handbooks/pandas_handbook.ipynb

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   },
   "cell_type": "code",
   "source": "import pandas as pd",
   "id": "716eb3e6d7fd0d66",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
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     "end_time": "2025-02-16T23:29:42.668359Z",
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   "cell_type": "code",
   "source": [
    "# Create DataFrame out of list of lists\n",
    "data = [\n",
    "    [1, 'Alice', 25],\n",
    "    [2, 'Bob', 30],\n",
    "    [3, 'Charlie', 35]\n",
    "]\n",
    "\n",
    "# Define column names\n",
    "columns = ['ID', 'Name', 'Age']\n",
    "\n",
    "# Create DataFrame\n",
    "df = pd.DataFrame(data, columns=columns)\n",
    "df"
   ],
   "id": "1237a2f0b0702bab",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   ID     Name  Age\n",
       "0   1    Alice   25\n",
       "1   2      Bob   30\n",
       "2   3  Charlie   35"
      ],
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Bob</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "execution_count": 3
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   "cell_type": "code",
   "source": [
    "# Create DataFrame out of a dict\n",
    "data_dict = {\n",
    "    'ID': [1, 2, 3],\n",
    "    'Name': ['Alice', 'Bob', 'Charlie'],\n",
    "    'Age': [25, 30, 35]\n",
    "}\n",
    "\n",
    "# Create DataFrame\n",
    "df = pd.DataFrame(data_dict)\n",
    "df"
   ],
   "id": "5a273b1e32700c8c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   ID     Name  Age\n",
       "0   1    Alice   25\n",
       "1   2      Bob   30\n",
       "2   3  Charlie   35"
      ],
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       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alice</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Bob</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
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     "execution_count": 7,
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   "execution_count": 7
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   "cell_type": "code",
   "source": [
    "# write CSV\n",
    "df.to_csv('data.csv', index=False)\n",
    "\n",
    "# read CSV\n",
    "df = pd.read_csv('data.csv')  # Assumes the file is in the same directory\n",
    "print(df.head())  # Display first 5 rows\n",
    "\n",
    "# Options:\n",
    "# sep=';' -> Change delimiter if CSV is not comma-separated.\n",
    "# header=None -> If there’s no header row, specify column names with names=['col1', 'col2'].\n",
    "# usecols=['col1', 'col3'] -> Load only specific columns.\n",
    "# dtype={'col1': str, 'col2': int} -> Define column data types."
   ],
   "id": "8202b1f79418f97",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ID     Name  Age\n",
      "0   1    Alice   25\n",
      "1   2      Bob   30\n",
      "2   3  Charlie   35\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
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     "end_time": "2025-02-16T23:32:14.259444Z",
     "start_time": "2025-02-16T23:32:14.252872Z"
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   },
   "cell_type": "code",
   "source": [
    "df.to_json('data.jsonl', orient='records', lines=True)\n",
    "\n",
    "df = pd.read_json('data.jsonl', orient='records', lines=True)\n",
    "\n",
    "df"
   ],
   "id": "d309c3c1948e46b2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   ID     Name  Age\n",
       "0   1    Alice   25\n",
       "1   2      Bob   30\n",
       "2   3  Charlie   35"
      ],
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Bob</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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     "execution_count": 9,
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   "execution_count": 9
  },
  {
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   },
   "cell_type": "code",
   "source": [
    "from IPython.display import display\n",
    "# How to display the first and last `n` rows of a DataFrame?**\n",
    "n = 3\n",
    "display(df.head(n))\n",
    "display(df.tail(n))"
   ],
   "id": "30f93ea2f8583445",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   ID     Name  Age\n",
       "0   1    Alice   25\n",
       "1   2      Bob   30\n",
       "2   3  Charlie   35"
      ],
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Bob</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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    {
     "data": {
      "text/plain": [
       "   ID     Name  Age\n",
       "0   1    Alice   25\n",
       "1   2      Bob   30\n",
       "2   3  Charlie   35"
      ],
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       "      <th>Age</th>\n",
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       "      <th>0</th>\n",
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       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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     "metadata": {},
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   ],
   "execution_count": 7
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### **Data Manipulation**",
   "id": "8bc7a042ce71239d"
  },
  {
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   },
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   ID\n",
       "0   1\n",
       "1   2\n",
       "2   3"
      ],
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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    {
     "data": {
      "text/plain": [
       "   ID     Name\n",
       "0   1    Alice\n",
       "1   2      Bob\n",
       "2   3  Charlie"
      ],
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       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Bob</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Charlie</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 12,
   "source": [
    "# How to select a single column and multiple columns in a DataFrame?\n",
    "display(df['ID'])\n",
    "display(df[['ID', 'Name']])"
   ],
   "id": "af02463acaf7ce8b"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-15T20:15:36.427738Z",
     "start_time": "2025-02-15T20:15:36.417897Z"
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   },
   "cell_type": "code",
   "source": [
    "# What is the difference between `.loc[]` and `.iloc[]`?\n",
    "import pandas as pd\n",
    "\n",
    "# case 1: the '.loc[]' is used for label-based indexing\n",
    "data = {'Name': ['Alice', 'Bob', 'Charlie'],\n",
    "        'Age': [25, 30, 35],\n",
    "        'City': ['NY', 'LA', 'SF']}\n",
    "df = pd.DataFrame(data, index=['A', 'B', 'C'])\n",
    "\n",
    "display(df.loc['A'])            # Select row with index label 'A'\n",
    "display(df.loc[['A', 'C']])     # Select multiple rows using labels\n",
    "\n",
    "# Select rows and specific columns\n",
    "print(f'{df.loc['A', 'Age']=}')                 # Single value\n",
    "display(df.loc[['A', 'B'], ['Name', 'City']])   # Subset\n",
    "\n",
    "# Slicing with labels (inclusive)\n",
    "display(df.loc['A':'B'])  # Includes 'B'"
   ],
   "id": "82f9bd556f8f7588",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name    Alice\n",
       "Age        25\n",
       "City       NY\n",
       "Name: A, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "      Name  Age City\n",
       "A    Alice   25   NY\n",
       "C  Charlie   35   SF"
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       "      <th>A</th>\n",
       "      <td>Alice</td>\n",
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       "      <td>NY</td>\n",
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       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>35</td>\n",
       "      <td>SF</td>\n",
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df.loc['A', 'Age']=np.int64(25)\n"
     ]
    },
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     "data": {
      "text/plain": [
       "    Name City\n",
       "A  Alice   NY\n",
       "B    Bob   LA"
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Alice</td>\n",
