Project_9_2_PandasAdvance-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "637c4fd6-e70d-4088-8357-b336dec891f2",
   "metadata": {},
   "source": [
    "### Q1. List any five functions of the pandas library with execution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9472f62d-faa4-4c1e-b85b-f11be0f33454",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0bd7a3b3-b8ba-4fee-8970-30ff57a9e0c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.read_csv() --> used to read a csv file\n",
    "df = pd.read_csv(\"https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5fd804e2-abda-474a-8deb-b334688c1099",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df.head() --> displays the top 5 rows of the datafame we can also get n number of enteries based on passing the value in head() function\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cc397284-f469-4303-bc3f-a081541f93a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "#info() --> gives us the summary of the dataframe including datatypes and memory useage\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "59884efb-0169-407c-b7c2-c864377809de",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describe() --> gives us statistical data of the dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d0214fcf-209b-4e23-9c00-abb44183e54a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    891\n",
       "Survived       891\n",
       "Pclass         891\n",
       "Name           891\n",
       "Sex            891\n",
       "Age            891\n",
       "SibSp          891\n",
       "Parch          891\n",
       "Ticket         891\n",
       "Fare           891\n",
       "Cabin          891\n",
       "Embarked       891\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#isnull() --> tells us if null value is present/not in dataset\n",
    "#count() --> counts the number of entries\n",
    "#isnull.count() --> gives the number of values in each coloumn\n",
    "df.isnull().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cd33ca4c-8e4a-4d1c-b58d-897ecbb94a6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "1    38.233441\n",
       "2    29.877630\n",
       "3    25.140620\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#groupby() is used to group data and then perform statistical operation on the grouped data\n",
    "#Average age of each category of pclass is displayed\n",
    "df.groupby('Pclass')['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2f5e220c-1522-4cf6-9df8-358bcbf99e82",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin  \n",
       "0        0         A/5 21171   7.2500   NaN  \n",
       "1        0          PC 17599  71.2833   C85  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN  \n",
       "3        0            113803  53.1000  C123  \n",
       "4        0            373450   8.0500   NaN  \n",
       "..     ...               ...      ...   ...  \n",
       "886      0            211536  13.0000   NaN  \n",
       "887      0            112053  30.0000   B42  \n",
       "888      2        W./C. 6607  23.4500   NaN  \n",
       "889      0            111369  30.0000  C148  \n",
       "890      0            370376   7.7500   NaN  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#used to drop coloumn embarked for droppping rows axis=0 and row index should be passed\n",
    "df.drop('Embarked',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad471bce-9066-4e1f-b81e-61c02bea8edc",
   "metadata": {},
   "source": [
    "### Q2. Given a Pandas DataFrame df with columns 'A', 'B', and 'C', write a Python function to re-index the DataFrame with a new index that starts from 1 and increments by 2 for each row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0be28502-1c20-4e4b-adcc-4bf39b23a6c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'A':[10,11,12,13,14,15],'B':[20,21,22,23,24,25],'C':[35,36,37,38,39,40]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "142ce415-48ed-4263-8f83-caee0f5e30af",
   "metadata": {},
   "outputs": [
    {
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       "      <td>40</td>\n",
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      "text/plain": [
       "    A   B   C\n",
       "0  10  20  35\n",
       "1  11  21  36\n",
       "2  12  22  37\n",
       "3  13  23  38\n",
       "4  14  24  39\n",
       "5  15  25  40"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bc5d42e7-15c7-47b1-af6d-20c8d69472d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "     A   B   C\n",
       "1   10  20  35\n",
       "3   11  21  36\n",
       "5   12  22  37\n",
       "7   13  23  38\n",
       "9   14  24  39\n",
       "11  15  25  40"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def reindex(df):\n",
    "    \n",
    "    new_index = pd.Series(range(1,len(df)*2,2))\n",
    "    df.index = new_index\n",
    "    return df\n",
    "                          \n",
    "    \n",
    "df_newindex = reindex(df)\n",
    "df_newindex"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31237a6c-44ca-48fc-934e-f50a3e677dd4",
   "metadata": {},
