Pandas_InClass_2 (Series Methods & DataFrames) 15.03.24.ipynb

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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='green'> <b>Importing Libraries </b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-04T19:06:15.654368Z",
     "start_time": "2023-04-04T19:06:10.963607Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'> <b>Basic Attributes & Methods of Series</b><font color='black'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**SOME COMMON ATTRIBUTES** [Official Pandas API Document](https://pandas.pydata.org/docs/reference/api/pandas.Series.html)<br>\n",
    "\n",
    "**Series.values:** Returns the values of the series as a Numpy array.\n",
    "\n",
    "**Series.index:** Returns the indices of the series.\n",
    "\n",
    "**Series.dtype:** Returns the data type of the series.\n",
    "\n",
    "**Series.size:** Returns the number of elements in the series.\n",
    "\n",
    "**Series.shape:** Returns the dimensions of the series.\n",
    "\n",
    "**Series.ndim:** Returns the number of dimensions of the series.\n",
    "\n",
    "**Series.head():** Returns the first n elements of the series.\n",
    "\n",
    "**Series.tail():** Returns the last n elements of the series.\n",
    "\n",
    "**Series.sample():** Returns a random n elements from the series.\n",
    "\n",
    "**Series.describe():** Returns the statistical summary of the series.\n",
    "\n",
    "**Series.sort_index:** Sorts the series by its indices.\n",
    "\n",
    "**Series.sort_values():** Sorts the series by its values.\n",
    "\n",
    "**Series.isnull():** Checks whether each element in the series is null (None).\n",
    "\n",
    "**Series.fillna():** Fills null values with a specified value.\n",
    "\n",
    "**Series.dropna():** Removes null values from the series.\n",
    "\n",
    "**Series.isin():** Checks whether the elements in the series are present in the given values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    38\n",
       "1    28\n",
       "2    14\n",
       "3    42\n",
       "4     7\n",
       "5    20\n",
       "6    38\n",
       "7    18\n",
       "dtype: int32"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "seri = pd.Series(np.random.randint(0, 50, 8))\n",
    "seri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    38\n",
       "1    28\n",
       "2    14\n",
       "3    42\n",
       "4     7\n",
       "dtype: int32"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.head()  #The default value is 5. It shows the first 5 values from the beginning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    38\n",
       "1    28\n",
       "2    14\n",
       "dtype: int32"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.head(3)  #We requested the first 3 values from the beginning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    42\n",
       "4     7\n",
       "5    20\n",
       "6    38\n",
       "7    18\n",
       "dtype: int32"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.tail()  #It returns the last 5 rows by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "seri.tail(2) #We requested to fetch the last 2 rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    38\n",
       "dtype: int32"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.sample()  #It returns a random row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4     7\n",
       "2    14\n",
       "7    18\n",
       "5    20\n",
       "1    28\n",
       "0    38\n",
       "6    38\n",
       "3    42\n",
       "dtype: int32"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.sort_values() #sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    42\n",
       "0    38\n",
       "6    38\n",
       "1    28\n",
       "5    20\n",
       "7    18\n",
       "2    14\n",
       "4     7\n",
       "dtype: int32"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.sort_values(ascending=False)  #The default value of the ascending parameter is True. If it's set to False, it sorts in descending order from largest to smallest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    38\n",
       "1    28\n",
       "2    14\n",
       "3    42\n",
       "4     7\n",
       "5    20\n",
       "6    38\n",
       "7    18\n",
       "dtype: int32"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7    18\n",
       "6    38\n",
       "5    20\n",
       "4     7\n",
       "3    42\n",
       "2    14\n",
       "1    28\n",
       "0    38\n",
       "dtype: int32"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.sort_index(ascending=False) #It sorted the indices in reverse order."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='green'> <b>DataFrames </b><font color='black'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'> <b>Creating a DataFrame</b><font color='black'>\n",
    "\n",
    "DataFrame is a two-dimensional data collection.\n",
    "\n",
    "It's a data structure where data is stored in a tabular format.\n",
    "\n",
    "Data sets are organized into rows and columns; a DataFrame can store multiple data sets.\n",
    "\n",
    "We can think of a DataFrame as a collection of Series objects that share the same index.\n",
    "\n",
    "We can perform various arithmetic operations on a DataFrame such as column/row selection and addition.\n",
    "\n",
    "We can import DataFrames from external storage; SQL database, CSV file, and Excel file.\n",
    "\n",
    "[SOURCE01](https://www.tutorialspoint.com/python_pandas/python_pandas_dataframe.htm), \n",
    "[SOURCE02](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html), \n",
    "[SOURCE03](https://morioh.com/p/2528ac775b1b), \n",
    "[SOURCE04](https://www.datacamp.com/community/tutorials/pandas-tutorial-dataframe-python), \n",
    "[SOURCE05](https://www.guru99.com/python-pandas-tutorial.html), \n",
    "[SOURCE06](https://realpython.com/pandas-dataframe/) &\n",
    "[SOURCE07](https://towardsdatascience.com/a-simple-guide-to-pandas-dataframes-b125f64e1453)<br>\n",
    "[VIDEO SOURCE01](https://www.youtube.com/watch?v=zmdjNSmRXF4), \n",
    "[VIDEO SOURCE02](https://www.youtube.com/watch?v=F6kmIpWWEdU) &\n",
    "[VIDEO SOURCE03](https://towardsdatascience.com/pandas-dataframe-basics-3c16eb35c4f3)<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='blue'> <b>Creating a DataFrame Using the Lists of Data & Columns</b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B  C\n",
       "0  1  2  3\n",
       "1  4  5  6"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_1 = [[1,2,3],[4,5,6]]\n",
