pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data \n",
    "df = pd.read_csv('filename.csv') # From a CSV file\n",
    "df = pd.read_excel('filename.xlsx') # From an Excel file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exporting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('filename.csv') # Write to a CSV file\n",
    "df.to_excel('filename.xlsx') # Write to an Excel file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Test Objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Build data frame from inputted data\n",
    "df = pd.DataFrame(data = {'Name': ['Bob', 'Sally', 'Scott', 'Katie'],\n",
    "\t'Physics': [68, 74, 77, 78],\n",
    "\t'Chemistry': [84, 100, 73, 90],\n",
    "\t'Algebra': [78, 88, 82, 87]})\n",
    "df\n",
    "df[:3].to_csv('saved_ratings.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      [Bob, 78]\n",
       "1    [Sally, 91]\n",
       "2    [Scott, 62]\n",
       "3    [Katie, 78]\n",
       "4    [John, 100]\n",
       "dtype: object"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Create a series from an iterable my_list\n",
    "my_list = [['Bob',78],\n",
    "          ['Sally',91], \n",
    "          ['Scott',62],\n",
    "          ['Katie',78],\n",
    "          ['John',100]]\n",
    "df1 = pd.Series(my_list) # Create a series from an iterable my_list\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-01-01      [Bob, 78]\n",
       "2020-01-02    [Sally, 91]\n",
       "2020-01-03    [Scott, 62]\n",
       "2020-01-04    [Katie, 78]\n",
       "2020-01-05    [John, 100]\n",
       "Freq: D, dtype: object"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a date index\n",
    "df1.index = pd.date_range('2020/1/1', periods=df1.shape[0]) \n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Viewing/Inspecting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>Name</th>\n",
       "      <th>Physics</th>\n",
       "      <th>Chemistry</th>\n",
       "      <th>Algebra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>68</td>\n",
       "      <td>84</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sally</td>\n",
       "      <td>74</td>\n",
       "      <td>100</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Scott</td>\n",
       "      <td>77</td>\n",
       "      <td>73</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Physics  Chemistry  Algebra\n",
       "0    Bob       68         84       78\n",
       "1  Sally       74        100       88\n",
       "2  Scott       77         73       82"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3) # First 3 rows of the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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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       "    }\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>Name</th>\n",
       "      <th>Physics</th>\n",
       "      <th>Chemistry</th>\n",
       "      <th>Algebra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sally</td>\n",
       "      <td>74</td>\n",
       "      <td>100</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Scott</td>\n",
       "      <td>77</td>\n",
       "      <td>73</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Katie</td>\n",
       "      <td>78</td>\n",
       "      <td>90</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Physics  Chemistry  Algebra\n",
       "1  Sally       74        100       88\n",
       "2  Scott       77         73       82\n",
       "3  Katie       78         90       87"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(3) # Last 3 rows of the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 4)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape #Number of rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   Name       4 non-null      object\n",
      " 1   Physics    4 non-null      int64 \n",
      " 2   Chemistry  4 non-null      int64 \n",
      " 3   Algebra    4 non-null      int64 \n",
      "dtypes: int64(3), object(1)\n",
      "memory usage: 256.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info() #Index, Datatype and Memory information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Physics</th>\n",
       "      <th>Chemistry</th>\n",
       "      <th>Algebra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>68</td>\n",
       "      <td>84</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sally</td>\n",
       "      <td>74</td>\n",
       "      <td>100</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Scott</td>\n",
       "      <td>77</td>\n",
       "      <td>73</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Katie</td>\n",
       "      <td>78</td>\n",
       "      <td>90</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>67</td>\n",
       "      <td>92</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Physics  Chemistry  Algebra\n",
       "0    Bob       68         84     78.0\n",
       "1  Sally       74        100     88.0\n",
       "2  Scott       77         73     82.0\n",
       "3  Katie       78         90     87.0\n",
       "4    Max       67         92      NaN"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Append row to the dataframe, missing data\n",
    "new_row = {'Name':'Max', 'Physics':67, 'Chemistry':92, 'Algebra':np.nan}\n",
    "df = df.append(new_row, ignore_index=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Physics</th>\n",
       "      <th>Chemistry</th>\n",
