrVSqjVauHq6irKlCmj9zAHqzhMo/TlqyVxjbVSypQpg+vXr8PX1xc7d+7ElClTADw+D8YSTrwlIvO7fPky4uPj8X//938oW7ZsqbyVhTVRqVT49NNPMWbMGFy+fBnp6emoV6+e3qF/UyQlJRU4kVn58uVx+/Ztk+v37t0bN2/exNSpUws8gdUcrCKMzJkzB++8847e5atJSUkICgoyy+Wrubm5WLVqFaKionDnzh3k5eXpvW7sjHMloWfPnujTpw9q1qyJ+/fvo1OnTgCAuLg41KhRQ3J3RFSS7t+/j3fffRf79u2DSqXCpUuXUL16dQwYMABlypTh/WkUZm9vj3r16pm9rq+vL2JiYuDn56c3HhMTg8qVK5tc//Dhwzhy5Iii5xhaRRjx8PDAnj17FLt8dcSIEVi1ahXefPNNNGjQQJFUqJQvv/wSfn5+SExMxMyZM3VJ/Pbt2/joo48kd0dEJWnkyJGws7NDYmIi6tatqxvv1asXwsLCGEbMqDj3nNm8ebNJ6xo0aBA+/vhjaLVatGnTBgAQFRWFsWPHYtSoUSbVBoA6deoYNXNrcVjNPCNPS01Nhaenp1lqeXl5YfXq1ejcubNZ6pUUrVaLwYMHIyIiwiAxE9GLp2LFiti1axcaN26sN+dEQkICGjVqhPT0dNktWo3Q0NAiL7ty5UqT1iWEwPjx47FgwQLdFP+Ojo4YN24cIiMjTaoNALt378akSZPwxRdfoGHDhgaTh7q7u5u8DqsII0pfvlq5cmVER0ejVq1a5mi3RHl4eODkyZMMI0QENzc3xMbGombNmnph5Pjx4+jQoQPu378vu8UXWkxMDAICAuDg4GDU+9PT03Hu3Dk4OTmhZs2aRtd5Wv59dZ4+KiCEgEqlMsv5h1YRRvz8/LBmzRo0b94ce/bswbvvvov169fjp59+QmJiInbv3m1S/Tlz5iAhIQFfffWVRR2iAYCQkBA0adIEI0eOlN0KEUnWuXNn+Pv7Y/LkyXBzc8Pp06dRrVo1vPfee8jLy8PGjRtlt/hCc3d3x8mTJ0vd3CP79+9/5ustW7Y0eR1Wcc5IUlISfH19ATyeKe7dd99F+/btodFoEBgYaHL9Q4cOYd++ffjvf/+L+vXrG+yiMvV4n5Jq1qyJzz//HDExMfD394eLi4ve68OHD5fUGRGVtJkzZ+KNN97A8ePHkZ2djbFjx+LPP/9ESkoKYmJiZLf3wiut+wbMETaexyrCiNKXr3p6eqJHjx4m15Fh+fLl8PT0xIkTJ3DixAm911QqFcMI0QvE3d0d586dw6JFi+Dm5ob09HT07NkTQ4cOhVarld0elSKnT59GgwYNoFarcfr06Wcu26hRI5PXZxWHaYYNG4Zff/0VNWvWRFxcHK5evQpXV1esW7cOM2fORGxsrOwWiYiks4abflozY29kpwS1Wo2kpCR4e3tDrVZDpVIVuOfGXOeMWMWekZK4fDUnJwfR0dGIj49Hnz594Obmhlu3bsHd3d1sE9cQESmpsL8909PT4ejoWMLdUGl25coVlC9fXvf/SrP4MPKsy1fNddLmtWvX0LFjRyQmJiIrKwvt2rWDm5sbZsyYgaysLCxevNgs61HCBx988MzXV6xYUUKdEJEsYWFhAB7/FRsZGQlnZ2fda7m5uTh69CiaNGkiqTvKV5oukKhWrVqB//8sb775JpYtW1bgbLDPY/FhxM7ODps2bUJERIRi6xgxYgQCAgJw6tQplCtXTjfeo0cPDBo0SLH1msPff/+t91yr1eLs2bNITU3VTY5DRNYtLi4OwOM9I2fOnIG9vb3uNXt7ezRu3BijR4+W1Z5VE0Lg+vXr8Pb2fu7eJ0s/a+LAgQNGT45m8WEEALp3746tW7cqdvnqwYMHcfjwYb1/wACg0Whw8+ZNRdZpLlu2bDEYy8vLw5AhQ/DSSy9J6IiISlr+/bVCQ0Mxf/58s0xSRUUjhECNGjXw559/ombNms9c9uHDhyXUVeljFWFE6ctX8/LyCjxB58aNG3BzczOptgxqtRphYWFo1aoVxo4dK7sdIiohps70ScWnVqt19wZ7Xhh5kVnF1TTPml1UpVIhISHBpPq9evWCh4cHlixZopsoqHz58ujWrRuqVq1qkf/Ad+zYgZCQENy9e1d2K0REVu2XX37BzJkzsWjRIjRo0EB2O4ox5WogqwgjSrtx4wY6dOgAIQQuXbqEgIAAXLp0CV5eXjhw4IDBZXKlSf6Ja/mEELh9+za2b9+OkJAQfPXVV5I6IyJ6MZQpUwaZmZnIycmBvb09nJyc9F5PSUmR1Jl5mRJGrOIwjdKqVKmCU6dOYd26dTh9+jTS09MxYMAA9O3b1+CHqrTJP3Etn1qtRvny5TFnzpznXmlDRESmmzdvnuwWSj2r2DPCy1eJiIiUMW3aNFSoUMHgd+2KFStw9+5djBs3TrfckCFD4OnpWex1WEUYeXqq9qcvXzXHvWNu3bqFQ4cO4c6dO8jLy9N7zRKmVL979y4uXLgAAKhdu7ZuMhsiIio5jx49QnZ2tt5Yab+6SaPRYO3atWjevLne+NGjR/Hee++ZZVI0qzhMo/Tlq6tWrcLgwYNhb2+PcuXK6U1MU9rv75