       "      <td>NY</td>\n",
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       "      <td>Bob</td>\n",
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     "data": {
      "text/plain": [
       "    Name  Age City\n",
       "A  Alice   25   NY\n",
       "B    Bob   30   LA"
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>Bob</td>\n",
       "      <td>30</td>\n",
       "      <td>LA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 30
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-15T20:18:40.255833Z",
     "start_time": "2025-02-15T20:18:40.248267Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# case 2: the '.iloc[]' is used for integer-based indexing\n",
    "\n",
    "print(f'{df.iloc[0]}=')       # Select first row (index 0)\n",
    "\n",
    "display(df.iloc[:2])    # Select first two rows\n",
    "\n",
    "# Select specific rows and columns by position\n",
    "display(df.iloc[0, 1])  # First row, second column\n",
    "display(df.iloc[[0, 2], [0, 2]])  # Select (0,0) and (2,2)\n"
   ],
   "id": "d379e6ff17cfc92e",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name    Alice\n",
      "Age        25\n",
      "City       NY\n",
      "Name: A, dtype: object=\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "    Name  Age City\n",
       "A  Alice   25   NY\n",
       "B    Bob   30   LA"
      ],
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       "<div>\n",
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       "      <th></th>\n",
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       "      <th>City</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25</td>\n",
       "      <td>NY</td>\n",
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       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>Bob</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "np.int64(25)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "      Name City\n",
       "A    Alice   NY\n",
       "C  Charlie   SF"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    </tr>\n",
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       "      <td>Alice</td>\n",
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       "      <th>C</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>SF</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 33
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-15T04:44:34.312561Z",
     "start_time": "2025-02-15T04:44:34.309071Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Filter rows based on a condition?\n",
    "df[df['Age'] >= 30]"
   ],
   "id": "a171fdd4fdc35265",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   ID     Name  Age\n",
       "1   2      Bob   30\n",
       "2   3  Charlie   35"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Bob</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# How to replace missing values in a DataFrame?\n",
    "df = df.fillna(value='fill_in_value')\n",
    "df = df.dropna()"
   ],
   "id": "eb9f47ec96ab2969"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-15T04:45:59.675298Z",
     "start_time": "2025-02-15T04:45:59.670383Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# How to add a new column to an existing DataFrame?\n",
    "df['id_mult'] = df['ID'] * 3\n",
    "df"
   ],
   "id": "8700a8653b16b972",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   ID     Name  Age  id_mult\n",
       "0   1    Alice   25        3\n",
       "1   2      Bob   30        6\n",
       "2   3  Charlie   35        9"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>id_mult</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>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Bob</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-15T04:46:27.784080Z",
     "start_time": "2025-02-15T04:46:27.781242Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# How to rename a column in Pandas?**\n",
    "df.rename(columns={'id_mult': 'id_multiplication'}, inplace=True)\n",
    "df"
   ],
   "id": "c1377ba22686209b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   ID     Name  Age  id_multiplication\n",
       "0   1    Alice   25                  3\n",
       "1   2      Bob   30                  6\n",
       "2   3  Charlie   35                  9"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>id_multiplication</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>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Bob</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-15T05:22:47.965952Z",
     "start_time": "2025-02-15T05:22:47.960313Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = {\n",
    "    'Category': ['A', 'B', 'A', 'B', 'A', 'C', 'C'],\n",
    "    'Value': [10, 20, 30, 40, 50, 60, 70]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Compute mean, sum, and count for each group\n",
    "result = df.groupby('Category').agg({'Value': ['mean', 'sum', 'count']})\n",
    "print(result)"
   ],
   "id": "94f3b79507f66767",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Value           \n",
      "          mean  sum count\n",
      "Category                 \n",
      "A         30.0   90     3\n",
      "B         30.0   60     2\n",
      "C         65.0  130     2\n"
     ]
    }
   ],
   "execution_count": 20
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### **Aggregation & Grouping**",
   "id": "41eec9c58f1d67e1"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-15T20:23:35.019408Z",
     "start_time": "2025-02-15T20:23:35.011391Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = {\n",
    "    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Emma', 'Frank', 'Grace', 'Helen', 'Ivy', 'Jack'],\n",
    "    'Age': [25, 30, 35, 28, 26, 33, 27, 31, 29, 34],\n",
    "    'City': ['NY', 'LA', 'SF', 'BOS', 'HOU', 'SEA', 'SEA', 'BOS', 'ATL', 'SEA'],\n",
    "    'Salary': [60000, 70000, 80000, 72000, 65000, 75000, 68000, 71000, 67000, 73000]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "display(df)\n",
    "\n",
    "# How to group data in Pandas?\n",
    "display(df.groupby('City').agg({'Salary': 'sum'}))\n",
    "\n",
    "# How to compute mean, sum and count per group\n",
    "display(df.groupby('City').agg({'Salary': ['mean', 'sum', 'count']}))"
   ],
   "id": "dfdfd3e67ee6318b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      Name  Age City  Salary\n",
       "0    Alice   25   NY   60000\n",
       "1      Bob   30   LA   70000\n",
       "2  Charlie   35   SF   80000\n",
       "3    David   28  BOS   72000\n",
       "4     Emma   26  HOU   65000\n",
       "5    Frank   33  SEA   75000\n",
       "6    Grace   27  SEA   68000\n",
       "7    Helen   31  BOS   71000\n",
       "8      Ivy   29  ATL   67000\n",
       "9     Jack   34  SEA   73000"
      ],
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       "<div>\n",
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       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
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       "      <td>60000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>30</td>\n",
       "      <td>LA</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>35</td>\n",
       "      <td>SF</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>28</td>\n",
       "      <td>BOS</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Emma</td>\n",
       "      <td>26</td>\n",
       "      <td>HOU</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Frank</td>\n",
       "      <td>33</td>\n",
       "      <td>SEA</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Grace</td>\n",
       "      <td>27</td>\n",
       "      <td>SEA</td>\n",
       "      <td>68000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Helen</td>\n",
       "      <td>31</td>\n",
       "      <td>BOS</td>\n",
       "      <td>71000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ivy</td>\n",
       "      <td>29</td>\n",
       "      <td>ATL</td>\n",
       "      <td>67000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jack</td>\n",
       "      <td>34</td>\n",