       "      <th>Algebra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>68.0</td>\n",
       "      <td>84</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sally</td>\n",
       "      <td>74.0</td>\n",
       "      <td>100</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Scott</td>\n",
       "      <td>77.0</td>\n",
       "      <td>73</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Katie</td>\n",
       "      <td>78.0</td>\n",
       "      <td>90</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>67.0</td>\n",
       "      <td>92</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Liz</td>\n",
       "      <td>83.0</td>\n",
       "      <td>77</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mike</td>\n",
       "      <td>79.0</td>\n",
       "      <td>87</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Scott</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Physics  Chemistry  Algebra\n",
       "0    Bob     68.0         84     78.0\n",
       "1  Sally     74.0        100     88.0\n",
       "2  Scott     77.0         73     82.0\n",
       "3  Katie     78.0         90     87.0\n",
       "4    Max     67.0         92      NaN\n",
       "5    Liz     83.0         77      NaN\n",
       "6    Sam      NaN         94     70.0\n",
       "7   Mike     79.0         87     90.0\n",
       "8  Scott      NaN         87      NaN"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Append multiple rows to the dataframe\n",
    "# List of series  \n",
    "list_of_series = [pd.Series(['Liz', 83, 77, np.nan], index=df.columns ) ,\n",
    "                pd.Series(['Sam', np.nan, 94,70], index=df.columns ) ,\n",
    "                pd.Series(['Mike', 79,87,90], index=df.columns ),\n",
    "                pd.Series(['Scott', np.nan,87,np.nan], index=df.columns ), ]\n",
    "# Pass a list of series to the append() to add multiple rows\n",
    "df = df.append(list_of_series , ignore_index=True)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Physics</th>\n",
       "      <th>Chemistry</th>\n",
       "      <th>Algebra</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>68.0</td>\n",
       "      <td>84</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sally</td>\n",
       "      <td>74.0</td>\n",
       "      <td>100</td>\n",
       "      <td>88.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Scott</td>\n",
       "      <td>77.0</td>\n",
       "      <td>73</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Katie</td>\n",
       "      <td>78.0</td>\n",
       "      <td>90</td>\n",
       "      <td>87.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>67.0</td>\n",
       "      <td>92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Liz</td>\n",
       "      <td>83.0</td>\n",
       "      <td>77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mike</td>\n",
       "      <td>79.0</td>\n",
       "      <td>87</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Scott</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Physics  Chemistry  Algebra     Sex\n",
       "0    Bob     68.0         84     78.0    Male\n",
       "1  Sally     74.0        100     88.0  Female\n",
       "2  Scott     77.0         73     82.0    Male\n",
       "3  Katie     78.0         90     87.0  Female\n",
       "4    Max     67.0         92      NaN    Male\n",
       "5    Liz     83.0         77      NaN  Female\n",
       "6    Sam      NaN         94     70.0  Female\n",
       "7   Mike     79.0         87     90.0    Male\n",
       "8  Scott      NaN         87      NaN    Male"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding new column to existing DataFrame in Pandas\n",
    "sex = ['Male','Female','Male','Female','Male','Female','Female','Male','Male']\n",
    "df['Sex'] = sex\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Chemistry'].dtypes # Check data type in pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Chemistry'] = df['Chemistry'].astype(float) #  Convert Integers to Floats in Pandas DataFrame\n",
    "df['Chemistry'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaN     2\n",
       "79.0    1\n",
       "83.0    1\n",
       "67.0    1\n",
       "78.0    1\n",
       "77.0    1\n",
       "74.0    1\n",
       "68.0    1\n",
       "Name: Physics, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Physics'].value_counts(dropna=False) # View unique values and counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>79.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>82.0</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>83.0</th>\n",
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       "      <td>NaN</td>\n",
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       "      <th>84.0</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>87.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>94.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bob</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Katie</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liz</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mike</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sally</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sam</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scott</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name  Physics  Chemistry  Algebra  Sex\n",
       "67.0     NaN      1.0        NaN      NaN  NaN\n",
       "68.0     NaN      1.0        NaN      NaN  NaN\n",
       "70.0     NaN      NaN        NaN      1.0  NaN\n",