KRkYH//Oc/WL16tS5E2djYIDg4GAsXLtSb/IiIiMwvIyMD48aNw08//YT79+8bvF7ap+FPSkoqcCKz8uXL4/bt22ZZh9osVUqh/MtXv/zyS5NrRUREIDIyEg8ePMDVq1dx5coV3cPUK3WUFhYWhv379+OXX35BamoqUlNTsW3bNuzfvx+jRo2S3R4RkdUbO3Ys9u7di0WLFsHBwQHLli3DpEmTULlyZaxevVp2e8/l6+tb4F2dY2JiULlyZbOswyr2jBQmPj4eOTk5JtfJzMzEe++9B7Xa8rLbpk2bsHHjRrRq1Uo31rlzZzg5OeHdd9/FokWL5DVHRPQC+OWXX7B69Wq0atUKoaGheP3111GjRg1Uq1YNa9asQd++fWW3+EyDBg3Cxx9/DK1Wq5u5OyoqCmPHjjXbH7VWEUaed/mqqQYMGIANGzZg/PjxJtcqaZmZmahQoYLBuLe3NzIzMyV0RET0YklJSdFd7uru7q67lPe1117DkCFDZLZWJGPGjMH9+/fx0Ucf6c53cXR0xLhx4xAeHm6WdVjFCaytW7fWe55/+WqbNm3wwQcfwNbWtMyVm5uLt956C//88w8aNmwIOzs7vdfnzp1rUn0lvfHGGyhXrhxWr16tuy/CP//8g5CQEKSkpOC3336T3CERkXVr1KgRFi5ciJYtW6Jt27Zo0qQJZs+ejQULFmDmzJm4ceOG7BaLJD09HefOnYOTkxNq1qwJBwcHs9W2ijCitClTpiAyMhK1a9dGhQoVDE5g3bt3r8Tunu3MmTPo2LEjsrKy0LhxYwDAqVOn4ODggN27d6N+/fqSOyQism5ffvklbGxsMHz4cPz222/o0qULhBDQarWYO3cuRowYIbtF6awqjCh1+WqZMmXw5Zdfon///mapV9IyMzOxZs0anD9/HgBQt25di5iwjYjIGl27dg0nTpxAjRo10KhRI9ntlApWEUaUvny1YsWKOHjwoEXe5Kiok9UQERHJYhVhZPDgwfjtt9/w1VdfoUWLFgCAQ4cOYfjw4WjXrp3JV4xMmzYNt2/fxoIFC8zRbokqiclqiIjo2aKiohAVFVXgxJmcJdxKwoiXl5fB5asAsG/fPrz77rsm35m2R48e2Lt3L8qVK4f69esbnMBqjhleleLo6Ihz584Z3Nk4ISEB9erVw6NHjyR1RkT0Ypg0aRI+//xzBAQEoFKlSnrnHQIFT9z5orGKS3uVvnzV09MTPXv2NLmODPmT1TwdRsw5WQ0RERVu8eLFWLVqFfr16ye7lVLLKsJIUFAQJkyYYHD56qRJkxAUFGRy/ZUrV5pcQ5aSmKyGiIgKl52dbXConPRZxWEapS9fzb/Z3tN3IkxLS0P37t1L9aW9QgiMHz8eCxYsMJisJjIyUnJ3RETWb9y4cXB1dUVERITsVkotqwgjgLKXr6rVaiQlJcHb21tv/M6dO/Dx8YFWqzV5HUpTcrIaIiIq3IgRI7B69Wo0atQIjRo1sqiJM0uKVRymyb98ddCgQXrjpl6+evr0ad3///XXX0hKStI9z83Nxc6dO+Hj42Nc0yXM1dUVr7zyiuw2iIheOKdPn0aTJk0AAGfPntV77emTWV9UVrFnRKnLV9Vqte4HpaDN5OTkhIULFxrM4UFERERFZxV7RpKSklCpUiWD8fLly+P27dtG171y5QqEEKhevTqOHTumN6Orvb09vL29YWNjY3R9IiIispIwotTlq9WqVYNWq0VISAjKlSuHatWqmdoqERG9AHr27IlVq1bB3d39uVNDlOa5qkqKVYQRJS9ftbOzw5YtW3jlCRERFZmHh4fuML+Hh4fkbko/qzhnROnLV0NCQtCkSROMHDnS5FpERESkzyrCSD6lLl+dMmUK5syZgzfeeAP+/v5wcXHRe3348OFmWQ8REdGLyKrCiFKePhflSSqVCgkJCSXYDRERWZL79+8jMjIS+/btK/BGeSkpKZI6Kz2s4pwRpfHOtkREZKx+/frh8uXLGDBgACpUqMC5RQrAPSNEREQKcnNzw6FDh3S3KyFD3DNSRDdu3MDPP/+MxMRE3Umy+TiVLxERFaZOnTr4559/ZLdRqjGMFEFUVBS6du2K6tWr4/z582jQoAGuXr0KIQRefvll2e0REVEp9s0332D8+PGIjIxEgwYNDO5N4+7uLqmz0kMtuwFLEB4ejtGjR+PMmTNwdHTEpk2bcP36dbRs2RL/+te/ZLdHRESlmKenJ9LS0tCmTRt4e3ujTJkyKFOmDDw9PVGmTBnZ7ZUKPGekCNzc3HDy5Em89NJLKFOmDA4dOoT69evj1KlT6NatG65evSq7RSIiKqWaNWsGW1tbjBgxosATWFu2bCmps9KDh2mKwMXFRXeeSKVKlRAfH4/69esDAO7duyezNSIiKuXOnj2LuLg41K5dW3YrpRbDSBG8+uqrOHToEOrWrYvOnTtj1KhROHPmDDZv3oxXX31VdntERFSKBQQE4Pr16wwjz8DDNEWQkJCA9PR0NGrUCBkZGRg1ahQOHz6MmjVrYu7cubyBHhERFWrDhg2YOHEixowZg4YNGxqcwNqoUSNJnZUeDCNEREQKUqsNrxVRqVQQQkClUiE3N1dCV6ULD