       "      <td>SEA</td>\n",
       "      <td>73000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "      Salary\n",
       "City        \n",
       "ATL    67000\n",
       "BOS   143000\n",
       "HOU    65000\n",
       "LA     70000\n",
       "NY     60000\n",
       "SEA   216000\n",
       "SF     80000"
      ],
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       "      <th>ATL</th>\n",
       "      <td>67000</td>\n",
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       "      <th>HOU</th>\n",
       "      <td>65000</td>\n",
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       "    <tr>\n",
       "      <th>LA</th>\n",
       "      <td>70000</td>\n",
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       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>60000</td>\n",
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       "      <th>SEA</th>\n",
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     "data": {
      "text/plain": [
       "       Salary              \n",
       "         mean     sum count\n",
       "City                       \n",
       "ATL   67000.0   67000     1\n",
       "BOS   71500.0  143000     2\n",
       "HOU   65000.0   65000     1\n",
       "LA    70000.0   70000     1\n",
       "NY    60000.0   60000     1\n",
       "SEA   72000.0  216000     3\n",
       "SF    80000.0   80000     1"
      ],
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       "<div>\n",
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       "    .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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ATL</th>\n",
       "      <td>67000.0</td>\n",
       "      <td>67000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOS</th>\n",
       "      <td>71500.0</td>\n",
       "      <td>143000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOU</th>\n",
       "      <td>65000.0</td>\n",
       "      <td>65000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LA</th>\n",
       "      <td>70000.0</td>\n",
       "      <td>70000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>60000.0</td>\n",
       "      <td>60000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEA</th>\n",
       "      <td>72000.0</td>\n",
       "      <td>216000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SF</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>80000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 37
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "51de50badfaf0bc6"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-15T20:27:56.628493Z",
     "start_time": "2025-02-15T20:27:56.621765Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# How to pivot a DataFrame?\n",
    "\n",
    "# Pivoting: Cities become columns, Salary is the value\n",
    "pivot_df = df.pivot_table(index='Name', columns='City', values='Salary')\n",
    "pivot_df\n"
   ],
   "id": "45997d5a0d8b8f06",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City         ATL      BOS      HOU       LA       NY      SEA       SF\n",
       "Name                                                                  \n",
       "Alice        NaN      NaN      NaN      NaN  60000.0      NaN      NaN\n",
       "Bob          NaN      NaN      NaN  70000.0      NaN      NaN      NaN\n",
       "Charlie      NaN      NaN      NaN      NaN      NaN      NaN  80000.0\n",
       "David        NaN  72000.0      NaN      NaN      NaN      NaN      NaN\n",
       "Emma         NaN      NaN  65000.0      NaN      NaN      NaN      NaN\n",
       "Frank        NaN      NaN      NaN      NaN      NaN  75000.0      NaN\n",
       "Grace        NaN      NaN      NaN      NaN      NaN  68000.0      NaN\n",
       "Helen        NaN  71000.0      NaN      NaN      NaN      NaN      NaN\n",
       "Ivy      67000.0      NaN      NaN      NaN      NaN      NaN      NaN\n",
       "Jack         NaN      NaN      NaN      NaN      NaN  73000.0      NaN"
      ],
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>City</th>\n",
       "      <th>ATL</th>\n",
       "      <th>BOS</th>\n",
       "      <th>HOU</th>\n",
       "      <th>LA</th>\n",
       "      <th>NY</th>\n",
       "      <th>SEA</th>\n",
       "      <th>SF</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alice</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bob</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Charlie</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>80000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>David</th>\n",
       "      <td>NaN</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emma</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frank</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grace</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Helen</th>\n",
       "      <td>NaN</td>\n",
       "      <td>71000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ivy</th>\n",
       "      <td>67000.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jack</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 40
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### **Sorting & Indexing**",
   "id": "ec1db60b72326394"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-15T20:31:53.008853Z",
     "start_time": "2025-02-15T20:31:53.003303Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# How to sort a Pandas DataFrame by multiple columns?\n",
    "df.sort_values(by=['Age', 'Salary'], ascending=[True, False])"
   ],
   "id": "b59f221afc78af08",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      Name  Age City  Salary\n",
       "0    Alice   25   NY   60000\n",
       "4     Emma   26  HOU   65000\n",
       "6    Grace   27  SEA   68000\n",
       "3    David   28  BOS   72000\n",
       "8      Ivy   29  ATL   67000\n",
       "1      Bob   30   LA   70000\n",
       "7    Helen   31  BOS   71000\n",
       "5    Frank   33  SEA   75000\n",
       "9     Jack   34  SEA   73000\n",
       "2  Charlie   35   SF   80000"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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</td>\n",
       "      <td>NY</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Emma</td>\n",
       "      <td>26</td>\n",
       "      <td>HOU</td>\n",
       "      <td>65000</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Grace</td>\n",
       "      <td>27</td>\n",
       "      <td>SEA</td>\n",
       "      <td>68000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>28</td>\n",
       "      <td>BOS</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ivy</td>\n",
       "      <td>29</td>\n",
       "      <td>ATL</td>\n",
       "      <td>67000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>30</td>\n",
       "      <td>LA</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Helen</td>\n",
       "      <td>31</td>\n",
       "      <td>BOS</td>\n",
       "      <td>71000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Frank</td>\n",
       "      <td>33</td>\n",
       "      <td>SEA</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jack</td>\n",
       "      <td>34</td>\n",
       "      <td>SEA</td>\n",
       "      <td>73000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>35</td>\n",
       "      <td>SF</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 41
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-15T20:33:53.205278Z",
     "start_time": "2025-02-15T20:33:53.200870Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# How to reset or set an index?\n",
    "df.set_index('Name')\n",
    "display(df)\n",
    "\n",
    "df.reset_index()\n",
    "display(df)"
   ],
   "id": "4c7635e7b460030d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      Name  Age City  Salary\n",
       "0    Alice   25   NY   60000\n",
       "1      Bob   30   LA   70000\n",
       "2  Charlie   35   SF   80000\n",
       "3    David   28  BOS   72000\n",
       "4     Emma   26  HOU   65000\n",
       "5    Frank   33  SEA   75000\n",
       "6    Grace   27  SEA   68000\n",
       "7    Helen   31  BOS   71000\n",