    "\n",
    "column_name = [\"A\", \"B\", \"C\"]\n",
    "\n",
    "df = pd.DataFrame(data = list_1, columns= column_name)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='blue'> <b>Creating a DataFrame Using a Numpy Arrays</b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  4,  7],\n",
       "       [10, 13, 16],\n",
       "       [19, 22, 25]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 = np.arange(1, 27, 3).reshape(3,3)\n",
    "arr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   2\n",
       "0   1   4   7\n",
       "1  10  13  16\n",
       "2  19  22  25"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(arr1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  5,  9],\n",
       "       [13, 17, 21],\n",
       "       [25, 29, 33]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2 = np.arange(1,36,4).reshape(3,3)\n",
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31</td>\n",
       "      <td>36</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    1   2   3\n",
       "0   1   6  11\n",
       "1  16  21  26\n",
       "2  31  36  41"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(arr2, columns= range(1,4))\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(arr1, columns= range(1,4)) #I tried a different method for column numbering.\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>B2</th>\n",
       "      <th>A3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A1  B2  A3\n",
       "0   1   4   7\n",
       "1  10  13  16\n",
       "2  19  22  25"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data = arr1, columns= [\"A1\", \"B2\", \"A3\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>B2</th>\n",
       "      <th>A3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B1</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B2</th>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B3</th>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A1  B2  A3\n",
       "B1   1   4   7\n",
       "B2  10  13  16\n",
       "B3  19  22  25"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(arr1, columns=[\"A1\", \"B2\", \"A3\"], index = [\"B1\", \"B2\", \"B3\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='blue'> <b>Creating a DataFrame Using a Dictionary</b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'Name':['Ayşe', 'Ahmet', 'Mehmet'],'Age':[20,30,40]}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name    [Ayşe, Ahmet, Mehmet]\n",
       "Age              [20, 30, 40]\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ayşe</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ahmet</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mehmet</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age\n",
       "0    Ayşe   20\n",
       "1   Ahmet   30\n",
       "2  Mehmet   40"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data = data) #parametre=argument   #In series, keys serve as indices, whereas in dataframes, keys are analogous to columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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",
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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>Job</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ayşe</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ahmet</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mehmet</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age  Job\n",
       "0    Ayşe   20  NaN\n",
       "1   Ahmet   30  NaN\n",
       "2  Mehmet   40  NaN"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data, columns= [\"Name\", \"Age\", \"Job\"]) #Due to the lack of job information, it resulted in NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>aa</th>\n",
       "      <th>bb</th>\n",
       "      <th>cc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [aa, bb, cc]\n",
       "Index: []"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data, columns= [\"aa\", \"bb\", \"cc\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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",
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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>bb</th>\n",
       "      <th>cc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ayşe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ahmet</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mehmet</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name   bb   cc\n",
       "0    Ayşe  NaN  NaN\n",
       "1   Ahmet  NaN  NaN\n",
       "2  Mehmet  NaN  NaN"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data, columns= [\"Name\", \"bb\", \"cc\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'> <b>Basic Attributes & Methods of DataFrames</b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_1 = {'Name':['Ayşe', 'Ahmet', 'Mehmet'],'Age':[20,30,40]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ayşe</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ahmet</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mehmet</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age\n",
       "0    Ayşe   20\n",
       "1   Ahmet   30\n",
       "2  Mehmet   40"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data = dict_1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ahmet</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Age\n",
       "0   Ayşe   20\n",
       "1  Ahmet   30"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ahmet</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mehmet</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age\n",
       "1   Ahmet   30\n",
       "2  Mehmet   40"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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",
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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mehmet</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age\n",
       "2  Mehmet   40"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Age'], dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Name'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Age'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 2 is out of bounds for axis 0 with size 2",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[42], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df\u001b[38;5;241m.\u001b[39mcolumns[\u001b[38;5;241m2\u001b[39m]\n",
