intermediate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In previous notebook, we have learned about the basic usage of pandas. In this notebook, we will learn more about pandas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In previous we talk about drop function, but we didn't talk about dropna and fillna function. Let's talk about it now.\n",
    "\n",
    "dropna function is used to drop rows with missing values. fillna function is used to fill missing values.\n",
    "Since we don't have any missing values in our data, we will create some missing values and then we will use dropna and fillna function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25.0</td>\n",
       "      <td>New York</td>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>22.0</td>\n",
       "      <td>None</td>\n",
       "      <td>35000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eva</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name   Age         City   Salary\n",
       "0    Alice  25.0     New York  40000.0\n",
       "1      Bob   NaN               50000.0\n",
       "2  Charlie  30.0      Chicago      NaN\n",
       "3    David  22.0         None  35000.0\n",
       "4      Eva  28.0  Los Angeles      NaN"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Create a DataFrame with NaN values\n",
    "data = {\n",
    "    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],\n",
    "    'Age': [25, None, 30, 22, 28],\n",
    "    'City': ['New York', '', 'Chicago', None, 'Los Angeles'],\n",
    "    'Salary': [40000, 50000, None, 35000, None]\n",
    "}\n",
    "\n",
    "demo = pd.DataFrame(data)\n",
    "demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5 entries, 0 to 4\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Name    5 non-null      object \n",
      " 1   Age     4 non-null      float64\n",
      " 2   City    4 non-null      object \n",
      " 3   Salary  3 non-null      float64\n",
      "dtypes: float64(2), object(2)\n",
      "memory usage: 292.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "demo.info()\n",
    "\n",
    "## We can see the number of non-null values in each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25.0</td>\n",
       "      <td>New York</td>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name   Age      City   Salary\n",
       "0  Alice  25.0  New York  40000.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### dropna() method\n",
    "demo.dropna() # drop all rows with any NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25.0</td>\n",
       "      <td>New York</td>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>0.0</td>\n",
       "      <td></td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>35000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eva</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name   Age         City   Salary\n",
       "0    Alice  25.0     New York  40000.0\n",
       "1      Bob   0.0               50000.0\n",
       "2  Charlie  30.0      Chicago      0.0\n",
       "3    David  22.0            0  35000.0\n",
       "4      Eva  28.0  Los Angeles      0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### fillna() method\n",
    "demo.fillna(0) # fill all NaN values with 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>40000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>26.25</td>\n",
       "      <td></td>\n",
       "      <td>50000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>30.00</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>41666.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>22.00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>35000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eva</td>\n",
       "      <td>28.00</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>41666.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name    Age         City        Salary\n",
       "0    Alice  25.00     New York  40000.000000\n",
       "1      Bob  26.25               50000.000000\n",
       "2  Charlie  30.00      Chicago  41666.666667\n",
       "3    David  22.00      Unknown  35000.000000\n",
       "4      Eva  28.00  Los Angeles  41666.666667"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fill NaN values with the mean of the column\\\n",
    "demo['Age'].fillna(demo['Age'].mean(), inplace=True)\n",
    "demo['Salary'].fillna(demo['Salary'].mean(), inplace=True)\n",
    "\n",
    "# fill string value with \"Unknown\"\n",
    "demo['City'].fillna('Unknown', inplace=True)\n",
    "demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Here, we can see all the missing values are filled butt in index 1 Bob City is neither filled with Unknown or showing NaN value. Because the city of Bob is using '' instead of NaN. So we have to replace '' with NaN and then we can fill it with Unknown."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>40000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>26.25</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>50000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>30.00</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>41666.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>22.00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>35000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eva</td>\n",
