Pandas Basic Documentation.ipynb

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    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/9/9f/Pandas_logo_2016.svg/540px-Pandas_logo_2016.svg.png\"/>\n",
    "\n",
    "<br>\n",
    "\n",
    "<a href=\"https://towardsdatascience.com/pandas-cheat-sheet-4c4eb6802a4b\"><h1>Pandas Basic Documentation </h1></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Create Data-Frame<h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "grades = pd.DataFrame(\n",
    "                 {'Math'     : [80, 89, 93,66, 84, 85,74,64],\n",
    "                  'Science'  : [94, 76, 88, 78, 88, 92, 60, 85],\n",
    "                  'English'  : [83, 76, 93, 96, 77, 85, 92, 60],\n",
    "                  'History'  : [96, 66, 76, 85, 78, 88, 69, 99]})"
   ]
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       "      <td>88</td>\n",
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       "      <th>6</th>\n",
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       "      <td>69</td>\n",
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       "      <th>7</th>\n",
       "      <td>64</td>\n",
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       "      <td>99</td>\n",
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      "text/plain": [
       "   Math  Science  English  History\n",
       "0    80       94       83       96\n",
       "1    89       76       76       66\n",
       "2    93       88       93       76\n",
       "3    66       78       96       85\n",
       "4    84       88       77       78\n",
       "5    85       92       85       88\n",
       "6    74       60       92       69\n",
       "7    64       85       60       99"
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     "execution_count": 3,
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   "source": [
    "grades"
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   "metadata": {},
   "source": [
    "*To display the top portion of the data frame, we can use the head function. By default, we will get the top 5 rows. We can pass in a specific number as n if we want another number.*"
   ]
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       "      <th>Math</th>\n",
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       "      <th>English</th>\n",
       "      <th>History</th>\n",
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       "   Math  Science  English  History\n",
       "0    80       94       83       96\n",
       "1    89       76       76       66\n",
       "2    93       88       93       76\n",
       "3    66       78       96       85\n",
       "4    84       88       77       78"
      ]
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   "source": [
    "grades.head()"
   ]
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       "   Math  Science  English  History\n",
       "0    80       94       83       96\n",
       "1    89       76       76       66\n",
       "2    93       88       93       76"
      ]
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    "grades.head(3)"
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   "source": [
    "<h4>Tail</h4>\n",
    "\n",
    "<br>\n",
    "*Similarly, we can get the opposite of head by calling tail on the data frame to get the last five rows and passing n as the argument to change the number displayed.*"
   ]
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   "cell_type": "code",
   "execution_count": 6,
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      "text/plain": [
       "   Math  Science  English  History\n",
       "3    66       78       96       85\n",
       "4    84       88       77       78\n",
       "5    85       92       85       88\n",
       "6    74       60       92       69\n",
       "7    64       85       60       99"
      ]
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     "execution_count": 6,
     "metadata": {},
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   "source": [
    "grades.tail()"
   ]
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       "   Math  Science  English  History\n",
       "5    85       92       85       88\n",
       "6    74       60       92       69\n",
       "7    64       85       60       99"
      ]
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "grades.tail(3)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Columns</h4>\n",
    "<br>\n",
    "*If we want to only the names of the dataframe, we can use the .columns function*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Math', 'Science', 'English', 'History'], dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Shape</h4>\n",
    "<br>\n",
    "*If you’re working with large data frames and it is difficult to count the number of rows or columns manually, we use .shape to find the dimensions.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 4)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Info</h4>\n",
    "<br>\n",
    "*Before starting any analysis, it is important to get an understanding of what data type you are working with. Using the .info() you can get an overview of the data types you have.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8 entries, 0 to 7\n",
      "Data columns (total 4 columns):\n",
      " #   Column   Non-Null Count  Dtype\n",
      "---  ------   --------------  -----\n",
      " 0   Math     8 non-null      int64\n",
      " 1   Science  8 non-null      int64\n",
      " 2   English  8 non-null      int64\n",
      " 3   History  8 non-null      int64\n",
      "dtypes: int64(4)\n",
      "memory usage: 384.0 bytes\n"
     ]
    }
   ],
   "source": [
    "grades.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Describe</h3>\n",
    "<br>\n",
    "*As well as .info(), you’ll most likely want to know the descriptive statistics of your datagram. Using the .describe() you can observe the count, mean, standard deviation, quantities, min, and max. In this example, I chained on .round(2) to clean up the output.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "      <th>count</th>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>79.38</td>\n",
       "      <td>82.62</td>\n",
       "      <td>82.75</td>\n",
       "      <td>82.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.53</td>\n",
       "      <td>11.07</td>\n",
       "      <td>11.76</td>\n",
       "      <td>11.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>64.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>66.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>72.00</td>\n",
       "      <td>77.50</td>\n",
       "      <td>76.75</td>\n",
       "      <td>74.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>82.00</td>\n",
       "      <td>86.50</td>\n",
       "      <td>84.00</td>\n",
       "      <td>81.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>86.00</td>\n",
       "      <td>89.00</td>\n",
       "      <td>92.25</td>\n",
       "      <td>90.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>93.00</td>\n",
       "      <td>94.00</td>\n",
       "      <td>96.00</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Math  Science  English  History\n",
       "count   8.00     8.00     8.00     8.00\n",
       "mean   79.38    82.62    82.75    82.12\n",
       "std    10.53    11.07    11.76    11.99\n",
       "min    64.00    60.00    60.00    66.00\n",
       "25%    72.00    77.50    76.75    74.25\n",