      "File \u001b[1;32mc:\\Users\\duman\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:5175\u001b[0m, in \u001b[0;36mIndex.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   5172\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(key) \u001b[38;5;129;01mor\u001b[39;00m is_float(key):\n\u001b[0;32m   5173\u001b[0m     \u001b[38;5;66;03m# GH#44051 exclude bool, which would return a 2d ndarray\u001b[39;00m\n\u001b[0;32m   5174\u001b[0m     key \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mcast_scalar_indexer(key)\n\u001b[1;32m-> 5175\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m getitem(key)\n\u001b[0;32m   5177\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mslice\u001b[39m):\n\u001b[0;32m   5178\u001b[0m     \u001b[38;5;66;03m# This case is separated from the conditional above to avoid\u001b[39;00m\n\u001b[0;32m   5179\u001b[0m     \u001b[38;5;66;03m# pessimization com.is_bool_indexer and ndim checks.\u001b[39;00m\n\u001b[0;32m   5180\u001b[0m     result \u001b[38;5;241m=\u001b[39m getitem(key)\n",
      "\u001b[1;31mIndexError\u001b[0m: index 2 is out of bounds for axis 0 with size 2"
     ]
    }
   ],
   "source": [
    "df.columns[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=3, step=1)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
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       "    }\n",
       "\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",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ayşe</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ahmet</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mehmet</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age\n",
       "0    Ayşe   20\n",
       "1   Ahmet   30\n",
       "2  Mehmet   40"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.mean of      Name  Age\n",
       "0    Ayşe   20\n",
       "1   Ahmet   30\n",
       "2  Mehmet   40>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Age.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'> <b>Indexing, Slicing & Selection</b><font color='black'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Source01](https://pandas.pydata.org/docs/user_guide/indexing.html),\n",
    "[Source02](https://www.geeksforgeeks.org/slicing-indexing-manipulating-and-cleaning-pandas-dataframe/),\n",
    "[Source03](https://www.tutorialspoint.com/python_pandas/python_pandas_indexing_and_selecting_data.htm),\n",
    "[Source04](https://www.dataquest.io/blog/tutorial-indexing-dataframes-in-pandas/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'isim': ['Ali', 'Ayşe', 'Fatma', 'Veli'],\n",
       " 'boy-cm': [170, 160, 170, 180],\n",
       " 'kilo-kg': [70, 55, 60, 80]}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data={\"isim\":[\"Ali\", \"Ayşe\", \"Fatma\", \"Veli\"], \"boy-cm\":[170,160,170,180], \"kilo-kg\":[70, 55, 60, 80]}\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>isim</th>\n",
       "      <th>boy-cm</th>\n",
       "      <th>kilo-kg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Ali</td>\n",
       "      <td>170</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>Ayşe</td>\n",
       "      <td>160</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>Fatma</td>\n",
       "      <td>170</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>Veli</td>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    isim  boy-cm  kilo-kg\n",
       "A    Ali     170       70\n",
       "B   Ayşe     160       55\n",
       "C  Fatma     170       60\n",
       "D   Veli     180       80"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data, index= [\"A\", \"B\", \"C\", \"D\"] )\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 3)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A      Ali\n",
       "B     Ayşe\n",
       "C    Fatma\n",
       "D     Veli\n",
       "Name: isim, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A      Ali\n",
       "B     Ayşe\n",
       "C    Fatma\n",
       "D     Veli\n",
       "Name: isim, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"isim\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['isim', 'boy-cm', 'kilo-kg'], dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'kilo_kg'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_19952\\2821439178.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkilo_kg\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   6292\u001b[0m             \u001b[1;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6293\u001b[0m             \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6294\u001b[0m         ):\n\u001b[0;32m   6295\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6296\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'kilo_kg'"
     ]
    }
   ],
   "source": [
    "df.kilo_kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    70\n",
       "B    55\n",
       "C    60\n",
       "D    80\n",
       "Name: kilo-kg, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"kilo-kg\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = df[\"kilo-kg\"]\n",
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>kilo-kg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   kilo-kg\n",
       "A       70\n",
       "B       55\n",
       "C       60\n",
       "D       80"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"kilo-kg\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = df[[\"kilo-kg\"]]\n",
    "type(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .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>boy-cm</th>\n",
       "      <th>kilo-kg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>170</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>160</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>170</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   boy-cm  kilo-kg\n",
       "A     170       70\n",
       "B     160       55\n",
       "C     170       60\n",
       "D     180       80"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"boy-cm\", \"kilo-kg\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"A\"] #It throws an error because it interprets it as a column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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",
       "\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",
       "      <th>isim</th>\n",
       "      <th>boy-cm</th>\n",
       "      <th>kilo-kg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Ali</td>\n",
       "      <td>170</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>Ayşe</td>\n",
       "      <td>160</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>Fatma</td>\n",