   "source": [
    "### Q3. You have a Pandas DataFrame df with a column named 'Values'. Write a Python function that iterates over the DataFrame and calculates the sum of the first three values in the 'Values' column. The function should print the sum to the console. For example, if the 'Values' column of df contains the values [10, 20, 30, 40, 50], your function should calculate and print the sum of the first three values, which is 60."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3185d026-5a51-41fc-a542-76c1e3dafd29",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"Values\":[10,20,30,40,50]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "50be108e-93fc-41aa-a174-9a0545ca2660",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>50</td>\n",
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      ],
      "text/plain": [
       "   Values\n",
       "0      10\n",
       "1      20\n",
       "2      30\n",
       "3      40\n",
       "4      50"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cd61a23a-8235-40f9-850a-2f02cea66a2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this will get the sum of the column values from index location 0-2\n",
    "df['Values'].iloc[0:3].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "412ea5a4-2ff1-4e26-a5bf-e2fa96e7c927",
   "metadata": {},
   "source": [
    "### Q4. Given a Pandas DataFrame df with a column 'Text', write a Python function to create a new column 'Word_Count' that contains the number of words in each row of the 'Text' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "41d97829-d1e9-4fd3-a07f-7c171fc386af",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Rock music approaches at high velocity.</td>\n",
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       "      <td>He had accidentally hacked into his company's server.</td>\n",
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       "      <td>Your girlfriend bought your favorite cookie crisp cereal but forgot to get milk.</td>\n",
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       "      <td>She finally understood that grief was her love with no place for it to go.</td>\n",
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       "      <th>8</th>\n",
       "      <td>He embraced his new life as an eggplant.</td>\n",
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       "      <td>If I don’t like something, I’ll stay away from it.</td>\n",
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       "      <th>10</th>\n",
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      ],
      "text/plain": [
       "                                                                                                                         Text\n",
       "0                                                                                     Rock music approaches at high velocity.\n",
       "1                                                                       He had accidentally hacked into his company's server.\n",
       "2                                                       The estate agent quickly marked out his territory on the dance floor.\n",
       "3                                                             The beauty of the sunset was obscured by the industrial cranes.\n",
       "4                               Swim at your own risk was taken as a challenge for the group of Kansas City college students.\n",
       "5                                            Your girlfriend bought your favorite cookie crisp cereal but forgot to get milk.\n",
       "6                                                  She finally understood that grief was her love with no place for it to go.\n",
       "7   She moved forward only because she trusted that the ending she now was going through must be followed by a new beginning.\n",
       "8                                                                                    He embraced his new life as an eggplant.\n",
       "9                                                                          If I don’t like something, I’ll stay away from it.\n",
       "10                                                                   We will not allow you to bring your pet armadillo along."
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"Text\":[\"Rock music approaches at high velocity.\",\n",
    "\"He had accidentally hacked into his company's server.\",\n",
    "\"The estate agent quickly marked out his territory on the dance floor.\",\n",
    "\"The beauty of the sunset was obscured by the industrial cranes.\",\n",
    "\"Swim at your own risk was taken as a challenge for the group of Kansas City college students.\",\n",
    "\"Your girlfriend bought your favorite cookie crisp cereal but forgot to get milk.\",\n",
    "\"She finally understood that grief was her love with no place for it to go.\",\n",
    "\"She moved forward only because she trusted that the ending she now was going through must be followed by a new beginning.\",\n",
    "\"He embraced his new life as an eggplant.\",\n",