       "50%    82.00    86.50    84.00    81.50\n",
       "75%    86.00    89.00    92.25    90.00\n",
       "max    93.00    94.00    96.00    99.00"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades.describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Quantile</h3>\n",
    "<br>\n",
    "*Using the .describe() function we automatically got quantiles for 25, 50, and 75. We can also state our own quantiles. Below I have selected 10%, 40%, and 70%. Note — we can pass in as many quantiles in the formula below.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Math</th>\n",
       "      <th>Science</th>\n",
       "      <th>English</th>\n",
       "      <th>History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.3</th>\n",
       "      <td>74.6</td>\n",
       "      <td>78.7</td>\n",
       "      <td>77.6</td>\n",
       "      <td>76.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.8</th>\n",
       "      <td>87.4</td>\n",
       "      <td>90.4</td>\n",
       "      <td>92.6</td>\n",
       "      <td>92.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Math  Science  English  History\n",
       "0.3  74.6     78.7     77.6     76.2\n",
       "0.8  87.4     90.4     92.6     92.8"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades.quantile([0.3,0.8])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Mean, Standard Deviation, Variance, Count, Median, Min, and Max</h3>\n",
    "<br>\n",
    "We can use a variety of functions on the dataframe to get an aggregated result. You can also pass in a column name to retrieve that column value only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.38"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades['Math'].mean().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.53"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades['Math'].std().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110.84"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades['Math'].var().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades['Math'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades['Math'].median().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades['Math'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades['Math'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Renaming Columns</h3>\n",
    "<br>\n",
    "*If we want to rename the column, for example, changing the column name to upper case.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>85</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>74</td>\n",
       "      <td>60</td>\n",
       "      <td>92</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>64</td>\n",
       "      <td>85</td>\n",
       "      <td>60</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MATH  Science  English  History\n",
       "0    80       94       83       96\n",
       "1    89       76       76       66\n",
       "2    93       88       93       76\n",
       "3    66       78       96       85\n",
       "4    84       88       77       78\n",
       "5    85       92       85       88\n",
       "6    74       60       92       69\n",
       "7    64       85       60       99"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades.rename(columns={'Math':'MATH'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>78</td>\n",
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       "      <th>5</th>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>85</td>\n",
       "      <td>88</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>74</td>\n",
       "      <td>60</td>\n",
       "      <td>92</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>64</td>\n",
       "      <td>85</td>\n",
       "      <td>60</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Math  Science  English  History\n",
       "0    80       94       83       96\n",
       "1    89       76       76       66\n",
       "2    93       88       93       76\n",
       "3    66       78       96       85\n",
       "4    84       88       77       78\n",
       "5    85       92       85       88\n",
       "6    74       60       92       69\n",
       "7    64       85       60       99"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Both pieces of code above will return the same result. Using the argument, inplace = True we are telling it to save it into itself, and remember the result. If we don’t state inplace = True we would have to code in the = , to state we want to save the value back into the grades dataframe.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Index Subsetting — iloc</h3>\n",
    "<br>\n",
    "    \n",
    "*Using iloc, which stands for index location, we can find a subset of the dataframe based on their index position. There are many ways we can index, so it is important to understand the different variations of using iloc below. The first part of the iloc represents the rows, and the second half is the columns.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>85</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>74</td>\n",
       "      <td>60</td>\n",
       "      <td>92</td>\n",
       "      <td>69</td>\n",
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       "      <th>7</th>\n",
       "      <td>64</td>\n",
       "      <td>85</td>\n",
       "      <td>60</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Math  Science  English  History\n",
       "0    80       94       83       96\n",
       "1    89       76       76       66\n",
       "2    93       88       93       76\n",
       "3    66       78       96       85\n",
       "4    84       88       77       78\n",
       "5    85       92       85       88\n",
       "6    74       60       92       69\n",
       "7    64       85       60       99"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Math       80\n",
       "Science    94\n",
       "English    83\n",
       "History    96\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades.iloc[0] #first column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80</td>\n",
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       "      <td>96</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   Math  Science  English  History\n",
       "0    80       94       83       96"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades.iloc[[0]] #first Row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Math</th>\n",
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       "      <th>English</th>\n",
       "      <th>History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66</td>\n",
       "      <td>78</td>\n",
       "      <td>96</td>\n",
       "      <td>85</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Math  Science  English  History\n",
       "3    66       78       96       85"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades.iloc[[3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    94\n",
       "1    76\n",
       "2    88\n",
       "3    78\n",
       "4    88\n",
       "5    92\n",
       "6    60\n",
       "7    85\n",
       "Name: Science, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades.iloc[:,1] #all rows second column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#4th row second column only\n",
    "grades.iloc[3,1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Math</th>\n",
       "      <th>Science</th>\n",
       "      <th>English</th>\n",
