Pandas 101 - Merge, Join, and Concatenate in Pandas.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "2b20c344",
   "metadata": {},
   "source": [
    "# Merge, Join, and Concatenate "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d5d0a50a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0b08cb55",
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FellowshipID</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>Skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>Frodo</td>\n",
       "      <td>Hiding</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002</td>\n",
       "      <td>Samwise</td>\n",
       "      <td>Gardening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003</td>\n",
       "      <td>Gandalf</td>\n",
       "      <td>Spells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1004</td>\n",
       "      <td>Pippin</td>\n",
       "      <td>Fireworks</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   FellowshipID FirstName     Skills\n",
       "0          1001     Frodo     Hiding\n",
       "1          1002   Samwise  Gardening\n",
       "2          1003   Gandalf     Spells\n",
       "3          1004    Pippin  Fireworks"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv(r\"C:\\Users\\alexf\\OneDrive\\Documents\\Pandas Tutorial\\LOTR.csv\")\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7b84eeef",
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FellowshipID</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>Frodo</td>\n",
       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002</td>\n",
       "      <td>Samwise</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1006</td>\n",
       "      <td>Legolas</td>\n",
       "      <td>2931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1007</td>\n",
       "      <td>Elrond</td>\n",
       "      <td>6520</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1008</td>\n",
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      "text/plain": [
       "   FellowshipID FirstName   Age\n",
       "0          1001     Frodo    50\n",
       "1          1002   Samwise    39\n",
       "2          1006   Legolas  2931\n",
       "3          1007    Elrond  6520\n",
       "4          1008  Barromir    51"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv(r\"C:\\Users\\alexf\\OneDrive\\Documents\\Pandas Tutorial\\LOTR 2.csv\")\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "de666ace",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>FellowshipID</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>Skills</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>Frodo</td>\n",
       "      <td>Hiding</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002</td>\n",
       "      <td>Samwise</td>\n",
       "      <td>Gardening</td>\n",
       "      <td>39</td>\n",
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      "text/plain": [
       "   FellowshipID FirstName     Skills  Age\n",
       "0          1001     Frodo     Hiding   50\n",
       "1          1002   Samwise  Gardening   39"
      ]
     },
     "execution_count": 13,
     "metadata": {},
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    }
   ],
   "source": [
    "df1.merge(df2, how = 'inner', on = ['FellowshipID', 'FirstName'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9e2dace6",
   "metadata": {},
   "outputs": [
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       "      <td>1002</td>\n",
       "      <td>Samwise</td>\n",
       "      <td>Gardening</td>\n",
       "      <td>39.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>1003</td>\n",
       "      <td>Gandalf</td>\n",
       "      <td>Spells</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1004</td>\n",
       "      <td>Pippin</td>\n",
       "      <td>Fireworks</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1006</td>\n",
       "      <td>Legolas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2931.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1007</td>\n",
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       "      <th>6</th>\n",
       "      <td>1008</td>\n",
       "      <td>Barromir</td>\n",
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      "text/plain": [
       "   FellowshipID FirstName     Skills     Age\n",
       "0          1001     Frodo     Hiding    50.0\n",
       "1          1002   Samwise  Gardening    39.0\n",
       "2          1003   Gandalf     Spells     NaN\n",
       "3          1004    Pippin  Fireworks     NaN\n",
       "4          1006   Legolas        NaN  2931.0\n",
       "5          1007    Elrond        NaN  6520.0\n",
       "6          1008  Barromir        NaN    51.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.merge(df2, how = 'outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f48ceaa9",
   "metadata": {},
   "outputs": [
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       "   FellowshipID FirstName     Skills   Age\n",
       "0          1001     Frodo     Hiding  50.0\n",
       "1          1002   Samwise  Gardening  39.0\n",
       "2          1003   Gandalf     Spells   NaN\n",
       "3          1004    Pippin  Fireworks   NaN"
      ]
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     "execution_count": 15,
     "metadata": {},
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   "source": [
    "df1.merge(df2, how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "10671135",
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       "   FellowshipID FirstName     Skills   Age\n",
       "0          1001     Frodo     Hiding    50\n",
       "1          1002   Samwise  Gardening    39\n",
       "2          1006   Legolas        NaN  2931\n",
       "3          1007    Elrond        NaN  6520\n",
       "4          1008  Barromir        NaN    51"
      ]
     },
     "execution_count": 16,
     "metadata": {},
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   "source": [
    "df1.merge(df2, how = 'right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fd9f1467",
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       "      <td>2931</td>\n",
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       "      <td>1002</td>\n",
       "      <td>Samwise</td>\n",
       "      <td>Gardening</td>\n",
       "      <td>1007</td>\n",
       "      <td>Elrond</td>\n",
       "      <td>6520</td>\n",
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       "      <th>9</th>\n",
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       "      <td>Gandalf</td>\n",
       "      <td>Spells</td>\n",
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       "      <td>Frodo</td>\n",
       "      <td>50</td>\n",
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       "      <td>Gandalf</td>\n",
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       "      <td>39</td>\n",
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       "      <td>Spells</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1003</td>\n",
       "      <td>Gandalf</td>\n",
       "      <td>Spells</td>\n",
       "      <td>1007</td>\n",
       "      <td>Elrond</td>\n",
       "      <td>6520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1003</td>\n",
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       "      <td>1001</td>\n",
