11.Handling Duplicate data .ipynb

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   "metadata": {},
   "source": [
    "# Dealing with Duplicate data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "17b1ccd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b7ad0b1c",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "   A  B\n",
       "0  1  1\n",
       "1  2  7\n",
       "2  3  3\n",
       "3  3  0\n",
       "4  2  8"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df = pd.DataFrame({'A':[1,2,3,3,2],\n",
    " 'B':[1,7,3,0,8]})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c67f8532",
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   "outputs": [
    {
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      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2     True\n",
       "3     True\n",
       "4     True\n",
       "Name: A, dtype: bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = df.A.duplicated(keep=False)\n",
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "03ebf353",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[mask,'B'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e88ffbf7",
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   "outputs": [
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      "text/plain": [
       "   A  B  C\n",
       "0  1  1  1\n",
       "1  2  0  0\n",
       "2  3  0  0\n",
       "3  3  0  0\n",
       "4  2  0  0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['C'] = df.A.mask(mask,0)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89260a2c",
   "metadata": {},
   "source": [
    "If need set value 0 to column B, where in column A are duplicated data first create mask by\n",
    "Series.duplicated and then use Series.mask:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eb999cc",
   "metadata": {},
   "source": [
    "### d invert mask use ~:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "77781e98",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   A  B  C\n",
       "0  1  1  0\n",
       "1  2  0  2\n",
       "2  3  0  3\n",
       "3  3  0  3\n",
       "4  2  0  2"
      ]
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   "source": [
    "df['C'] = df.A.mask(~mask, 0)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7251c29",
   "metadata": {},
   "source": [
    "### Drop duplicated\n",
    "    pd.drop_duplicate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d75f25b8",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   A  B\n",
       "0  1  1\n",
       "1  2  7\n",
       "2  3  3\n",
       "3  3  0\n",
       "4  2  8"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A':[1,2,3,3,2],\n",
    "'B':[1,7,3,0,8]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9b3aa32d",
   "metadata": {},
   "outputs": [
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       "   A  B\n",
       "0  1  1\n",
       "3  3  0\n",
       "4  2  8"
      ]
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     "execution_count": 14,
     "metadata": {},
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   ],
   "source": [
    "# keep only the last value\n",
    "df.drop_duplicates(subset=['A'], keep='last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0b835297",
   "metadata": {},
   "outputs": [
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       "  </thead>\n",
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       "   A  B\n",
       "0  1  1\n",
       "1  2  7\n",
       "2  3  3"
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   "source": [
    "# keep only the first value, default value\n",
    "df.drop_duplicates(subset=['A'], keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fe6d07ca",
   "metadata": {},
   "outputs": [
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       "   A  B\n",
       "0  1  1"
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     "execution_count": 17,
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   ],
   "source": [
    "# drop all duplicated values\n",
    "df.drop_duplicates(subset=['A'], keep=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a86e12a",
   "metadata": {},
   "source": [
    "### When you don't want to get a copy of a data frame, but to modify the existing one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d1cdb148",
   "metadata": {},
   "outputs": [
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       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
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      "text/plain": [
       "   A  B\n",
       "0  1  1\n",
       "1  2  7\n",
       "2  3  3\n",
       "3  3  0\n",
       "4  2  8"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A':[1,2,3,3,2],\n",
    " 'B':[1,7,3,0,8]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "28eac042",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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      "text/plain": [
       "   A  B\n",
       "0  1  1\n",
       "1  2  7\n",
       "2  3  3"
      ]
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     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.drop_duplicates(subset=['A'], inplace=True)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8f2cb69",
   "metadata": {},
   "source": [
    "### Counting and getting unique elements"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cd5ffd2",
   "metadata": {},
   "source": [
    "#### Number of unique elements in a series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1684a953",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " id_numbers = pd.Series([111, 112, 112, 114, 115, 118, 114, 118, 112])\n",
    "\n",
    "id_numbers.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7eb4592",
   "metadata": {},
   "source": [
    "#### Get unique elements in a series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e7774cb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([111, 112, 114, 115, 118], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id_numbers.unique()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6ee51a2",
   "metadata": {},
   "source": [
    "#### Number of unique elements in each group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4e7e0af9",
   "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>Group</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Group  ID\n",
       "0     A   1\n",
       "1     B   1\n",
       "2     A   2\n",
       "3     A   3\n",
       "4     B   3\n",
       "5     A   2\n",
       "6     B   1\n",
       "7     A   2\n",
       "8     A   1\n",
       "9     B   3"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Group': list('ABAABABAAB'),\n",
    " 'ID': [1, 1, 2, 3, 3, 2, 1, 2, 1, 3]})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9c3b6da6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group\n",
       "A    3\n",
       "B    2\n",
       "Name: ID, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df.groupby('Group')['ID'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea871c71",
   "metadata": {},
   "source": [
    "#### Get of unique elements in each group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "059c6edf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group\n",
       "A    [1, 2, 3]\n",
       "B       [1, 3]\n",
       "Name: ID, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df.groupby('Group')['ID'].unique()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "012fd15b",
   "metadata": {},
   "source": [
    "#### Get unique values from a column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "50b6b978",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3], dtype=int64)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"A\":[1,1,2,3,1,1],\"B\":[5,4,3,4,6,7]})\n",
    "\n",
    "df[\"A\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0c517ebb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 4, 3, 6, 7], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"B\"].unique()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa6bf2a9",
   "metadata": {},
   "source": [
    "##### To get the unique values in column A as a list (note that unique() can be used in two slightly  different ways)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2585b7af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.unique(df['A']).tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89688262",
   "metadata": {},
   "source": [
    "### Here is a more complex example. Say we want to find the unique values from column 'B' where 'A' is equal to 1.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a497edfe",
   "metadata": {},
   "source": [
    "First, let's introduce a duplicate so you can see how it works. Let's replace the 6 in row '4', column\n",
    "'B' with a 4:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "09a253c8",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B\n",
       "0  1.0  5.0\n",
       "1  1.0  4.0\n",
       "2  2.0  3.0\n",
       "3  3.0  4.0\n",
       "4  1.0  6.0\n",
       "5  1.0  7.0\n",
       "4  NaN  4.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['4', 'B'] = 4\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ee47a17a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5.0, 4.0, 6.0, 7.0]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.unique(df[df['A'] == 1]['B']).tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50215199",
   "metadata": {},
   "source": [
    "### Explanation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "364cafd0",
   "metadata": {},
   "source": [
    "This can be broken down by thinking of the inner DataFrame first:\n",
    "    \n",
    "df['A'] == 1\n",
    "\n",
    "This finds values in column A that are equal to 1, and applies True or False to them. We can then\n",
    "use this to select values from column 'B' of the DataFrame (the outer DataFrame selection)\n",
    "For comparison, here is the list if we don't use unique. It retrieves every value in column 'B' where\n",
    "column 'A' is 1.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d4305eb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5.0, 4.0, 6.0, 7.0]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['A'] == 1]['B'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "511df8b7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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