{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handling Duplicated and Missing Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting ready\n",
    "Before starting, we are going to create a `DataFrame` containing duplicated and missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Surname</th>\n",
       "      <th>Maths</th>\n",
       "      <th>Spanish</th>\n",
       "      <th>Chemistry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andrea</td>\n",
       "      <td>Smith</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bert</td>\n",
       "      <td>Williams</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Carl</td>\n",
       "      <td>Addison</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>Brown</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Elena</td>\n",
       "      <td>Miller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name   Surname  Maths  Spanish  Chemistry\n",
       "0  Andrea     Smith   10.0      8.0       10.0\n",
       "1    Bert  Williams    NaN      9.0        9.0\n",
       "2    Carl   Addison    9.0      NaN        9.5\n",
       "3   David     Brown    8.5      8.5        8.5\n",
       "4   Elena    Miller    NaN      9.0        9.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({    \n",
    "    'Name': ['Andrea', 'Bert', 'Carl', 'David', 'Elena', 'Andrea', 'Louis', 'Lucy', 'Mary', 'Zoe', 'Bert', 'Louis', 'Elena', 'Bert'],         \n",
    " 'Surname': ['Smith', 'Williams', 'Addison', 'Brown', 'Miller',  'Smith', 'Taylor', 'Brown', 'Moore', 'Anderson', 'Steward', 'Smith', 'Brown', 'Williams'],         \n",
    "\n",
    "    'Maths' : [10.,  np.nan, 9.0, 8.5, np.nan, 10., 10.0, 7.5, 9.0, 6.5, 7.5, 10.0, 8.5, np.nan], \n",
    "    'Spanish' : [8.0, 9.0, np.nan, 8.5, 9.0, 8.0, 10., 1.0, 7.5, 6.5, 8.5, 10.0, 9.5, 9.0],\n",
    "    'Chemistry' :  [10., 9.0, 9.5, 8.5, 9.0,  10., 10.0, 7.5, np.nan, 6.5, 10., 10.0, 7.5, 9.0]\n",
    "})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to do it \n",
    "\n",
    "### Duplicates\n",
    "\n",
    "1. Check for duplicated rows by calling the method `duplicated`. This returns a pandas series with a flag which allows us to identify the duplicated values. By default, for each set of duplicated values, the first occurrence is set on `False` and all others on `True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     False\n",
       "1     False\n",
       "2     False\n",
       "3     False\n",
       "4     False\n",
       "5      True\n",
       "6     False\n",
       "7     False\n",
       "8     False\n",
       "9     False\n",
       "10    False\n",
       "11    False\n",
       "12    False\n",
       "13     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Inspect the duplicated rows in our `DataFrame`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Surname</th>\n",
       "      <th>Maths</th>\n",
       "      <th>Spanish</th>\n",
       "      <th>Chemistry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Andrea</td>\n",
       "      <td>Smith</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bert</td>\n",
       "      <td>Williams</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name   Surname  Maths  Spanish  Chemistry\n",
       "5   Andrea     Smith   10.0      8.0       10.0\n",
       "13    Bert  Williams    NaN      9.0        9.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicated = df.duplicated()\n",
    "df[duplicated]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Find duplicates on specific column(s) by using the input `subset`. \n",
    "For example, we can check for students with the same name as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Surname</th>\n",
       "      <th>Maths</th>\n",
       "      <th>Spanish</th>\n",
       "      <th>Chemistry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Andrea</td>\n",
       "      <td>Smith</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bert</td>\n",
       "      <td>Steward</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Louis</td>\n",
       "      <td>Smith</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Elena</td>\n",
       "      <td>Brown</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bert</td>\n",
       "      <td>Williams</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name   Surname  Maths  Spanish  Chemistry\n",
       "5   Andrea     Smith   10.0      8.0       10.0\n",
       "10    Bert   Steward    7.5      8.5       10.0\n",
       "11   Louis     Smith   10.0     10.0       10.0\n",
       "12   Elena     Brown    8.5      9.5        7.5\n",
       "13    Bert  Williams    NaN      9.0        9.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicated_name =df.duplicated(subset=['Name'])\n",
    "df[duplicated_name]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Find duplicated across a specifi set of columns. For example, we can check for students with the same name and surname as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Surname</th>\n",
       "      <th>Maths</th>\n",
       "      <th>Spanish</th>\n",
       "      <th>Chemistry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Andrea</td>\n",
       "      <td>Smith</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bert</td>\n",
       "      <td>Williams</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name   Surname  Maths  Spanish  Chemistry\n",
       "5   Andrea     Smith   10.0      8.0       10.0\n",
       "13    Bert  Williams    NaN      9.0        9.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicated_name =df.duplicated(subset=['Name', 'Surname'])\n",
