Lecture5-pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Pandas**\n",
    "\n",
    "Pandas is an Python package that is widely used for data data analysis and machine learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First data type we will try to use is one dimensional arrays (\"columns\") called `Series`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     2\n",
      "1     8\n",
      "2    99\n",
      "3     1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "mylist = [2, 8, 99,1]\n",
    "\n",
    "myvar = pd.Series(mylist)\n",
    "\n",
    "print(myvar)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the data gets labeled by indexes starting from 0, otherwise you can specify the labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "2\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "print(myvar[0])\n",
    "myvar2 = pd.Series(mylist, index = [\"a\", \"b\", \"c\",\"d\"])\n",
    "print(myvar2[0]) #but you can still use the index to access\n",
    "print(myvar2[\"a\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also create \"series\" from a dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key1    888\n",
      "key3    777\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "mydict = {\"key1\": 888, \"key2\": 123, \"key3\": 777}\n",
    "\n",
    "myvar = pd.Series(mydict, index = [\"key1\",\"key3\"]) #index is optional\n",
    "\n",
    "print(myvar)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To work with tables rather than vectors, we need to use `DataFrame`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    cats  age\n",
      "0   grey    3\n",
      "1  black    7\n",
      "2  white    2\n"
     ]
    }
   ],
   "source": [
    "mydataset = {\n",
    "  'cats': [\"grey\", \"black\", \"white\"],\n",
    "  'age': [3, 7, 2]\n",
    "}\n",
    "\n",
    "myvar = pd.DataFrame(mydataset)\n",
    "\n",
    "print(myvar) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To access the rows, use `loc`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cats    white\n",
       "age         2\n",
       "Name: 2, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myvar.loc[2] #a named index will work as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(myvar.loc[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How to load files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        course1  course2  course3  course4\n",
      "name                                      \n",
      "Marie        24       18       30       20\n",
      "John         18       27       29       27\n",
      "Kati         21       24       28       27\n",
      "Olaf         30       30       29       30\n",
      "Mark         24       25       27       21\n",
      "Olga         23       26       30       30\n",
      "Jean         18       28       26       24\n",
      "Alex         19       20       27       27\n",
      "Pier         24       29       23       26\n",
      "Bennet       24       25       26       30\n",
      "Luis         20       20       18       18\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('example.csv',index_col=0)\n",
    "\n",
    "print(df) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can control how many rows it prints:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       course1  course2  course3  course4\n",
      "name                                     \n",
      "Marie       24       18       30       20\n",
      "...        ...      ...      ...      ...\n",
      "Luis        20       20       18       18\n",
      "\n",
      "[11 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "pd.options.display.max_rows = 3\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Type `pd.read` and press `tab` to see what else it can read."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.read"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To have a quick look at what you've read, do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       course1  course2  course3  course4\n",
      "name                                     \n",
      "Marie       24       18       30       20\n",
      "John        18       27       29       27\n",
      "Kati        21       24       28       27\n",
      "Olaf        30       30       29       30\n",
      "Mark        24       25       27       21\n",
      "        course1  course2  course3  course4\n",
      "name                                      \n",
      "Jean         18       28       26       24\n",
      "Alex         19       20       27       27\n",
      "Pier         24       29       23       26\n",
      "Bennet       24       25       26       30\n",
      "Luis         20       20       18       18\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 11 entries, Marie to Luis\n",
      "Data columns (total 4 columns):\n",
      " #   Column   Non-Null Count  Dtype\n",
      "---  ------   --------------  -----\n",
      " 0   course1  11 non-null     int64\n",
      " 1   course2  11 non-null     int64\n",
      " 2   course3  11 non-null     int64\n",
      " 3   course4  11 non-null     int64\n",
      "dtypes: int64(4)\n",