       "      <th>History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80</td>\n",
       "      <td>94</td>\n",
       "      <td>83</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>89</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>93</td>\n",
       "      <td>88</td>\n",
       "      <td>93</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66</td>\n",
       "      <td>78</td>\n",
       "      <td>96</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84</td>\n",
       "      <td>88</td>\n",
       "      <td>77</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>85</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>74</td>\n",
       "      <td>60</td>\n",
       "      <td>92</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>64</td>\n",
       "      <td>85</td>\n",
       "      <td>60</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Math  Science  English  History\n",
       "0    80       94       83       96\n",
       "1    89       76       76       66\n",
       "2    93       88       93       76\n",
       "3    66       78       96       85\n",
       "4    84       88       77       78\n",
       "5    85       92       85       88\n",
       "6    74       60       92       69\n",
       "7    64       85       60       99"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Math</th>\n",
       "      <th>Science</th>\n",
       "      <th>English</th>\n",
       "      <th>History</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
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       "      <td>76</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66</td>\n",
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      "text/plain": [
       "   Math  Science  English  History\n",
       "1    89       76       76       66\n",
       "3    66       78       96       85"
      ]
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # row 1 and 3, with all columns\n",
    "grades.iloc[[1,3],:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "      <td>88</td>\n",
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       "      <th>5</th>\n",
       "      <td>92</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
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       "      <td>60</td>\n",
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      ],
      "text/plain": [
       "   Science  English\n",
       "0       94       83\n",
       "1       76       76\n",
       "2       88       93\n",
       "3       78       96\n",
       "4       88       77\n",
       "5       92       85\n",
       "6       60       92\n",
       "7       85       60"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all rows and columns 1 to 3 (*not including 3)\n",
    "grades.iloc[:,1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>88</td>\n",
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       "      <td>69</td>\n",
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      ],
      "text/plain": [
       "   Math  Science  English  History\n",
       "0    80       94       83       96\n",
       "1    89       76       76       66\n",
       "2    93       88       93       76\n",
       "3    66       78       96       85\n",
       "4    84       88       77       78\n",
       "5    85       92       85       88\n",
       "6    74       60       92       69\n",
       "7    64       85       60       99"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "      <th>7</th>\n",
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      "text/plain": [
       "   Math  Science  English\n",
       "4    84       88       77\n",
       "5    85       92       85\n",
       "6    74       60       92\n",
       "7    64       85       60"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from row 5 till last row, and first col to 4th col (not including 4th)\n",
    "grades.iloc[4:,:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice we included a : in some situations. The : represents a range symbol, with the numbers on the left of the : stating the starting position and the right the index up to, but not including. For example, grades.iloc[:, 1:3] it is broken into two parts.\n",
    "<br>\n",
    "<ol>\n",
    "    <li>The first : → all the rows, since there is no start index preceding it, and no end index following it</li>\n",
    "    <li>The second 1:3 → from column 1 up to, but NOT including column 3. Meaning the result will only return columns 1 and 2 for this example.</li>\n",
    "    </ol>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Location Subsetting — loc</h3>\n",
    "<br>\n",
    "*On the opposite side of subsetting, we have location-based subsetting. Here we reference the name of the variables we want and not the index.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    80\n",
       "1    89\n",
       "2    93\n",
       "3    66\n",
       "4    84\n",
       "5    85\n",
       "6    74\n",
       "7    64\n",
       "Name: Math, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all rows in Math column\n",
    "grades.loc[:,'Math']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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      "text/plain": [
       "   Math  English\n",
       "2    93       93\n",
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     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rows 3 and 4, for Math and English\n",
    "grades.loc[[2,3],['Math','English']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       "      <td>69</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>64</td>\n",
       "      <td>85</td>\n",
       "      <td>60</td>\n",
       "      <td>99</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   Math  Science  English  History\n",
       "0    80       94       83       96\n",
       "1    89       76       76       66\n",
       "2    93       88       93       76\n",
       "3    66       78       96       85\n",
       "4    84       88       77       78\n",
       "5    85       92       85       88\n",
       "6    74       60       92       69\n",
       "7    64       85       60       99"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>64</td>\n",
       "      <td>85</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Math  Science  English\n",
       "4    84       88       77\n",
       "5    85       92       85\n",
       "6    74       60       92\n",
       "7    64       85       60"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#row 5 onwards, and Math TO English\n",
    "grades.loc[4: ,'Math':'English']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Science</th>\n",
       "      <th>English</th>\n",
       "      <th>History</th>\n",
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       "  </thead>\n",
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       "      <th>2</th>\n",
       "      <td>88</td>\n",
       "      <td>93</td>\n",
       "      <td>76</td>\n",
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       "      <th>3</th>\n",
       "      <td>78</td>\n",
       "      <td>96</td>\n",
       "      <td>85</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>88</td>\n",
       "      <td>77</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   Science  English  History\n",
       "0       94       83       96\n",
       "1       76       76       66\n",
       "2       88       93       76\n",
       "3       78       96       85\n",