       "73.0     NaN      NaN        1.0      NaN  NaN\n",
       "74.0     NaN      1.0        NaN      NaN  NaN\n",
       "77.0     NaN      1.0        1.0      NaN  NaN\n",
       "78.0     NaN      1.0        NaN      1.0  NaN\n",
       "79.0     NaN      1.0        NaN      NaN  NaN\n",
       "82.0     NaN      NaN        NaN      1.0  NaN\n",
       "83.0     NaN      1.0        NaN      NaN  NaN\n",
       "84.0     NaN      NaN        1.0      NaN  NaN\n",
       "87.0     NaN      NaN        2.0      1.0  NaN\n",
       "88.0     NaN      NaN        NaN      1.0  NaN\n",
       "90.0     NaN      NaN        1.0      1.0  NaN\n",
       "92.0     NaN      NaN        1.0      NaN  NaN\n",
       "94.0     NaN      NaN        1.0      NaN  NaN\n",
       "100.0    NaN      NaN        1.0      NaN  NaN\n",
       "Bob      1.0      NaN        NaN      NaN  NaN\n",
       "Female   NaN      NaN        NaN      NaN  4.0\n",
       "Katie    1.0      NaN        NaN      NaN  NaN\n",
       "Liz      1.0      NaN        NaN      NaN  NaN\n",
       "Male     NaN      NaN        NaN      NaN  5.0\n",
       "Max      1.0      NaN        NaN      NaN  NaN\n",
       "Mike     1.0      NaN        NaN      NaN  NaN\n",
       "Sally    1.0      NaN        NaN      NaN  NaN\n",
       "Sam      1.0      NaN        NaN      NaN  NaN\n",
       "Scott    2.0      NaN        NaN      NaN  NaN"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(pd.Series.value_counts) # Unique values and counts for all columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     84.0\n",
       "1    100.0\n",
       "2     73.0\n",
       "3     90.0\n",
       "4     92.0\n",
       "5     77.0\n",
       "6     94.0\n",
       "7     87.0\n",
       "8     87.0\n",
       "Name: Chemistry, dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Chemistry'] # Returns column with label col as Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Mike</td>\n",
       "      <td>90.0</td>\n",
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       "      <th>8</th>\n",
       "      <td>Scott</td>\n",
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      "text/plain": [
       "    Name  Algebra\n",
       "0    Bob     78.0\n",
       "1  Sally     88.0\n",
       "2  Scott     82.0\n",
       "3  Katie     87.0\n",
       "4    Max      NaN\n",
       "5    Liz      NaN\n",
       "6    Sam     70.0\n",
       "7   Mike     90.0\n",
       "8  Scott      NaN"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Name','Algebra']] # Returns columns as a new DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name          Bob\n",
       "Physics        68\n",
       "Chemistry      84\n",
       "Algebra        78\n",
       "Sex          Male\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0] # Selection by position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    68.0\n",
       "1    74.0\n",
       "2    77.0\n",
       "3    78.0\n",
       "4    67.0\n",
       "5    83.0\n",
       "6     NaN\n",
       "7    79.0\n",
       "8     NaN\n",
       "Name: Physics, dtype: float64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,1] # Second column 'Name' of data frame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68.0"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0,1] # First element of Second column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>88.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Scott</td>\n",
       "      <td>77.0</td>\n",
       "      <td>73.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>90.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>67.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Liz</td>\n",
       "      <td>83.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mike</td>\n",
       "      <td>79.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Scott</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Student  Physics  Chemistry  Math     Sex\n",
       "0     Bob     68.0       84.0  78.0    Male\n",
       "1   Sally     74.0      100.0  88.0  Female\n",
       "2   Scott     77.0       73.0  82.0    Male\n",
       "3   Katie     78.0       90.0  87.0  Female\n",
       "4     Max     67.0       92.0   NaN    Male\n",
       "5     Liz     83.0       77.0   NaN  Female\n",
       "6     Sam      NaN       94.0  70.0  Female\n",
       "7    Mike     79.0       87.0  90.0    Male\n",
       "8   Scott      NaN       87.0   NaN    Male"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.rename({'Name':'Student','Algebra':'Math'}, axis='columns')# Rename columns\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
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       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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      "text/plain": [
       "   Student  Physics  Chemistry   Math    Sex\n",
       "0    False    False      False  False  False\n",
       "1    False    False      False  False  False\n",
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       "3    False    False      False  False  False\n",
       "4    False    False      False   True  False\n",
       "5    False    False      False   True  False\n",
       "6    False     True      False  False  False\n",
       "7    False    False      False  False  False\n",
       "8    False     True      False   True  False"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_for_nan = df.isnull() # Checks for null Values, Returns Boolean Arrray\n",
