dataframe_3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "649768ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "55b00a85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     A    B   C\n",
      "0  1.0  5.0   9\n",
      "1  2.0  NaN  10\n",
      "2  NaN  NaN  11\n",
      "3  4.0  8.0  12\n"
     ]
    }
   ],
   "source": [
    "data = {\n",
    "    'A': [1, 2, np.nan, 4],\n",
    "    'B': [5, np.nan, np.nan, 8],\n",
    "    'C': [9, 10, 11, 12]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "749b198c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       A      B      C\n",
      "0  False  False  False\n",
      "1  False   True  False\n",
      "2   True   True  False\n",
      "3  False  False  False\n"
     ]
    }
   ],
   "source": [
    "print(df.isna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8b9be8af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop rows with missing values\n",
    "df_dropped_rows = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cdf611bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B   C\n",
       "0  1.0  5.0   9\n",
       "3  4.0  8.0  12"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dropped_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2c996cef",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dropped_rows = df.dropna(thresh=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "24427cd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "     A    B   C\n",
       "0  1.0  5.0   9\n",
       "1  2.0  NaN  10\n",
       "3  4.0  8.0  12"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dropped_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1a71314b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop columns with missing values\n",
    "df_dropped_columns = df.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7b34bd75",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
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       "      <td>11</td>\n",
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       "      <td>12</td>\n",
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      "text/plain": [
       "    C\n",
       "0   9\n",
       "1  10\n",
       "2  11\n",
       "3  12"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dropped_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ca41cb62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     A    B   C\n",
       "0  1.0  5.0   9\n",
       "1  2.0  0.0  10\n",
       "2  0.0  0.0  11\n",
       "3  4.0  8.0  12"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill missing values with a specific value (e.g., 0)\n",
    "df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1dcddee5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.333333</td>\n",
       "      <td>6.5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A    B   C\n",
       "0  1.000000  5.0   9\n",
       "1  2.000000  6.5  10\n",
       "2  2.333333  6.5  11\n",
       "3  4.000000  8.0  12"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill missing values with the mean of the column\n",
    "df.fillna(df.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9d3f2976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B   C\n",
       "0  1.0  5.0   9\n",
       "1  2.0  5.0  10\n",
       "2  2.0  5.0  11\n",
       "3  4.0  8.0  12"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill missing values with the previous value in the column (forward fill)\n",
    "df.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cbfb1eb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B   C\n",
       "0  1.0  5.0   9\n",
       "1  2.0  8.0  10\n",
       "2  4.0  8.0  11\n",
       "3  4.0  8.0  12"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill missing values with the next value in the column (backward fill)\n",
    "df.fillna(method='bfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3329fcb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2., nan,  4.])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get unique value\n",
    "df['A'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4e2b2e2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get number of unique value\n",
    "df['A'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "af9f3d3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    1\n",
       "2.0    1\n",
       "4.0    1\n",
       "Name: A, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the count of each unique value\n",
    "df['A'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c906be3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Math  Physics  Chemistry\n",
      "Alice      85       80         70\n",
      "Bob        90       85         75\n",
      "Charlie    78       86         72\n",
      "David      92       92         89\n",
      "Eva        88       95         85\n"
     ]
    }
   ],
   "source": [
    "data = {\n",
    "    'Math': [85, 90, 78, 92, 88],\n",
    "    'Physics': [80, 85, 86, 92, 95],\n",
    "    'Chemistry': [70, 75, 72, 89, 85]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data, index=['Alice', 'Bob', 'Charlie', 'David', 'Eva'])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "89fa8d5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Row-wise operation: average score per student\n",
    "row_average = df.apply(lambda row: row.mean(), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "245161c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alice      78.333333\n",
       "Bob        83.333333\n",
       "Charlie    78.666667\n",
       "David      91.000000\n",
       "Eva        89.333333\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7e42cb2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Column-wise operation: average score per subject\n",
    "col_average = df.apply(lambda col: col.mean(), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c1433d74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Math         86.6\n",
       "Physics      87.6\n",
       "Chemistry    78.2\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2e3f8b51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Math', 'Physics', 'Chemistry'], dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6e872594",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Alice', 'Bob', 'Charlie', 'David', 'Eva'], dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0d20a751",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\n",
    "    'Region': ['North', 'North', 'South', 'South', 'East', 'East', 'West', 'West'],\n",
    "    'Product': ['A', 'B', 'A', 'B', 'A', 'B', 'A', 'B'],\n",
    "    'Sales': [100, 150, 200, 250, 300, 350, 400, 450]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e10371bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Product</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>North</td>\n",
       "      <td>A</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>North</td>\n",
       "      <td>B</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>South</td>\n",
       "      <td>A</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>South</td>\n",
       "      <td>B</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>East</td>\n",
       "      <td>A</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>East</td>\n",
       "      <td>B</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>West</td>\n",
       "      <td>A</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>West</td>\n",
       "      <td>B</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Region Product  Sales\n",
       "0  North       A    100\n",
       "1  North       B    150\n",
       "2  South       A    200\n",
       "3  South       B    250\n",
       "4   East       A    300\n",
       "5   East       B    350\n",
       "6   West       A    400\n",
       "7   West       B    450"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "01e2285b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_table = df.pivot_table(\n",
    "                values='Sales',\n",
    "                index='Region',\n",
    "                columns='Product',\n",
    "                aggfunc='sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "77ef37a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\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",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Product</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>East</th>\n",
       "      <td>300</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North</th>\n",
       "      <td>100</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South</th>\n",
       "      <td>200</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West</th>\n",
       "      <td>400</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Product    A    B\n",
       "Region           \n",
       "East     300  350\n",
       "North    100  150\n",
       "South    200  250\n",
       "West     400  450"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "856a1e52",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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