      "memory usage: 440.0+ bytes\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "pd.options.display.max_rows = 999 #to fix back from 3\n",
    "print(df.head())\n",
    "print(df.tail())\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that there are no null objects, if there were, we would probably need to delete them before analysing the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        course1  course2  course3  course4\n",
      "name                                      \n",
      "Marie      24.0     18.0       30       20\n",
      "John       18.0     27.0       29       27\n",
      "Kati       21.0     24.0       28       27\n",
      "Olaf       30.0     30.0       29       30\n",
      "Mark        NaN     25.0       27       21\n",
      "Olga       23.0     26.0       30       30\n",
      "Jean       18.0     28.0       26       24\n",
      "Alex       19.0      NaN       27       27\n",
      "Pier       24.0     29.0       23       26\n",
      "Bennet     24.0     25.0       26       30\n",
      "Luis       20.0     20.0       18       18\n"
     ]
    }
   ],
   "source": [
    "df2 = pd.read_csv('example_empty.csv', index_col=0)\n",
    "\n",
    "print(df2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 11 entries, Marie to Luis\n",
      "Data columns (total 4 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   course1  10 non-null     float64\n",
      " 1   course2  10 non-null     float64\n",
      " 2   course3  11 non-null     int64  \n",
      " 3   course4  11 non-null     int64  \n",
      "dtypes: float64(2), int64(2)\n",
      "memory usage: 440.0+ bytes\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df2.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's clean this data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        course1  course2  course3  course4\n",
      "name                                      \n",
      "Marie      24.0     18.0       30       20\n",
      "John       18.0     27.0       29       27\n",
      "Kati       21.0     24.0       28       27\n",
      "Olaf       30.0     30.0       29       30\n",
      "Olga       23.0     26.0       30       30\n",
      "Jean       18.0     28.0       26       24\n",
      "Pier       24.0     29.0       23       26\n",
      "Bennet     24.0     25.0       26       30\n",
      "Luis       20.0     20.0       18       18\n"
     ]
    }
   ],
   "source": [
    "new_df2 = df2.dropna() #returns a new dataframe\n",
    "print(new_df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        course1  course2  course3  course4\n",
      "name                                      \n",
      "Marie      24.0     18.0       30       20\n",
      "John       18.0     27.0       29       27\n",
      "Kati       21.0     24.0       28       27\n",
      "Olaf       30.0     30.0       29       30\n",
      "Olga       23.0     26.0       30       30\n",
      "Jean       18.0     28.0       26       24\n",
      "Pier       24.0     29.0       23       26\n",
      "Bennet     24.0     25.0       26       30\n",
      "Luis       20.0     20.0       18       18\n"
     ]
    }
   ],
   "source": [
    "df2.dropna(inplace = True) #deletes directly (you might not have space otherwise)\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>course1</th>\n",
       "      <th>course2</th>\n",
       "      <th>course3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Marie</th>\n",
       "      <td>24.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John</th>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kati</th>\n",
       "      <td>21.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Olaf</th>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Olga</th>\n",
       "      <td>23.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jean</th>\n",
       "      <td>18.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pier</th>\n",
       "      <td>24.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bennet</th>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luis</th>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        course1  course2  course3\n",
       "name                             \n",
       "Marie      24.0     18.0       30\n",
       "John       18.0     27.0       29\n",
       "Kati       21.0     24.0       28\n",
       "Olaf       30.0     30.0       29\n",
       "Olga       23.0     26.0       30\n",
       "Jean       18.0     28.0       26\n",
       "Pier       24.0     29.0       23\n",
       "Bennet     24.0     25.0       26\n",
       "Luis       20.0     20.0       18"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.drop(\"course4\", axis='columns') #how to drop a column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        course1  course2  course3  course4\n",
      "name                                      \n",
      "Marie        24       18       30       20\n",
      "John         18       27       29       27\n",
      "Kati         21       24       28       27\n",
      "Olaf         30       30       29       30\n",
      "Mark         24       25       27       21\n",
      "Olga         23       26       30       30\n",