       "4       88       77       78"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades.loc[0:4,\"Science\":]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*For our data, we only have location subset names for the columns. If we had row labels, we could reference them as well. We will see this example further down the post.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Sort Values</h3>\n",
    "<br>\n",
    "*If we want to sort our dataframe based on a specific value, we can use sort_values() by default values are sorted in ascending, we can pass in a False parameter to change it to descending.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>History</th>\n",
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       "      <td>78</td>\n",
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       "      <th>5</th>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>85</td>\n",
       "      <td>88</td>\n",
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       "      <td>74</td>\n",
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       "      <td>69</td>\n",
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       "      <td>64</td>\n",
       "      <td>85</td>\n",
       "      <td>60</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Math  Science  English  History\n",
       "0    80       94       83       96\n",
       "1    89       76       76       66\n",
       "2    93       88       93       76\n",
       "3    66       78       96       85\n",
       "4    84       88       77       78\n",
       "5    85       92       85       88\n",
       "6    74       60       92       69\n",
       "7    64       85       60       99"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Math</th>\n",
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       "      <th>English</th>\n",
       "      <th>History</th>\n",
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       "  </thead>\n",
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       "      <th>4</th>\n",
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       "      <td>88</td>\n",
       "      <td>77</td>\n",
       "      <td>78</td>\n",
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       "      <td>89</td>\n",
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       "      <td>76</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>64</td>\n",
       "      <td>85</td>\n",
       "      <td>60</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Math  Science  English  History\n",
       "3    66       78       96       85\n",
       "2    93       88       93       76\n",
       "6    74       60       92       69\n",
       "5    85       92       85       88\n",
       "0    80       94       83       96\n",
       "4    84       88       77       78\n",
       "1    89       76       76       66\n",
       "7    64       85       60       99"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades.sort_values('English',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Concat</h3>\n",
    "<br>\n",
    "*Using concat, we can merge two data frames together based on an axis. For example, we can add new values to our dataframe in two scenarios.*\n",
    "\n",
    "<ol>\n",
    "    <li>  Add more rows — axis = 0\n",
    "       </li>\n",
    "       \n",
    "    <li> Add more columns — axis = 1 </li>\n",
    "       \n",
    "    </ol>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# concat\n",
    "grades2 = pd.DataFrame(\n",
    "                        {'Math'     : [77,55,93,76],\n",
    "                         'Science'  : [88,60,90,74],\n",
    "                         'English'  : [84,76,66,90],\n",
    "                         'History'  : [77,69,92,81]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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      "text/plain": [
       "   Math  Science  English  History\n",
       "0    77       88       84       77\n",
       "1    55       60       76       69\n",
       "2    93       90       66       92\n",
       "3    76       74       90       81"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "finalgrade=pd.concat([grades,grades2],axis=0).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>99</td>\n",
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       "      <th>8</th>\n",
       "      <td>77</td>\n",
       "      <td>88</td>\n",
       "      <td>84</td>\n",
       "      <td>77</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>55</td>\n",
       "      <td>60</td>\n",
       "      <td>76</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "      <td>66</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>76</td>\n",
       "      <td>74</td>\n",
       "      <td>90</td>\n",
       "      <td>81</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Math  Science  English  History\n",
       "0     80       94       83       96\n",
       "1     89       76       76       66\n",
       "2     93       88       93       76\n",
       "3     66       78       96       85\n",
       "4     84       88       77       78\n",
       "5     85       92       85       88\n",
       "6     74       60       92       69\n",
       "7     64       85       60       99\n",
       "8     77       88       84       77\n",
       "9     55       60       76       69\n",
       "10    93       90       66       92\n",
       "11    76       74       90       81"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalgrade"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*First, we created a second data frame with additional grade values called grades2. Then we concat the new dataframe onto the old one. We state the axis as 0 because we are adding more rows. If we were adding an entirely new subject such as Geography, then we would want to use the axis = 1.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> For Column Add </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "grades3 = pd.DataFrame(\n",
    "                        {'CSE' : [77,55,93,76,85,98,45,75,65,62,87,89]\n",
    "                         })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>9</th>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CSE\n",
       "0    77\n",
       "1    55\n",
       "2    93\n",
       "3    76\n",
       "4    85\n",
       "5    98\n",
       "6    45\n",
       "7    75\n",
       "8    65\n",
       "9    62\n",
       "10   87\n",
       "11   89"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "finalgrade2=pd.concat([finalgrade,grades3],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
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       "      <td>87</td>\n",
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       "      <td>76</td>\n",
       "      <td>74</td>\n",
       "      <td>90</td>\n",
       "      <td>81</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Math  Science  English  History  CSE\n",
       "0     80       94       83       96   77\n",
       "1     89       76       76       66   55\n",
       "2     93       88       93       76   93\n",
       "3     66       78       96       85   76\n",
       "4     84       88       77       78   85\n",
       "5     85       92       85       88   98\n",
       "6     74       60       92       69   45\n",
       "7     64       85       60       99   75\n",
       "8     77       88       84       77   65\n",