    "df[duplicated_name]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Remove the duplicated values from the `DataFrame` by calling the method `drop_duplicates`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Chemistry</th>\n",
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       "      <th>0</th>\n",
       "      <td>Andrea</td>\n",
       "      <td>Smith</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>Bert</td>\n",
       "      <td>Williams</td>\n",
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       "      <td>9.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Carl</td>\n",
       "      <td>Addison</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>Brown</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Elena</td>\n",
       "      <td>Miller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Louis</td>\n",
       "      <td>Taylor</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>Brown</td>\n",
       "      <td>7.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mary</td>\n",
       "      <td>Moore</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Zoe</td>\n",
       "      <td>Anderson</td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bert</td>\n",
       "      <td>Steward</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Louis</td>\n",
       "      <td>Smith</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Elena</td>\n",
       "      <td>Brown</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name   Surname  Maths  Spanish  Chemistry\n",
       "0   Andrea     Smith   10.0      8.0       10.0\n",
       "1     Bert  Williams    NaN      9.0        9.0\n",
       "2     Carl   Addison    9.0      NaN        9.5\n",
       "3    David     Brown    8.5      8.5        8.5\n",
       "4    Elena    Miller    NaN      9.0        9.0\n",
       "6    Louis    Taylor   10.0     10.0       10.0\n",
       "7     Lucy     Brown    7.5      1.0        7.5\n",
       "8     Mary     Moore    9.0      7.5        NaN\n",
       "9      Zoe  Anderson    6.5      6.5        6.5\n",
       "10    Bert   Steward    7.5      8.5       10.0\n",
       "11   Louis     Smith   10.0     10.0       10.0\n",
       "12   Elena     Brown    8.5      9.5        7.5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing Values\n",
    "\n",
    "6. Check if a particular column has missing values using the method `isna`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     False\n",
       "1      True\n",
       "2     False\n",
       "3     False\n",
       "4      True\n",
       "5     False\n",
       "6     False\n",
       "7     False\n",
       "8     False\n",
       "9     False\n",
       "10    False\n",
       "11    False\n",
       "12    False\n",
       "13     True\n",
       "Name: Maths, dtype: bool"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Maths'].isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Elena</td>\n",
       "      <td>Miller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bert</td>\n",
       "      <td>Williams</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name   Surname  Maths  Spanish  Chemistry\n",
       "1    Bert  Williams    NaN      9.0        9.0\n",
       "4   Elena    Miller    NaN      9.0        9.0\n",
       "13   Bert  Williams    NaN      9.0        9.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Maths'].isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. Check for missing values in all columns of a `DataFrame` with the methods `isnull` and `sum`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name         0\n",
       "Surname      0\n",
       "Maths        3\n",
       "Spanish      1\n",
       "Chemistry    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8. Drop the rows with missing values"
   ]
  },
  {
   "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>Name</th>\n",
       "      <th>Surname</th>\n",
       "      <th>Maths</th>\n",
       "      <th>Spanish</th>\n",
       "      <th>Chemistry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andrea</td>\n",
       "      <td>Smith</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>Brown</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Andrea</td>\n",
       "      <td>Smith</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Louis</td>\n",
       "      <td>Taylor</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>Brown</td>\n",
       "      <td>7.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Zoe</td>\n",
       "      <td>Anderson</td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bert</td>\n",
       "      <td>Steward</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Louis</td>\n",
       "      <td>Smith</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Elena</td>\n",
       "      <td>Brown</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name   Surname  Maths  Spanish  Chemistry\n",
       "0   Andrea     Smith   10.0      8.0       10.0\n",
       "3    David     Brown    8.5      8.5        8.5\n",
       "5   Andrea     Smith   10.0      8.0       10.0\n",
       "6    Louis    Taylor   10.0     10.0       10.0\n",
       "7     Lucy     Brown    7.5      1.0        7.5\n",
       "9      Zoe  Anderson    6.5      6.5        6.5\n",
       "10    Bert   Steward    7.5      8.5       10.0\n",
       "11   Louis     Smith   10.0     10.0       10.0\n",
       "12   Elena     Brown    8.5      9.5        7.5"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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