      "Jean         18       28       26       24\n",
      "Alex         19       20       27       27\n",
      "Bennet       24       25       26       30\n",
      "Luis         20       20       18       18\n"
     ]
    }
   ],
   "source": [
    "print(df.drop('Pier', axis='rows')) #how to drop a row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also fill the null values with something. For example, let's give 18 to students without marks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        course1  course2  course3  course4\n",
      "name                                      \n",
      "Marie      24.0     18.0       30       20\n",
      "John       18.0     27.0       29       27\n",
      "Kati       21.0     24.0       28       27\n",
      "Olaf       30.0     30.0       29       30\n",
      "Mark       18.0     25.0       27       21\n",
      "Olga       23.0     26.0       30       30\n",
      "Jean       18.0     28.0       26       24\n",
      "Alex       19.0     18.0       27       27\n",
      "Pier       24.0     29.0       23       26\n",
      "Bennet     24.0     25.0       26       30\n",
      "Luis       20.0     20.0       18       18\n"
     ]
    }
   ],
   "source": [
    "df2 = pd.read_csv('example_empty.csv', index_col=0)\n",
    "df2.fillna(18, inplace = True)\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Actually, we probably need names as index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        course1  course2  course3  course4\n",
      "name                                      \n",
      "Marie        24       18       30       20\n",
      "John         18       27       29       27\n",
      "Kati         21       24       28       27\n",
      "Olaf         30       30       29       30\n",
      "Mark         24       25       27       21\n",
      "Olga         23       26       30       30\n",
      "Jean         18       28       26       24\n",
      "Alex         19       20       27       27\n",
      "Pier         24       29       23       26\n",
      "Bennet       24       25       26       30\n",
      "Luis         20       20       18       18\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('example.csv', index_col=0)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's edit more:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       course1  course2  course3  course4\n",
      "name                                     \n",
      "Marie       24       18       30       20\n",
      "John    failed       27       29       27\n",
      "Kati        21       24       28       27\n",
      "Olaf        30       30       29       30\n",
      "Mark        24       25       27       21\n",
      "Olga        23       26       30       30\n",
      "Jean    failed       28       26       24\n",
      "Alex        19       20       27       27\n",
      "Pier        24       29       23       26\n",
      "Bennet      24       25       26       30\n",
      "Luis        20       20       18       18\n"
     ]
    }
   ],
   "source": [
    "df[\"course1\"] = df[\"course1\"].replace(18, \"failed\")\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Basic statistics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>course1</th>\n",
       "      <th>course2</th>\n",
       "      <th>course3</th>\n",
       "      <th>course4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>22.272727</td>\n",
       "      <td>24.727273</td>\n",
       "      <td>26.636364</td>\n",
       "      <td>25.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.552208</td>\n",
       "      <td>3.926599</td>\n",
       "      <td>3.529100</td>\n",
       "      <td>4.203894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>19.500000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>22.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>28.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         course1    course2    course3    course4\n",
       "count  11.000000  11.000000  11.000000  11.000000\n",
       "mean   22.272727  24.727273  26.636364  25.454545\n",
       "std     3.552208   3.926599   3.529100   4.203894\n",
       "min    18.000000  18.000000  18.000000  18.000000\n",
       "25%    19.500000  22.000000  26.000000  22.500000\n",
       "50%    23.000000  25.000000  27.000000  27.000000\n",
       "75%    24.000000  27.500000  29.000000  28.500000\n",
       "max    30.000000  30.000000  30.000000  30.000000"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('example.csv', index_col=0)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "course1    22.272727\n",
       "course2    24.727273\n",
       "course3    26.636364\n",
       "course4    25.454545\n",
       "dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean() #column average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.272727272727273"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"course1\"].mean() #column average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Marie     23.00\n",
       "John      25.25\n",
       "Kati      25.00\n",
       "Olaf      29.75\n",
       "Mark      24.25\n",
       "Olga      27.25\n",
       "Jean      24.00\n",