       "      <td>170</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>Veli</td>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    isim  boy-cm  kilo-kg\n",
       "A    Ali     170       70\n",
       "B   Ayşe     160       55\n",
       "C  Fatma     170       60\n",
       "D   Veli     180       80"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isim</th>\n",
       "      <th>boy-cm</th>\n",
       "      <th>kilo-kg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Ali</td>\n",
       "      <td>170</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>Ayşe</td>\n",
       "      <td>160</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>Fatma</td>\n",
       "      <td>170</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    isim  boy-cm  kilo-kg\n",
       "A    Ali     170       70\n",
       "B   Ayşe     160       55\n",
       "C  Fatma     170       60"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"A\": \"C\"] #We instructed it to go from A to C, inclusive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isim</th>\n",
       "      <th>boy-cm</th>\n",
       "      <th>kilo-kg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Ali</td>\n",
       "      <td>170</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  isim  boy-cm  kilo-kg\n",
       "A  Ali     170       70"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'> <b>Creating a New Column</b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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",
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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>isim</th>\n",
       "      <th>boy-cm</th>\n",
       "      <th>kilo-kg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Ali</td>\n",
       "      <td>170</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>Ayşe</td>\n",
       "      <td>160</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>Fatma</td>\n",
       "      <td>170</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>Veli</td>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    isim  boy-cm  kilo-kg\n",
       "A    Ali     170       70\n",
       "B   Ayşe     160       55\n",
       "C  Fatma     170       60\n",
       "D   Veli     180       80"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"BMI\"] = round(df[\"kilo-kg\"] / (df[\"boy-cm\"] / 100) **2, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>isim</th>\n",
       "      <th>boy-cm</th>\n",
       "      <th>kilo-kg</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Ali</td>\n",
       "      <td>170</td>\n",
       "      <td>70</td>\n",
       "      <td>24.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>Ayşe</td>\n",
       "      <td>160</td>\n",
       "      <td>55</td>\n",
       "      <td>21.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>Fatma</td>\n",
       "      <td>170</td>\n",
       "      <td>60</td>\n",
       "      <td>20.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>Veli</td>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "      <td>24.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    isim  boy-cm  kilo-kg    BMI\n",
       "A    Ali     170       70  24.22\n",
       "B   Ayşe     160       55  21.48\n",
       "C  Fatma     170       60  20.76\n",
       "D   Veli     180       80  24.69"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"new\"] = np.arange(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>boy-cm</th>\n",
       "      <th>kilo-kg</th>\n",
       "      <th>BMI</th>\n",
       "      <th>new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>A</th>\n",
       "      <td>Ali</td>\n",
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       "      <td>24.22</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>Ayşe</td>\n",
       "      <td>160</td>\n",
       "      <td>55</td>\n",
       "      <td>21.48</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>Fatma</td>\n",
       "      <td>170</td>\n",
       "      <td>60</td>\n",
       "      <td>20.76</td>\n",
       "      <td>2</td>\n",
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       "      <th>D</th>\n",
       "      <td>Veli</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "    isim  boy-cm  kilo-kg    BMI  new\n",
       "A    Ali     170       70  24.22    0\n",
       "B   Ayşe     160       55  21.48    1\n",
       "C  Fatma     170       60  20.76    2\n",
       "D   Veli     180       80  24.69    3"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>22</td>\n",
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       "      <td>Never-married</td>\n",
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       "      <td>0</td>\n",
       "      <td>20</td>\n",
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       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32561 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age         workclass  fnlwgt   education  education-num  \\\n",
       "0       39         State-gov   77516   Bachelors           13.0   \n",
       "1       50  Self-emp-not-inc   83311   Bachelors           13.0   \n",
       "2       38           Private  215646     HS-grad            9.0   \n",
       "3       53           Private  234721        11th            7.0   \n",
       "4       28           Private  338409   Bachelors           13.0   \n",
       "...    ...               ...     ...         ...            ...   \n",
       "32556   27           Private  257302  Assoc-acdm           12.0   \n",
       "32557   40           Private  154374     HS-grad            9.0   \n",
       "32558   58           Private  151910     HS-grad            9.0   \n",
       "32559   22           Private  201490     HS-grad            9.0   \n",
       "32560   52      Self-emp-inc  287927     HS-grad            9.0   \n",
       "\n",
       "           marital-status         occupation   relationship   race     sex  \\\n",
       "0           Never-married       Adm-clerical  Not-in-family  White    Male   \n",
       "1      Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
       "2                Divorced  Handlers-cleaners  Not-in-family  White    Male   \n",
       "3      Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n",
       "4      Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n",
       "...                   ...                ...            ...    ...     ...   \n",
       "32556  Married-civ-spouse       Tech-support           Wife  White  Female   \n",