    "\"If I don’t like something, I’ll stay away from it.\",\n",
    "\"We will not allow you to bring your pet armadillo along.\"]})\n",
    "\n",
    "#set the coloumnwidth to 250 charecters\n",
    "pd.set_option(\"display.max_colwidth\" , 250)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "486d7516-1f10-4d9e-8494-288301d51b34",
   "metadata": {},
   "outputs": [],
   "source": [
    "#new coloumn to add the number of words using\n",
    "df[\"Word_Count\"] = df[\"Text\"].apply( lambda x : len(x.split()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6879c082-1dcc-416a-9813-0a7342df0777",
   "metadata": {},
   "outputs": [
    {
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       "      <th>7</th>\n",
       "      <td>She moved forward only because she trusted that the ending she now was going through must be followed by a new beginning.</td>\n",
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       "      <td>If I don’t like something, I’ll stay away from it.</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>We will not allow you to bring your pet armadillo along.</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                         Text  \\\n",
       "0                                                                                     Rock music approaches at high velocity.   \n",
       "1                                                                       He had accidentally hacked into his company's server.   \n",
       "2                                                       The estate agent quickly marked out his territory on the dance floor.   \n",
       "3                                                             The beauty of the sunset was obscured by the industrial cranes.   \n",
       "4                               Swim at your own risk was taken as a challenge for the group of Kansas City college students.   \n",
       "5                                            Your girlfriend bought your favorite cookie crisp cereal but forgot to get milk.   \n",
       "6                                                  She finally understood that grief was her love with no place for it to go.   \n",
       "7   She moved forward only because she trusted that the ending she now was going through must be followed by a new beginning.   \n",
       "8                                                                                    He embraced his new life as an eggplant.   \n",
       "9                                                                          If I don’t like something, I’ll stay away from it.   \n",
       "10                                                                   We will not allow you to bring your pet armadillo along.   \n",
       "\n",
       "    Word_Count  \n",
       "0            6  \n",
       "1            8  \n",
       "2           12  \n",
       "3           11  \n",
       "4           18  \n",
       "5           13  \n",
       "6           15  \n",
       "7           22  \n",
       "8            8  \n",
       "9           10  \n",
       "10          11  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0874fcf0-23ae-48cd-b1b3-a0f4b078d48b",
   "metadata": {},
   "source": [
    "### Q5. How are DataFrame.size() and DataFrame.shape() different?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c0d8d9b-f903-4960-a341-92b6f9ade568",
   "metadata": {},
   "source": [
    "DataFrame.size:\n",
    "DataFrame.size returns the total number of elements in the DataFrame.\n",
    "It represents the total count of cells in the DataFrame, including both rows and columns.\n",
    "The size is calculated as the product of the number of rows and the number of columns.\n",
    "This attribute returns a single integer value.\n",
    "\n",
    "DataFrame.shape:\n",
    "DataFrame.shape returns a tuple representing the dimensions of the DataFrame.\n",
    "The tuple contains two values: the number of rows and the number of columns, respectively.\n",
    "It provides a convenient way to quickly check the size of the DataFrame in terms of rows and columns.\n",
    "This attribute returns a tuple of two integers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "66d2ca40-79d4-4869-be64-b1cfb1f470c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'A':[10,11,12,13,14,15],'B':[20,21,22,23,24,25],'C':[35,36,37,38,39,40]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4a631ea3-5e77-49a8-ba32-7889e60cc41a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "    A   B   C\n",
       "0  10  20  35\n",
       "1  11  21  36\n",
       "2  12  22  37\n",
       "3  13  23  38\n",
       "4  14  24  39\n",
       "5  15  25  40"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "933129c9-3c3e-4089-a22a-e0b42bcd1b8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e03b456b-2a88-446f-8e02-a4bdd8f222d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 3)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c174927-7ba2-418f-a867-724b9fc14653",
   "metadata": {},
   "source": [