       "8      Ivy   29  ATL   67000\n",
       "9     Jack   34  SEA   73000"
      ],
      "text/html": [
       "<div>\n",
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       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
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       "      <td>70000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>35</td>\n",
       "      <td>SF</td>\n",
       "      <td>80000</td>\n",
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       "      <th>3</th>\n",
       "      <td>David</td>\n",
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       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Emma</td>\n",
       "      <td>26</td>\n",
       "      <td>HOU</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Frank</td>\n",
       "      <td>33</td>\n",
       "      <td>SEA</td>\n",
       "      <td>75000</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Grace</td>\n",
       "      <td>27</td>\n",
       "      <td>SEA</td>\n",
       "      <td>68000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Helen</td>\n",
       "      <td>31</td>\n",
       "      <td>BOS</td>\n",
       "      <td>71000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ivy</td>\n",
       "      <td>29</td>\n",
       "      <td>ATL</td>\n",
       "      <td>67000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jack</td>\n",
       "      <td>34</td>\n",
       "      <td>SEA</td>\n",
       "      <td>73000</td>\n",
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      "text/plain": [
       "      Name  Age City  Salary\n",
       "0    Alice   25   NY   60000\n",
       "1      Bob   30   LA   70000\n",
       "2  Charlie   35   SF   80000\n",
       "3    David   28  BOS   72000\n",
       "4     Emma   26  HOU   65000\n",
       "5    Frank   33  SEA   75000\n",
       "6    Grace   27  SEA   68000\n",
       "7    Helen   31  BOS   71000\n",
       "8      Ivy   29  ATL   67000\n",
       "9     Jack   34  SEA   73000"
      ],
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       "      <td>Bob</td>\n",
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       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
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       "      <td>80000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
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       "      <td>72000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Emma</td>\n",
       "      <td>26</td>\n",
       "      <td>HOU</td>\n",
       "      <td>65000</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Frank</td>\n",
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       "      <td>75000</td>\n",
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       "      <td>SEA</td>\n",
       "      <td>68000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Helen</td>\n",
       "      <td>31</td>\n",
       "      <td>BOS</td>\n",
       "      <td>71000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ivy</td>\n",
       "      <td>29</td>\n",
       "      <td>ATL</td>\n",
       "      <td>67000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jack</td>\n",
       "      <td>34</td>\n",
       "      <td>SEA</td>\n",
       "      <td>73000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 42
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### **Merging & Joining**",
   "id": "68c1dce8fe860892"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-15T20:56:27.699258Z",
     "start_time": "2025-02-15T20:56:27.681420Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# How to merge two DataFrames?\n",
    "import pandas as pd\n",
    "\n",
    "df1 = pd.DataFrame({\n",
    "    'ID': [1, 2, 3, 4, 5],\n",
    "    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Emma'],\n",
    "    'Age': [25, 30, 35, 28, 26]\n",
    "})\n",
    "\n",
    "df2 = pd.DataFrame({\n",
    "    'ID': [3, 4, 5, 6, 7],  # Overlapping and new IDs\n",
    "    'City': ['NY', 'LA', 'SF', 'CHI', 'HOU'],\n",
    "    'Salary': [80000, 72000, 75000, 68000, 70000]\n",
    "})\n",
    "\n",
    "# Display both DataFrames\n",
    "print('DataFrame 1:')\n",
    "display(df1)\n",
    "\n",
    "print('DataFrame 2:')\n",
    "display(df2)\n",
    "\n",
    "inner_merge = pd.merge(df1, df2, on='ID', how='inner')\n",
    "print('Inner join: Keeps only matching IDs')\n",
    "display(inner_merge)\n",
    "\n",
    "left_merge = pd.merge(df1, df2, on='ID', how='left')\n",
    "print('Left Join: Keeps all rows from df1, fills missing values from df2')\n",
    "display(left_merge)\n",
    "\n",
    "right_merge = pd.merge(df1, df2, on='ID', how='right')\n",
    "print('Right Join: Keeps all rows from df2, fills missing values from df1')\n",
    "display(right_merge)\n",
    "\n",
    "outer_merge = pd.merge(df1, df2, on='ID', how='outer')\n",
    "print('Outer Join: Keeps all unique values from both DataFrames')\n",
    "display(outer_merge)"
   ],
   "id": "cb50468e25192d45",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame 1:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "   ID     Name  Age\n",
       "0   1    Alice   25\n",
       "1   2      Bob   30\n",
       "2   3  Charlie   35\n",
       "3   4    David   28\n",
       "4   5     Emma   26"
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     "text": [
      "DataFrame 2:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "   ID City  Salary\n",
       "0   3   NY   80000\n",
       "1   4   LA   72000\n",
       "2   5   SF   75000\n",
       "3   6  CHI   68000\n",
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     "text": [
      "Inner join: Keeps only matching IDs\n"
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       "   ID     Name  Age City  Salary\n",
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      "Left Join: Keeps all rows from df1, fills missing values from df2\n"
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       "   ID     Name  Age City   Salary\n",
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      "Right Join: Keeps all rows from df2, fills missing values from df1\n"
     ]
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     "data": {
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       "   ID     Name   Age City  Salary\n",
       "0   3  Charlie  35.0   NY   80000\n",
       "1   4    David  28.0   LA   72000\n",
       "2   5     Emma  26.0   SF   75000\n",
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     "text": [
      "Outer Join: Keeps all unique values from both DataFrames\n"
     ]
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       "   ID     Name   Age City   Salary\n",
       "0   1    Alice  25.0  NaN      NaN\n",
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       "2   3  Charlie  35.0   NY  80000.0\n",
       "3   4    David  28.0   LA  72000.0\n",
       "4   5     Emma  26.0   SF  75000.0\n",
       "5   6      NaN   NaN  CHI  68000.0\n",
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   "execution_count": 44
  },
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   "source": [
    "\n",
    "# What is the difference between `merge()`, `join()`, and `concat()` in Pandas?\n",
    "\n",
    "### merge() – SQL-style Joins (Flexible)\n",
    "\n",
    "- Similar to SQL JOIN operations.\n",
    "- Uses keys (columns) to match rows.\n",
    "- Supports inner, left, right, and outer joins.\n",
    "\n",
    "### join() – Index-Based Merging\n",
    "\n",
    "- Similar to merge(), but joins on the index instead of columns.\n",
    "- Default join type is left.\n",
    "- Can be used with set_index().\n",
    "\n",
    "### concat() – Stacking DataFrames (Row-wise or Column-wise)\n",
    "\n",
    "- Used for appending (row-wise) or extending (column-wise).\n",
    "- Does not require keys like merge().\n",
    "- Works with axis=0 (rows) or axis=1 (columns).\n"
   ],
   "id": "7b6376ae6715336f"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### **Simple Pandas Programming Tasks**",
   "id": "2fad740395c612a8"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-15T21:04:14.753659Z",