       "32557  Married-civ-spouse  Machine-op-inspct        Husband  White    Male   \n",
       "32558             Widowed       Adm-clerical      Unmarried  White  Female   \n",
       "32559       Never-married       Adm-clerical            NaN  White    Male   \n",
       "32560  Married-civ-spouse    Exec-managerial           Wife  White  Female   \n",
       "\n",
       "       capital-gain  capital-loss  hours-per-week native-country salary  \n",
       "0              2174             0              40  United-States  <=50K  \n",
       "1                 0             0              13  United-States  <=50K  \n",
       "2                 0             0              40  United-States  <=50K  \n",
       "3                 0             0              40  United-States  <=50K  \n",
       "4                 0             0              40           Cuba  <=50K  \n",
       "...             ...           ...             ...            ...    ...  \n",
       "32556             0             0              38  United-States  <=50K  \n",
       "32557             0             0              40  United-States   >50K  \n",
       "32558             0             0              40  United-States  <=50K  \n",
       "32559             0             0              20  United-States  <=50K  \n",
       "32560         15024             0              40  United-States   >50K  \n",
       "\n",
       "[32561 rows x 15 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"adult_eda.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
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       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         workclass  fnlwgt  education  education-num  \\\n",
       "0   39         State-gov   77516  Bachelors           13.0   \n",
       "1   50  Self-emp-not-inc   83311  Bachelors           13.0   \n",
       "2   38           Private  215646    HS-grad            9.0   \n",
       "3   53           Private  234721       11th            7.0   \n",
       "4   28           Private  338409  Bachelors           13.0   \n",
       "\n",
       "       marital-status         occupation   relationship   race     sex  \\\n",
       "0       Never-married       Adm-clerical  Not-in-family  White    Male   \n",
       "1  Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
       "2            Divorced  Handlers-cleaners  Not-in-family  White    Male   \n",
       "3  Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n",
       "4  Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week native-country salary  \n",
       "0          2174             0              40  United-States  <=50K  \n",
       "1             0             0              13  United-States  <=50K  \n",
       "2             0             0              40  United-States  <=50K  \n",
       "3             0             0              40  United-States  <=50K  \n",
       "4             0             0              40           Cuba  <=50K  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>58</td>\n",
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       "      <td>&lt;=50K</td>\n",
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       "    <tr>\n",
       "      <th>32560</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-inc</td>\n",
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       "      <td>9.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
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       "      <td>&gt;50K</td>\n",
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      ],
      "text/plain": [
       "       age     workclass  fnlwgt   education  education-num  \\\n",
       "32556   27       Private  257302  Assoc-acdm           12.0   \n",
       "32557   40       Private  154374     HS-grad            9.0   \n",
       "32558   58       Private  151910     HS-grad            9.0   \n",
       "32559   22       Private  201490     HS-grad            9.0   \n",
       "32560   52  Self-emp-inc  287927     HS-grad            9.0   \n",
       "\n",
       "           marital-status         occupation relationship   race     sex  \\\n",
       "32556  Married-civ-spouse       Tech-support         Wife  White  Female   \n",
       "32557  Married-civ-spouse  Machine-op-inspct      Husband  White    Male   \n",
       "32558             Widowed       Adm-clerical    Unmarried  White  Female   \n",
       "32559       Never-married       Adm-clerical          NaN  White    Male   \n",
       "32560  Married-civ-spouse    Exec-managerial         Wife  White  Female   \n",
       "\n",
       "       capital-gain  capital-loss  hours-per-week native-country salary  \n",
       "32556             0             0              38  United-States  <=50K  \n",
       "32557             0             0              40  United-States   >50K  \n",
       "32558             0             0              40  United-States  <=50K  \n",
       "32559             0             0              20  United-States  <=50K  \n",
       "32560         15024             0              40  United-States   >50K  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                 int64\n",
       "workclass          object\n",
       "fnlwgt              int64\n",
       "education          object\n",
       "education-num     float64\n",
       "marital-status     object\n",
       "occupation         object\n",
       "relationship       object\n",
       "race               object\n",
       "sex                object\n",
       "capital-gain        int64\n",
       "capital-loss        int64\n",
       "hours-per-week      int64\n",
       "native-country     object\n",
       "salary             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes  #dtypes is used in DataFrames, while dtype is used in Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "488415"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size  #The product of the number of rows and columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
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       "      <td>338409</td>\n",
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       "      <td>Cuba</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>32556</th>\n",
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       "      <th>32557</th>\n",
       "      <td>40</td>\n",
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       "      <th>32558</th>\n",
       "      <td>58</td>\n",