       "      <td>28.00</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>41666.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name    Age         City        Salary\n",
       "0    Alice  25.00     New York  40000.000000\n",
       "1      Bob  26.25      Unknown  50000.000000\n",
       "2  Charlie  30.00      Chicago  41666.666667\n",
       "3    David  22.00      Unknown  35000.000000\n",
       "4      Eva  28.00  Los Angeles  41666.666667"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace empty string with None value\n",
    "demo['City'].replace('', None, inplace=True)\n",
    "# convert None string to \"Unknown\"\n",
    "demo['City'].fillna('Unknown', inplace=True)\n",
    "\n",
    "demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We will import Section_A data which we created in previous notebook to learn more about pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"Section_A.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Science</th>\n",
       "      <th>English</th>\n",
       "      <th>History</th>\n",
       "      <th>Maths</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>Jocelin</td>\n",
       "      <td>Male</td>\n",
       "      <td>14</td>\n",
       "      <td>43</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>Corrine</td>\n",
       "      <td>Male</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>49</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>Merrick</td>\n",
       "      <td>Male</td>\n",
       "      <td>15</td>\n",
       "      <td>65</td>\n",
       "      <td>89</td>\n",
       "      <td>96</td>\n",
       "      <td>34</td>\n",
       "      <td>284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>Gilberta</td>\n",
       "      <td>Male</td>\n",
       "      <td>15</td>\n",
       "      <td>53</td>\n",
       "      <td>30</td>\n",
       "      <td>90</td>\n",
       "      <td>64</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>Peggy</td>\n",
       "      <td>Male</td>\n",
       "      <td>13</td>\n",
       "      <td>91</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>45</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21</td>\n",
       "      <td>Amery</td>\n",
       "      <td>Male</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>96</td>\n",
       "      <td>67</td>\n",
       "      <td>17</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>25</td>\n",
       "      <td>Jone</td>\n",
       "      <td>Male</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>87</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26</td>\n",
       "      <td>Rae</td>\n",
       "      <td>Female</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>59</td>\n",
       "      <td>18</td>\n",
       "      <td>64</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27</td>\n",
       "      <td>Charmine</td>\n",
       "      <td>Female</td>\n",
       "      <td>14</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>83</td>\n",
       "      <td>40</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>28</td>\n",
       "      <td>Hanan</td>\n",
       "      <td>Male</td>\n",
       "      <td>14</td>\n",
       "      <td>60</td>\n",
       "      <td>36</td>\n",
       "      <td>86</td>\n",
       "      <td>92</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id      Name  Gender  Age  Science  English  History  Maths  Total\n",
       "0   5   Jocelin    Male   14       43        6        3     21     73\n",
       "1   9   Corrine    Male   15       39       16       22     49    126\n",
       "2  13   Merrick    Male   15       65       89       96     34    284\n",
       "3  16  Gilberta    Male   15       53       30       90     64    237\n",
       "4  17     Peggy    Male   13       91       45       44     45    225\n",
       "5  21     Amery    Male   13       76       96       67     17    256\n",
       "6  25      Jone    Male   14       17       87       16     50    170\n",
       "7  26       Rae  Female   15       16       59       18     64    157\n",
       "8  27  Charmine  Female   14       93       93       83     40    309\n",
       "9  28     Hanan    Male   14       60       36       86     92    274"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I think age column is not important for us, so we will drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop age column\n",
    "df.drop('Age', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can make two new dataframe from this data frame. One will have all male and other all female."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# male students\n",
    "S1_male = df.loc[df.Gender==\"Male\"]\n",