    "check_for_nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   Student  Physics  Chemistry   Math   Sex\n",
       "0     True     True       True   True  True\n",
       "1     True     True       True   True  True\n",
       "2     True     True       True   True  True\n",
       "3     True     True       True   True  True\n",
       "4     True     True       True  False  True\n",
       "5     True     True       True  False  True\n",
       "6     True    False       True   True  True\n",
       "7     True     True       True   True  True\n",
       "8     True    False       True  False  True"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value = df.notnull() # Opposite of df2.isnull()\n",
    "value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  Student  Physics  Chemistry  Math     Sex\n",
       "0     Bob     68.0       84.0  78.0    Male\n",
       "1   Sally     74.0      100.0  88.0  Female\n",
       "2   Scott     77.0       73.0  82.0    Male\n",
       "3   Katie     78.0       90.0  87.0  Female\n",
       "7    Mike     79.0       87.0  90.0    Male"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_null_row = df.dropna() # Drop all rows that contain null values\n",
    "drop_null_row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "  Student  Chemistry     Sex\n",
       "0     Bob       84.0    Male\n",
       "1   Sally      100.0  Female\n",
       "2   Scott       73.0    Male\n",
       "3   Katie       90.0  Female\n",
       "4     Max       92.0    Male\n",
       "5     Liz       77.0  Female\n",
       "6     Sam       94.0  Female\n",
       "7    Mike       87.0    Male\n",
       "8   Scott       87.0    Male"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_null_col = df.dropna(axis=1) # Drop all columns that contain null values\n",
    "drop_null_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Sam</td>\n",
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       "      <td>70.0</td>\n",
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       "      <th>7</th>\n",
       "      <td>Mike</td>\n",
       "      <td>79.0</td>\n",
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       "      <td>90.0</td>\n",
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       "      <td>Male</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Student  Physics  Chemistry  Math     Sex\n",
       "0     Bob     68.0       84.0  78.0    Male\n",
       "1   Sally     74.0      100.0  88.0  Female\n",
       "2   Scott     77.0       73.0  82.0    Male\n",
       "3   Katie     78.0       90.0  87.0  Female\n",
       "4     Max     67.0       92.0   0.0    Male\n",
       "5     Liz     83.0       77.0   0.0  Female\n",
       "6     Sam      0.0       94.0  70.0  Female\n",
       "7    Mike     79.0       87.0  90.0    Male\n",
       "8   Scott      0.0       87.0   0.0    Male"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "replace_null = df.fillna(0) # Replace all null values with 0\n",
    "replace_null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Scott</td>\n",
       "      <td>77.00</td>\n",
       "      <td>73.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Katie</td>\n",
       "      <td>78.00</td>\n",
       "      <td>90.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>67.00</td>\n",
       "      <td>92.0</td>\n",
       "      <td>82.5</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Liz</td>\n",
       "      <td>83.00</td>\n",
       "      <td>77.0</td>\n",
       "      <td>82.5</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sam</td>\n",
       "      <td>75.14</td>\n",
       "      <td>94.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mike</td>\n",
       "      <td>79.00</td>\n",
       "      <td>87.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Scott</td>\n",
       "      <td>75.14</td>\n",
       "      <td>87.0</td>\n",
       "      <td>82.5</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Student  Physics  Chemistry  Math     Sex\n",
       "0     Bob    68.00       84.0  78.0    Male\n",
       "1   Sally    74.00      100.0  88.0  Female\n",
       "2   Scott    77.00       73.0  82.0    Male\n",
       "3   Katie    78.00       90.0  87.0  Female\n",
       "4     Max    67.00       92.0  82.5    Male\n",
       "5     Liz    83.00       77.0  82.5  Female\n",
       "6     Sam    75.14       94.0  70.0  Female\n",
       "7    Mike    79.00       87.0  90.0    Male\n",
       "8   Scott    75.14       87.0  82.5    Male"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace all null values with the mean (mean can be replaced with almost any function from the statistics module)\n",
    "df = round(df.fillna(df.mean()),2) \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Student</th>\n",
       "      <th>Physics</th>\n",
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       "      <th>Math</th>\n",
       "      <th>Sex</th>\n",
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       "      <td>Bob</td>\n",
       "      <td>68.00</td>\n",
       "      <td>84</td>\n",
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       "      <td>Sally</td>\n",
       "      <td>74.00</td>\n",
       "      <td>A</td>\n",
       "      <td>88.0</td>\n",
       "      <td>Female</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Scott</td>\n",
       "      <td>77.00</td>\n",