    "### Q6. Which function of pandas do we use to read an excel file?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f53a83b7-58a4-46eb-a29f-59196582cf6a",
   "metadata": {},
   "source": [
    "read_excel() is a pandas function used to read data from Excel files and create a DataFrame. It is part of the pandas I/O (Input/Output) tools that allow you to read data from and write data to different file formats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "89d6c4a8-d434-4385-8a38-751562ae1877",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
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       "      <th>0</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>This sheet allows you to format datetimes for use with LUSID</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>If you have any questions please visit our:</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "   Unnamed: 0  Unnamed: 1  Unnamed: 2  \\\n",
       "0         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN   \n",
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       "3         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN   \n",
       "\n",
       "                                                      Unnamed: 3 Unnamed: 4  \\\n",
       "0                                                            NaN        NaN   \n",
       "1                                             Datetimes in LUSID        NaN   \n",
       "2                                                            NaN        NaN   \n",
       "3   This sheet allows you to format datetimes for use with LUSID        NaN   \n",
       "4                    If you have any questions please visit our:        NaN   \n",
       "\n",
       "  Unnamed: 5  Unnamed: 6 Unnamed: 7 Unnamed: 8 Unnamed: 9  \n",
       "0        NaN         NaN        NaN        NaN        NaN  \n",
       "1        NaN         NaN        NaN        NaN        NaN  \n",
       "2        NaN         NaN        NaN        NaN        NaN  \n",
       "3        NaN         NaN        NaN        NaN        NaN  \n",
       "4        NaN         NaN        NaN        NaN        NaN  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_excel(\"LUSID Excel - Setting up your market data.xlsx\").head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2c6c832-2722-40a4-a08a-cfb9859e2193",
   "metadata": {},
   "source": [
    "### Q7. You have a Pandas DataFrame df that contains a column named 'Email' that contains email addresses in the format 'username@domain.com'. Write a Python function that creates a new column 'Username' in df that contains only the username part of each email address.The username is the part of the email address that appears before the '@' symbol. For example, if the email address is 'john.doe@example.com', the 'Username' column should contain 'john.doe'. Your function should extract the username from each email address and store it in the new 'Username' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1d8bd194-dec5-44fc-a230-3d36e739d2ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"Email\" : [\"abc@domain.com\",\"xyx@domain.com\",\"dani@domain.com\",\"john.doe@example.com\",\"rachit@example.com\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "34dce4f8-2051-42e9-a344-254e31573986",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "                  Email\n",
       "0        abc@domain.com\n",
       "1        xyx@domain.com\n",
       "2       dani@domain.com\n",
       "3  john.doe@example.com\n",
       "4    rachit@example.com"
      ]
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     "execution_count": 24,
     "metadata": {},
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "562c115d-ae3a-47aa-b566-f4daad734495",
   "metadata": {},
   "outputs": [],
   "source": [
    "#this will basically split based on the seperator @ and then 0 part will the first half and 1 woll give the second half\n",
    "df[\"Username\"] = df[\"Email\"].apply(lambda x : x.split('@')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7fbbdb2c-74d0-4c28-bcf5-4ed90fb8adac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>john.doe@example.com</td>\n",
       "      <td>john.doe</td>\n",
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       "      <td>rachit</td>\n",
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      "text/plain": [
       "                  Email  Username\n",
       "0        abc@domain.com       abc\n",
       "1        xyx@domain.com       xyx\n",
       "2       dani@domain.com      dani\n",
       "3  john.doe@example.com  john.doe\n",
       "4    rachit@example.com    rachit"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cc136de-5221-45b0-95d3-4c082671a315",
   "metadata": {},
   "source": [
    "### Q8. You have a Pandas DataFrame df with columns 'A', 'B', and 'C'. Write a Python function that selects all rows where the value in column 'A' is greater than 5 and the value in column 'B' is less than 10. The function should return a new DataFrame that contains only the selected rows.\n",
    "<br>For example, if df contains the following values:\n",
    "<br> A B C\n",