9MUw/Hjx3Hu3DkAQL169eDv7y+5IyIiKu04i/fzcc9IEdy4cQO9e/dGTEwMPD09AQCpqalo3rw51q1bhypVqshtkIiIyIJxnpEiGDhwILRaLc6dO4eUlBSkpKTg3LlzyMvLw8CBA2W3R0REpdz333+PFi1aoHLlyrh27RoAYN68edi2bZvkzkoHhpEi2L9/PxYtWqR3JnTt2rWxcOFCHDhwQGJnRERU2i1atAhhYWHo3LkzUlNTdeeIeHp6Yt68eXKbKyUYRorA19cXWq3WYDw3NxeVK1eW0BEREVmKhQsXYunSpfj0009hY2OjGw8ICMCZM2ckdlZ6MIwUwaxZs/Cf//wHx48f140dP34cI0aMwOzZsyV2RkREpd2VK1fQtGlTg3EHBwdkZGRI6Kj04dU0RdC/f39kZmYiMDAQtraPN1lOTg5sbW3xwQcf4IMPPtAtm5KSIqtNIiIqhfz8/HDy5EmDOal27tyJunXrSuqqdGEYKQIe0yMiImOFhYVh6NChePToEYQQOHbsGH788UdMmzYNy5Ytk91eqcBLe4mIiBS2Zs0aTJw4EfHx8QAAHx8fTJw4EQMGDJDcWenAMFJE8fHxWLlyJeLj4zF//nx4e3vjv//9L6pWraq7aR4REdHT/vnnHwgh4OzsjMzMTJw9exYxMTGoV68eOnToILu9UoEnsBbB/v370bBhQxw9ehSbN29Geno6AODUqVOYMGGC5O6IiKg069atG1avXg0AyM7ORteuXTF37lx0794dixYtktxd6cAwUgTjx4/HlClTsGfPHtjb2+vG27Rpg99//11iZ0REVNrFxsbi9ddfBwBs3LgRFSpUwLVr17B69WosWLBAcnelA8NIEZw5cwY9evQwGPf29sa9e/ckdERERJYiMzMTbm5uAIDdu3ejZ8+eUKvVePXVV3Wzsb7oGEaKwNPTE7dv3zYYj4uLg4+Pj4SOiIjIUtSoUQNbt27F9evXsWvXLrRv3x4AcOfOHbi7u0vurnRgGCmC9957D+PGjUNSUhJUKhXy8vIQExOD0aNHIzg4WHZ7RERUikVGRmL06NHQaDQIDAxEUFAQgMd7SQqaDO1FxKtpiiA7OxtDhw7FqlWrkJubC1tbW+Tk5KBv375YtWqV3vS+RERET0tKSsLt27fRuHFjqNWP9wMcO3YM7u7uqFOnjuTu5GMYKYbr16/jzJkzSE9PR9OmTVGzZk3ZLREREVk8hpFChIWFFXnZuXPnKtgJERGRdeN08IWIi4vTex4bG4ucnBzUrl0bAHDx4kXY2NjA399fRntERERWg2GkEPv27dP9/9y5c+Hm5obvvvsOZcqUAQD8/fffCA0N1V07TkRERMbhYZoi8PHxwe7duw2mfT979izat2+PW7duSeqMiIjI8vHS3iJIS0vD3bt3Dcbv3r2Lhw8fSuiIiIjIejCMFEGPHj0QGhqKzZs348aNG7hx4wY2bdqEAQMGoGfPnrLbIyIismg8TFMEmZmZGD16NFasWAGtVgsAsLW1xYABAzBr1iy4uLhI7pCIiMhyMYwUQ0ZGBuLj4wEAL730EkMIERGRGTCMEBERkVQ8Z4SIiIikYhghIiIiqRhGiIiISCqGESIiIpKKYYSIiIikYhghIiIiqRhGiIiISKr/B9j7CxVxri/DAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df.isnull().sum()/df.shape[0]).sort_values().plot.bar(ylim=(0,1),grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Matplotlib <a name=\"introduction\"></a>\n",
    "\n",
    "* \"A picture is worth a thousand words.\"\n",
    "    * more like \"A picture is worth a few lines of code.\"\n",
    "* development started in 2003 by John D. Hunter, a neurobiologist (inspired by MATLAB software)\n",
    "* generating basic plots in *matplotlib* is simple, mastering the library can be little bit less pleseant (we skip this part)\n",
    "* you can have as much control as you want, but you can also concede as much control as you want \n",
    "* [**gallery**](https://matplotlib.org/stable/gallery/index.html)\n",
    "    * can get help to problems like \"I want to make a figure that looks something I've seen somewhere.\" (hard to google)\n",