       "Alex      23.25\n",
       "Pier      25.50\n",
       "Bennet    26.25\n",
       "Luis      19.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(axis=1) #rows average"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will add a new column with the average by rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        course1  course2  course3  course4   mean\n",
      "name                                             \n",
      "Marie        24       18       30       20  23.00\n",
      "John         18       27       29       27  25.25\n",
      "Kati         21       24       28       27  25.00\n",
      "Olaf         30       30       29       30  29.75\n",
      "Mark         24       25       27       21  24.25\n",
      "Olga         23       26       30       30  27.25\n",
      "Jean         18       28       26       24  24.00\n",
      "Alex         19       20       27       27  23.25\n",
      "Pier         24       29       23       26  25.50\n",
      "Bennet       24       25       26       30  26.25\n",
      "Luis         20       20       18       18  19.00\n"
     ]
    }
   ],
   "source": [
    "df[\"mean\"]=df.mean(axis=1)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To sort the values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        course1  course2  course3  course4   mean\n",
      "name                                             \n",
      "Olaf         30       30       29       30  29.75\n",
      "Olga         23       26       30       30  27.25\n",
      "Bennet       24       25       26       30  26.25\n",
      "Pier         24       29       23       26  25.50\n",
      "John         18       27       29       27  25.25\n",
      "Kati         21       24       28       27  25.00\n",
      "Mark         24       25       27       21  24.25\n",
      "Jean         18       28       26       24  24.00\n",
      "Alex         19       20       27       27  23.25\n",
      "Marie        24       18       30       20  23.00\n",
      "Luis         20       20       18       18  19.00\n"
     ]
    }
   ],
   "source": [
    "df.sort_values(by=['mean'], inplace=True, ascending=False)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To save this result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('example_mean.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.0"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"Jean\"].mean() #to get average from a specific row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How to change values (let's apply penalty to grades):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        course1  course2  course3  course4   mean\n",
      "name                                             \n",
      "Olaf         30       28       29       30  29.75\n",
      "Olga         23       24       30       30  27.25\n",
      "Bennet       24       23       26       30  26.25\n",
      "Pier         24       27       23       26  25.50\n",
      "John         18       25       29       27  25.25\n",
      "Kati         21       22       28       27  25.00\n",
      "Mark         24       23       27       21  24.25\n",
      "Jean         18       26       26       24  24.00\n",
      "Alex         19       18       27       27  23.25\n",
      "Marie        24       16       30       20  23.00\n",
      "Luis         20       18       18       18  19.00\n"
     ]
    }
   ],
   "source": [
    "df[\"course2\"]=df[\"course2\"]-2\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select all who failed the first course:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>course1</th>\n",
       "      <th>course2</th>\n",
       "      <th>course3</th>\n",
       "      <th>course4</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</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>John</th>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>29</td>\n",
       "      <td>27</td>\n",
       "      <td>25.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jean</th>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>24</td>\n",
       "      <td>24.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      course1  course2  course3  course4   mean\n",
       "name                                           \n",
       "John       18       25       29       27  25.25\n",
       "Jean       18       26       26       24  24.00"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['course1'] == 18]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting all failed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>course1</th>\n",
       "      <th>course2</th>\n",
       "      <th>course3</th>\n",
       "      <th>course4</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</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>Olaf</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Olga</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bennet</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pier</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kati</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mark</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jean</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alex</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marie</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luis</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        course1  course2  course3  course4   mean\n",