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       "      <td>Widowed</td>\n",
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       "      <td>22</td>\n",
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       "      <td>9.0</td>\n",
       "      <td>Never-married</td>\n",
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       "      <td>0</td>\n",
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       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>32560</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-inc</td>\n",
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       "      <td>HS-grad</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32561 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age         workclass  fnlwgt   education  education-num  \\\n",
       "0       39         State-gov   77516   Bachelors           13.0   \n",
       "1       50  Self-emp-not-inc   83311   Bachelors           13.0   \n",
       "2       38           Private  215646     HS-grad            9.0   \n",
       "3       53           Private  234721        11th            7.0   \n",
       "4       28           Private  338409   Bachelors           13.0   \n",
       "...    ...               ...     ...         ...            ...   \n",
       "32556   27           Private  257302  Assoc-acdm           12.0   \n",
       "32557   40           Private  154374     HS-grad            9.0   \n",
       "32558   58           Private  151910     HS-grad            9.0   \n",
       "32559   22           Private  201490     HS-grad            9.0   \n",
       "32560   52      Self-emp-inc  287927     HS-grad            9.0   \n",
       "\n",
       "           marital-status         occupation   relationship   race     sex  \\\n",
       "0           Never-married       Adm-clerical  Not-in-family  White    Male   \n",
       "1      Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
       "2                Divorced  Handlers-cleaners  Not-in-family  White    Male   \n",
       "3      Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n",
       "4      Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n",
       "...                   ...                ...            ...    ...     ...   \n",
       "32556  Married-civ-spouse       Tech-support           Wife  White  Female   \n",
       "32557  Married-civ-spouse  Machine-op-inspct        Husband  White    Male   \n",
       "32558             Widowed       Adm-clerical      Unmarried  White  Female   \n",
       "32559       Never-married       Adm-clerical            NaN  White    Male   \n",
       "32560  Married-civ-spouse    Exec-managerial           Wife  White  Female   \n",
       "\n",
       "       capital-gain  capital-loss  hours-per-week native-country salary  \n",
       "0              2174             0              40  United-States  <=50K  \n",
       "1                 0             0              13  United-States  <=50K  \n",
       "2                 0             0              40  United-States  <=50K  \n",
       "3                 0             0              40  United-States  <=50K  \n",
       "4                 0             0              40           Cuba  <=50K  \n",
       "...             ...           ...             ...            ...    ...  \n",
       "32556             0             0              38  United-States  <=50K  \n",
       "32557             0             0              40  United-States   >50K  \n",
       "32558             0             0              40  United-States  <=50K  \n",
       "32559             0             0              20  United-States  <=50K  \n",
       "32560         15024             0              40  United-States   >50K  \n",
       "\n",
       "[32561 rows x 15 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32561, 15)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ndim  #It shows the size or dimension, which is unrelated to the number of rows and columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>salary</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8105</th>\n",
       "      <td>32</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>113543</td>\n",
       "      <td>Prof-school</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
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       "      <td>0</td>\n",
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       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age     workclass  fnlwgt    education  education-num  \\\n",
       "8105   32  Self-emp-inc  113543  Prof-school           15.0   \n",
       "\n",
       "          marital-status      occupation relationship   race   sex  \\\n",
       "8105  Married-civ-spouse  Prof-specialty      Husband  White  Male   \n",
       "\n",
       "      capital-gain  capital-loss  hours-per-week native-country salary  \n",
       "8105             0             0              40  United-States   >50K  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>11071</th>\n",
       "      <td>58</td>\n",
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       "      <td>430005</td>\n",
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       "      <th>29742</th>\n",
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       "      <th>14948</th>\n",
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       "      <td>White</td>\n",
       "      <td>Male</td>\n",
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       "      <th>20799</th>\n",
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       "      <td>&gt;50K</td>\n",
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       "      <th>25933</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>102945</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
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       "      <td>White</td>\n",
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       "      <td>0</td>\n",
       "      <td>52</td>\n",
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       "      <th>13442</th>\n",
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       "      <td>0</td>\n",
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       "      <th>21494</th>\n",
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       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>United-States</td>\n",
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       "      <th>20508</th>\n",
       "      <td>45</td>\n",
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       "      <td>Female</td>\n",