     "start_time": "2025-02-15T21:04:14.748605Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Create a DataFrame with 3 columns and 5 rows of random integers between 1 and 100\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def generate_random_dataframe(rows: int, cols: int, fill_value: float|int|None = None) -> pd.DataFrame:\n",
    "    if fill_value is None:\n",
    "        data = np.random.randint(0, 101, size=(rows, cols))\n",
    "    else:\n",
    "        data = np.full((rows, cols), fill_value)\n",
    "    col_names = [f'Col_{i + 1}' for i in range(cols)]  # Column names: Col_1, Col_2, ...\n",
    "    return pd.DataFrame(data, columns=col_names)\n",
    "\n",
    "df = generate_random_dataframe(rows=5, cols=3)\n",
    "display(df)\n"
   ],
   "id": "538cc6af2c0e9068",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Col_1  Col_2  Col_3\n",
       "0     20     66     98\n",
       "1     57     32     61\n",
       "2     81     14     70\n",
       "3     76     49     39\n",
       "4     75     39     22"
      ],
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       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>32</td>\n",
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       "      <th>2</th>\n",
       "      <td>81</td>\n",
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       "      <td>70</td>\n",
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       "      <th>3</th>\n",
       "      <td>76</td>\n",
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       "      <td>39</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75</td>\n",
       "      <td>39</td>\n",
       "      <td>22</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 46
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-15T21:08:08.713462Z",
     "start_time": "2025-02-15T21:08:08.706458Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Create a DataFrame with missing values\n",
    "data = {\n",
    "    'A': [10, 20, np.nan, 40, 50, np.nan, 30, 80, 90, 25, 15],\n",
    "    'B': [100, 200, 150, np.nan, 300, 350, np.nan, 400, 450, 250, np.nan],\n",
    "    'C': ['X', 'Y', 'Z', 'X', 'Y', 'Z', 'X', 'Y', 'Z', 'X', 'Y'],\n",
    "    'D': [1, 2, 3, 4, 5, 6, 7, 2, 3, 1, 2]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "display(df)"
   ],
   "id": "dab56801b865404",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       A      B  C  D\n",
       "0   10.0  100.0  X  1\n",
       "1   20.0  200.0  Y  2\n",
       "2    NaN  150.0  Z  3\n",
       "3   40.0    NaN  X  4\n",
       "4   50.0  300.0  Y  5\n",
       "5    NaN  350.0  Z  6\n",
       "6   30.0    NaN  X  7\n",
       "7   80.0  400.0  Y  2\n",
       "8   90.0  450.0  Z  3\n",
       "9   25.0  250.0  X  1\n",
       "10  15.0    NaN  Y  2"
      ],
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>100.0</td>\n",
       "      <td>X</td>\n",
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       "      <th>1</th>\n",
       "      <td>20.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>Z</td>\n",
       "      <td>3</td>\n",
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       "      <th>3</th>\n",
       "      <td>40.0</td>\n",
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       "      <td>X</td>\n",
       "      <td>4</td>\n",
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       "      <th>4</th>\n",
       "      <td>50.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>Z</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>X</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>80.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>90.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>Z</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>25.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>X</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 51
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-15T21:08:09.843019Z",
     "start_time": "2025-02-15T21:08:09.840017Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Find the number of missing values in each column.\n",
    "\n",
    "print(f'{df.isna().sum()}=')"
   ],
   "id": "e4278689a4ca34e3",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A    2\n",
      "B    3\n",
      "C    0\n",
      "D    0\n",
      "dtype: int64=\n",
      "A    2\n",
      "B    3\n",
      "C    0\n",
      "D    0\n",
      "dtype: int64=\n"
     ]
    }
   ],
   "execution_count": 52
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-15T21:09:57.453710Z",
     "start_time": "2025-02-15T21:09:57.447425Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Replace all NaN values with the mean of their respective columns\n",
    "df_filled = df.copy()\n",
    "df_filled.fillna(value=df_filled.mean(numeric_only=True), inplace=True)\n",
    "display(df_filled)"
   ],
   "id": "801abb9d8a97fbd1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       A      B  C  D\n",
       "0   10.0  100.0  X  1\n",
       "1   20.0  200.0  Y  2\n",
       "2   40.0  150.0  Z  3\n",
       "3   40.0  275.0  X  4\n",
       "4   50.0  300.0  Y  5\n",
       "5   40.0  350.0  Z  6\n",
       "6   30.0  275.0  X  7\n",
       "7   80.0  400.0  Y  2\n",
       "8   90.0  450.0  Z  3\n",
       "9   25.0  250.0  X  1\n",
       "10  15.0  275.0  Y  2"
      ],
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>200.0</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>Z</td>\n",
       "      <td>3</td>\n",
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       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>40.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>Z</td>\n",
       "      <td>6</td>\n",
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       "      <td>30.0</td>\n",
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       "      <td>X</td>\n",
       "      <td>7</td>\n",
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       "      <th>7</th>\n",
       "      <td>80.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>90.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>Z</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>25.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>X</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 54
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-15T21:14:02.904201Z",
     "start_time": "2025-02-15T21:14:02.896347Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Extract all rows where column `A` has values between 10 and 50\n",
    "filtered_df = df[df['A'].between(10, 50, inclusive='both')]\n",
    "display(filtered_df)\n",
    "\n",
    "filtered_df = df[df['A'].notna() & (10 <= df['A']) & (df['A'] <= 50)]\n",
    "display(filtered_df)\n"
   ],
   "id": "41e459e8b43dae99",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       A      B  C  D\n",
       "0   10.0  100.0  X  1\n",
       "1   20.0  200.0  Y  2\n",
       "3   40.0    NaN  X  4\n",
       "4   50.0  300.0  Y  5\n",
       "6   30.0    NaN  X  7\n",
       "9   25.0  250.0  X  1\n",
       "10  15.0    NaN  Y  2"
      ],
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       "      <th>3</th>\n",
       "      <td>40.0</td>\n",
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       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>X</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>25.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>X</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
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     "data": {
      "text/plain": [
       "       A      B  C  D\n",
       "0   10.0  100.0  X  1\n",
       "1   20.0  200.0  Y  2\n",
       "3   40.0    NaN  X  4\n",
       "4   50.0  300.0  Y  5\n",
       "6   30.0    NaN  X  7\n",
       "9   25.0  250.0  X  1\n",
       "10  15.0    NaN  Y  2"
      ],
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>X</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>X</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>25.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>X</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 60