       "name                                             \n",
       "Olaf      False    False    False    False  False\n",
       "Olga      False    False    False    False  False\n",
       "Bennet    False    False    False    False  False\n",
       "Pier      False    False    False    False  False\n",
       "John       True    False    False    False  False\n",
       "Kati      False    False    False    False  False\n",
       "Mark      False    False    False    False  False\n",
       "Jean       True    False    False    False  False\n",
       "Alex      False     True    False    False  False\n",
       "Marie     False    False    False    False  False\n",
       "Luis      False     True     True     True  False"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df==18 # \"a mask\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "      course1  course2  course3  course4   mean\n",
      "name                                           \n",
      "John       18       25       29       27  25.25\n",
      "Jean       18       26       26       24  24.00\n",
      "Alex       19       18       27       27  23.25\n",
      "Luis       20       18       18       18  19.00\n"
     ]
    }
   ],
   "source": [
    "failed=df[(df == 18).any(axis=1)]\n",
    "print(type(failed))\n",
    "print(failed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see who passed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        course1  course2  course3  course4   mean\n",
      "name                                             \n",
      "Olaf         30       28       29       30  29.75\n",
      "Olga         23       24       30       30  27.25\n",
      "Bennet       24       23       26       30  26.25\n",
      "Pier         24       27       23       26  25.50\n",
      "Kati         21       22       28       27  25.00\n",
      "Mark         24       23       27       21  24.25\n",
      "Marie        24       16       30       20  23.00\n"
     ]
    }
   ],
   "source": [
    "print(pd.concat([df,failed]).drop_duplicates(keep=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name\n",
      "Olaf      False\n",
      "Olga      False\n",
      "Bennet    False\n",
      "Pier      False\n",
      "John      False\n",
      "Kati      False\n",
      "Mark      False\n",
      "Jean      False\n",
      "Alex      False\n",
      "Marie     False\n",
      "Luis      False\n",
      "John       True\n",
      "Jean       True\n",
      "Alex       True\n",
      "Luis       True\n",
      "dtype: bool\n"
     ]
    }
   ],
   "source": [
    "print(pd.concat([df,failed]).duplicated())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>course1</th>\n",
       "      <th>course2</th>\n",
       "      <th>course3</th>\n",
       "      <th>course4</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>course1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.299813</td>\n",
       "      <td>0.232057</td>\n",
       "      <td>0.265426</td>\n",
       "      <td>0.614297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>course2</th>\n",
       "      <td>0.299813</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.143672</td>\n",
       "      <td>0.571658</td>\n",
       "      <td>0.728376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>course3</th>\n",
       "      <td>0.232057</td>\n",
       "      <td>0.143672</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.490822</td>\n",
       "      <td>0.643489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>course4</th>\n",
       "      <td>0.265426</td>\n",
       "      <td>0.571658</td>\n",
       "      <td>0.490822</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.840995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.614297</td>\n",
       "      <td>0.728376</td>\n",
       "      <td>0.643489</td>\n",
       "      <td>0.840995</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          course1   course2   course3   course4      mean\n",
       "course1  1.000000  0.299813  0.232057  0.265426  0.614297\n",
       "course2  0.299813  1.000000  0.143672  0.571658  0.728376\n",
       "course3  0.232057  0.143672  1.000000  0.490822  0.643489\n",
       "course4  0.265426  0.571658  0.490822  1.000000  0.840995\n",
       "mean     0.614297  0.728376  0.643489  0.840995  1.000000"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr() #numbers were random, so probably no correlation here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "df[\"course1\"].plot(kind = 'hist')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**groupby fucntion:**\n",
    "\n",
    "The groupby function in pandas is a very useful function for grouping and aggregating data. It allows you to group a DataFrame by one or more columns and apply a function to each group.\n",
    "\n",
    "For example, consider the following DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>Animal</th>\n",