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       "      <td>Male</td>\n",
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       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3760</th>\n",
       "      <td>37</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>291518</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
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       "    <tr>\n",
       "      <th>20569</th>\n",
       "      <td>26</td>\n",
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       "    <tr>\n",
       "      <th>7646</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>196125</td>\n",
       "      <td>11th</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25328</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>159589</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12084</th>\n",
       "      <td>40</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>269733</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6453</th>\n",
       "      <td>42</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>170230</td>\n",
       "      <td>Masters</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>14084</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16796</th>\n",
       "      <td>48</td>\n",
       "      <td>Private</td>\n",
       "      <td>83444</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16619</th>\n",
       "      <td>36</td>\n",
       "      <td>Private</td>\n",
       "      <td>175360</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>13550</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12622</th>\n",
       "      <td>52</td>\n",
       "      <td>Private</td>\n",
       "      <td>229983</td>\n",
       "      <td>Prof-school</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>3103</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24724</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>171637</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age         workclass  fnlwgt     education  education-num  \\\n",
       "11071   58           Private  430005          10th            6.0   \n",
       "29742   46           Private  270565     Assoc-voc           11.0   \n",
       "14948   39  Self-emp-not-inc  336793       Masters           14.0   \n",
       "20799   40           Private   96509  Some-college           10.0   \n",
       "25933   38           Private  102945     Bachelors           13.0   \n",
       "13442   20           Private  131611  Some-college           10.0   \n",
       "21494   28  Self-emp-not-inc  282398       HS-grad            9.0   \n",
       "20508   45           Private  116163       HS-grad            9.0   \n",
       "28225   39           Private  249720     Bachelors           13.0   \n",
       "11493   37           Private  267085  Some-college           10.0   \n",
       "3760    37      Self-emp-inc  291518     Bachelors           13.0   \n",
       "20569   26           Private  222539  Some-college           10.0   \n",
       "7646    31           Private  196125          11th            7.0   \n",
       "25328   31           Private  159589       HS-grad            9.0   \n",
       "12084   40         State-gov  269733     Assoc-voc           11.0   \n",
       "6453    42       Federal-gov  170230       Masters            NaN   \n",
       "16796   48           Private   83444     Assoc-voc           11.0   \n",
       "16619   36           Private  175360       Masters           14.0   \n",
       "12622   52           Private  229983   Prof-school           15.0   \n",
       "24724   32           Private  171637  Some-college           10.0   \n",
       "\n",
       "           marital-status        occupation   relationship  \\\n",
       "11071  Married-civ-spouse  Transport-moving        Husband   \n",
       "29742  Married-civ-spouse    Prof-specialty        Husband   \n",
       "14948  Married-civ-spouse      Craft-repair        Husband   \n",
       "20799  Married-civ-spouse    Prof-specialty        Husband   \n",
       "25933  Married-civ-spouse             Sales        Husband   \n",
       "13442       Never-married      Adm-clerical            NaN   \n",
       "21494            Divorced      Craft-repair  Not-in-family   \n",
       "20508           Separated   Exec-managerial  Not-in-family   \n",
       "28225            Divorced   Exec-managerial      Unmarried   \n",
       "11493  Married-civ-spouse      Craft-repair        Husband   \n",
       "3760   Married-civ-spouse      Craft-repair        Husband   \n",
       "20569  Married-civ-spouse      Craft-repair        Husband   \n",
       "7646   Married-civ-spouse      Craft-repair        Husband   \n",
       "25328       Never-married     Other-service  Not-in-family   \n",
       "12084       Never-married      Craft-repair  Not-in-family   \n",
       "6453        Never-married   Exec-managerial  Not-in-family   \n",
       "16796  Married-civ-spouse   Exec-managerial        Husband   \n",
       "16619       Never-married      Adm-clerical  Not-in-family   \n",
       "12622  Married-civ-spouse    Prof-specialty           Wife   \n",
       "24724       Never-married      Adm-clerical            NaN   \n",
       "\n",
       "                     race     sex  capital-gain  capital-loss  hours-per-week  \\\n",
       "11071               White    Male             0             0              50   \n",
       "29742               White    Male             0             0              60   \n",
       "14948               White    Male             0             0              55   \n",
       "20799  Amer-Indian-Eskimo    Male             0             0              40   \n",
       "25933               White    Male             0             0              52   \n",
       "13442               White    Male             0             0              48   \n",
       "21494               White    Male             0             0              35   \n",
       "20508               White  Female             0             0              40   \n",
       "28225               Black  Female             0             0              60   \n",
       "11493               White    Male             0             0              40   \n",
       "3760                White    Male         15024             0              55   \n",
       "20569               White    Male             0             0              40   \n",