    "<br>0 3 5 1\n",
    "<br>1 8 2 7\n",
    "<br>2 6 9 4\n",
    "<br>3 2 3 5\n",
    "<br>4 9 1 2\n",
    "\n",
    "<br>Your function should select the following rows: \n",
    "<br> A B C\n",
    "<br>1 8 2 7\n",
    "<br>2 6 9 4\n",
    "<br>4 9 1 2\n",
    "<br>The function should return a new DataFrame that contains only the selected rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "675d94eb-5976-450d-8295-a8657956cfe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"A\":[3,8,6,2,9],\"B\":[5,2,9,3,1],\"C\":[1,7,4,5,2]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f3b2226f-194a-40d9-a098-28fb22a8f0dd",
   "metadata": {},
   "outputs": [
    {
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       "   A  B  C\n",
       "0  3  5  1\n",
       "1  8  2  7\n",
       "2  6  9  4\n",
       "3  2  3  5\n",
       "4  9  1  2"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8a19f1bb-3cad-419b-bb0a-432d82f36306",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df[(df[\"A\"]>5) & (df[\"B\"]<10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2a04b424-a0b7-49ce-9ae1-3661c6df3d59",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   A  B  C\n",
       "1  8  2  7\n",
       "2  6  9  4\n",
       "4  9  1  2"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df1"
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  {
   "cell_type": "markdown",
   "id": "20a9b695-60c8-45a7-bc56-6f780e3b27eb",
   "metadata": {},
   "source": [
    "### Q9. Given a Pandas DataFrame df with a column 'Values', write a Python function to calculate the mean, median, and standard deviation of the values in the 'Values' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9c30e792-f883-48c6-9870-d89490709ad9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"Values\":[3,8,6,2,9,3,1,3,4,13,3,1,4,3]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dd84ad07-162d-4f4d-b599-6f7c7dfb7f00",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>10</th>\n",
       "      <td>3</td>\n",
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       "      <th>11</th>\n",
       "      <td>1</td>\n",
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       "      <th>12</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Values\n",
       "0        3\n",
       "1        8\n",
       "2        6\n",
       "3        2\n",
       "4        9\n",
       "5        3\n",
       "6        1\n",
       "7        3\n",
       "8        4\n",
       "9       13\n",
       "10       3\n",
       "11       1\n",
       "12       4\n",
       "13       3"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "eacd947e-48f5-4245-bd6d-b6ae7fc38dd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.500000</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.391165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13.000000</td>\n",
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      "text/plain": [
       "          Values\n",
       "count  14.000000\n",
       "mean    4.500000\n",
       "std     3.391165\n",
       "min     1.000000\n",
       "25%     3.000000\n",
       "50%     3.000000\n",
       "75%     5.500000\n",
       "max    13.000000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using inbuilt function\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "43c4e872-b141-47fe-81c0-7e2c2de36b6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Mean : 4.5 \n",
      " Median : 3.0 \n",
      " Standard Deviation : 3.391164991562634 \n"
     ]
    }
   ],
   "source": [
    "#using custom function of each\n",
    "meann = df[\"Values\"].mean()\n",
    "mediann = df[\"Values\"].median()\n",
    "stdd = df[\"Values\"].std()\n",
    "print(f\" Mean : {meann} \")\n",
    "print(f\" Median : {mediann} \")\n",
    "print(f\" Standard Deviation : {stdd} \")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeb2a208-abfc-4678-915f-a54f4bedc6e5",
   "metadata": {},
   "source": [
    "### Q10. Given a Pandas DataFrame df with a column 'Sales' and a column 'Date', write a Python function to create a new column 'MovingAverage' that contains the moving average of the sales for the past 7 days for each row in the DataFrame. The moving average should be calculated using a window of size 7 and should include the current day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7f490327-2fef-4496-906b-2c7e1b46111a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sales = pd.Series([3400,5452,6341,4311,5432,9842,9831,2134,5431,6522,6423,1294,9582,8582,8958,5923,9238,5835,9323,6542,9389,2322,9242])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "aff80bf8-b678-41d2-8cfc-c30f4bee7073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     3400\n",
       "1     5452\n",
       "2     6341\n",
       "3     4311\n",
       "4     5432\n",
       "5     9842\n",
       "6     9831\n",
       "7     2134\n",
       "8     5431\n",
       "9     6522\n",
       "10    6423\n",
       "11    1294\n",
       "12    9582\n",
       "13    8582\n",
       "14    8958\n",
       "15    5923\n",
       "16    9238\n",