       "      <th>Size</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dog</td>\n",
       "      <td>Small</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cat</td>\n",
       "      <td>Small</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dog</td>\n",
       "      <td>Medium</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cat</td>\n",
       "      <td>Large</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dog</td>\n",
       "      <td>Large</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Animal    Size  Weight\n",
       "0    Dog   Small       5\n",
       "1    Cat   Small       3\n",
       "2    Dog  Medium      15\n",
       "3    Cat   Large      10\n",
       "4    Dog   Large      20"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.DataFrame({'Animal': ['Dog', 'Cat', 'Dog', 'Cat', 'Dog'],\n",
    "                   'Size': ['Small', 'Small', 'Medium', 'Large', 'Large'],\n",
    "                   'Weight': [5, 3, 15, 10, 20]})\n",
    "\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to group the data by the Animal column and calculate the mean weight for each group, you can use the groupby function like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Animal\n",
       "Cat     6.500000\n",
       "Dog    13.333333\n",
       "Name: Weight, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.groupby('Animal')['Weight'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also group the data by multiple columns and apply multiple functions to each group. For example, to group the data by both the Animal and Size columns and calculate the mean and median weight for each group, you can use the groupby function like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animal</th>\n",
       "      <th>Size</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Cat</th>\n",
       "      <th>Large</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Dog</th>\n",
       "      <th>Large</th>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medium</th>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               mean  median\n",
       "Animal Size                \n",
       "Cat    Large   10.0    10.0\n",
       "       Small    3.0     3.0\n",
       "Dog    Large   20.0    20.0\n",
       "       Medium  15.0    15.0\n",
       "       Small    5.0     5.0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.groupby(['Animal', 'Size'])['Weight'].agg(['mean', 'median'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Applying more advanced functions:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>course1</th>\n",
       "      <th>course2</th>\n",
       "      <th>course3</th>\n",
       "      <th>course4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Marie</th>\n",
       "      <td>4.898979</td>\n",
       "      <td>4.242641</td>\n",
       "      <td>5.477226</td>\n",
       "      <td>4.472136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John</th>\n",
       "      <td>4.242641</td>\n",
       "      <td>5.196152</td>\n",
       "      <td>5.385165</td>\n",
       "      <td>5.196152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kati</th>\n",
       "      <td>4.582576</td>\n",
       "      <td>4.898979</td>\n",
       "      <td>5.291503</td>\n",
       "      <td>5.196152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Olaf</th>\n",
       "      <td>5.477226</td>\n",
       "      <td>5.477226</td>\n",
       "      <td>5.385165</td>\n",
       "      <td>5.477226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mark</th>\n",
       "      <td>4.898979</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.196152</td>\n",
       "      <td>4.582576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Olga</th>\n",
       "      <td>4.795832</td>\n",
       "      <td>5.099020</td>\n",
       "      <td>5.477226</td>\n",
       "      <td>5.477226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jean</th>\n",
       "      <td>4.242641</td>\n",
       "      <td>5.291503</td>\n",
       "      <td>5.099020</td>\n",
       "      <td>4.898979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alex</th>\n",
       "      <td>4.358899</td>\n",
       "      <td>4.472136</td>\n",
       "      <td>5.196152</td>\n",
       "      <td>5.196152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pier</th>\n",
       "      <td>4.898979</td>\n",
       "      <td>5.385165</td>\n",
       "      <td>4.795832</td>\n",
       "      <td>5.099020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bennet</th>\n",
       "      <td>4.898979</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.099020</td>\n",
       "      <td>5.477226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luis</th>\n",
       "      <td>4.472136</td>\n",
       "      <td>4.472136</td>\n",
       "      <td>4.242641</td>\n",
       "      <td>4.242641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         course1   course2   course3   course4\n",
       "name                                          \n",
       "Marie   4.898979  4.242641  5.477226  4.472136\n",
       "John    4.242641  5.196152  5.385165  5.196152\n",