    "S1_female = df.loc[df.Gender=='Female']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Since we have separate table for male and female , we don't need to keep Gender columns, because it will be same for all rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop Gender columns for both table\n",
    "S1_male = S1_male.drop(\"Gender\", axis=1)\n",
    "S2_female = S1_female.drop(\"Gender\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can check which student get highest and lowest marks in total. To find highest marks, we will use \"idxmax\" function and to find lowest marks, we will use \"idxmin\" function.\n",
    "\n",
    "Also need to use loc function to get the full row of that student so we can see all information about that student. Without loc function, we will only get the highest or lowest total marks of that student nothng else."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''Without loc method we get only highest mark here. we don't get the row. '''\n",
    "\n",
    "S1_male['Total'].idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id              121\n",
       "Name       Patrizia\n",
       "Science          79\n",
       "English          88\n",
       "History          96\n",
       "Maths            98\n",
       "Total           361\n",
       "Name: 41, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''But if we use loc function and then idxmax() function we get the row.'''\n",
    "# highest row\n",
    "\n",
    "highest_male = S1_male.loc[S1_male['Total'].idxmax()]\n",
    "highest_male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id               5\n",
       "Name       Jocelin\n",
       "Science         43\n",
       "English          6\n",
       "History          3\n",
       "Maths           21\n",
       "Total           73\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''Use idxmin() function to get the lowest row.'''\n",
    "# lowest row\n",
    "\n",
    "lowest_male = S1_male.loc[S1_male['Total'].idxmin()]\n",
    "lowest_male"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can also merge these two dataframes to get the full information about the student who got highest and lowest marks.\n",
    "The method we will use is \"concat\" function.\n",
    "There are some other function also to merge the dataframes. We will learn about them later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>highest</th>\n",
       "      <th>lowest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>121</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>Patrizia</td>\n",
       "      <td>Jocelin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Science</th>\n",
       "      <td>79</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English</th>\n",
       "      <td>88</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>History</th>\n",
       "      <td>96</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maths</th>\n",
       "      <td>98</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>361</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          highest   lowest\n",
       "id            121        5\n",
       "Name     Patrizia  Jocelin\n",
       "Science        79       43\n",
       "English        88        6\n",
       "History        96        3\n",
       "Maths          98       21\n",
       "Total         361       73"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''pd.concat() function is used to merge two columns. axis=1 is used to merge columns. axis=0 is used to merge rows. and also we will use rename() function to rename the columns name. If we don't use rename() function then the columns name will be there index number'''\n",
    "# merge columns\n",
    "merged_df = pd.concat([highest_male, lowest_male], axis=1)\n",
    "merged_df = merged_df.rename(columns={merged_df.columns[0]: 'highest', merged_df.columns[1]: 'lowest'})\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"We can also use keys parameter to rename the columns name. keys parameter is used to merge columns with index. Both rename() function and keys parameter are same. It is up to you which one you want to use.\"\"\"\n",
    "# merge column with index\n",
    "\n",
    "merged_df = pd.concat([highest_male, lowest_male], axis=1, keys=['highest_male', 'lowest_male'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Transpose function is used to change the row into column and column into row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>id</th>\n",
       "      <th>Name</th>\n",
       "      <th>Science</th>\n",
       "      <th>English</th>\n",
       "      <th>History</th>\n",
       "      <th>Maths</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>highest_male</th>\n",
       "      <td>121</td>\n",
       "      <td>Patrizia</td>\n",
       "      <td>79</td>\n",
       "      <td>88</td>\n",
       "      <td>96</td>\n",
       "      <td>98</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lowest_male</th>\n",
       "      <td>5</td>\n",
       "      <td>Jocelin</td>\n",