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-15T21:22:54.865767Z",
     "start_time": "2025-02-15T21:22:54.860844Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Drop duplicate rows based on a specific column\n",
    "\n",
    "df_deduplicated = df_filled.drop_duplicates(subset=['C'], keep='first', inplace=False)\n",
    "display(df_deduplicated)"
   ],
   "id": "72dc0973f8eaa2b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      A      B  C  D\n",
       "0  10.0  100.0  X  1\n",
       "1  20.0  200.0  Y  2\n",
       "2  40.0  150.0  Z  3"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>X</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>Z</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 62
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### **Data Transformations**",
   "id": "44d3dae0e2a5b3bd"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-15T21:30:47.549957Z",
     "start_time": "2025-02-15T21:30:47.544841Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Convert a column to datetime format\n",
    "import pandas as pd\n",
    "\n",
    "data = {\n",
    "    'ID': [1, 2, 3, 4, 5, 6, 7],\n",
    "    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Emma', 'Frank', 'Grace'],\n",
    "    'Timestamp_Str': ['2024-01-01 10:30:00', '2024-02-15 14:45:00', '2024-03-20 08:10:00',\n",
    "                      '2024-04-10 16:20:00', '2024-05-05 12:00:00', '2024-06-18 09:30:00', '2024-07-25 18:15:00'],\n",
    "    'Epoch_Time': [1704100200, 1708008300, 1710912600, 1712763600, 1714910400, 1718693400, 1721912100]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "df['dt_from_str'] = pd.to_datetime(df['Timestamp_Str'])\n",
    "df['dt_from_str'] = pd.to_datetime(df['Timestamp_Str'], format='%Y-%m-%d %H:%M:%S')\n",
    "\n",
    "df['dt_from_epoch'] = pd.to_datetime(df['Epoch_Time'], unit='s')\n",
    "display(df)\n"
   ],
   "id": "61261d616a039edd",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   ID     Name        Timestamp_Str  Epoch_Time         dt_from_str  \\\n",
       "0   1    Alice  2024-01-01 10:30:00  1704100200 2024-01-01 10:30:00   \n",
       "1   2      Bob  2024-02-15 14:45:00  1708008300 2024-02-15 14:45:00   \n",
       "2   3  Charlie  2024-03-20 08:10:00  1710912600 2024-03-20 08:10:00   \n",
       "3   4    David  2024-04-10 16:20:00  1712763600 2024-04-10 16:20:00   \n",
       "4   5     Emma  2024-05-05 12:00:00  1714910400 2024-05-05 12:00:00   \n",
       "5   6    Frank  2024-06-18 09:30:00  1718693400 2024-06-18 09:30:00   \n",
       "6   7    Grace  2024-07-25 18:15:00  1721912100 2024-07-25 18:15:00   \n",
       "\n",
       "        dt_from_epoch  \n",
       "0 2024-01-01 09:10:00  \n",
       "1 2024-02-15 14:45:00  \n",
       "2 2024-03-20 05:30:00  \n",
       "3 2024-04-10 15:40:00  \n",
       "4 2024-05-05 12:00:00  \n",
       "5 2024-06-18 06:50:00  \n",
       "6 2024-07-25 12:55:00  "
      ],
      "text/html": [
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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>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Timestamp_Str</th>\n",
       "      <th>Epoch_Time</th>\n",
       "      <th>dt_from_str</th>\n",
       "      <th>dt_from_epoch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alice</td>\n",
       "      <td>2024-01-01 10:30:00</td>\n",
       "      <td>1704100200</td>\n",
       "      <td>2024-01-01 10:30:00</td>\n",
       "      <td>2024-01-01 09:10:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Bob</td>\n",
       "      <td>2024-02-15 14:45:00</td>\n",
       "      <td>1708008300</td>\n",
       "      <td>2024-02-15 14:45:00</td>\n",
       "      <td>2024-02-15 14:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>2024-03-20 08:10:00</td>\n",
       "      <td>1710912600</td>\n",
       "      <td>2024-03-20 08:10:00</td>\n",
       "      <td>2024-03-20 05:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>David</td>\n",
       "      <td>2024-04-10 16:20:00</td>\n",
       "      <td>1712763600</td>\n",
       "      <td>2024-04-10 16:20:00</td>\n",
       "      <td>2024-04-10 15:40:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Emma</td>\n",
       "      <td>2024-05-05 12:00:00</td>\n",
       "      <td>1714910400</td>\n",
       "      <td>2024-05-05 12:00:00</td>\n",
       "      <td>2024-05-05 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Frank</td>\n",
       "      <td>2024-06-18 09:30:00</td>\n",
       "      <td>1718693400</td>\n",
       "      <td>2024-06-18 09:30:00</td>\n",
       "      <td>2024-06-18 06:50:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Grace</td>\n",
       "      <td>2024-07-25 18:15:00</td>\n",
       "      <td>1721912100</td>\n",
       "      <td>2024-07-25 18:15:00</td>\n",
       "      <td>2024-07-25 12:55:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 65
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-15T21:32:49.070141Z",
     "start_time": "2025-02-15T21:32:49.061737Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Calculate the rolling mean of a numeric column with a window size of 3.\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "data = {\n",
    "    'ID': range(1, 11),\n",
    "    'Value': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "df['Rolling_Mean'] = df['Value'].rolling(window=3).mean()\n",
    "display(df)\n",
    "\n",
    "df['Rolling_Mean'] = df['Value'].rolling(window=3, min_periods=1).mean()\n",
    "display(df)\n"
   ],
   "id": "7e7f69997bcaead0",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   ID  Value  Rolling_Mean\n",
       "0   1     10           NaN\n",
       "1   2     20           NaN\n",
       "2   3     30          20.0\n",
       "3   4     40          30.0\n",
       "4   5     50          40.0\n",
       "5   6     60          50.0\n",
       "6   7     70          60.0\n",
       "7   8     80          70.0\n",
       "8   9     90          80.0\n",
       "9  10    100          90.0"
      ],
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       "    <tr>\n",
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       "      <td>40.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>70</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
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       "      <td>70.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>90</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "   ID  Value  Rolling_Mean\n",
       "0   1     10          10.0\n",
       "1   2     20          15.0\n",
       "2   3     30          20.0\n",
       "3   4     40          30.0\n",
       "4   5     50          40.0\n",
       "5   6     60          50.0\n",
       "6   7     70          60.0\n",
       "7   8     80          70.0\n",
       "8   9     90          80.0\n",
       "9  10    100          90.0"
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       "      <th>6</th>\n",
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       "      <td>70</td>\n",
       "      <td>60.0</td>\n",
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       "      <td>80</td>\n",
       "      <td>70.0</td>\n",
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       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>90</td>\n",
       "      <td>80.0</td>\n",
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       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>90.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 67
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-15T21:33:59.310564Z",
     "start_time": "2025-02-15T21:33:59.306190Z"
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   },
   "cell_type": "code",
   "source": [
    "# Normalize a numerical column to a range between 0 and 1\n",
    "df['normalized_value'] = (df['Value'] - df['Value'].min()) / (df['Value'].max() - df['Value'].min())\n",
    "display(df)"
   ],
   "id": "4264a8e9e45d6645",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   ID  Value  Rolling_Mean  normalized_value\n",
       "0   1     10          10.0          0.000000\n",