       "      <td>73</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Katie</td>\n",
       "      <td>78.00</td>\n",
       "      <td>90</td>\n",
       "      <td>87.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>67.00</td>\n",
       "      <td>92</td>\n",
       "      <td>82.5</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Liz</td>\n",
       "      <td>83.00</td>\n",
       "      <td>77</td>\n",
       "      <td>82.5</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sam</td>\n",
       "      <td>75.14</td>\n",
       "      <td>94</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mike</td>\n",
       "      <td>79.00</td>\n",
       "      <td>87</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Scott</td>\n",
       "      <td>75.14</td>\n",
       "      <td>87</td>\n",
       "      <td>82.5</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Student  Physics Chemistry  Math     Sex\n",
       "0     Bob    68.00        84  78.0    Male\n",
       "1   Sally    74.00         A  88.0  Female\n",
       "2   Scott    77.00        73  82.0    Male\n",
       "3   Katie    78.00        90  87.0  Female\n",
       "4     Max    67.00        92  82.5    Male\n",
       "5     Liz    83.00        77  82.5  Female\n",
       "6     Sam    75.14        94  70.0  Female\n",
       "7    Mike    79.00        87  90.0    Male\n",
       "8   Scott    75.14        87  82.5    Male"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one = df.replace(100,'A') # Replace all values equal to 1 with 'one'\n",
    "one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filter, Sort, and Groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Student</th>\n",
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       "      <th>Chemistry</th>\n",
       "      <th>Math</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Liz</td>\n",
       "      <td>83.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>82.5</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Student  Physics  Chemistry  Math     Sex\n",
       "5     Liz     83.0       77.0  82.5  Female"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fil_80 = df[df['Physics'] > 80] # Rows where Physics is greater than 80\n",
    "fil_80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>88.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>Katie</td>\n",
       "      <td>78.00</td>\n",
       "      <td>90.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>Female</td>\n",
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       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>67.00</td>\n",
       "      <td>92.0</td>\n",
       "      <td>82.5</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sam</td>\n",
       "      <td>75.14</td>\n",
       "      <td>94.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Scott</td>\n",
       "      <td>75.14</td>\n",
       "      <td>87.0</td>\n",
       "      <td>82.5</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Student  Physics  Chemistry  Math     Sex\n",
       "0     Bob    68.00       84.0  78.0    Male\n",
       "1   Sally    74.00      100.0  88.0  Female\n",
       "3   Katie    78.00       90.0  87.0  Female\n",
       "4     Max    67.00       92.0  82.5    Male\n",
       "6     Sam    75.14       94.0  70.0  Female\n",
       "8   Scott    75.14       87.0  82.5    Male"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fil = df[(df['Chemistry'] > 80) & (df['Math'] < 90)] # Rows where Chemistry >80, Math <90\n",
    "fil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>7</th>\n",
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       "      <td>74.00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sam</td>\n",
       "      <td>75.14</td>\n",
       "      <td>94.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Scott</td>\n",
       "      <td>77.00</td>\n",
       "      <td>73.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Scott</td>\n",
       "      <td>75.14</td>\n",
       "      <td>87.0</td>\n",
       "      <td>82.5</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Student  Physics  Chemistry  Math     Sex\n",
       "0     Bob    68.00       84.0  78.0    Male\n",
       "3   Katie    78.00       90.0  87.0  Female\n",
       "5     Liz    83.00       77.0  82.5  Female\n",
       "4     Max    67.00       92.0  82.5    Male\n",
       "7    Mike    79.00       87.0  90.0    Male\n",
       "1   Sally    74.00      100.0  88.0  Female\n",
       "6     Sam    75.14       94.0  70.0  Female\n",
       "2   Scott    77.00       73.0  82.0    Male\n",
       "8   Scott    75.14       87.0  82.5    Male"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ascending = df.sort_values('Student') # Sort values by Students'name in ascending order\n",
    "ascending"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>90.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>75.14</td>\n",
       "      <td>87.0</td>\n",
       "      <td>82.5</td>\n",
       "      <td>Male</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>68.00</td>\n",
       "      <td>84.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Liz</td>\n",
       "      <td>83.00</td>\n",
       "      <td>77.0</td>\n",
       "      <td>82.5</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Scott</td>\n",
       "      <td>77.00</td>\n",
       "      <td>73.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Student  Physics  Chemistry  Math     Sex\n",