       "      <td>43</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               id      Name Science English History Maths Total\n",
       "highest_male  121  Patrizia      79      88      96    98   361\n",
       "lowest_male     5   Jocelin      43       6       3    21    73"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show highest and lowest marks as rows\n",
    "merged_df = merged_df.transpose()\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For female students, we will find the highest and lowest marks in each subject. then use concat function to merge them. We will use \"axis\" parameter to merge them in column wise. and apply \"transpose\" function to change the row into column and column into row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# highest lowest mark obtain by female student\n",
    "\n",
    "highest_female = S1_female.loc[S1_female['Total'].idxmax()]\n",
    "lowest_female = S1_female.loc[S1_female['Total'].idxmin()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>id</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Science</th>\n",
       "      <th>English</th>\n",
       "      <th>History</th>\n",
       "      <th>Maths</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Highest_girl</th>\n",
       "      <td>69</td>\n",
       "      <td>Nil</td>\n",
       "      <td>Female</td>\n",
       "      <td>84</td>\n",
       "      <td>97</td>\n",
       "      <td>70</td>\n",
       "      <td>96</td>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lowest_girl</th>\n",
       "      <td>154</td>\n",
       "      <td>Milo</td>\n",
       "      <td>Female</td>\n",
       "      <td>47</td>\n",
       "      <td>16</td>\n",
       "      <td>55</td>\n",
       "      <td>21</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               id  Name  Gender Science English History Maths Total\n",
       "Highest_girl   69   Nil  Female      84      97      70    96   347\n",
       "Lowest_girl   154  Milo  Female      47      16      55    21   139"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergeBy_Concat = pd.concat([highest_female, lowest_female], axis=1, keys= ['Highest_girl', 'Lowest_girl'])\n",
    "mergeBy_Concat = mergeBy_Concat.transpose()\n",
    "mergeBy_Concat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There is another function called join() function. join() function is also used to merge columns. But join() function is used to merge columns with rows. join() function is used to merge columns with index. Both join() function and keys parameter are same.\n",
    "\n",
    "since the highest_female variable is a series type variable, we can't use join() function on it. So we will convert it into dataframe type variable and then we will use join() function on it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge highest and lowest marks obtained by female students using join() function\n",
    "merged_female = highest_female.to_frame().join(lowest_female.to_frame(), lsuffix='_highest', rsuffix='_lowest')\n",
    "merged_female = merged_female.rename(columns={merged_female.columns[0]: 'highest', merged_female.columns[1]: 'lowest'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>highest</th>\n",
       "      <th>lowest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>69</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>Nil</td>\n",
       "      <td>Milo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Science</th>\n",
       "      <td>84</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English</th>\n",
       "      <td>97</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>History</th>\n",
       "      <td>70</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maths</th>\n",
       "      <td>96</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>347</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        highest  lowest\n",
       "id           69     154\n",
       "Name        Nil    Milo\n",
       "Gender   Female  Female\n",
       "Science      84      47\n",
       "English      97      16\n",
       "History      70      55\n",
       "Maths        96      21\n",
       "Total       347     139"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_female"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Another example of join function. where two dataframes are merged."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alice</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Bob</td>\n",
       "      <td>30</td>\n",
       "      <td>Doctor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>22</td>\n",
       "      <td>Engineer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>David</td>\n",
       "      <td>28</td>\n",
       "      <td>Teacher</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID     Name  Age Occupation\n",
       "0   1    Alice   25        NaN\n",
       "1   2      Bob   30     Doctor\n",
       "2   3  Charlie   22   Engineer\n",
       "3   4    David   28    Teacher"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame 1\n",