       "1   2     20          15.0          0.111111\n",
       "2   3     30          20.0          0.222222\n",
       "3   4     40          30.0          0.333333\n",
       "4   5     50          40.0          0.444444\n",
       "5   6     60          50.0          0.555556\n",
       "6   7     70          60.0          0.666667\n",
       "7   8     80          70.0          0.777778\n",
       "8   9     90          80.0          0.888889\n",
       "9  10    100          90.0          1.000000"
      ],
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     },
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    }
   ],
   "execution_count": 69
  },
  {
   "metadata": {
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   },
   "cell_type": "code",
   "source": [
    "# Find the most frequently occurring value(s) in a column.\n",
    "\n",
    "data = {\n",
    "    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Emma', 'Frank', 'Grace', 'Helen', 'Ivy', 'Jack'],\n",
    "    'Age': [25, 30, 35, 28, 26, 33, 27, 31, 29, 34],\n",
    "    'City': ['NY', 'LA', 'SF', 'BOS', 'HOU', 'SEA', 'SEA', 'BOS', 'ATL', 'SEA'],\n",
    "    'Salary': [60000, 70000, 80000, 72000, 65000, 75000, 68000, 71000, 67000, 73000]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "display(df)\n",
    "\n",
    "# df['City'].mode()\n",
    "\n",
    "# Find the top 3 most common values in a column\n",
    "top_3 = df['City'].value_counts().nlargest(3)\n",
    "print(top_3)\n",
    "\n",
    "# Find the top value\n",
    "print(top_3.index.values[0])"
   ],
   "id": "75fb6c9456bd35be",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      Name  Age City  Salary\n",
       "0    Alice   25   NY   60000\n",
       "1      Bob   30   LA   70000\n",
       "2  Charlie   35   SF   80000\n",
       "3    David   28  BOS   72000\n",
       "4     Emma   26  HOU   65000\n",
       "5    Frank   33  SEA   75000\n",
       "6    Grace   27  SEA   68000\n",
       "7    Helen   31  BOS   71000\n",
       "8      Ivy   29  ATL   67000\n",
       "9     Jack   34  SEA   73000"
      ],
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       "  <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",
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       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
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       "      <td>60000</td>\n",
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       "    <tr>\n",
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       "      <td>Bob</td>\n",
       "      <td>30</td>\n",
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       "      <td>70000</td>\n",
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       "      <td>Charlie</td>\n",
       "      <td>35</td>\n",
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       "      <td>80000</td>\n",
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       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>28</td>\n",
       "      <td>BOS</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Emma</td>\n",
       "      <td>26</td>\n",
       "      <td>HOU</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Frank</td>\n",
       "      <td>33</td>\n",
       "      <td>SEA</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Grace</td>\n",
       "      <td>27</td>\n",
       "      <td>SEA</td>\n",
       "      <td>68000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Helen</td>\n",
       "      <td>31</td>\n",
       "      <td>BOS</td>\n",
       "      <td>71000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ivy</td>\n",
       "      <td>29</td>\n",
       "      <td>ATL</td>\n",
       "      <td>67000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jack</td>\n",
       "      <td>34</td>\n",
       "      <td>SEA</td>\n",
       "      <td>73000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "City\n",
      "SEA    3\n",
      "BOS    2\n",
      "NY     1\n",
      "Name: count, dtype: int64\n",
      "SEA\n"
     ]
    }
   ],
   "execution_count": 76
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-15T21:46:58.010082Z",
     "start_time": "2025-02-15T21:46:58.005290Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Calculate the cumulative sum of a column.\n",
    "\n",
    "# The cumulative sum (also called running total) computes the sum of a column progressively row by row.\n",
    "df['Cumulative_Sum'] = df['Salary'].fillna(0).cumsum()\n",
    "display(df)\n",
    "\n",
    "df['Cumulative_Sum'] = df.groupby('City')['Salary'].cumsum()\n",
    "display(df)\n"
   ],
   "id": "ca9a576bd192ffe9",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      Name  Age City  Salary  Cumulative_Sum\n",
       "0    Alice   25   NY   60000           60000\n",
       "1      Bob   30   LA   70000          130000\n",
       "2  Charlie   35   SF   80000          210000\n",
       "3    David   28  BOS   72000          282000\n",
       "4     Emma   26  HOU   65000          347000\n",
       "5    Frank   33  SEA   75000          422000\n",
       "6    Grace   27  SEA   68000          490000\n",
       "7    Helen   31  BOS   71000          561000\n",
       "8      Ivy   29  ATL   67000          628000\n",
       "9     Jack   34  SEA   73000          701000"
      ],
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       "      <td>70000</td>\n",
       "      <td>130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>35</td>\n",
       "      <td>SF</td>\n",
       "      <td>80000</td>\n",
       "      <td>210000</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>28</td>\n",
       "      <td>BOS</td>\n",
       "      <td>72000</td>\n",
       "      <td>282000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Emma</td>\n",
       "      <td>26</td>\n",
       "      <td>HOU</td>\n",
       "      <td>65000</td>\n",
       "      <td>347000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Frank</td>\n",
       "      <td>33</td>\n",
       "      <td>SEA</td>\n",
       "      <td>75000</td>\n",
       "      <td>422000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Grace</td>\n",
       "      <td>27</td>\n",
       "      <td>SEA</td>\n",
       "      <td>68000</td>\n",
       "      <td>490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Helen</td>\n",
       "      <td>31</td>\n",
       "      <td>BOS</td>\n",
       "      <td>71000</td>\n",
       "      <td>561000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ivy</td>\n",
       "      <td>29</td>\n",
       "      <td>ATL</td>\n",
       "      <td>67000</td>\n",
       "      <td>628000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jack</td>\n",
       "      <td>34</td>\n",
       "      <td>SEA</td>\n",
       "      <td>73000</td>\n",
       "      <td>701000</td>\n",
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       "</div>"
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    {
     "data": {
      "text/plain": [
       "      Name  Age City  Salary  Cumulative_Sum\n",
       "0    Alice   25   NY   60000           60000\n",
       "1      Bob   30   LA   70000           70000\n",
       "2  Charlie   35   SF   80000           80000\n",
       "3    David   28  BOS   72000           72000\n",
       "4     Emma   26  HOU   65000           65000\n",
       "5    Frank   33  SEA   75000           75000\n",
       "6    Grace   27  SEA   68000          143000\n",
       "7    Helen   31  BOS   71000          143000\n",
       "8      Ivy   29  ATL   67000           67000\n",
       "9     Jack   34  SEA   73000          216000"
      ],
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       "      <td>LA</td>\n",
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       "      <td>70000</td>\n",
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       "      <td>65000</td>\n",
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       "      <td>SEA</td>\n",
       "      <td>75000</td>\n",
       "      <td>75000</td>\n",
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       "    <tr>\n",