       "      <td>Frodo</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1004</td>\n",
       "      <td>Pippin</td>\n",
       "      <td>Fireworks</td>\n",
       "      <td>1002</td>\n",
       "      <td>Samwise</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1004</td>\n",
       "      <td>Pippin</td>\n",
       "      <td>Fireworks</td>\n",
       "      <td>1006</td>\n",
       "      <td>Legolas</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1004</td>\n",
       "      <td>Pippin</td>\n",
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       "      <td>Elrond</td>\n",
       "      <td>6520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1004</td>\n",
       "      <td>Pippin</td>\n",
       "      <td>Fireworks</td>\n",
       "      <td>1008</td>\n",
       "      <td>Barromir</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    FellowshipID_x FirstName_x     Skills  FellowshipID_y FirstName_y   Age\n",
       "0             1001       Frodo     Hiding            1001       Frodo    50\n",
       "1             1001       Frodo     Hiding            1002     Samwise    39\n",
       "2             1001       Frodo     Hiding            1006     Legolas  2931\n",
       "3             1001       Frodo     Hiding            1007      Elrond  6520\n",
       "4             1001       Frodo     Hiding            1008    Barromir    51\n",
       "5             1002     Samwise  Gardening            1001       Frodo    50\n",
       "6             1002     Samwise  Gardening            1002     Samwise    39\n",
       "7             1002     Samwise  Gardening            1006     Legolas  2931\n",
       "8             1002     Samwise  Gardening            1007      Elrond  6520\n",
       "9             1002     Samwise  Gardening            1008    Barromir    51\n",
       "10            1003     Gandalf     Spells            1001       Frodo    50\n",
       "11            1003     Gandalf     Spells            1002     Samwise    39\n",
       "12            1003     Gandalf     Spells            1006     Legolas  2931\n",
       "13            1003     Gandalf     Spells            1007      Elrond  6520\n",
       "14            1003     Gandalf     Spells            1008    Barromir    51\n",
       "15            1004      Pippin  Fireworks            1001       Frodo    50\n",
       "16            1004      Pippin  Fireworks            1002     Samwise    39\n",
       "17            1004      Pippin  Fireworks            1006     Legolas  2931\n",
       "18            1004      Pippin  Fireworks            1007      Elrond  6520\n",
       "19            1004      Pippin  Fireworks            1008    Barromir    51"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.merge(df2, how = 'cross')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "242affa2",
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>1008.0</td>\n",
       "      <td>Barromir</td>\n",
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       "     FellowshipID  FellowshipID_Left FirstName_Left     Skills  \\\n",
       "0.0          1001             1001.0          Frodo     Hiding   \n",
       "1.0          1002             1002.0        Samwise  Gardening   \n",
       "2.0          1003             1003.0        Gandalf     Spells   \n",
       "3.0          1004             1004.0         Pippin  Fireworks   \n",
       "NaN             0                NaN            NaN        NaN   \n",
       "NaN             1                NaN            NaN        NaN   \n",
       "NaN             2                NaN            NaN        NaN   \n",
       "NaN             3                NaN            NaN        NaN   \n",
       "NaN             4                NaN            NaN        NaN   \n",
       "\n",
       "     FellowshipID_Right FirstName_Right     Age  \n",
       "0.0                 NaN             NaN     NaN  \n",
       "1.0                 NaN             NaN     NaN  \n",
       "2.0                 NaN             NaN     NaN  \n",
       "3.0                 NaN             NaN     NaN  \n",
       "NaN              1001.0           Frodo    50.0  \n",
       "NaN              1002.0         Samwise    39.0  \n",
       "NaN              1006.0         Legolas  2931.0  \n",
       "NaN              1007.0          Elrond  6520.0  \n",
       "NaN              1008.0        Barromir    51.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df1.join(df2, on = 'FellowshipID', how = 'outer', lsuffix = '_Left',rsuffix = '_Right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eef5006c",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "             FirstName_Left     Skills FirstName_Right     Age\n",
       "FellowshipID                                                  \n",
       "1001                  Frodo     Hiding           Frodo    50.0\n",
       "1002                Samwise  Gardening         Samwise    39.0\n",
       "1003                Gandalf     Spells             NaN     NaN\n",
       "1004                 Pippin  Fireworks             NaN     NaN\n",
       "1006                    NaN        NaN         Legolas  2931.0\n",
       "1007                    NaN        NaN          Elrond  6520.0\n",
       "1008                    NaN        NaN        Barromir    51.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df4 = df1.set_index('FellowshipID').join(df2.set_index('FellowshipID'), lsuffix = '_Left',rsuffix = '_Right', how = 'outer')\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
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   "id": "079d4901",
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       "   FellowshipID FirstName     Skills  FellowshipID FirstName   Age\n",
       "0        1001.0     Frodo     Hiding          1001     Frodo    50\n",
       "1        1002.0   Samwise  Gardening          1002   Samwise    39\n",
       "2        1003.0   Gandalf     Spells          1006   Legolas  2931\n",
       "3        1004.0    Pippin  Fireworks          1007    Elrond  6520\n",
       "4           NaN       NaN        NaN          1008  Barromir    51"
      ]
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     "execution_count": 29,
     "metadata": {},
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  {
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   "execution_count": 30,
   "id": "19e39c9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alexf\\AppData\\Local\\Temp\\ipykernel_6472\\3062608662.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df1.append(df2)\n"
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       "   FellowshipID FirstName     Skills     Age\n",
       "0          1001     Frodo     Hiding     NaN\n",
       "1          1002   Samwise  Gardening     NaN\n",
       "2          1003   Gandalf     Spells     NaN\n",
       "3          1004    Pippin  Fireworks     NaN\n",
       "0          1001     Frodo        NaN    50.0\n",
       "1          1002   Samwise        NaN    39.0\n",
       "2          1006   Legolas        NaN  2931.0\n",
       "3          1007    Elrond        NaN  6520.0\n",
       "4          1008  Barromir        NaN    51.0"
      ]
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"
    }
   },
   "cell_type": "markdown",
   "id": "09c9a2db",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}