       "9     55       60       76       69   62\n",
       "10    93       90       66       92   87\n",
       "11    76       74       90       81   89"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalgrade2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "finalgrade2=finalgrade2.rename(columns={\"CSE\":\"Cse\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>77</td>\n",
       "      <td>65</td>\n",
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       "      <td>60</td>\n",
       "      <td>76</td>\n",
       "      <td>69</td>\n",
       "      <td>62</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "      <td>66</td>\n",
       "      <td>92</td>\n",
       "      <td>87</td>\n",
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       "      <td>89</td>\n",
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      ],
      "text/plain": [
       "    Math  Science  English  History  Cse\n",
       "0     80       94       83       96   77\n",
       "1     89       76       76       66   55\n",
       "2     93       88       93       76   93\n",
       "3     66       78       96       85   76\n",
       "4     84       88       77       78   85\n",
       "5     85       92       85       88   98\n",
       "6     74       60       92       69   45\n",
       "7     64       85       60       99   75\n",
       "8     77       88       84       77   65\n",
       "9     55       60       76       69   62\n",
       "10    93       90       66       92   87\n",
       "11    76       74       90       81   89"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalgrade2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    12.000000\n",
       "mean     75.583333\n",
       "std      16.132735\n",
       "min      45.000000\n",
       "25%      64.250000\n",
       "50%      76.500000\n",
       "75%      87.500000\n",
       "max      98.000000\n",
       "Name: Cse, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalgrade2['Cse'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Boolean Indexing</h3>\n",
    "<br>\n",
    "*A useful technique in pandas is called boolean indexing. It is essentially a filtering technique to find values based on a boolean condition (i.e., True or False). The general syntax for a boolean index is as follows.*\n",
    "\n",
    "df[df[column]>80]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "fg=finalgrade2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>75</td>\n",
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       "      <th>8</th>\n",
       "      <td>77</td>\n",
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       "      <td>77</td>\n",
       "      <td>65</td>\n",
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       "      <th>9</th>\n",
       "      <td>55</td>\n",
       "      <td>60</td>\n",
       "      <td>76</td>\n",
       "      <td>69</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "      <td>66</td>\n",
       "      <td>92</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>76</td>\n",
       "      <td>74</td>\n",
       "      <td>90</td>\n",
       "      <td>81</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Math  Science  English  History  Cse\n",
       "0     80       94       83       96   77\n",
       "1     89       76       76       66   55\n",
       "2     93       88       93       76   93\n",
       "3     66       78       96       85   76\n",
       "4     84       88       77       78   85\n",
       "5     85       92       85       88   98\n",
       "6     74       60       92       69   45\n",
       "7     64       85       60       99   75\n",
       "8     77       88       84       77   65\n",
       "9     55       60       76       69   62\n",
       "10    93       90       66       92   87\n",
       "11    76       74       90       81   89"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Math, Science, English, History, Cse]\n",
       "Index: []"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fg[fg[\"Cse\"]<40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     94\n",
       "1     76\n",
       "2     88\n",
       "4     88\n",
       "5     92\n",
       "10    90\n",
       "Name: Science, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subsetting but only calling a column\n",
    "# math er shapekkhe science er value return korbe\n",
    "fg[fg[\"Math\"]>=80]['Science']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    85\n",
       "Name: Math, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fg[fg[\"Cse\"]>95][\"Math\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     66\n",
       "2     76\n",
       "4     78\n",
       "5     88\n",
       "10    92\n",
       "Name: History, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subsetting but only calling a column using iloc\n",
    "fg[fg[\"Math\"]>80].iloc[:,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Math</th>\n",
       "      <th>Science</th>\n",
       "      <th>English</th>\n",
       "      <th>History</th>\n",
       "      <th>Cse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>89</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>66</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Math  Science  English  History  Cse\n",
       "1    89       76       76       66   55"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## AND  conditions\n",
    "fg[(fg[\"Math\"]>80) &(fg[\"Cse\"]<80)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "      <th>History</th>\n",
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       "      <th>0</th>\n",
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       "      <td>77</td>\n",
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       "      <td>89</td>\n",
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       "      <td>66</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>93</td>\n",
       "      <td>88</td>\n",
       "      <td>93</td>\n",
       "      <td>76</td>\n",
       "      <td>93</td>\n",
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       "      <th>3</th>\n",
       "      <td>66</td>\n",
       "      <td>78</td>\n",
       "      <td>96</td>\n",
       "      <td>85</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84</td>\n",
       "      <td>88</td>\n",
       "      <td>77</td>\n",
       "      <td>78</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>85</td>\n",
       "      <td>88</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>74</td>\n",
       "      <td>60</td>\n",
       "      <td>92</td>\n",
       "      <td>69</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>64</td>\n",
       "      <td>85</td>\n",
       "      <td>60</td>\n",
       "      <td>99</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>77</td>\n",
       "      <td>88</td>\n",
       "      <td>84</td>\n",
       "      <td>77</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>55</td>\n",
       "      <td>60</td>\n",
       "      <td>76</td>\n",
       "      <td>69</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "      <td>66</td>\n",
       "      <td>92</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>76</td>\n",
       "      <td>74</td>\n",
       "      <td>90</td>\n",
       "      <td>81</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Math  Science  English  History  Cse\n",
       "0     80       94       83       96   77\n",
       "1     89       76       76       66   55\n",