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       "      <td>Grace</td>\n",
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       "      <td>SEA</td>\n",
       "      <td>68000</td>\n",
       "      <td>143000</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Helen</td>\n",
       "      <td>31</td>\n",
       "      <td>BOS</td>\n",
       "      <td>71000</td>\n",
       "      <td>143000</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ivy</td>\n",
       "      <td>29</td>\n",
       "      <td>ATL</td>\n",
       "      <td>67000</td>\n",
       "      <td>67000</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jack</td>\n",
       "      <td>34</td>\n",
       "      <td>SEA</td>\n",
       "      <td>73000</td>\n",
       "      <td>216000</td>\n",
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       "  </tbody>\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 81
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-15T21:54:41.619625Z",
     "start_time": "2025-02-15T21:54:41.614076Z"
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   },
   "cell_type": "code",
   "source": [
    "# Write a function to detect outliers in a column using the IQR method.\n",
    "import pandas as pd\n",
    "\n",
    "def detect_outliers_iqr(df: pd.DataFrame, column: str) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    Detect outliers in a column using the IQR (Interquartile Range) method.\n",
    "\n",
    "    Args:\n",
    "        df (pd.DataFrame): Input DataFrame.\n",
    "        column (str): Column to check for outliers.\n",
    "\n",
    "    Returns:\n",
    "        pd.DataFrame: A subset of the DataFrame containing only the outliers.\n",
    "    \"\"\"\n",
    "    Q1 = df[column].quantile(0.25)  # First quartile (25%)\n",
    "    Q3 = df[column].quantile(0.75)  # Third quartile (75%)\n",
    "    IQR = Q3 - Q1  # Interquartile range\n",
    "\n",
    "    # Define outlier boundaries\n",
    "    lower_bound = Q1 - 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "\n",
    "    # Filter outliers\n",
    "    outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
    "\n",
    "    return outliers\n",
    "\n",
    "\n",
    "data = {\n",
    "    'ID': range(1, 11),\n",
    "    'Values': [10, 12, 14, 18, 22, 30, 32, 34, 100, 150]  # 100 and 150 are outliers\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "outliers_df = detect_outliers_iqr(df, 'Values')\n",
    "display(outliers_df)\n",
    "\n",
    "# remove outliers from the DataFrame\n",
    "df_clean = df[~df['Values'].isin(outliers_df['Values'])]\n",
    "display(df_clean)"
   ],
   "id": "528f016d2220225a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   ID  Values\n",
       "8   9     100\n",
       "9  10     150"
      ],
      "text/html": [
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      "text/plain": [
       "   ID  Values\n",
       "0   1      10\n",
       "1   2      12\n",
       "2   3      14\n",
       "3   4      18\n",
       "4   5      22\n",
       "5   6      30\n",
       "6   7      32\n",
       "7   8      34"
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       "      <td>32</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
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    }
   ],
   "execution_count": 83
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-15T22:02:34.948107Z",
     "start_time": "2025-02-15T22:02:34.874632Z"
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   },
   "cell_type": "code",
   "source": [
    "# Compute correlation between numeric columns in a DataFrame\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = {\n",
    "    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Emma', 'Frank', 'Grace', 'Helen', 'Ivy', 'Jack'],\n",
    "    'Age': [25, 30, 35, 28, 40, 50, 27, 45, 38, 29],\n",
    "    'Salary': [60000, 70000, 80000, 72000, 90000, 100000, 68000, 95000, 87000, 73000],\n",
    "    'Shift': ['Morning', 'Afternoon', 'Night', 'Morning', 'Night', 'Afternoon',\n",
    "              'Morning', 'Night', 'Afternoon', 'Morning']  # Categorical\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "correlation_matrix = df[['Age', 'Salary']].corr()\n",
    "display(correlation_matrix)\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "plt.title(\"Correlation Matrix Heatmap\")\n",
    "plt.show()\n",
    "\n",
    "# Convert the Shift column into numerical values using Label Encoding:\n",
    "df['Shift_Num'] = df['Shift'].astype('category').cat.codes\n",
    "correlation_matrix_with_categorical = df[['Age', 'Salary', 'Shift_Num']].corr()\n",
    "display(correlation_matrix_with_categorical)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "sns.heatmap(correlation_matrix_with_categorical, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "plt.title(\"Correlation Matrix Heatmap\")\n",
    "plt.show()\n"
   ],
   "id": "2717bbb1f097f2f6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "             Age    Salary\n",
       "Age     1.000000  0.980226\n",
       "Salary  0.980226  1.000000"
      ],
      "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>Age</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.980226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>0.980226</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "                Age    Salary  Shift_Num\n",
       "Age        1.000000  0.980226   0.032476\n",
       "Salary     0.980226  1.000000   0.083538\n",
       "Shift_Num  0.032476  0.083538   1.000000"
      ],
      "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>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Shift_Num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.980226</td>\n",
       "      <td>0.032476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>0.980226</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.083538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shift_Num</th>\n",
       "      <td>0.032476</td>\n",
       "      <td>0.083538</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 90
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-15T22:04:35.427487Z",
     "start_time": "2025-02-15T22:04:35.422536Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Apply a custom function to a column using `.apply()`\n",
    "import pandas as pd\n",
    "\n",
    "data = {\n",
    "    'First_Name': ['Alice', 'Bob', 'Charlie', 'David'],\n",
    "    'Last_Name': ['Smith', 'Johnson', 'Williams', 'Brown'],\n",
    "    'Age': [25, 30, 35, 40]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Define a function to apply row-wise\n",
    "def create_full_name(row):\n",
    "    return f'{row['First_Name']} {row['Last_Name']}'\n",
    "\n",
    "df['Full_Name'] = df.apply(create_full_name, axis=1)\n",
    "display(df)\n"
   ],
   "id": "90a884d18226dc22",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  First_Name Last_Name  Age         Full_Name\n",
       "0      Alice     Smith   25       Alice Smith\n",
       "1        Bob   Johnson   30       Bob Johnson\n",
       "2    Charlie  Williams   35  Charlie Williams\n",
       "3      David     Brown   40       David Brown"
      ],
      "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>First_Name</th>\n",
       "      <th>Last_Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Full_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>Smith</td>\n",
       "      <td>25</td>\n",
       "      <td>Alice Smith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Johnson</td>\n",
       "      <td>30</td>\n",
       "      <td>Bob Johnson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>Williams</td>\n",
       "      <td>35</td>\n",
       "      <td>Charlie Williams</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>Brown</td>\n",
       "      <td>40</td>\n",
       "      <td>David Brown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 91
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "fb13cb0ab31adaa5"
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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