Tutorial 9- Python Pandas Tutorials .ipynb

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   "source": [
    "### Tutorial 9- Python Pandas Tutorials- Part 1\n",
    "\n",
    "In Part we are going to learn about\n",
    "1. Pandas Dataframe\n",
    "2. Pandas Series\n",
    "3. Pandas Basic Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd0f0f2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "abe8c71c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1c8e36b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11],\n",
       "       [12, 13, 14, 15],\n",
       "       [16, 17, 18, 19]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,20).reshape(5,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7430aaab",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Create Dataframe\n",
    "df=pd.DataFrame(data=np.arange(0,20).reshape(5,4),index=[\"Row1\",\n",
    "                                                      \"Row2\",\"Row3\",\n",
    "                                                      \"Row4\",\"Row5\"],columns=[\"Column1\",\n",
    "                                                                             \"Column2\",\n",
    "                                                                             \"Column3\",\n",
    "                                                                             \"Column4\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "79322dcd",
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       "      <td>7</td>\n",
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       "      <th>Row3</th>\n",
       "      <td>8</td>\n",
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       "      <td>11</td>\n",
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       "      <th>Row4</th>\n",
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       "      <td>15</td>\n",
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       "      <th>Row5</th>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "      Column1  Column2  Column3  Column4\n",
       "Row1        0        1        2        3\n",
       "Row2        4        5        6        7\n",
       "Row3        8        9       10       11\n",
       "Row4       12       13       14       15\n",
       "Row5       16       17       18       19"
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   "source": [
    "df.head()"
   ]
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   "execution_count": 8,
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       "      <td>7</td>\n",
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       "      <th>Row3</th>\n",
       "      <td>8</td>\n",
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       "      <td>10</td>\n",
       "      <td>11</td>\n",
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       "      <th>Row4</th>\n",
       "      <td>12</td>\n",
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       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row5</th>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
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       "      Column1  Column2  Column3  Column4\n",
       "Row1        0        1        2        3\n",
       "Row2        4        5        6        7\n",
       "Row3        8        9       10       11\n",
       "Row4       12       13       14       15\n",
       "Row5       16       17       18       19"
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   "source": [
    "df.tail()"
   ]
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   "execution_count": 9,
   "id": "5fd56def",
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   "outputs": [
    {
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       "pandas.core.frame.DataFrame"
      ]
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     "execution_count": 9,
     "metadata": {},
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   "source": [
    "type(df)"
   ]
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   "cell_type": "code",
   "execution_count": 10,
   "id": "b9689b0c",
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 5 entries, Row1 to Row5\n",
      "Data columns (total 4 columns):\n",
      " #   Column   Non-Null Count  Dtype\n",
      "---  ------   --------------  -----\n",
      " 0   Column1  5 non-null      int32\n",
      " 1   Column2  5 non-null      int32\n",
      " 2   Column3  5 non-null      int32\n",
      " 3   Column4  5 non-null      int32\n",
      "dtypes: int32(4)\n",
      "memory usage: 120.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0475bb62",
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       "      <th>Column4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.324555</td>\n",
       "      <td>6.324555</td>\n",
       "      <td>6.324555</td>\n",
       "      <td>6.324555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Column1    Column2    Column3    Column4\n",
       "count   5.000000   5.000000   5.000000   5.000000\n",
       "mean    8.000000   9.000000  10.000000  11.000000\n",
       "std     6.324555   6.324555   6.324555   6.324555\n",
       "min     0.000000   1.000000   2.000000   3.000000\n",
       "25%     4.000000   5.000000   6.000000   7.000000\n",
       "50%     8.000000   9.000000  10.000000  11.000000\n",
       "75%    12.000000  13.000000  14.000000  15.000000\n",
       "max    16.000000  17.000000  18.000000  19.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.describe()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1ad08456",
   "metadata": {},
   "outputs": [
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       "      <th>Row4</th>\n",
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       "      <th>Row5</th>\n",
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       "      <td>19</td>\n",
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      "text/plain": [
       "      Column1  Column2  Column3  Column4\n",
       "Row1        0        1        2        3\n",
       "Row2        4        5        6        7\n",
       "Row3        8        9       10       11\n",
       "Row4       12       13       14       15\n",
       "Row5       16       17       18       19"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Indexing\n",
    "## columnname,rowindex[loc],rowindex columnindex number[.iloc]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "79d36316",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##columnname\n",
    "type(df['Column1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e4183cf8",
   "metadata": {},
   "outputs": [
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       "      Column1  Column2  Column3\n",
       "Row1        0        1        2\n",
       "Row2        4        5        6\n",
       "Row3        8        9       10\n",
       "Row4       12       13       14\n",
       "Row5       16       17       18"
      ]
     },
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     "metadata": {},
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   "source": [
    "df[['Column1','Column2','Column3']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0d169272",
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>Row3</th>\n",
       "      <td>8</td>\n",
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       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row4</th>\n",