       "2     93       88       93       76   93\n",
       "3     66       78       96       85   76\n",
       "4     84       88       77       78   85\n",
       "5     85       92       85       88   98\n",
       "6     74       60       92       69   45\n",
       "7     64       85       60       99   75\n",
       "8     77       88       84       77   65\n",
       "9     55       60       76       69   62\n",
       "10    93       90       66       92   87\n",
       "11    76       74       90       81   89"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     96\n",
       "1     66\n",
       "2     76\n",
       "3     85\n",
       "4     78\n",
       "5     88\n",
       "6     69\n",
       "7     99\n",
       "8     77\n",
       "9     69\n",
       "10    92\n",
       "Name: History, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# OR conditions but only want History score\n",
    "\n",
    "fg[(fg[\"Math\"]>80) |(fg[\"Cse\"]<80)]['History']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Looking above, we can combine multiple boolean conditions to drill down the filtering abilities. Using the & and | we can state conditions. The & (AND) will return the result if BOTH conditions are met. The | (OR) will return the result if either side of the condition is satisfied.*\n",
    "<br>\n",
    "<br>\n",
    "*Observe the code above. We can see that adding iloc or the column name can provide us with a result which returns only the selected columns if the conditions are met.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Subsetting Columns</h3>\n",
    "<h4>You can subset columns in pandas as a series or a dataframe.</h4>\n",
    "*Series is a type of list in pandas that can take integer values, string values, double values, and more. But in Pandas Series, we return an object in the form of a list, having index starting from 0 to n, Where n is the length of values in series. Series can only contain a single list with index, whereas dataframe can be made of more than one series, or we can say that a dataframe is a collection of series that can be used to analyze the data.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     77\n",
       "1     55\n",
       "2     93\n",
       "3     76\n",
       "4     85\n",
       "5     98\n",
       "6     45\n",
       "7     75\n",
       "8     65\n",
       "9     62\n",
       "10    87\n",
       "11    89\n",
       "Name: Cse, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fg['Cse']\n",
    "# subseting a column as a series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cse</th>\n",
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       "      <td>76</td>\n",
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       "      <td>75</td>\n",
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       "      <td>65</td>\n",
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       "      <th>9</th>\n",
       "      <td>62</td>\n",
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       "      <th>10</th>\n",
       "      <td>87</td>\n",
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       "      <td>89</td>\n",
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       "</table>\n",
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      "text/plain": [
       "    Cse\n",
       "0    77\n",
       "1    55\n",
       "2    93\n",
       "3    76\n",
       "4    85\n",
       "5    98\n",
       "6    45\n",
       "7    75\n",
       "8    65\n",
       "9    62\n",
       "10   87\n",
       "11   89"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subseting as a dataframe\n",
    "fg[[\"Cse\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>6</th>\n",
       "      <td>45</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>75</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>65</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>62</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>87</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>89</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Cse  Math\n",
       "0    77    80\n",
       "1    55    89\n",
       "2    93    93\n",
       "3    76    66\n",
       "4    85    84\n",
       "5    98    85\n",
       "6    45    74\n",
       "7    75    64\n",
       "8    65    77\n",
       "9    62    55\n",
       "10   87    93\n",
       "11   89    76"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# multiple columns\n",
    "fg[['Cse','Math']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Adding Columns</h3>\n",
    "    <br>\n",
    "*Adding more columns to a dataframe is as simple as creating a new column name and setting the values equal to it.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "fg[\"Name\"]=[\"Shamim\",\"Tamim\",\"Shakib\",\"Riad\",\"Mushfiq\",\"Mash\",\"Sabbir\",\"Souyma\",\"Liton\",\"Musta\",\"kohli\",\"Rubel\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>76</td>\n",
       "      <td>Riad</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>84</td>\n",
       "      <td>88</td>\n",
       "      <td>77</td>\n",
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       "      <td>Mushfiq</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>85</td>\n",
       "      <td>88</td>\n",
       "      <td>98</td>\n",
       "      <td>Mash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>74</td>\n",
       "      <td>60</td>\n",
       "      <td>92</td>\n",
       "      <td>69</td>\n",
       "      <td>45</td>\n",
       "      <td>Sabbir</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>64</td>\n",
       "      <td>85</td>\n",
       "      <td>60</td>\n",
       "      <td>99</td>\n",
       "      <td>75</td>\n",
       "      <td>Souyma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>77</td>\n",
       "      <td>88</td>\n",
       "      <td>84</td>\n",
       "      <td>77</td>\n",
       "      <td>65</td>\n",
       "      <td>Liton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>55</td>\n",
       "      <td>60</td>\n",
       "      <td>76</td>\n",
       "      <td>69</td>\n",
       "      <td>62</td>\n",
       "      <td>Musta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "      <td>66</td>\n",
       "      <td>92</td>\n",
       "      <td>87</td>\n",
       "      <td>kohli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>76</td>\n",
       "      <td>74</td>\n",
       "      <td>90</td>\n",
       "      <td>81</td>\n",
       "      <td>89</td>\n",
       "      <td>Rubel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Math  Science  English  History  Cse     Name\n",
       "0     80       94       83       96   77   Shamim\n",
       "1     89       76       76       66   55    Tamim\n",
       "2     93       88       93       76   93   Shakib\n",
       "3     66       78       96       85   76     Riad\n",
       "4     84       88       77       78   85  Mushfiq\n",
       "5     85       92       85       88   98     Mash\n",
       "6     74       60       92       69   45   Sabbir\n",
       "7     64       85       60       99   75   Souyma\n",
       "8     77       88       84       77   65    Liton\n",
       "9     55       60       76       69   62    Musta\n",
       "10    93       90       66       92   87    kohli\n",
       "11    76       74       90       81   89    Rubel"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "fg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "fg['Gender']    = ['M','F', 'M', 'M', 'F', 'F', 'F', 'M',\"F\",\"M\",\"F\",\"M\"]\n",