       "17    5835\n",
       "18    9323\n",
       "19    6542\n",
       "20    9389\n",
       "21    2322\n",
       "22    9242\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "54aa09de-c46c-454e-8db8-bae5fe713639",
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.Series([\"01/06/2023\",\"07/06/2023\",\"08/06/2023\",\"11/06/2023\",\"12/06/2023\",\"13/06/2023\",\"17/06/2023\",\"19/06/2023\",\"22/06/2023\",\"23/06/2023\",\"24/06/2023\",\"01/07/2023\",\"07/07/2023\",\"08/07/2023\",\"10/07/2023\",\"12/07/2023\",\"13/07/2023\",\"16/07/2023\",\"22/07/2023\",\"23/07/2023\",\"28/07/2023\",\"01/06/2023\",\"03/06/2023\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7ffbb8a8-d82c-4c19-809c-6bf856966576",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     01/06/2023\n",
       "1     07/06/2023\n",
       "2     08/06/2023\n",
       "3     11/06/2023\n",
       "4     12/06/2023\n",
       "5     13/06/2023\n",
       "6     17/06/2023\n",
       "7     19/06/2023\n",
       "8     22/06/2023\n",
       "9     23/06/2023\n",
       "10    24/06/2023\n",
       "11    01/07/2023\n",
       "12    07/07/2023\n",
       "13    08/07/2023\n",
       "14    10/07/2023\n",
       "15    12/07/2023\n",
       "16    13/07/2023\n",
       "17    16/07/2023\n",
       "18    22/07/2023\n",
       "19    23/07/2023\n",
       "20    28/07/2023\n",
       "21    01/06/2023\n",
       "22    03/06/2023\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "63b8c49b-c457-433b-be70-a65dfc6429b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"Sales\":sales,\"Dates\":dates})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "baf35399-3535-41d9-9a00-891297ed1fc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4311</td>\n",
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       "      <th>4</th>\n",
       "      <td>5432</td>\n",
       "      <td>12/06/2023</td>\n",
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       "      <td>17/06/2023</td>\n",
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       "      <th>7</th>\n",
       "      <td>2134</td>\n",
       "      <td>19/06/2023</td>\n",
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       "      <th>8</th>\n",
       "      <td>5431</td>\n",
       "      <td>22/06/2023</td>\n",
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       "      <th>9</th>\n",
       "      <td>6522</td>\n",
       "      <td>23/06/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6423</td>\n",
       "      <td>24/06/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1294</td>\n",
       "      <td>01/07/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9582</td>\n",
       "      <td>07/07/2023</td>\n",
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       "      <th>13</th>\n",
       "      <td>8582</td>\n",
       "      <td>08/07/2023</td>\n",
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       "      <th>14</th>\n",
       "      <td>8958</td>\n",
       "      <td>10/07/2023</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5923</td>\n",
       "      <td>12/07/2023</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9238</td>\n",
       "      <td>13/07/2023</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5835</td>\n",
       "      <td>16/07/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9323</td>\n",
       "      <td>22/07/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6542</td>\n",
       "      <td>23/07/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>9389</td>\n",
       "      <td>28/07/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2322</td>\n",
       "      <td>01/06/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9242</td>\n",
       "      <td>03/06/2023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sales       Dates\n",
       "0    3400  01/06/2023\n",
       "1    5452  07/06/2023\n",
       "2    6341  08/06/2023\n",
       "3    4311  11/06/2023\n",
       "4    5432  12/06/2023\n",
       "5    9842  13/06/2023\n",
       "6    9831  17/06/2023\n",
       "7    2134  19/06/2023\n",
       "8    5431  22/06/2023\n",
       "9    6522  23/06/2023\n",
       "10   6423  24/06/2023\n",
       "11   1294  01/07/2023\n",
       "12   9582  07/07/2023\n",
       "13   8582  08/07/2023\n",
       "14   8958  10/07/2023\n",
       "15   5923  12/07/2023\n",
       "16   9238  13/07/2023\n",
       "17   5835  16/07/2023\n",
       "18   9323  22/07/2023\n",
       "19   6542  23/07/2023\n",
       "20   9389  28/07/2023\n",
       "21   2322  01/06/2023\n",
       "22   9242  03/06/2023"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b39f3a9d-ea24-450d-bc50-24c7f992c5e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"MovingAverage\"] = df[\"Sales\"].rolling(window=7).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f6af72c4-7aa0-4648-a599-dd2d0e305ce9",
   "metadata": {},
   "outputs": [
    {
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       "\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>Sales</th>\n",
       "      <th>Dates</th>\n",