    "* plotting consists of many layers, from general 'contour this 2D array' to very specific 'color this screen pixel'\n",
    "    * key is allowing both levels to coexist in one package\n",
    "* *matplotlib* has 2 interfaces:\n",
    "    1. \"state-machine environment\" (based on MATLAB)\n",
    "    2. a object-oriented interface\n",
    "* this often creates confusion (multiple, conflicting, solutions on the web)\n",
    "* another common confusion is the relationship of *Matplotlib, pyplot and pylab*\n",
    "    * Matplotlib is the whole package\n",
    "    * `matplotlib.pyplot` is a module in matplotlib\n",
    "    * `pylab` is a a convenience module doing a bulk import of `pyplot` and `numpy`\n",
    "\n",
    "* [anatomy of the plot](https://matplotlib.org/examples/showcase/anatomy.html) from matplotlib\n",
    "\n",
    "<img src= \"https://matplotlib.org/_images/anatomy.png\" width= \"600\" height=\"400\">\n",
    "\n",
    "* the *figure* keeps track of all the child *Axes*, titles,legends, etc.\n",
    "    * the figure can have any number of *Axes*\n",
    "* *Axes* is 'a plot', i.e. the region of the image with the data space\n",
    "    * given *Axes* object can only be in one Figure\n",
    "    * *Axes* contains 2 (3 in case of 3D) *Axis* objects which take care of the data limits (conrolled via `set_xlim()` method)\n",
    "    * each *Axes* has a title (`set_title()`), an x- and y-labels (`set_xlabel()`)\n",
    "* *Artist* is anything you can see on the figure, e.g. text objects, Line2D objects, etc.\n",
    "\n",
    "* `matplolib.pyplot` functions make some changes to a figure, e.g. create a figure, plot some lines, etc.\n",
    "    * the plotting functions are directed to the current axes\n",
    "\n",
    "* all of plotting functions expect `np.array` or `array-like` data objects (for majority of cases works out of the box)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# increasing the size of the figure\n",
    "plt.figure(figsize = (20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "gather": {
     "logged": 1677517840825
    }
   },
   "outputs": [],
   "source": [
    "from time import sleep\n",
    "for style in plt.style.available:\n",
    "    plt.style.use(style)\n",
    "    print(style)\n",
    "    plt.figure(figsize=(5,2))\n",
    "    plt.plot(np.sin(np.linspace(0,2*np.pi)))\n",
    "    plt.show()\n",
    "    sleep(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')\n",
    "# minimum example of pyplot\n",
    "x = np.linspace(0, 2, 100)\n",
    "\n",
    "# we can also specify only \"y\" and use default x-axis: plt.plot(x, label='linear')\n",