    "fg['Class']     = ['A', 'A', 'C', 'B', 'C', 'A', 'B', 'C','B+','C+','A','A-']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    Math  Science  English  History  Cse     Name Gender Class\n",
       "0     80       94       83       96   77   Shamim      M     A\n",
       "1     89       76       76       66   55    Tamim      F     A\n",
       "2     93       88       93       76   93   Shakib      M     C\n",
       "3     66       78       96       85   76     Riad      M     B\n",
       "4     84       88       77       78   85  Mushfiq      F     C\n",
       "5     85       92       85       88   98     Mash      F     A\n",
       "6     74       60       92       69   45   Sabbir      F     B\n",
       "7     64       85       60       99   75   Souyma      M     C\n",
       "8     77       88       84       77   65    Liton      F    B+\n",
       "9     55       60       76       69   62    Musta      M    C+\n",
       "10    93       90       66       92   87    kohli      F     A\n",
       "11    76       74       90       81   89    Rubel      M    A-"
      ]
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     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Reorder Columns</h3>\n",
    "*If the example above, it would make more sense to have the Student as the first column. If we wanted to reorder the columns, we can create a list of the order we want the dataframe columns to be in, and index them on the dataframe.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Math', 'Science', 'English', 'History', 'Cse', 'Name', 'Gender',\n",
       "       'Class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fg.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "col=['Name','Gender','Math','English','Cse','Science','History','Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "fg=fg[col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "       Name Gender  Math  English  Cse  Science  History Class\n",
       "0    Shamim      M    80       83   77       94       96     A\n",
       "1     Tamim      F    89       76   55       76       66     A\n",
       "2    Shakib      M    93       93   93       88       76     C\n",
       "3      Riad      M    66       96   76       78       85     B\n",
       "4   Mushfiq      F    84       77   85       88       78     C\n",
       "5      Mash      F    85       85   98       92       88     A\n",
       "6    Sabbir      F    74       92   45       60       69     B\n",
       "7    Souyma      M    64       60   75       85       99     C\n",
       "8     Liton      F    77       84   65       88       77    B+\n",
       "9     Musta      M    55       76   62       60       69    C+\n",
       "10    kohli      F    93       66   87       90       92     A\n",
       "11    Rubel      M    76       90   89       74       81    A-"
      ]
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     "execution_count": 70,
     "metadata": {},
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    }
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   "source": [
    "fg"
   ]
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  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "fg.rename(columns={\"Class\":\"Grade\"},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Rubel</td>\n",
       "      <td>M</td>\n",
       "      <td>76</td>\n",
       "      <td>90</td>\n",
       "      <td>89</td>\n",
       "      <td>74</td>\n",
       "      <td>81</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name Gender  Math  English  Cse  Science  History Grade\n",
       "0    Shamim      M    80       83   77       94       96     A\n",
       "1     Tamim      F    89       76   55       76       66     A\n",
       "2    Shakib      M    93       93   93       88       76     C\n",
       "3      Riad      M    66       96   76       78       85     B\n",
       "4   Mushfiq      F    84       77   85       88       78     C\n",
       "5      Mash      F    85       85   98       92       88     A\n",
       "6    Sabbir      F    74       92   45       60       69     B\n",
       "7    Souyma      M    64       60   75       85       99     C\n",
       "8     Liton      F    77       84   65       88       77    B+\n",
       "9     Musta      M    55       76   62       60       69    C+\n",
       "10    kohli      F    93       66   87       90       92     A\n",
       "11    Rubel      M    76       90   89       74       81    A-"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12 entries, 0 to 11\n",
      "Data columns (total 8 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   Name     12 non-null     object\n",
      " 1   Gender   12 non-null     object\n",
      " 2   Math     12 non-null     int64 \n",
      " 3   English  12 non-null     int64 \n",
      " 4   Cse      12 non-null     int64 \n",
      " 5   Science  12 non-null     int64 \n",
      " 6   History  12 non-null     int64 \n",
      " 7   Grade    12 non-null     object\n",
      "dtypes: int64(5), object(3)\n",
      "memory usage: 896.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "fg.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 8)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fg.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Pivot Table</h3>\n",
    "A pivot table is a table of statistics that summarizes the data of a more extensive table (such as from a database, spreadsheet, or business intelligence program). This summary might include sums, averages, or other statistics, which the pivot table groups together in a meaningful way.\n",
    "<br>\n",
    "Pivot tables are a technique in data processing. They arrange and rearrange (or “pivot”) statistics to draw attention to useful information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>Cse</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
       "      <th>F</th>\n",
       "      <td>80</td>\n",
       "      <td>75.67</td>\n",
       "      <td>82.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>77</td>\n",
       "      <td>83.00</td>\n",
       "      <td>96.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>94.0</td>\n",
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       "      <th>A-</th>\n",
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       "      <td>89</td>\n",
       "      <td>90.00</td>\n",
       "      <td>81.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
       "      <th>F</th>\n",
       "      <td>45</td>\n",
       "      <td>92.00</td>\n",
       "      <td>69.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>76</td>\n",
       "      <td>96.00</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B+</th>\n",
       "      <th>F</th>\n",
       "      <td>65</td>\n",
       "      <td>84.00</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">C</th>\n",
       "      <th>F</th>\n",
       "      <td>85</td>\n",
       "      <td>77.00</td>\n",
       "      <td>78.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>84</td>\n",
       "      <td>76.50</td>\n",
       "      <td>87.5</td>\n",
       "      <td>78.5</td>\n",
       "      <td>86.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C+</th>\n",
       "      <th>M</th>\n",
       "      <td>62</td>\n",
       "      <td>76.00</td>\n",
