Python & Numpy for DA(3).ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cb45734d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "25f72f9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a sample DataFrame\n",
    "data = {'Name': ['Imen', 'Karthika', 'Cosimo', 'Cathrine'],\n",
    "        'Age': [25, 30, 22, 35],\n",
    "        'Salary': [50000, 60000, 45000, 70000]}\n",
    "\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6eb92c29",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Imen</td>\n",
       "      <td>25</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Karthika</td>\n",
       "      <td>30</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cosimo</td>\n",
       "      <td>22</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cathrine</td>\n",
       "      <td>35</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  Age  Salary\n",
       "0      Imen   25   50000\n",
       "1  Karthika   30   60000\n",
       "2    Cosimo   22   45000\n",
       "3  Cathrine   35   70000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1ff3bfb9",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>Imen</td>\n",
       "      <td>Karthika</td>\n",
       "      <td>Cosimo</td>\n",
       "      <td>Cathrine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>50000</td>\n",
       "      <td>60000</td>\n",
       "      <td>45000</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1       2         3\n",
       "Name     Imen  Karthika  Cosimo  Cathrine\n",
       "Age        25        30      22        35\n",
       "Salary  50000     60000   45000     70000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transposed_df = df.T\n",
    "transposed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cfc8a531",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>Imen</td>\n",
       "      <td>Karthika</td>\n",
       "      <td>Cosimo</td>\n",
       "      <td>Cathrine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>50000</td>\n",
       "      <td>60000</td>\n",
       "      <td>45000</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            0         1       2         3\n",
       "Name     Imen  Karthika  Cosimo  Cathrine\n",
       "Age        25        30      22        35\n",
       "Salary  50000     60000   45000     70000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transposing_df = df.transpose()\n",
    "transposing_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "60337648",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Imen</td>\n",
       "      <td>HR</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Karthika</td>\n",
       "      <td>IT</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Cosimo</td>\n",
       "      <td>Finance</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID      Name Department  Salary\n",
       "0   1      Imen         HR   50000\n",
       "1   2  Karthika         IT   60000\n",
       "2   3    Cosimo    Finance   45000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create two sample DataFrames\n",
    "data1 = {'ID': [1, 2, 3, 4],\n",
    "         'Name': ['Imen', 'Karthika', 'Cosimo', 'Cathrine'],\n",
    "         'Department': ['HR', 'IT', 'Finance', 'IT']}\n",
    "\n",
    "data2 = {'ID': [1, 2, 3, 5],\n",
    "         'Salary': [50000, 60000, 45000, 70000],\n",
    "         'Department': ['HR', 'IT', 'Finance', 'Marketing']}\n",
    "\n",
    "df1 = pd.DataFrame(data1)\n",
    "df2 = pd.DataFrame(data2)\n",
    "\n",
    "merged_df = pd.merge(df1, df2, on=['ID', \"Department\"], how='inner')\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "82d40a95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Department   Salary         Experience\n",
      "                 mean     sum        max\n",
      "0    Finance  52500.0  105000          4\n",
      "1         HR  52500.0  105000          3\n",
      "2         IT  65000.0  130000          7\n"
     ]
    }
   ],
   "source": [
    "# Create a sample DataFrame\n",
    "data = {'Department': ['HR', 'IT', 'Finance', 'IT', 'HR', 'Finance'],\n",
    "        'Salary': [50000, 60000, 45000, 70000, 55000, 60000],\n",
    "        'Experience': [2, 5, 1, 7, 3, 4]}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Group by 'Department' and aggregate data with multiple functions\n",
    "aggregated_df = df.groupby('Department').agg({\n",
    "    'Salary': ['mean', 'sum'],\n",
    "    'Experience': 'max'\n",
    "}).reset_index()\n",
    "\n",
    "# Display the aggregated DataFrame\n",
    "print(aggregated_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6f47af3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DateTime</th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01 08:30:00</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>08:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-01-02 12:45:00</td>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>12:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-01-03 18:15:00</td>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>18:15:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "             DateTime        Date      Time\n",
       "0 2012-01-01 08:30:00  2012-01-01  08:30:00\n",
       "1 2012-01-02 12:45:00  2012-01-02  12:45:00\n",
       "2 2012-01-03 18:15:00  2012-01-03  18:15:00"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a sample DataFrame with a DateTime column\n",
    "data = {'DateTime': ['2012-01-01 08:30:00', '2012-01-02 12:45:00', '2012-01-03 18:15:00']}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Convert the 'DateTime' column to a datetime type\n",
    "df['DateTime'] = pd.to_datetime(df['DateTime'])\n",
    "\n",
    "#Extract date and time components\n",
    "df['Date'] = df['DateTime'].dt.date\n",
    "df['Time'] = df['DateTime'].dt.time\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "33f9ac5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DataFrame\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-07</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-08</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-10</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Value\n",
       "2012-01-01     10\n",
       "2012-01-02     15\n",
       "2012-01-03     20\n",
       "2012-01-04     25\n",
       "2012-01-05     30\n",
       "2012-01-06     35\n",
       "2012-01-07     40\n",
       "2012-01-08     45\n",
       "2012-01-09     50\n",
       "2012-01-10     55"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a sample DataFrame with time-series data\n",
    "date_rng = pd.date_range(start='2012-01-01', end='2012-01-10', freq='D')\n",
    "data = {'Value': [10, 15, 20, 25, 30, 35, 40, 45, 50, 55]}\n",
    "df = pd.DataFrame(data, index=date_rng)\n",
    "\n",
    "resampled_df = df.resample('W').mean()\n",
    "\n",
    "print(\"Original DataFrame:\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f6437257",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resampled DataFrame:\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-08</th>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-15</th>\n",
       "      <td>52.5</td>\n",
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      "text/plain": [
       "            Value\n",
       "2012-01-01   10.0\n",
       "2012-01-08   30.0\n",
       "2012-01-15   52.5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Resampled DataFrame:\")\n",
    "resampled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1402405e",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Value</th>\n",
       "      <th>Rolling_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>40</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>45</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Value  Rolling_mean\n",
       "0     10           NaN\n",
       "1     15           NaN\n",
       "2     20          15.0\n",
       "3     25          20.0\n",
       "4     30          25.0\n",
       "5     35          30.0\n",
       "6     40          35.0\n",
       "7     45          40.0\n",
       "8     50          45.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a sample DataFrame\n",
    "data = {'Value': [10, 15, 20, 25, 30, 35, 40, 45, 50]}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "rolling_mean = df['Value'].rolling(window=3).mean()\n",
    "df['Rolling_mean'] = rolling_mean\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9e45dfe4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Value</th>\n",
       "      <th>10</th>\n",
       "      <th>15</th>\n",
       "      <th>20</th>\n",
       "      <th>25</th>\n",
       "      <th>30</th>\n",
       "      <th>35</th>\n",
       "      <th>40</th>\n",
       "      <th>45</th>\n",
       "      <th>50</th>\n",
       "      <th>55</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Value     10  15  20  25  30  35  40  45  50  55\n",
       "Category                                        \n",
       "A          1   0   1   0   0   1   0   0   0   1\n",
       "B          0   1   0   1   0   0   0   0   1   0\n",
       "C          0   0   0   0   1   0   1   1   0   0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a sample DataFrame\n",
    "data = {'Category': ['A', 'B', 'A', 'B', 'C', 'A', 'C', 'C', 'B', 'A'],\n",
    "        'Value': [10, 15, 20, 25, 30, 35, 40, 45, 50, 55]}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "cross_tab = pd.crosstab(df['Category'], df['Value'])\n",
    "cross_tab"
   ]
  }
 ],
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