DataTalks_Merge II.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create data set by using dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_1 = {\n",
    "        'subject_id': ['1', '2', '3', '4', '5'],\n",
    "        'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'], \n",
    "        'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', 'Atiches']}\n",
    "\n",
    "data_2 = {\n",
    "        'subject_id': ['4', '5', '6', '7', '8'],\n",
    "        'first_name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'], \n",
    "        'last_name': ['Bonder', 'Black', 'Balwner', 'Brice', 'Btisan']}\n",
    "\n",
    "data_3 = {\n",
    "        'subject_id': ['1', '2', '3', '4', '5', '7', '8', '9', '10', '11'],\n",
    "        'test_id': [51, 15, 15, 61, 16, 14, 15, 1, 61, 16]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assign each to a variable called data1, data2, data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = pd.DataFrame(data_1, columns = [\"subject_id\", \"first_name\", \"last_name\"])\n",
    "data2 = pd.DataFrame(data_2, columns = [\"sublect_id\", \"first_name\", \"last_name\"])\n",
    "data3 = pd.DataFrame(data_3, columns = [\"subject_id\", \"test_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Anderson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Ackerman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Ali</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name last_name\n",
       "0          1       Alex  Anderson\n",
       "1          2        Amy  Ackerman\n",
       "2          3      Allen       Ali\n",
       "3          4      Alice      Aoni\n",
       "4          5     Ayoung   Atiches"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Join the two dataframes along rows and assign all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>sublect_id</th>\n",
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       "  </thead>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Ackerman</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Ali</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Billy</td>\n",
       "      <td>Bonder</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bran</td>\n",
       "      <td>Balwner</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bryce</td>\n",
       "      <td>Brice</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Btisan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name last_name sublect_id\n",
       "0          1       Alex  Anderson        NaN\n",
       "1          2        Amy  Ackerman        NaN\n",
       "2          3      Allen       Ali        NaN\n",
       "3          4      Alice      Aoni        NaN\n",
       "4          5     Ayoung   Atiches        NaN\n",
       "0        NaN      Billy    Bonder        NaN\n",
       "1        NaN      Brian     Black        NaN\n",
       "2        NaN       Bran   Balwner        NaN\n",
       "3        NaN      Bryce     Brice        NaN\n",
       "4        NaN      Betty    Btisan        NaN"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data = pd.concat([data1, data2], sort = False)\n",
    "all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>subject_id</th>\n",
       "      <th>sublect_id</th>\n",
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       "      <td>Alex</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Ackerman</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Ali</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Billy</td>\n",
       "      <td>Bonder</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Bran</td>\n",
       "      <td>Balwner</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Bryce</td>\n",
       "      <td>Brice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Btisan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  first_name last_name subject_id sublect_id\n",
       "0       Alex  Anderson          1        NaN\n",
       "1        Amy  Ackerman          2        NaN\n",
       "2      Allen       Ali          3        NaN\n",
       "3      Alice      Aoni          4        NaN\n",
       "4     Ayoung   Atiches          5        NaN\n",
       "0      Billy    Bonder        NaN        NaN\n",
       "1      Brian     Black        NaN        NaN\n",
       "2       Bran   Balwner        NaN        NaN\n",
       "3      Bryce     Brice        NaN        NaN\n",
       "4      Betty    Btisan        NaN        NaN"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = data1.append(data2)\n",
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Join the two dataframes along columns and assing to all_data_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>subject_id</th>\n",
       "      <th>test_id</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Anderson</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Ackerman</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Ali</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "      <td>4</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name last_name subject_id  test_id\n",
       "0          1       Alex  Anderson          1       51\n",
       "1          2        Amy  Ackerman          2       15\n",
       "2          3      Allen       Ali          3       15\n",
       "3          4      Alice      Aoni          4       61\n",
       "4          5     Ayoung   Atiches          5       16\n",
       "5        NaN        NaN       NaN          7       14\n",
       "6        NaN        NaN       NaN          8       15\n",
       "7        NaN        NaN       NaN          9        1\n",
       "8        NaN        NaN       NaN         10       61\n",
       "9        NaN        NaN       NaN         11       16"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data_col = pd.concat([data1, data3], axis = 1)\n",
    "all_data_col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merge all_data and data3 along the subject_id value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <th>first_name</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Alex</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>51</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Ackerman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Ali</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
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      "text/plain": [
       "  subject_id first_name last_name sublect_id  test_id\n",
       "0          1       Alex  Anderson        NaN       51\n",
       "1          2        Amy  Ackerman        NaN       15\n",
       "2          3      Allen       Ali        NaN       15\n",
       "3          4      Alice      Aoni        NaN       61\n",
       "4          5     Ayoung   Atiches        NaN       16"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(all_data, data3, on = \"subject_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.merge(data1, data2, on='subject_id', how='inner')\n",
    "pd.merge(data1, data2, on='subject_id', how='outer')"
   ]
  }
 ],
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