       "1   Sally    74.00      100.0  88.0  Female\n",
       "6     Sam    75.14       94.0  70.0  Female\n",
       "4     Max    67.00       92.0  82.5    Male\n",
       "3   Katie    78.00       90.0  87.0  Female\n",
       "7    Mike    79.00       87.0  90.0    Male\n",
       "8   Scott    75.14       87.0  82.5    Male\n",
       "0     Bob    68.00       84.0  78.0    Male\n",
       "5     Liz    83.00       77.0  82.5  Female\n",
       "2   Scott    77.00       73.0  82.0    Male"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "descending = df.sort_values('Chemistry',ascending=False) # Sort values by Chemistry in descending order\n",
    "descending"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student</th>\n",
       "      <th>Physics</th>\n",
       "      <th>Chemistry</th>\n",
       "      <th>Math</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>67.00</td>\n",
       "      <td>92.0</td>\n",
       "      <td>82.5</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>68.00</td>\n",
       "      <td>84.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sally</td>\n",
       "      <td>74.00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sam</td>\n",
       "      <td>75.14</td>\n",
       "      <td>94.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Scott</td>\n",
       "      <td>75.14</td>\n",
       "      <td>87.0</td>\n",
       "      <td>82.5</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Scott</td>\n",
       "      <td>77.00</td>\n",
       "      <td>73.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Katie</td>\n",
       "      <td>78.00</td>\n",
       "      <td>90.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mike</td>\n",
       "      <td>79.00</td>\n",
       "      <td>87.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Liz</td>\n",
       "      <td>83.00</td>\n",
       "      <td>77.0</td>\n",
       "      <td>82.5</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Student  Physics  Chemistry  Math     Sex\n",
       "4     Max    67.00       92.0  82.5    Male\n",
       "0     Bob    68.00       84.0  78.0    Male\n",
       "1   Sally    74.00      100.0  88.0  Female\n",
       "6     Sam    75.14       94.0  70.0  Female\n",
       "8   Scott    75.14       87.0  82.5    Male\n",
       "2   Scott    77.00       73.0  82.0    Male\n",
       "3   Katie    78.00       90.0  87.0  Female\n",
       "7    Mike    79.00       87.0  90.0    Male\n",
       "5     Liz    83.00       77.0  82.5  Female"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(['Physics','Chemistry'],ascending=[True,False]) # Sort values by Physics in ascending order then Chemistry in descending order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>Student</th>\n",
       "      <th>Physics</th>\n",
       "      <th>Chemistry</th>\n",
       "      <th>Math</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>Sally</td>\n",
       "      <td>74.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>Bob</td>\n",
       "      <td>68.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Student  Physics  Chemistry  Math\n",
       "Sex                                     \n",
       "Female   Sally     74.0      100.0  88.0\n",
       "Male       Bob     68.0       84.0  78.0"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_by = df.groupby(['Sex']) # Returns a groupby object for values from one column\n",
    "group_by.first() # Print the first value in each group "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\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>Physics</th>\n",
       "      <th>Chemistry</th>\n",
       "      <th>Math</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>77.535</td>\n",
       "      <td>90.25</td>\n",
       "      <td>81.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>73.228</td>\n",
       "      <td>84.60</td>\n",
       "      <td>83.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Physics  Chemistry    Math\n",
       "Sex                               \n",
       "Female   77.535      90.25  81.875\n",
       "Male     73.228      84.60  83.000"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average = df.groupby('Sex').agg(np.mean) # Find the average across all columns for every unique Sex group\n",
    "average"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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",
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       "\n",
       "    .dataframe thead tr th {\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">len</th>\n",
       "      <th colspan=\"2\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"2\" halign=\"left\">std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Chemistry</th>\n",
       "      <th>Physics</th>\n",
       "      <th>Chemistry</th>\n",
       "      <th>Physics</th>\n",
       "      <th>Chemistry</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</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>Female</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>90.25</td>\n",
       "      <td>77.535</td>\n",
       "      <td>9.742518</td>\n",
       "      <td>4.013091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>84.60</td>\n",
       "      <td>73.228</td>\n",
       "      <td>7.092249</td>\n",
       "      <td>5.415710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             len              mean               std          \n",
       "       Chemistry Physics Chemistry Physics Chemistry   Physics\n",