       "      <th>MovingAverage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3400</td>\n",
       "      <td>01/06/2023</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5452</td>\n",
       "      <td>07/06/2023</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6341</td>\n",
       "      <td>08/06/2023</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4311</td>\n",
       "      <td>11/06/2023</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5432</td>\n",
       "      <td>12/06/2023</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9842</td>\n",
       "      <td>13/06/2023</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9831</td>\n",
       "      <td>17/06/2023</td>\n",
       "      <td>6372.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2134</td>\n",
       "      <td>19/06/2023</td>\n",
       "      <td>6191.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5431</td>\n",
       "      <td>22/06/2023</td>\n",
       "      <td>6188.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6522</td>\n",
       "      <td>23/06/2023</td>\n",
       "      <td>6214.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6423</td>\n",
       "      <td>24/06/2023</td>\n",
       "      <td>6516.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1294</td>\n",
       "      <td>01/07/2023</td>\n",
       "      <td>5925.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9582</td>\n",
       "      <td>07/07/2023</td>\n",
       "      <td>5888.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8582</td>\n",
       "      <td>08/07/2023</td>\n",
       "      <td>5709.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8958</td>\n",
       "      <td>10/07/2023</td>\n",
       "      <td>6684.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5923</td>\n",
       "      <td>12/07/2023</td>\n",
       "      <td>6754.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9238</td>\n",
       "      <td>13/07/2023</td>\n",
       "      <td>7142.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5835</td>\n",
       "      <td>16/07/2023</td>\n",
       "      <td>7058.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9323</td>\n",
       "      <td>22/07/2023</td>\n",
       "      <td>8205.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6542</td>\n",
       "      <td>23/07/2023</td>\n",
       "      <td>7771.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>9389</td>\n",
       "      <td>28/07/2023</td>\n",
       "      <td>7886.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2322</td>\n",
       "      <td>01/06/2023</td>\n",
       "      <td>6938.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9242</td>\n",
       "      <td>03/06/2023</td>\n",
       "      <td>7413.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sales       Dates  MovingAverage\n",
       "0    3400  01/06/2023            NaN\n",
       "1    5452  07/06/2023            NaN\n",
       "2    6341  08/06/2023            NaN\n",
       "3    4311  11/06/2023            NaN\n",
       "4    5432  12/06/2023            NaN\n",
       "5    9842  13/06/2023            NaN\n",
       "6    9831  17/06/2023    6372.714286\n",
       "7    2134  19/06/2023    6191.857143\n",
       "8    5431  22/06/2023    6188.857143\n",
       "9    6522  23/06/2023    6214.714286\n",
       "10   6423  24/06/2023    6516.428571\n",
       "11   1294  01/07/2023    5925.285714\n",
       "12   9582  07/07/2023    5888.142857\n",
       "13   8582  08/07/2023    5709.714286\n",
       "14   8958  10/07/2023    6684.571429\n",
       "15   5923  12/07/2023    6754.857143\n",
       "16   9238  13/07/2023    7142.857143\n",
       "17   5835  16/07/2023    7058.857143\n",
       "18   9323  22/07/2023    8205.857143\n",
       "19   6542  23/07/2023    7771.571429\n",
       "20   9389  28/07/2023    7886.857143\n",
       "21   2322  01/06/2023    6938.857143\n",
       "22   9242  03/06/2023    7413.000000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f589b579-d0b1-4d17-8f9f-61e8784f6255",
   "metadata": {},
   "source": [
    "### Q11. You have a Pandas DataFrame df with a column 'Date'. Write a Python function that creates a new column 'Weekday' in the DataFrame. The 'Weekday' column should contain the weekday name (e.g. Monday, Tuesday) corresponding to each date in the 'Date' column.\n",
    "<br>For example, if df contains the following values:\n",
    "<br><br>Date\n",
    "<br>0 2023-01-01\n",
    "<br>1 2023-01-02\n",
    "<br>2 2023-01-03\n",
    "<br>3 2023-01-04\n",
    "<br>4 2023-01-05\n",
    "<br>Your function should create the following DataFrame:\n",
    "<br>\n",
    "<br>Date Weekday\n",
    "<br>0 2023-01-01 Sunday\n",
    "<br>1 2023-01-02 Monday\n",
    "<br>2 2023-01-03 Tuesday\n",
    "<br>3 2023-01-04 Wednesday\n",
    "<br>4 2023-01-05 Thursday\n",
    "<br>The function should return the modified DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "93d13261-e61c-4a0a-a5ba-7e1fb4622879",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"Date\" : [\"2023-01-01\",\"2023-01-02\",\"2023-01-03\",\"2023-01-04\",\"2023-01-05\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "88edae22-895d-4adb-b543-5ccb83882a2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>Date</th>\n",