       "Kati    4.582576  4.898979  5.291503  5.196152\n",
       "Olaf    5.477226  5.477226  5.385165  5.477226\n",
       "Mark    4.898979  5.000000  5.196152  4.582576\n",
       "Olga    4.795832  5.099020  5.477226  5.477226\n",
       "Jean    4.242641  5.291503  5.099020  4.898979\n",
       "Alex    4.358899  4.472136  5.196152  5.196152\n",
       "Pier    4.898979  5.385165  4.795832  5.099020\n",
       "Bennet  4.898979  5.000000  5.099020  5.477226\n",
       "Luis    4.472136  4.472136  4.242641  4.242641"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now this is a nonsense example\n",
    "#but it shows how to apply a function to all emelents\n",
    "df = pd.read_csv('example.csv', index_col=0)\n",
    "df.apply(np.sqrt) #note that this won't change df itself, do df=df.apply(...)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use your own functions as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "def myf(x):\n",
    "    return x+10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>course1</th>\n",
       "      <th>course2</th>\n",
       "      <th>course3</th>\n",
       "      <th>course4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Marie</th>\n",
       "      <td>34</td>\n",
       "      <td>28</td>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John</th>\n",
       "      <td>28</td>\n",
       "      <td>37</td>\n",
       "      <td>39</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kati</th>\n",
       "      <td>31</td>\n",
       "      <td>34</td>\n",
       "      <td>38</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Olaf</th>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mark</th>\n",
       "      <td>34</td>\n",
       "      <td>35</td>\n",
       "      <td>37</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Olga</th>\n",
       "      <td>33</td>\n",
       "      <td>36</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jean</th>\n",
       "      <td>28</td>\n",
       "      <td>38</td>\n",
       "      <td>36</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alex</th>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pier</th>\n",
       "      <td>34</td>\n",
       "      <td>39</td>\n",
       "      <td>33</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bennet</th>\n",
       "      <td>34</td>\n",
       "      <td>35</td>\n",
       "      <td>36</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luis</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        course1  course2  course3  course4\n",
       "name                                      \n",
       "Marie        34       28       40       30\n",
       "John         28       37       39       37\n",
       "Kati         31       34       38       37\n",
       "Olaf         40       40       39       40\n",
       "Mark         34       35       37       31\n",
       "Olga         33       36       40       40\n",
       "Jean         28       38       36       34\n",
       "Alex         29       30       37       37\n",
       "Pier         34       39       33       36\n",
       "Bennet       34       35       36       40\n",
       "Luis         30       30       28       28"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(myf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or it works with a lambda:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>course1</th>\n",
       "      <th>course2</th>\n",
       "      <th>course3</th>\n",
       "      <th>course4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Marie</th>\n",
       "      <td>576</td>\n",
       "      <td>324</td>\n",
       "      <td>900</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John</th>\n",
       "      <td>324</td>\n",
       "      <td>729</td>\n",
       "      <td>841</td>\n",
       "      <td>729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kati</th>\n",
       "      <td>441</td>\n",
       "      <td>576</td>\n",
       "      <td>784</td>\n",
       "      <td>729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Olaf</th>\n",
       "      <td>900</td>\n",
       "      <td>900</td>\n",
       "      <td>841</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mark</th>\n",
       "      <td>576</td>\n",
       "      <td>625</td>\n",
       "      <td>729</td>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Olga</th>\n",
       "      <td>529</td>\n",
       "      <td>676</td>\n",
       "      <td>900</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jean</th>\n",
       "      <td>324</td>\n",
       "      <td>784</td>\n",
       "      <td>676</td>\n",
       "      <td>576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alex</th>\n",
       "      <td>361</td>\n",
       "      <td>400</td>\n",
       "      <td>729</td>\n",
       "      <td>729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pier</th>\n",
       "      <td>576</td>\n",
       "      <td>841</td>\n",
       "      <td>529</td>\n",
       "      <td>676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bennet</th>\n",
       "      <td>576</td>\n",
       "      <td>625</td>\n",
       "      <td>676</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luis</th>\n",