       "Sex                                                           \n",
       "Female       4.0     4.0     90.25  77.535  9.742518  4.013091\n",
       "Male         5.0     5.0     84.60  73.228  7.092249  5.415710"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    " Create a pivot table that groups by Sex \n",
    " and calculates the mean of Physics and Chemistry'''\n",
    "pivot_table = df.pivot_table(index='Sex',\n",
    "                             values=['Physics','Chemistry'],\n",
    "                             aggfunc=[len, np.mean, np.std])\n",
    "pivot_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     84.0\n",
       "1    100.0\n",
       "2     82.0\n",
       "3     90.0\n",
       "4     92.0\n",
       "5     83.0\n",
       "6     94.0\n",
       "7     90.0\n",
       "8     87.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a series containing maximum value of each row\n",
    "max_row = df.max(axis=1)\n",
    "max_row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Student      Scott\n",
       "Physics         83\n",
       "Chemistry      100\n",
       "Math            90\n",
       "Sex           Male\n",
       "dtype: object"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a series containing maximum value of each column without skipping NaN\n",
    "max_col = df.max(skipna=False)\n",
    "max_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>Physics</th>\n",
       "      <th>Chemistry</th>\n",
       "      <th>Math</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75.142222</td>\n",
       "      <td>87.111111</td>\n",
       "      <td>82.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.084992</td>\n",
       "      <td>8.343327</td>\n",
       "      <td>5.95294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>70.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>74.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>82.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75.140000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>82.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>87.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>83.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>90.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Physics   Chemistry      Math\n",
       "count   9.000000    9.000000   9.00000\n",
       "mean   75.142222   87.111111  82.50000\n",
       "std     5.084992    8.343327   5.95294\n",
       "min    67.000000   73.000000  70.00000\n",
       "25%    74.000000   84.000000  82.00000\n",
       "50%    75.140000   87.000000  82.50000\n",
       "75%    78.000000   92.000000  87.00000\n",
       "max    83.000000  100.000000  90.00000"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() # Summary statistics for numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Physics      75.142222\n",
       "Chemistry    87.111111\n",
       "Math         82.500000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean() # Returns the mean of all columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>Physics</th>\n",
       "      <th>Chemistry</th>\n",
       "      <th>Math</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Physics</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.383500</td>\n",
       "      <td>0.275638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chemistry</th>\n",
       "      <td>-0.383500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.045301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Math</th>\n",
       "      <td>0.275638</td>\n",
       "      <td>0.045301</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Physics  Chemistry      Math\n",
       "Physics    1.000000  -0.383500  0.275638\n",
       "Chemistry -0.383500   1.000000  0.045301\n",
       "Math       0.275638   0.045301  1.000000"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr() # Returns the correlation between columns in a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Student      9\n",
       "Physics      9\n",
       "Chemistry    9\n",
       "Math         9\n",
       "Sex          9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count() # Returns the number of non-null values in each DataFrame column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Student      Scott\n",
       "Physics         83\n",
       "Chemistry      100\n",
       "Math            90\n",
       "Sex           Male\n",
       "dtype: object"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.max() # Returns the highest value in each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Student         Bob\n",
       "Physics          67\n",
       "Chemistry        73\n",
       "Math             70\n",
       "Sex          Female\n",
       "dtype: object"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.min() # Returns the lowest value in each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Physics      75.14\n",
       "Chemistry    87.00\n",
       "Math         82.50\n",
       "dtype: float64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.median() # Returns the median of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Physics      5.084992\n",
       "Chemistry    8.343327\n",
       "Math         5.952940\n",
       "dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.std() # Returns the standard deviation of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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