       "      <td>69.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Cse  English  History  Math  Science\n",
       "Grade Gender                                      \n",
       "A     F        80    75.67     82.0  89.0     86.0\n",
       "      M        77    83.00     96.0  80.0     94.0\n",
       "A-    M        89    90.00     81.0  76.0     74.0\n",
       "B     F        45    92.00     69.0  74.0     60.0\n",
       "      M        76    96.00     85.0  66.0     78.0\n",
       "B+    F        65    84.00     77.0  77.0     88.0\n",
       "C     F        85    77.00     78.0  84.0     88.0\n",
       "      M        84    76.50     87.5  78.5     86.5\n",
       "C+    M        62    76.00     69.0  55.0     60.0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fg.pivot_table(index=['Grade','Gender']).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Science</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grade</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</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 rowspan=\"4\" valign=\"top\">A</th>\n",
       "      <th>Mash</th>\n",
       "      <th>F</th>\n",
       "      <td>98</td>\n",
       "      <td>85</td>\n",
       "      <td>88</td>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shamim</th>\n",
       "      <th>M</th>\n",
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       "      <td>96</td>\n",
       "      <td>80</td>\n",
       "      <td>94</td>\n",
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       "      <th>Tamim</th>\n",
       "      <th>F</th>\n",
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       "      <td>66</td>\n",
       "      <td>89</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kohli</th>\n",
       "      <th>F</th>\n",
       "      <td>87</td>\n",
       "      <td>66</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A-</th>\n",
       "      <th>Rubel</th>\n",
       "      <th>M</th>\n",
       "      <td>89</td>\n",
       "      <td>90</td>\n",
       "      <td>81</td>\n",
       "      <td>76</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
       "      <th>Riad</th>\n",
       "      <th>M</th>\n",
       "      <td>76</td>\n",
       "      <td>96</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sabbir</th>\n",
       "      <th>F</th>\n",
       "      <td>45</td>\n",
       "      <td>92</td>\n",
       "      <td>69</td>\n",
       "      <td>74</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B+</th>\n",
       "      <th>Liton</th>\n",
       "      <th>F</th>\n",
       "      <td>65</td>\n",
       "      <td>84</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">C</th>\n",
       "      <th>Mushfiq</th>\n",
       "      <th>F</th>\n",
       "      <td>85</td>\n",
       "      <td>77</td>\n",
       "      <td>78</td>\n",
       "      <td>84</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shakib</th>\n",
       "      <th>M</th>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>76</td>\n",
       "      <td>93</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Souyma</th>\n",
       "      <th>M</th>\n",
       "      <td>75</td>\n",
       "      <td>60</td>\n",
       "      <td>99</td>\n",
       "      <td>64</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C+</th>\n",
       "      <th>Musta</th>\n",
       "      <th>M</th>\n",
       "      <td>62</td>\n",
       "      <td>76</td>\n",
       "      <td>69</td>\n",
       "      <td>55</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Cse  English  History  Math  Science\n",
       "Grade Name    Gender                                      \n",
       "A     Mash    F        98       85       88    85       92\n",
       "      Shamim  M        77       83       96    80       94\n",
       "      Tamim   F        55       76       66    89       76\n",
       "      kohli   F        87       66       92    93       90\n",
       "A-    Rubel   M        89       90       81    76       74\n",
       "B     Riad    M        76       96       85    66       78\n",
       "      Sabbir  F        45       92       69    74       60\n",
       "B+    Liton   F        65       84       77    77       88\n",
       "C     Mushfiq F        85       77       78    84       88\n",
       "      Shakib  M        93       93       76    93       88\n",
       "      Souyma  M        75       60       99    64       85\n",
       "C+    Musta   M        62       76       69    55       60"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fg.pivot_table(index=['Grade',\"Name\",\"Gender\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Group By</h3>\n",
    "A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Science</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>83.67</td>\n",
       "      <td>80.0</td>\n",
       "      <td>72.50</td>\n",
       "      <td>82.33</td>\n",
       "      <td>78.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>72.33</td>\n",
       "      <td>83.0</td>\n",
       "      <td>78.67</td>\n",
       "      <td>79.83</td>\n",
       "      <td>84.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Math  English    Cse  Science  History\n",
       "Gender                                         \n",
       "F       83.67     80.0  72.50    82.33    78.33\n",
       "M       72.33     83.0  78.67    79.83    84.33"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fg.groupby(by='Gender').mean().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The great thing about the groupby function in pandas is chaining on several aggregate functions using the agg function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Math</th>\n",
       "      <th colspan=\"2\" halign=\"left\">English</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Cse</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Science</th>\n",
       "      <th colspan=\"2\" halign=\"left\">History</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>median</th>\n",
       "      <th>sum</th>\n",
       "      <th>median</th>\n",
       "      <th>sum</th>\n",
       "      <th>median</th>\n",
       "      <th>sum</th>\n",
       "      <th>median</th>\n",
       "      <th>sum</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>502</td>\n",
       "      <td>84.5</td>\n",
       "      <td>480</td>\n",
       "      <td>80.5</td>\n",
       "      <td>435</td>\n",
       "      <td>75.0</td>\n",
       "      <td>494</td>\n",
       "      <td>88.0</td>\n",
       "      <td>470</td>\n",
       "      <td>77.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>434</td>\n",
       "      <td>71.0</td>\n",
       "      <td>498</td>\n",
       "      <td>86.5</td>\n",
       "      <td>472</td>\n",
       "      <td>76.5</td>\n",
       "      <td>479</td>\n",
       "      <td>81.5</td>\n",
       "      <td>506</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Math        English         Cse        Science        History       \n",
       "        sum median     sum median  sum median     sum median     sum median\n",
       "Gender                                                                     \n",
       "F       502   84.5     480   80.5  435   75.0     494   88.0     470   77.5\n",
       "M       434   71.0     498   86.5  472   76.5     479   81.5     506   83.0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fg.groupby(by=\"Gender\").agg(['sum','median']).round(2)"
   ]
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