Chapter01/Merge_Examples.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "products = {'CampaignYear': [2015, 2016, 2017, 2018, 2019 , 2020, 2021],\n",
    "        'ProductPrice': [199, 199, 199, 299, 299, 349, 349 ],\n",
    "        'ProductVersion': ['v1', 'v1', 'v1', 'v2', 'v2', 'v2', 'v3']\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_products = pd.DataFrame(products, columns = ['CampaignYear', 'ProductPrice', 'ProductVersion'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
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       "      <td>2015</td>\n",
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       "      <td>2017</td>\n",
       "      <td>199</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>299</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>299</td>\n",
       "      <td>v2</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020</td>\n",
       "      <td>349</td>\n",
       "      <td>v2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021</td>\n",
       "      <td>349</td>\n",
       "      <td>v3</td>\n",
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      "text/plain": [
       "   CampaignYear  ProductPrice ProductVersion\n",
       "0          2015           199             v1\n",
       "1          2016           199             v1\n",
       "2          2017           199             v1\n",
       "3          2018           299             v2\n",
       "4          2019           299             v2\n",
       "5          2020           349             v2\n",
       "6          2021           349             v3"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "revenue = {'CampaignYear': [ 2016, 2017, 2018, 2019 , 2020, 2021],\n",
    "        'Revenue': [9473, 8422, 9987, 7994, 9530, 9444 ]\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_revenue = pd.DataFrame(revenue, columns = ['CampaignYear', 'Revenue'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
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       "      <th>CampaignYear</th>\n",
       "      <th>Revenue</th>\n",
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       "      <td>2018</td>\n",
       "      <td>9987</td>\n",
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       "      <td>7994</td>\n",
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       "   CampaignYear  Revenue\n",
       "0          2016     9473\n",
       "1          2017     8422\n",
       "2          2018     9987\n",
       "3          2019     7994\n",
       "4          2020     9530\n",
       "5          2021     9444"
      ]
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     "execution_count": 132,
     "metadata": {},
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   "source": [
    "df_revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combined = pd.merge(df_products, df_revenue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
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       "   CampaignYear  ProductPrice ProductVersion  Revenue\n",
       "0          2016           199             v1     9473\n",
       "1          2017           199             v1     8422\n",
       "2          2018           299             v2     9987\n",
       "3          2019           299             v2     7994\n",
       "4          2020           349             v2     9530\n",
       "5          2021           349             v3     9444"
      ]
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     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df_combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combined_outer = pd.merge(df_products, df_revenue, \\\n",
    "                             how = 'outer')\n",
    "df_combined_inner = pd.merge(df_products, df_revenue, \\\n",
    "                             how = 'inner')\n",
    "df_combined_left = pd.merge(df_products, df_revenue, \\\n",
    "                            how = 'left')\n",
    "df_combined_right = pd.merge(df_products, df_revenue, \\\n",
    "                             how = 'right')"
   ]
  },
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   "execution_count": 136,
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       "   CampaignYear  ProductPrice ProductVersion  Revenue\n",
       "0          2015           199             v1      NaN\n",
       "1          2016           199             v1   9473.0\n",
       "2          2017           199             v1   8422.0\n",
       "3          2018           299             v2   9987.0\n",
       "4          2019           299             v2   7994.0\n",
       "5          2020           349             v2   9530.0\n",
       "6          2021           349             v3   9444.0"
      ]
     },
     "execution_count": 136,
     "metadata": {},
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    "df_combined_outer"
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       "   CampaignYear  ProductPrice ProductVersion  Revenue\n",
       "0          2016           199             v1     9473\n",
       "1          2017           199             v1     8422\n",
       "2          2018           299             v2     9987\n",
       "3          2019           299             v2     7994\n",
       "4          2020           349             v2     9530\n",
       "5          2021           349             v3     9444"
      ]
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     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "df_combined_inner\n"
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       "   CampaignYear  ProductPrice ProductVersion  Revenue\n",
       "0          2015           199             v1      NaN\n",
       "1          2016           199             v1   9473.0\n",
       "2          2017           199             v1   8422.0\n",
       "3          2018           299             v2   9987.0\n",
       "4          2019           299             v2   7994.0\n",
       "5          2020           349             v2   9530.0\n",
       "6          2021           349             v3   9444.0"
      ]
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     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
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    "df_combined_left "
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>199</td>\n",
       "      <td>v1</td>\n",
       "      <td>8422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>299</td>\n",
       "      <td>v2</td>\n",
       "      <td>9987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>299</td>\n",
       "      <td>v2</td>\n",
       "      <td>7994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>349</td>\n",
       "      <td>v2</td>\n",
       "      <td>9530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021</td>\n",
       "      <td>349</td>\n",
       "      <td>v3</td>\n",
       "      <td>9444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CampaignYear  ProductPrice ProductVersion  Revenue\n",
       "0          2016           199             v1     9473\n",
       "1          2017           199             v1     8422\n",
       "2          2018           299             v2     9987\n",
       "3          2019           299             v2     7994\n",
       "4          2020           349             v2     9530\n",
       "5          2021           349             v3     9444"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined_right "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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