    "demo1 = {\n",
    "    'ID': [1, 2, 3, 4],\n",
    "    'Name': ['Alice', 'Bob', 'Charlie', 'David'],\n",
    "    'Age': [25, 30, 22, 28]\n",
    "}\n",
    "df1 = pd.DataFrame(demo1)\n",
    "\n",
    "# DataFrame 2\n",
    "demo2 = {\n",
    "    'ID': [3, 4, 2],\n",
    "    'Occupation': ['Engineer', 'Teacher', 'Doctor']\n",
    "}\n",
    "df2 = pd.DataFrame(demo2)\n",
    "\n",
    "mer_join = df1.join(df2.set_index('ID'), on='ID')\n",
    "mer_join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We can visualize the students data using plot function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'here we demosntrate how to plot a bar chart using plot function and we show how to change the color of the graph and the title of the graph. We also show how to change the size of the graph using the figsize parameter.'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot df Maths columns as a bar chart\n",
    "df.plot(kind='bar', x='Name', y='Total', figsize=(10, 5), title='Total Marks of Students', color='green')\n",
    "\n",
    "'''here we demosntrate how to plot a bar chart using plot function and we show how to change the color of the graph and the title of the graph. We also show how to change the size of the graph using the figsize parameter.'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here showing highest and lowest student data as bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Total Marks of Students'}>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kGc8995xhGIYxZcoUo0GDBoavr6/h4eFhVKlSxZg6daqRkZFxw8yGYRiHDh0yunfvbvj6+hru7u5GgwYNjNjY2Bzzrpfx7w4fPmyMHz/eaNSokREQEGAULlzYKFmypNGhQwdjzZo1DnMvX75sDB8+3ChZsqRhs9kcfi5//vmn0a1bN6No0aJG8eLFjSFDhhi//vprjqWuDcMwPv/8c6Nq1aqGm5ubUa1aNWPZsmVGRESEw1LXV7333ntG/fr1DQ8PD8PLy8uoWbOm8fzzzxt//PGHfc61fpeGYRgtWrQwWrRo4TD2/vvvG+XLlzdcXFzs/z7t3LnTePzxx42QkBDDzc3NCAgIMDp27Ghs3779pj8/AChobIZxC3c7AgAAAICT454fAAAAAJZA+QEAAABgCZQfAAAAAJZA+QEAAABgCZQfAAAAAJbglN/zk52drT/++ENeXl43/UZqAAAAAPcuwzB0/vx5lS5d+qbf6eaU5eePP/5QcHCw2TEAAAAAFBAnTpxQ2bJlbzjHKcuPl5eXpCtv0Nvb2+Q0AAAAAMySkpKi4OBge0e4EacsP1cvdfP29qb8AAAAALil22FY8AAAAACAJVB+AAAAAFgC5QcAAACAJTjlPT8AAABAXsnOzlZGRobZMfB/XF1d5eLikiv7ovwAAAAA/ycjI0NHjhxRdna22VHwF76+vipVqtRdf8fnbZefH374Qa+88op27NihU6dO6YsvvlCXLl3s2w3D0IQJE/T+++8rKSlJTZs21TvvvKPw8HD7nLNnz2r48OH66quvVKhQIXXr1k1vvvmmihUrdldvBgAAALhThmHo1KlTcnFxUXBw8E2/MBN5zzAMpaWlKTExUZIUFBR0V/u77fJz4cIF1a5dW/3799ejjz6aY/usWbM0Z84cRUdHKywsTOPGjVPbtm21d+9eubu7S5J69+6tU6dOadWqVcrMzFS/fv00ePBgLV68+K7eDAAAAHCnLl++rLS0NJUuXVpFixY1Ow7+j4eHhyQpMTFRAQEBd3UJnM0wDOOOX2yzOZz5MQxDpUuX1rPPPqvnnntOkpScnKzAwEBFRUWpV69e2rdvn6pVq6Zt27bpvvvukyStXLlS7du31//+9z+VLl06x3EuXbqkS5cu2Z9f/SKj5ORkvucHAAAAuSI9PV1HjhxRaGio/T+4UTBcvHhRR48eVVhYmP2EylUpKSny8fG5pW6Qq+fyjhw5ovj4eLVu3do+5uPjo4YNG2rTpk2SpE2bNsnX19defCSpdevWKlSokLZs2XLN/U6fPl0+Pj72R3BwcG7GBgAAAOzu9r4S5L7c+p3kavmJj4+XJAUGBjqMBwYG2rfFx8crICDAYXvhwoXl5+dnn/N3Y8eOVXJysv1x4sSJ3IwNAAAAwAKcYrU3Nzc3ubm5mR0DAAAAgBPL1fJTqlQpSVJCQoLDSgwJCQmqU6eOfc7V1Rquunz5ss6ePWt/PQAAAFBQhI75Ol+Pd3RGh3w9XlRUlEaOHKmkpKR8Pa4ZcvWyt7CwMJUqVUqrV6+2j6WkpGjLli1q3LixJKlx48ZKSkrSjh077HPWrFmj7OxsNWzYMDfjAAAAAJbw559/6p///KdCQkLk5uamUqVKqW3bttq4ceNNX/vYY4/pwIED+ZDSfLd95ic1NVW///67/fmRI0e0e/du+fn5KSQkRCNHjtSUKVMUHh5uX+q6dOnS9hXhqlatqnbt2mnQoEGaP3++MjMzNWzYMPXq1euaK70BAAAAuLFu3bopIyND0dHRKl++vBISErR69WqdOXPmpq/18PCwzOp2t33mZ/v27apbt67q1q0rSRo1apTq1q2r8ePHS5Kef/55DR8+XIMHD9Y//vEPpaamauXKlQ5L0i1atEhVqlTRgw8+qPbt26tZs2Z67733cuktAQAAANaRlJSkH3/8UTNnzlSrVq1Urlw5NWjQQGPHjlWnTp3sc4YMGaLAwEC5u7urRo0aio2NlXTlsjdfX1+HfX755ZeqV6+e3N3dVb58eU2aNEmXL1+2b7fZbFqwYIG6du2qokWLKjw8XMuXL3fYx2+//aaOHTvK29tbXl5euv/++3Xo0CH79gULFqhq1apyd3dXlSpVNG/evDz6Cf1/t33mp2XLlrrRVwPZbDZNnjxZkydPvu4cPz8/vtAUAAAAyAXFihVTsWLFFBMTo0aNGuVYKCw7O1sPP/ywzp8/r//+97+qUKGC9u7de90vC/3xxx/Vp08fzZkzx15YBg8eLEmaMGGCfd6kSZM0a9YsvfLKK3rrrbfUu3dvHTt2TH5+fjp58qSaN2+uli1bas2aNfL29tbGjRvtBWrRokUaP3683n77bdWtW1e7du3SoEGD5OnpqYiIiDz6STnJam8A7k37qlQ1O8Jdqxq3z+wIAACLK1y4sKKiouy3ldSrV08tWrRQr169VKtWLX3//ffaunWr9u3bp0qVKkmSypcvf939TZo0SWPGjLGXkPLly+vll1/W888/71B++vbtq8cff1ySNG3aNM2ZM0dbt25Vu3btNHfuXPn4+GjJkiVydXWVJPuxpSsl6rXXXtOjjz4q6craAXv37tW7775L+QEAAABwfd26dVOHDh30448/avPmzVqxYoVmzZqlBQsWKDExUWXLlnUoHzeyZ88ebdy4UVOnTrWPZWVlKT09XWlpaSpatKgkqVatWvbtnp6e8vb2tq/qvHv3bt1///324vNXFy5c0KFDhzRgwAANGjTIPn758mX5+Pjc0fu/VZQfAAAA4B7g7u6uNm3aqE2bNho3bpwGDhyoCRMm6Lnnnrut/aSmpmrSpEn2szJ/P8ZVfy82NptN2dnZknTDBRRSU1MlSe+//36O1Z6vdylebqH8AAAAAPegatWqKSYmRrVq1dL//vc/HThw4JbO/tSrV0/79+9XxYoV7/jYtWrVUnR0tDIzM3OUpMDAQJUuXVqHDx9W79697/gYd4LyAwAAADixM2fOqEePHurfv79q1aolLy8vbd++XbNmzVLnzp3VokULNW/eXN26ddPrr7+uihUrKi4uTjabTe3atcuxv/Hjx6tjx44KCQlR9+7dVahQIe3Zs0e//vqrpkyZckuZhg0bprfeeku9evXS2LFj5ePjo82bN6tBgwaqXLmyJk2apBEjRsjHx0ft2rXTpUuXtH37dp07d06jRo3K7R+RHeUHAAAAuIGjMzqYHeGGihUrpoYNG+qNN97QoUOHlJmZqeDgYA0aNEj/+c9/JEmff/65nnvuOT3++OO6cOGCKlasqBkzZlxzf23btlVsbKwmT56smTNnytXVVVWqVNHAgQNvOZO/v7/WrFmj0aNHq0WLFnJxcVGdOnXUtGlTSdLAgQNVtGhRvfLKKxo9erQ8PT1Vs2ZNjRw58q5/HjdiM260bnUBlZKSIh8fHyUnJ8vb29vsOADuEKu9AQAKkvT0dB05ckRhYWEO97bAfDf63dxON+DMD+DEakbXNDvCXfnU7AAAAMBSCpkdAAAAAADyA+UHAAAAgCVQfgAAAABYAuUHAAAAgCVQfgAAAABYAuUHAAAAgCVQfgAAAABYAuUHAAAAgCXwJacAAADAjUz0yefjJefv8W5BVFSURo4cqaSkJEnSxIkTFRMTo927d9/0tbczN69x5gcAAABwYn379pXNZsvxaNeuXZ4d87nnntPq1avzbP95hTM/AAAAgJNr166dIiMjHcbc3Nzy7HjFihVTsWLF8mz/eYUzPwAAAICTc3NzU6lSpRwexYsXlyTZbDYtWLBAXbt2VdGiRRUeHq7ly5c7vH758uUKDw+Xu7u7WrVqpejoaNlsNvtlbn83ceJE1alTx/583bp1atCggTw9PeXr66umTZvq2LFjDq/56KOPFBoaKh8fH/Xq1Uvnz5/P1Z/BraD8AAAAAPe4SZMmqWfPnvr555/Vvn179e7dW2fPnpUkHTlyRN27d1eXLl20Z88eDRkyRC+++OIt7/vy5cvq0qWLWrRooZ9//lmbNm3S4MGDZbPZ7HMOHTqkmJgYxcbGKjY2VuvXr9eMGTNy/X3eDOUHAAAAcHKxsbH2S9GuPqZNm2bf3rdvXz3++OOqWLGipk2bptTUVG3dulWS9O6776py5cp65ZVXVLlyZfXq1Ut9+/a95WOnpKQoOTlZHTt2VIUKFVS1alVFREQoJCTEPic7O1tRUVGqUaOG7r//fj311FOm3DPEPT8AAACAk2vVqpXeeecdhzE/Pz/7P9eqVcv+z56envL29lZiYqIkaf/+/frHP/7h8NoGDRrc8rH9/PzUt29ftW3bVm3atFHr1q3Vs2dPBQUF2eeEhobKy8vL/jwoKMh+/PzEmR8AAADAyXl6eqpixYoOj7+WH1dXV4f5NptN2dnZuXb8yMhIbdq0SU2aNNEnn3yiSpUqafPmzfl2/FtF+QEAAAAsrHLlytq+fbvD2LZt2257P3Xr1tXYsWP1008/qUaNGlq8eHFuRcw1lB8AAADAyV26dEnx8fEOj9OnT9/Sa4cMGaK4uDi98MILOnDggD799FNFRUVJksOiBddz5MgRjR07Vps2bdKxY8f03Xff6eDBg6paterdvKU8wT0/AAAAwI1MTDY7wU2tXLnS4R4b6coZnbi4uJu+NiwsTEuXLtWzzz6rN998U40bN9aLL76of/7zn7f0XUFFixZVXFycoqOjdebMGQUFBWno0KEaMmTIHb+fvGIzDMMwO8TtSklJkY+Pj5KTk+Xt7W12HMA0NaNrmh3hrnw6/bLZEe5a1bh9ZkcAAOSS9PR0HTlyRGFhYXJ3dzc7jqmmTp2q+fPn68SJE2ZHkXTj383tdAPO/AAAAAAWN2/ePP3jH/+Qv7+/Nm7cqFdeeUXDhg0zO1auo/wAAAAAFnfw4EFNmTJFZ8+eVUhIiJ599lmNHTvW7Fi5jvIDAAAAWNwbb7yhN954w+wYeY7V3gAAAABYAuUHAAAAgCVQfgAAAABYAuUHAAAAgCVQfgAAAABYAuUHAAAAgCWw1DWsa6KP2QnuXliI2QkAALjn1Yyuma/H+yXil1zfZ1RUlEaOHKmkpKRc37cz4cwPAAAA4MT69u2rLl265Bhft26dbDabkpKS9Nhjj+nAgQO3tL+oqCj5+vrmbsgCgjM/AAAAwD3Ow8NDHh4e+XrMrKws2Ww2FSpUcM63FJwkAAAAAPLE38/m7NmzR61atZKXl5e8vb1Vv359bd++XevWrVO/fv2UnJwsm80mm82miRMnSpLOnTunPn36qHjx4ipatKgefvhhHTx4MMcxli9frmrVqsnNzU0bNmyQq6ur4uPjHfKMHDlS999/f368dQeUHwAAAMBievfurbJly2rbtm3asWOHxowZI1dXVzVp0kSzZ8+Wt7e3Tp06pVOnTum5556TdOXyuu3bt2v58uXatGmTDMNQ+/btlZmZad9vWlqaZs6cqQULFui3337Tfffdp/Lly+ujjz6yz8nMzNSiRYvUv3//fH/fXPYGAAAAOLnY2FgVK1bMYSwrK+u6848fP67Ro0erSpUqkqTw8HD7Nh8fH9lsNpUqVco+dvDgQS1fvlwbN25UkyZNJEmLFi1ScHCwYmJi1KNHD0lXis28efNUu3Zt+2sHDBigyMhIjR49WpL01VdfKT09XT179rzLd337OPMDAAAAOLlWrVpp9+7dDo8FCxZcd/6oUaM0cOBAtW7dWjNmzNChQ4duuP99+/apcOHCatiwoX3M399flStX1r59++xjRYoUUa1atRxe27dvX/3+++/avHmzpCuXx/Xs2VOenp538lbvCuUHAAAAcHKenp6qWLGiw6NMmTLXnT9x4kT99ttv6tChg9asWaNq1arpiy++uOscHh4estlsDmMBAQF65JFHFBkZqYSEBK1YscKUS94kyg8AAABgSZUqVdIzzzyj7777To8++qgiIyMlXTl78/dL5qpWrarLly9ry5Yt9rEzZ85o//79qlat2k2PNXDgQH3yySd67733VKFCBTVt2jR338wtovwAAAAAFnLx4kUNGzZM69at07Fjx7Rx40Zt27ZNVatWlSSFhoYqNTVVq1ev1unTp5WWlqbw8HB17txZgwYN0oYNG7Rnzx49+eSTKlOmjDp37nzTY7Zt21be3t6aMmWK+vXrl9dv8bpY8AAAAAC4gV8ifjE7Qq5ycXHRmTNn1KdPHyUkJKhEiRJ69NFHNWnSJElSkyZN9PTTT+uxxx7TmTNnNGHCBE2cOFGRkZH697//rY4dOyojI0PNmzfXN998I1dX15ses1ChQurbt6+mTZumPn365PVbvC6bYRiGaUe/QykpKfLx8VFycrK8vb3NjgNnNdHH7AR3rWZYiNkR7sqn0y+bHeGuVY3bd/NJAACnkJ6eriNHjigsLEzu7u5mx7nnDBgwQH/++aeWL19+26+90e/mdroBZ34AAAAA5Jnk5GT98ssvWrx48R0Vn9xE+QEAAACQZzp37qytW7fq6aefVps2bUzNQvkBAAAAkGfWrVtndgQ7VnsDAAAAYAmUHwAAAACWQPkBAAAAYAmUHwAAAACWQPkBAAAAYAmUHwAAAACWwFLXAAAAwA3sq1I1X49XNW5fvh7vVvTt21dJSUmKiYkxO8pd4cwPAAAA4MT69u0rm82mp59+Ose2oUOHymazqW/fvre0r6NHj8pms2n37t25G7KAoPwAAAAATi44OFhLlizRxYsX7WPp6elavHixQkJCTExWsFB+AAAAACdXr149BQcHa9myZfaxZcuWKSQkRHXr1rWPrVy5Us2aNZOvr6/8/f3VsWNHHTp0yL49LCxMklS3bl3ZbDa1bNnS4TivvvqqgoKC5O/vr6FDhyozM9O+bd68eQoPD5e7u7sCAwPVvXv3PHq3d47yAwAAANwD+vfvr8jISPvzDz74QP369XOYc+HCBY0aNUrbt2/X6tWrVahQIXXt2lXZ2dmSpK1bt0qSvv/+e506dcqhTK1du1aHDh3S2rVrFR0draioKEVFRUmStm/frhEjRmjy5Mnav3+/Vq5cqebNm+fxO759LHgAAAAA3AOefPJJjR07VseOHZMkbdy4UUuWLNG6devsc7p16+bwmg8++EAlS5bU3r17VaNGDZUsWVKS5O/vr1KlSjnMLV68uN5++225uLioSpUq6tChg1avXq1Bgwbp+PHj8vT0VMeOHeXl5aVy5co5nHEqKHL9zE9WVpbGjRunsLAweXh4qEKFCnr55ZdlGIZ9jmEYGj9+vIKCguTh4aHWrVvr4MGDuR0FAAAAsIySJUuqQ4cOioqKUmRkpDp06KASJUo4zDl48KAef/xxlS9fXt7e3goNDZUkHT9+/Kb7r169ulxcXOzPg4KClJiYKElq06aNypUrp/Lly+upp57SokWLlJaWlntvLpfkevmZOXOm3nnnHb399tvat2+fZs6cqVmzZumtt96yz5k1a5bmzJmj+fPna8uWLfL09FTbtm2Vnp6e23EAAAAAy+jfv7+ioqIUHR2t/v3759j+yCOP6OzZs3r//fe1ZcsWbdmyRZKUkZFx0327uro6PLfZbPbL5by8vLRz5059/PHHCgoK0vjx41W7dm0lJSXd/ZvKRblefn766Sd17txZHTp0UGhoqLp3766HHnrIfv2gYRiaPXu2XnrpJXXu3Fm1atXShx9+qD/++OO664ZfunRJKSkpDg8AAAAAjtq1a6eMjAxlZmaqbdu2DtvOnDmj/fv366WXXtKDDz6oqlWr6ty5cw5zihQpIunK1Vy3q3DhwmrdurVmzZqln3/+WUePHtWaNWvu/M3kgVwvP02aNNHq1at14MABSdKePXu0YcMGPfzww5KkI0eOKD4+Xq1bt7a/xsfHRw0bNtSmTZuuuc/p06fLx8fH/ggODs7t2AAAAIDTc3Fx0b59+7R3716HS9SkK/fs+Pv767333tPvv/+uNWvWaNSoUQ5zAgIC5OHhoZUrVyohIUHJycm3dNzY2FjNmTNHu3fv1rFjx/Thhx8qOztblStXzrX3lhtyfcGDMWPGKCUlRVWqVJGLi4uysrI0depU9e7dW5IUHx8vSQoMDHR4XWBgoH3b340dO9bhF5OSkkIBAgAAQL6oGrfP7Ai3xdvb+5rjhQoV0pIlSzRixAjVqFFDlStX1pw5cxyWsy5cuLDmzJmjyZMna/z48br//vsdFky4Hl9fXy1btkwTJ05Uenq6wsPD9fHHH6t69eq59K5yR66Xn08//VSLFi3S4sWLVb16de3evVsjR45U6dKlFRERcUf7dHNzk5ubWy4nBQAAAJzf1eWmr+evt5a0bt1ae/fuddj+14XJJGngwIEaOHDgTY8xe/Zs+z83a9bslkqS2XK9/IwePVpjxoxRr169JEk1a9bUsWPHNH36dEVERNiXzEtISFBQUJD9dQkJCapTp05uxwEAAAAASXlQftLS0lSokOOtRC4uLvaVIMLCwlSqVCmtXr3aXnZSUlK0ZcsW/fOf/8ztOMhDoWO+NjvCXTnqbnYCAAAA5KdcLz+PPPKIpk6dqpCQEFWvXl27du3S66+/bl9qz2azaeTIkZoyZYrCw8MVFhamcePGqXTp0urSpUtuxwEAAAAASXlQft566