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       "      <th>1</th>\n",
       "      <td>2023-01-02</td>\n",
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       "      <td>2023-01-03</td>\n",
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       "      <td>2023-01-04</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-01-05</td>\n",
       "    </tr>\n",
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       "</div>"
      ],
      "text/plain": [
       "         Date\n",
       "0  2023-01-01\n",
       "1  2023-01-02\n",
       "2  2023-01-03\n",
       "3  2023-01-04\n",
       "4  2023-01-05"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "86834c9c-26a8-4cf0-877c-1b1f901813d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_weekday_column(df):\n",
    "    # Convert the 'Date' column to datetime type\n",
    "    df['Date'] = pd.to_datetime(df['Date'])\n",
    "\n",
    "    # Create the 'Weekday' column containing the weekday name\n",
    "    df['Weekday'] = df['Date'].dt.strftime('%A')\n",
    "\n",
    "    return df\n",
    "\n",
    "df1 = add_weekday_column(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0365eec2-3d19-4b35-a031-ab7e3635c4e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-01-02</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-01-03</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-01-04</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-01-05</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date    Weekday\n",
       "0 2023-01-01     Sunday\n",
       "1 2023-01-02     Monday\n",
       "2 2023-01-03    Tuesday\n",
       "3 2023-01-04  Wednesday\n",
       "4 2023-01-05   Thursday"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de03d325-5bd5-40d0-bb84-2be4c4808c23",
   "metadata": {},
   "source": [
    "### Q12. Given a Pandas DataFrame df with a column 'Date' that contains timestamps, write a Python function to select all rows where the date is between '2023-01-01' and '2023-01-31'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e1e33c70-354f-4919-841b-e6248174b7d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'Date': ['2023-01-01', '2023-01-15', '2023-01-25', '2023-02-05']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "37cd5598-07fa-48ae-ac1e-980f8cb5599a",
   "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>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-01-15</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>2023-01-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-02-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         Date\n",
       "0  2023-01-01\n",
       "1  2023-01-15\n",
       "2  2023-01-25\n",
       "3  2023-02-05"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "bc8626a1-028c-4502-9131-e8b8ed8229a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def date_range(df):\n",
    "    df['Date'] = pd.to_datetime(df['Date'])\n",
    "    rows = (df['Date'] >= '2023-01-01') & (df['Date'] <= '2023-01-31')\n",
    "    return df[rows]\n",
    "\n",
    "df1 = date_range(df)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "dcd259da-5b67-4586-bb4f-b45db3af7204",
   "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>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-01-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-01-25</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "        Date\n",
       "0 2023-01-01\n",
       "1 2023-01-15\n",
       "2 2023-01-25"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a18522f-8837-4d27-ba0b-fc1772bade77",
   "metadata": {},
   "source": [
    "### Q13. To use the basic functions of pandas, what is the first and foremost necessary library that needs to be imported?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c1f4059-05ff-4e4f-bfc5-cabac8596e31",
   "metadata": {},
   "source": [
    "The first and foremost necessary library that needs to be imported to use the basic functions of pandas is pandas itself. To use the pandas library in Python, you need to import it using the import statement\n",
    "\n",
    "The as pd part in the import statement is optional but commonly used as a shorthand to refer to the pandas library with the alias pd. This allows you to use pandas functions and classes more conveniently using the alias pd.\n",
    "\n",
    "Once you import the pandas library, you can access all its functions, data structures (such as DataFrame and Series), and other capabilities for data manipulation, analysis, and visualization. pandas is a powerful and widely-used library for data handling in Python, and it serves as the foundation for working with tabular data in various formats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "09915637-6483-4ea2-b181-0ecc84723a1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  }
 ],
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