       "7646                White    Male             0             0              40   \n",
       "25328               White    Male             0             0              85   \n",
       "12084               White    Male             0             0              40   \n",
       "6453                White  Female         14084             0              60   \n",
       "16796               White    Male             0             0              43   \n",
       "16619               White    Male         13550             0              50   \n",
       "12622               White  Female          3103             0              30   \n",
       "24724               White  Female             0             0              40   \n",
       "\n",
       "      native-country salary  \n",
       "11071  United-States   >50K  \n",
       "29742  United-States  <=50K  \n",
       "14948  United-States   >50K  \n",
       "20799  United-States   >50K  \n",
       "25933  United-States   >50K  \n",
       "13442  United-States  <=50K  \n",
       "21494  United-States  <=50K  \n",
       "20508  United-States  <=50K  \n",
       "28225  United-States  <=50K  \n",
       "11493  United-States  <=50K  \n",
       "3760   United-States   >50K  \n",
       "20569  United-States   >50K  \n",
       "7646   United-States  <=50K  \n",
       "25328  United-States  <=50K  \n",
       "12084  United-States  <=50K  \n",
       "6453   United-States   >50K  \n",
       "16796  United-States   >50K  \n",
       "16619  United-States   >50K  \n",
       "12622  United-States   >50K  \n",
       "24724  United-States  <=50K  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(20)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 22,
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      ],
      "text/plain": [
       "         age  workclass  fnlwgt  education  education-num  marital-status  \\\n",
       "0      False      False   False      False          False           False   \n",
       "1      False      False   False      False          False           False   \n",
       "2      False      False   False      False          False           False   \n",
       "3      False      False   False      False          False           False   \n",
       "4      False      False   False      False          False           False   \n",
       "...      ...        ...     ...        ...            ...             ...   \n",
       "32556  False      False   False      False          False           False   \n",
       "32557  False      False   False      False          False           False   \n",
       "32558  False      False   False      False          False           False   \n",
       "32559  False      False   False      False          False           False   \n",
       "32560  False      False   False      False          False           False   \n",
       "\n",
       "       occupation  relationship   race    sex  capital-gain  capital-loss  \\\n",
       "0           False         False  False  False         False         False   \n",
       "1           False         False  False  False         False         False   \n",
       "2           False         False  False  False         False         False   \n",
       "3           False         False  False  False         False         False   \n",
       "4           False         False  False  False         False         False   \n",
       "...           ...           ...    ...    ...           ...           ...   \n",
       "32556       False         False  False  False         False         False   \n",
       "32557       False         False  False  False         False         False   \n",
       "32558       False         False  False  False         False         False   \n",
       "32559       False          True  False  False         False         False   \n",
       "32560       False         False  False  False         False         False   \n",
       "\n",
       "       hours-per-week  native-country  salary  \n",
       "0               False           False   False  \n",
       "1               False           False   False  \n",
       "2               False           False   False  \n",
       "3               False           False   False  \n",
       "4               False           False   False  \n",
       "...               ...             ...     ...  \n",
       "32556           False           False   False  \n",
       "32557           False           False   False  \n",
       "32558           False           False   False  \n",
       "32559           False           False   False  \n",
       "32560           False           False   False  \n",
       "\n",
       "[32561 rows x 15 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                  0\n",
       "workclass            0\n",
       "fnlwgt               0\n",
       "education            0\n",
       "education-num      802\n",
       "marital-status       0\n",
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       "relationship      5068\n",
       "race                 0\n",
       "sex                  0\n",
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       "native-country       0\n",
       "salary               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
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   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
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     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32561 entries, 0 to 32560\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   age             32561 non-null  int64  \n",
      " 1   workclass       32561 non-null  object \n",
      " 2   fnlwgt          32561 non-null  int64  \n",
      " 3   education       32561 non-null  object \n",
      " 4   education-num   31759 non-null  float64\n",
      " 5   marital-status  32561 non-null  object \n",
      " 6   occupation      32561 non-null  object \n",
      " 7   relationship    27493 non-null  object \n",
      " 8   race            32561 non-null  object \n",
      " 9   sex             32561 non-null  object \n",
      " 10  capital-gain    32561 non-null  int64  \n",
      " 11  capital-loss    32561 non-null  int64  \n",
      " 12  hours-per-week  32561 non-null  int64  \n",
      " 13  native-country  32561 non-null  object \n",
      " 14  salary          32561 non-null  object \n",
      "dtypes: float64(1), int64(5), object(9)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  }
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