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      "text/plain": [
       "      Column1  Column2  Column3  Column4\n",
       "Row3        8        9       10       11\n",
       "Row4       12       13       14       15"
      ]
     },
     "execution_count": 21,
     "metadata": {},
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   ],
   "source": [
    "##using row index name loc\n",
    "df.loc[['Row3','Row4']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9ceec48e",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>Row2</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row3</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row4</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row5</th>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Column1  Column2  Column3  Column4\n",
       "Row1        0        1        2        3\n",
       "Row2        4        5        6        7\n",
       "Row3        8        9       10       11\n",
       "Row4       12       13       14       15\n",
       "Row5       16       17       18       19"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ad535711",
   "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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       "      <th>Column2</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>Row3</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>Row4</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Column1  Column2\n",
       "Row3        8        9\n",
       "Row4       12       13"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2:4,0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e185a687",
   "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>Column2</th>\n",
       "      <th>Column3</th>\n",
       "      <th>Column4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Row3</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row4</th>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row5</th>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Column2  Column3  Column4\n",
       "Row3        9       10       11\n",
       "Row4       13       14       15\n",
       "Row5       17       18       19"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ed1cc9b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3],\n",
       "       [ 5,  6,  7],\n",
       "       [ 9, 10, 11],\n",
       "       [13, 14, 15],\n",
       "       [17, 18, 19]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##convert dataframe into arrays\n",
    "df.iloc[:,1:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9fdbd21f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Column1    0\n",
       "Column2    0\n",
       "Column3    0\n",
       "Column4    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Basic operations\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "eab8b52f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(data=[[1,np.nan,2],[1,3,4]],index=[\"Row1\",\n",
    "                                                      \"Row2\"],columns=[\"Column1\",\n",
    "                                                                             \"Column2\",\n",
    "                                                                             \"Column3\",\n",
    "                                                                             ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d93f1cb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>Column1</th>\n",
       "      <th>Column2</th>\n",
       "      <th>Column3</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Row1</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row2</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Column1  Column2  Column3\n",
       "Row1        1      NaN        2\n",
       "Row2        1      3.0        4"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f4c51420",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Column1    0\n",
       "Column2    1\n",
       "Column3    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b237842a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Column1     True\n",
       "Column2    False\n",
       "Column3     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()==0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ecad2a5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</table>\n",
       "</div>"
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      "text/plain": [
       "      Column1  Column2  Column3\n",
       "Row1        1      NaN        2\n",
       "Row2        1      3.0        4"
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     "execution_count": 43,
     "metadata": {},
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    "df"
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  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "825eacd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    1\n",
       "4    1\n",
       "Name: Column3, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Column3'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "490fffb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Row2</th>\n",
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       "      <td>7</td>\n",
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       "      <th>Row3</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>Row4</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
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       "    <tr>\n",
       "      <th>Row5</th>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Column1  Column2  Column3  Column4\n",
       "Row1        0        1        2        3\n",
       "Row2        4        5        6        7\n",
       "Row3        8        9       10       11\n",
       "Row4       12       13       14       15\n",
       "Row5       16       17       18       19"
      ]
     },
     "execution_count": 49,
     "metadata": {},
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "954951bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  5,  9, 13, 17])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Column2'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6c8fc245",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Row3</th>\n",
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       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>Row4</th>\n",
       "      <td>12</td>\n",
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       "      <td>14</td>\n",
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       "    <tr>\n",
       "      <th>Row5</th>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
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       "      <td>19</td>\n",
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      "text/plain": [
       "      Column1  Column2  Column3  Column4\n",
       "Row2        4        5        6        7\n",
       "Row3        8        9       10       11\n",
       "Row4       12       13       14       15\n",
       "Row5       16       17       18       19"
      ]
     },
     "execution_count": 53,
     "metadata": {},
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   ],
   "source": [
    "df[df['Column2']>2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15b67c66",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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