y2NGzdO//rXv5SYmKjSpUtryJAhGj9+vH3O888/rwsXLmjw4MFKSkpSs2bNtHLlSrm786d4AAAAAHkj18uPl5eXZs+e7XAD1N/ZbDZNnjxZkydPzu3DAwAAAHfl7wsAwHxXb6G5W7lefgAAAABn5OrqKpvNpj///FMlS5aUzWYzO5LlGYahjIwM/fnnnypUqJD9S1jvFOUHAAAA0JVFusqWLav//e9/Onr0qNlx8BdFixZVSEhIjoXVbhflBwAAAPg/xYoVU3h4uDIzM82Ogv/j4uKiwoUL58qZOMoPAAAA8BcuLi5ycXExOwbywN2dNwIAAAAAJ0H5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlkD5AQAAAGAJlB8AAAAAlpAn5efkyZN68skn5e/vLw8PD9WsWVPbt2+3bzcMQ+PHj1dQUJA8PDzUunVrHTx4MC+iAAAAAICkPCg/586dU9OmTeXq6qoVK1Zo7969eu2111S8eHH7nFmzZmnOnDmaP3++tmzZIk9PT7Vt21bp6em5HQcAAAAAJEmFc3uHM2fOVHBwsCIjI+1jYWFh9n82DEOzZ8/WSy+9pM6dO0uSPvzwQwUGBiomJka9evXK7UgAAAAAkPtnfpYvX6777rtPPXr0UEBAgOrWrav333/fvv3IkSOKj49X69at7WM+Pj5q2LChNm3adM19Xrp0SSkpKQ4PAAAAALgduV5+Dh8+rHfeeUfh4eH69ttv9c9//lMjRoxQdHS0JCk+Pl6SFBgY6PC6wMBA+7a/mz59unx8fOyP4ODg3I4NAAAA4B6X6+UnOztb9erV07Rp01S3bl0NHjxYgwYN0vz58+94n2PHjlVycrL9ceLEiVxMDAAAAMAKcr38BAUFqVq1ag5jVatW1fHjxyVJpUqVkiQlJCQ4zElISLBv+zs3Nzd5e3s7PAAAAADgduR6+WnatKn279/vMHbgwAGVK1dO0pXFD0qVKqXVq1fbt6ekpGjLli1q3LhxbscBAAAAAEl5sNrbM888oyZNmmjatGnq2bOntm7dqvfee0/vvfeeJMlms2nkyJGaMmWKwsPDFRYWpnHjxql06dLq0qVLbscBAAAAAEl5UH7+8Y9/6IsvvtDYsWM1efJkhYWFafbs2erdu7d9zvPPP68LFy5o8ODBSkpKUrNmzbRy5Uq5u7vndhwAAAAAkCTZDMMwzA5xu1JSUuTj46Pk5GTu/zFR6JivzY5wV466P2F2hLtWMyzE7Ah35dPpl82OcNeqxu0zOwIAAJZ2O90g1+/5AQAAAICCiPIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsgfIDAAAAwBIoPwAAAAAsobDZAQAAgPMKHfO12RHu2tEZHcyOACCfcOYHAAAAgCVQfgAAAABYAuUHAAAAgCVQfgAAAABYAuUHAAAAgCVQfgAAAABYQp6XnxkzZshms2nkyJH2sfT0dA0dOlT+/v4qVqyYunXrpoSEhLyOAgAAAMDC8rT8bNu2Te+++65q1arlMP7MM8/oq6++0meffab169frjz/+0KOPPpqXUQAAAABYXJ6Vn9TUVPXu3Vvvv/++ihcvbh9PTk7WwoUL9frrr+uBBx5Q/fr1FRkZqZ9++kmbN2/OqzgAAAAALC7Pys/QoUPVoUMHtW7d2mF8x44dyszMdBivUqWKQkJCtGnTpmvu69KlS0pJSXF4AAAAAMDtKJwXO12yZIl27typbdu25dgWHx+vIkWKyNfX12E8MDBQ8fHx19zf9OnTNWnSpLyICgAAAMAicv3Mz4kTJ/Tvf/9bixYtkru7e67sc+zYsUpOTrY/Tpw4kSv7BQAAAGAduV5+duzYocTERNWrV0+FCxdW4cKFtX79es2ZM0eFCxdWYGCgMjIylJSU5PC6hIQElSpV6pr7dHNzk7e3t8MDAAAAAG5Hrl/29uCDD+qXX35xGOvXr5+qVKmiF154QcHBwXJ1ddXq1avVrVs3SdL+/ft1/PhxNW7cOLfjAAAAAICkPCg/Xl5eqlGjhsOYp6en/P397eMDBgzQqFGj5OfnJ29vbw0fPlyNGzdWo0aNcjsOAAAAAEjKowUPbuaNN95QoUKF1K1bN126dElt27bVvHnzzIgCAAAAwCLypfysW7fO4bm7u7vmzp2ruXPn5sfhAQAAACDvvucHAAAAAAoSyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAASyic2zucPn26li1bpri4OHl4eKhJkyaaOXOmKleubJ+Tnp6uZ599VkuWLNGlS5fUtm1bzZs3T4GBgbkdBwAAAAXcvipVzY5wV6rG7TM7Am5Rrp/5Wb9+vYYOHarNmzdr1apVyszM1EMPPaQLFy7Y5zzzzDP66quv9Nlnn2n9+vX6448/9Oijj+Z2FAAAAACwy/UzPytXrnR4HhUVpYCAAO3YsUPNmzdXcnKyFi5cqMWLF+uBBx6QJEVGRqpq1aravHmzGjVqlNuRAAAAACDv7/lJTk6WJPn5+UmSduzYoczMTLVu3do+p0qVKgoJCdGmTZuuuY9Lly4pJSXF4QEAAAAAtyNPy092drZGjhyppk2bqkaNGpKk+Ph4FSlSRL6+vg5zAwMDFR8ff839TJ8+XT4+PvZHcHBwXsYGAAAAcA/K0/IzdOhQ/frrr1qyZMld7Wfs2LFKTk62P06cOJFLCQEAAABYRa7f83PVsGHDFBsbqx9++EFly5a1j5cqVUoZGRlKSkpyOPuTkJCgUqVKXXNfbm5ucnNzy6uoAAAAACwg18/8GIahYcOG6YsvvtCaNWsUFhbmsL1+/fpydXXV6tWr7WP79+/X8ePH1bhx49yOAwAAAACS8uDMz9ChQ7V48WJ9+eWX8vLyst/H4+PjIw8PD/n4+GjAgAEaNWqU/Pz85O3treHDh6tx48as9AYAAAAgz+R6+XnnnXckSS1btnQYj4yMVN++fSVJb7zxhgoVKqRu3bo5fMkpAAAAAOSVXC8/hmHcdI67u7vmzp2ruXPn5vbhAQAAAOCa8vx7fgAAAACgIKD8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAAS6D8AAAAALAEyg8AAAAASyhsdgAAAABTTfQxO8HdmZhsdgLAaXDmBwAAAIAlUH4AAAAAWALlBwAAAIAlUH4AAAAAWALlBwAAAIAlUH4AAAAAWALlBwAAAIAlUH4AAAAAWALlBwAAAIAlUH4AAAAAWEJhswMAAADgztWMrml2hLv2qdkBYBmc+QEAAABgCZQfAAAAAJZA+QEAAABgCZQfAAAAAJZA+QEAAABgCZQfAAAAAJZA+QEAAABgCaaWn7lz5yo0NFTu7u5q2LChtm7damYcAAAAAPcw08rPJ598olGjRmnChAnauXOnateurbZt2yoxMdGsSAAAAADuYYXNOvDrr7+uQYMGqV+/fpKk+fPn6+uvv9YHH3ygMWPGOMy9dOmSLl26ZH+enJwsSUpJScm/wMgh+1Ka2RHuSorNMDvCXcu6mGV2hLuSmuXc+SX+fwhw9s8Cyfk/D5z9s0By/s8DPgvMdfXnbxg3/9+yzbiVWbksIyNDRYsW1dKlS9WlSxf7eEREhJKSkvTll186zJ84caImTZqUzykBAAAAOIsTJ06obNmyN5xjypmf06dPKysrS4GBgQ7jgYGBiouLyzF/7NixGjVqlP15dna2zp49K39/f9lstjzPCxREKSkpCg4O1okTJ+Tt7W12HACACfgsAK6c8Tl//rxKly5907mmXfZ2O9zc3OTm5uYw5uvra04YoIDx9vbmAw8ALI7PAlidj4/PLc0zZcGDEiVKyMXFRQkJCQ7jCQkJKlWqlBmRAAAAANzjTCk/RYoUUf369bV69Wr7WHZ2tlavXq3GjRubEQkAAADAPc60y95GjRqliIgI3XfffWrQoIFmz56tCxcu2Fd/A3Bjbm5umjBhQo5LQgEA1sFnAXB7TFnt7aq3335br7zyiuLj41WnTh3NmTNHDRs2NCsOAAAAgHuYqeUHAAAAAPKLKff8AAAAAEB+o/wAAAAAsATKDwAAAABLoPwAAAAAsATKDwAAAABLoPwAAAAAsATKD+CEMjIytH//fl2+fNnsKAAAkxw6dEgvvfSSHn/8cSUmJkqSVqxYod9++83kZEDBRfkBnEhaWpoGDBigokWLqnr16jp+/Lgkafjw4ZoxY4bJ6QAA+WX9+vWqWbOmtmzZomXLlik1NVWStGfPHk2YMMHkdEDBRfkBnMjYsWO1Z88erVu3Tu7u7vbx1q1b65NPPjExGQAgP40ZM0ZTpkzRqlWrVKRIEfv4Aw88oM2bN5uYDCjYCpsdAMCti4mJ0SeffKJGjRrJZrPZx6tXr65Dhw6ZmAwAkJ9++eUXLV68OMd4QECATp8+bUIiwDlw5gdwIn/++acCAgJyjF+4cMGhDAEA7m2+vr46depUjvFdu3apTJkyJiQCnAPlB3Ai9913n77++mv786uFZ8GCBWrcuLFZsQAA+axXr1564YUXFB8fL5vNpuzsbG3cuFHPPfec+vTpY3Y8oMDisjfAiUybNk0PP/yw9u7dq8uXL+vNN9/U3r179dNPP2n9+vVmxwMA5JNp06Zp6NChCg4OVlZWlqpVq6asrCw98cQTeumll8yOBxRYNsMwDLNDALh1hw4d0owZM7Rnzx6lpqaqXr16euGFF1SzZk2zowEA8tnx48f166+/KjU1VXXr1lV4eLjZkYACjfIDAAAAwBK47A0o4FJSUm55rre3dx4mAQCYadSoUbc89/XXX8/DJIDzovwABZyvr+9NV3IzDEM2m01ZWVn5lAoAkN927dp1S/NY/RO4Pi57Awq421nIoEWLFnmYBAAAwLlRfgAAAABYApe9AU4oLS1Nx48fV0ZGhsN4rVq1TEoEAMhv27dv16effnrNz4Nly5aZlAoo2Cg/gBP5888/1a9fP61YseKa27nnBwCsYcmSJerTp4/atm2r7777Tg899JAOHDighIQEde3a1ex4QIFVyOwAAG7dyJEjlZSUpC1btsjDw0MrV65UdHS0wsPDtXz5crPjAQDyybRp0/TGG2/oq6++UpEiRfTmm28qLi5OPXv2VEhIiNnxgAKLe34AJxIUFKQvv/xSDRo0kLe3t7Zv365KlSpp+fLlmjVrljZs2GB2RABAPvD09NRvv/2m0NBQ+fv7a926dapZs6b27dunBx54QKdOnTI7IlAgceYHcCIXLlxQQECAJKl48eL6888/JUk1a9bUzp07zYwGAMhHxYsX1/nz5yVJZcqU0a+//ipJSkpKUlpampnRgAKN8gM4kcqVK2v//v2SpNq1a+vdd9/VyZMnNX/+fAUFBZmcDgCQX5o3b65Vq1ZJknr06KF///vfGjRokB5//HE9+OCDJqcDCi4uewOcyH//+19dvnxZffv21Y4dO9SuXTudPXtWRYoUUVRUlB577DGzIwIA8sHZs2eVnp6u0qVLKzs7W7NmzdJPP/2k8PBwvfTSSypevLjZEYECifIDOLG0tDTFxcUpJCREJUqUMDsOAABAgUb5AQAAcFKJiYlKTExUdna2wzjf+wZcG9/zAzgRwzC0dOlSrV279pofdnypHQBYw44dOxQREaF9+/bp73/HttlsfO8bcB2UH8CJjBw5Uu+++65atWqlwMBA2Ww2syMBAEzQv39/VapUSQsXLuTzALgNXPYGOBE/Pz/997//Vfv27c2OAgAwkZeXl3bt2qWKFSuaHQVwKix1DTgRHx8flS9f3uwYAACTPfjgg9qzZ4/ZMQCnw5kfwIlER0dr5cqV+uCDD+Th4WF2HACASU6fPq2IiAg1aNBANWrUkKurq8P2Tp06mZQMKNgoP4ATuXjxorp27aqNGzcqNDQ0x4fdzp07TUoGAMhPX331lZ566imlpKTk2MaCB8D1seAB4EQiIiK0Y8cOPfnkk9zgCgAWNnz4cD355JMaN26cAgMDzY4DOA3O/ABOxNPTU99++62aNWtmdhQAgIm8vLy0e/duVahQwewogFNhwQPAiQQHB8vb29vsGAAAkz366KNau3at2TEAp8Nlb4ATee211/T8889r/vz5Cg0NNTsOAMAklSpV0tixY7VhwwbVrFkzxz2gI0aMMCkZULBx2RvgRIoXL660tDRdvnxZRYsWzfFhd/bsWZOSAQDyU1hY2HW32Ww2HT58OB/TAM6DMz+AE5k9e7bZEQAABcCRI0fMjgA4Jc78APegGTNm6Omnn5avr6/ZUQAAJvL29tbu3bv5gmzg/7DgAXAPmjZtGpfAAQDE37gBR5Qf4B7Ehx0AAEBOlB8AAAAAlkD5AQAAAGAJlB8AAIB7lM1mMzsCUKBQfgAAAO5R3AMKOKL8APeg+++/Xx4eHmbHAADkkcmTJystLS3H+MWLFzV58mT78xUrVqhMmTL5GQ0o0PieH8CJuLi46NSpUwoICHAYP3PmjAICApSVlWVSMgBAfuLzALgznPkBnMj1/lZx6dIlFSlSJJ/TAADMYhjGNe/n2bNnj/z8/ExIBDiHwmYHAHBzc+bMkXTlxtUFCxaoWLFi9m1ZWVn64YcfVKVKFbPiAQDySfHixWWz2WSz2VSpUiWHApSVlaXU1FQ9/fTTJiYECjYuewOcQFhYmCTp2LFjKlu2rFxcXOzbihQpotDQUE2ePFkNGzY0KyIAIB9ER0fLMAz1799fs2fPlo+Pj33b1c+Dxo0bm5gQKNgoP4ATadWqlZYtW6bixYubHQUAYKL169eradOmKlyYi3iA28E9P4ATWbt2rUPxycrK0u7du3Xu3DkTUwEA8puXl5f27dtnf/7ll1+qS5cu+s9//qOMjAwTkwEFG+UHcCIjR47UwoULJV0pPs2bN1e9evUUHBysdevWmRsOAJBvhgwZogMHDkiSDh8+rMcee0xFixbVZ599pueff97kdEDBRfkBnMhnn32m2rVrS5K++uorHT16VHFxcXrmmWf04osvmpwOAJBfDhw4oDp16ki68tnQokULLV68WFFRUfr888/NDQcUYJQfwImcOXNGpUqVkiR988036tGjhypVqqT+/fvrl19+MTkdACC/GIah7OxsSdL333+v9u3bS5KCg4N1+vRpM6MBBRrlB3AigYGB2rt3r7KysrRy5Uq1adNGkpSWluawAhwA4N523333acqUKfroo4+0fv16dejQQZJ05MgRBQYGmpwOKLhYIgRwIv369VPPnj0VFBQkm82m1q1bS5K2bNnC9/wAgIXMnj1bvXv3VkxMjF588UVVrFhRkrR06VI1adLE5HRAwcVS14CTWbp0qU6cOKEePXqobNmykq5874Ovr686d+5scjoAgJnS09Pl4uIiV1dXs6MABRLlB3BS6enpcnd3NzsGAMAkSUlJWrp0qQ4dOqTRo0fLz89PO3fuVGBgoMqUKWN2PKBA4p4fwIlkZWXp5ZdfVpkyZVSsWDEdPnxYkjRu3Dj7EtgAgHvfzz//rPDwcM2cOVOvvvqqkpKSJEnLli3T2LFjzQ0HFGCUH8CJTJ06VVFRUZo1a5aKFCliH69Ro4YWLFhgYjIAQH4aNWqU+vXrp4MHDzpcBdC+fXv98MMPJiYDCjbKD+BEPvzwQ7333nvq3bu3w+putWvXVlxcnInJAAD5adu2bRoyZEiO8TJlyig+Pt6ERIBzoPwATuTkyZP2FX3+Kjs7W5mZmSYkAgCYwc3NTSkpKTnGDxw4oJIlS5qQCHAOlB/AiVSrVk0//vhjjvGlS5eqbt26JiQCAJihU6dOmjx5sv0PXzabTcePH9cLL7ygbt26mZwOKLj4nh/AiYwfP14RERE6efKksrOztWzZMu3fv18ffvihYmNjzY4HAMgnr732mrp3766AgABdvHhRLVq0UHx8vBo3bqypU6eaHQ8osFjqGnAyP/74oyZPnqw9e/YoNTVV9erV0/jx4/XQQw+ZHQ0AkM82bNign3/+2f55cPXLrwFcG+UHAADAyfBdb8CdofwATigjI0OJiYnKzs52GA8JCTEpEQAgP7m7u6tBgwZq0aKFWrVqpcaNG8vDw8PsWECBR/kBnMjBgwfVv39//fTTTw7jhmHIZrMpKyvLpGQAgPy0YcMG/fDDD1q3bp1++uknXb58Wffdd59atGihli1bqk2bNmZHBAokyg/gRJo2barChQtrzJgxCgoKks1mc9heu3Ztk5IBAMxy+fJlbdu2Te+++64WLVqk7Oxs/hgGXAervQFOZPfu3dqxY4eqVKlidhQAgMkOHDigdevW2R+XLl1Sx44d1bJlS7OjAQUW5QdwItWqVdPp06fNjgEAMFmZMmV08eJFtWzZUi1bttQLL7ygWrVq5bgiAIAjvuQUKOBSUlLsj5kzZ+r555/XunXrdObMGYdt1/qmbwDAvalkyZJKS0tTfHy84uPjlZCQoIsXL5odCyjwuOcHKOAKFSrk8Je8q4sb/BULHgCA9SQlJemHH37Q+vXrtX79eu3du1d16tRRq1at+KJT4DooP0ABt379+lue26JFizxMAgAoiM6cOaN169bpyy+/1Mcff8yCB8ANUH4AAACczLJly+wLHezdu1d+fn5q1qyZWrZsqRYtWrD6J3AdlB/Aifz888/XHLfZbHJ3d1dISIjc3NzyORUAIL8FBASoefPm9rJTs2ZNsyMBToHyAziRv9//83eurq567LHH9O6778rd3T0fkwEAABR8lB/AiXz55Zd64YUXNHr0aDVo0ECStHXrVr322muaMGGCLl++rDFjxuixxx7Tq6++anJaAEBeysrKUkxMjPbt2yfpytchdO7cWS4uLiYnAwouyg/gRBo0aKCXX35Zbdu2dRj/9ttvNW7cOG3dulUxMTF69tlndejQIZNSAgDy2u+//6727dvr5MmTqly5siRp//79Cg4O1tdff60KFSqYnBAomCg/gBPx8PDQrl27VKVKFYfxuLg41a1bVxcvXtTRo0dVrVo1paWlmZQSAJDX2rdvL8MwtGjRIvn5+Um6surbk08+qUKFCunrr782OSFQMPElp4ATqVKlimbMmKGMjAz7WGZmpmbMmGEvRCdPnlRgYKBZEQEA+WD9+vWaNWuWvfhIkr+/v2bMmHFbX5EAWE1hswMAuHVz585Vp06dVLZsWdWqVUuS9MsvvygrK0uxsbGSpMOHD+tf//qXmTEBAHnMzc1N58+fzzGempqqIkWKmJAIcA5c9gY4mfPnz2vRokU6cOCAJKly5cp64okn5OXlZXIyAEB+6dOnj3bu3KmFCxfaF8DZsmWLBg0apPr16ysqKsrcgEABRfkBAABwMklJSYqIiNBXX30lV1dXSVcug+7cubMiIyPl6+trbkCggKL8AAXc8uXL9fDDD8vV1VXLly+/4dxOnTrlUyoAQEHw+++/25e6rlq1qipWrGhyIqBgo/wABVyhQoUUHx+vgIAAFSp0/TVKbDabsrKy8jEZACA/jRo16pbnvv7663mYBHBeLHgAFHDZ2dnX/GcAgLXs2rXrlubZbLY8TgI4L878AE5m9erVWr16tRITEx3KkM1m08KFC01MBgAAULBx5gdwIpMmTdLkyZN13333KSgoiL/uAQAA3AbO/ABOJCgoSLNmzdJTTz1ldhQAAACnc/27pwEUOBkZGWrSpInZMQAAAJwS5QdwIgMHDtTixYvNjgEAAOCUuOwNKOD+urRpdna2oqOjVatWLdWqVcv+xXZXsbQpAADA9VF+gAKuVatWtzTPZrNpzZo1eZwGAADAeVF+AAAAAFgC9/wAAAAAsATKDwAAAABLoPwAAAAAsATKDwAAAABLoPwAAAAAsATKDwAAAABLoPwAAAAAsIT/Bza5yq53K7etAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_df.plot(kind='bar', y=['Science','English','History','Maths'], figsize=(10, 5), title='Total Marks of Students')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}