    "plt.plot(x, x, label='linear',  linewidth=2.0)\n",
    "plt.plot(x, x**2, label='quadratic')\n",
    "plt.plot(x, np.sqrt(x),'k^:',label='sqrt')\n",
    "\n",
    "plt.xlabel('x label')\n",
    "plt.ylabel('y label')\n",
    "\n",
    "plt.title(\"Basic plots\")\n",
    "\n",
    "plt.legend(loc = 'best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* for multiple subplots: `fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, sharey=True, figsize=(7, 4))`\n",
    "* call `plt.subplot()` and specify three numbers:\n",
    "    * number of rows\n",
    "    * number of columns\n",
    "    * subplot number you want to activate.\n",
    "* if subplots are too squished `plt.tight_layout()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range (1, 5):\n",
    "    plt.subplot(2, 2, i)\n",
    "    plt.text(0.5,0.5, str((2, 2, i)), ha='center', fontsize = 10) #again, just a plot\n",
    "    plt.tight_layout() \n",
    "    plt.grid(True) # add the grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for multiple figures and axes \n",
    "def f(x):\n",
    "    return np.cos(2*np.pi*x)\n",
    "\n",
    "x1 = np.arange(0.0, 5.0, 0.1)\n",
    "x2 = np.arange(0.0, 5.0, 0.02)\n",
    "\n",
    "plt.figure(1) # optional, since figure(1) will be created by default\n",
    "plt.subplot(211)\n",
    "plt.plot(x1, f(x1), 'bo', x2, f(x2), 'k')\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.plot(x2, np.tan(2*np.pi*x2), 'r--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu, sigma, n = 100, 15, 10000\n",
    "x = np.random.normal(mu, sigma, n)\n",
    "\n",
    "# the histogram of the data\n",
    "plt.hist(x, bins = 50, density= True, facecolor='g')\n",
    "\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('Probability')\n",
    "plt.title('Histogram of X')\n",
    "\n",
    "# meaningful text\n",
    "plt.text(60, .025, f'$\\mu={mu},\\ \\sigma={sigma}$')\n",
    "# tail events text\n",
    "plt.text(40, .00025, f\"I've seen better times.\")\n",
    "\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = plt.subplot(111)\n",
    "t = np.arange(0.0, 5.0, 0.01)\n",
    "s = np.cos(2*np.pi*t)\n",
    "line, = plt.plot(t, s, lw=2)\n",
    "plt.annotate(\"'go home, you are drunk'-arrow'\", xy=(4.5, -1.7), xytext=(0.3, 1.7),\n",
    "             arrowprops=dict(facecolor='black', shrink=0.05),\n",
    "             )\n",
    "plt.ylim(-2, 2)\n",
    "\n",
    "# actually saving\n",
    "plt.savefig('auxiliary/go_home_you_drunk.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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