       "      <td>400</td>\n",
       "      <td>400</td>\n",
       "      <td>324</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        course1  course2  course3  course4\n",
       "name                                      \n",
       "Marie       576      324      900      400\n",
       "John        324      729      841      729\n",
       "Kati        441      576      784      729\n",
       "Olaf        900      900      841      900\n",
       "Mark        576      625      729      441\n",
       "Olga        529      676      900      900\n",
       "Jean        324      784      676      576\n",
       "Alex        361      400      729      729\n",
       "Pier        576      841      529      676\n",
       "Bennet      576      625      676      900\n",
       "Luis        400      400      324      324"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda x: x*x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just for one column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Marie     576\n",
       "John      324\n",
       "Kati      441\n",
       "Olaf      900\n",
       "Mark      576\n",
       "Olga      529\n",
       "Jean      324\n",
       "Alex      361\n",
       "Pier      576\n",
       "Bennet    576\n",
       "Luis      400\n",
       "Name: course1, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"course1\"].apply(lambda x:x*x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How to apply to a column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "course1    576\n",
       "course2    625\n",
       "course3    676\n",
       "course4    900\n",
       "Name: Bennet, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"Bennet\"].apply(lambda x:x*x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        course1  course2  course3  course4\n",
      "name                                      \n",
      "Marie        24       18       30       20\n",
      "John         18       27       29       27\n",
      "Kati         21       24       28       27\n",
      "Olaf         30       30       29       30\n",
      "Mark         24       25       27       21\n",
      "Olga         23       26       30       30\n",
      "Jean         18       28       26       24\n",
      "Alex         19       20       27       27\n",
      "Pier         24       29       23       26\n",
      "Bennet      576      625      676      900\n",
      "Luis         20       20       18       18\n"
     ]
    }
   ],
   "source": [
    "df.loc[\"Bennet\"]=df.loc[\"Bennet\"].apply(lambda x:x*x) #to actually change df\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **What if we have just a txt file and not a proper csv?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           0       1\n",
      "0     453320  606542\n",
      "1     520651  594591\n",
      "...      ...     ...\n",
      "4998  341057  341338\n",
      "4999  374464  284441\n",
      "\n",
      "[5000 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "pd.options.display.max_rows = 5\n",
    "pd.options.display.max_columns = 5\n",
    "#just say that the delimter is a space and that there is no header\n",
    "df = pd.read_csv('s3.txt',delim_whitespace=True, header=None) \n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df[0], df[1], \"og\",  markersize=0.5)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **What if I don't want to use pandas, but still have to read data from a file?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "#specify int or it's going to assume floats\n",
    "my_data=np.genfromtxt('s3.txt',dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[453320 606542]\n",
      " [520651 594591]\n",
      " [516664 660290]\n",
      " ...\n",
      " [351482 334662]\n",
      " [341057 341338]\n",
      " [374464 284441]]\n"
     ]
    }
   ],
   "source": [
    "print(my_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "#format %d is here to print as integers (\"decimals\")\n",
    "np.savetxt(\"saved_test.txt\",my_data,fmt='%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_grades=np.genfromtxt('example.csv',delimiter=',',skip_header=1)\n",
    "#yes, there are ways to skip columns as well, but they become ugly, might as well use pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[nan 24. 18. 30. 20.]\n",
      " [nan 18. 27. 29. 27.]\n",
      " [nan 21. 24. 28. 27.]\n",
      " [nan 30. 30. 29. 30.]\n",
      " [nan 24. 25. 27. 21.]\n",
      " [nan 23. 26. 30. 30.]\n",
      " [nan 18. 28. 26. 24.]\n",
      " [nan 19. 20. 27. 27.]\n",
      " [nan 24. 29. 23. 26.]\n",
      " [nan 24. 25. 26. 30.]\n",
      " [nan 20. 20. 18. 18.]]\n"
     ]
    }
   ],
   "source": [
    "print(my_grades)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can you read and write files in \"pure python\" without numpy and pandas? Yes, but it becomes as comppilcated as in C/Fortran, so most times you have no reason to do that, that's why we are not considering that."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:my_env2]",
   "language": "python",
   "name": "conda-env-my_env2-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}