11.1 Merging Datasets on Keys (common columns).ipynb

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    "![title](img/cover4.png)"
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   "source": [
    "<center><em>Copyright! This material is protected, please do not copy or distribute.    by:Taher Assaf</em></center>"
   ]
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   "source": [
    "***\n",
    "<h1 align=\"center\">Udemy course : Python Bootcamp for Data Science 2021 Numpy Pandas & Seaborn</h1> \n",
    "\n",
    "***"
   ]
  },
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   "metadata": {},
   "source": [
    "## 11.1 Merging Datasets on Keys (common columns)"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "First we import pandas:"
   ]
  },
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   "source": [
    "import pandas as pd"
   ]
  },
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   "source": [
    "Lets now creat two dataframes using the function **pd.DataFrame()**:"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>states</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Florida</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "       states  population\n",
       "0  California          40\n",
       "1     Georgia          10\n",
       "2     Florida          21\n",
       "3     Arizona           7"
      ]
     },
     "execution_count": 2,
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   "source": [
    "data1 = pd.DataFrame({'states': ['California', 'Georgia', 'Florida', 'Arizona'],\n",
    "                      'population': [40, 10, 21,7]})\n",
    "data1"
   ]
  },
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       "      <th>states</th>\n",
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       "      <td>Arizona</td>\n",
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       "      <td>Colorado</td>\n",
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       "      <td>Indiana</td>\n",
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       "     states  area\n",
       "0   Arizona   113\n",
       "1  Colorado   104\n",
       "2   Indiana    36\n",
       "3   Florida    65"
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    "data2 = pd.DataFrame({'states': ['Arizona', 'Colorado', 'Indiana', 'Florida', ],\n",
    "                      'area': [113, 104, 36, 65]})\n",
    "data2"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "We can merge the above dataframes based on a common column using the function **merge()**:"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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       "      <th>states</th>\n",
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       "  </thead>\n",
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       "    states  population  area\n",
       "0  Florida          21    65\n",
       "1  Arizona           7   113"
      ]
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     "execution_count": 4,
     "metadata": {},
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   "source": [
    "pd.merge(data1, data2)"
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   "source": [
    "We can as well specify the common column for merging using the argument **(on = )**:"
   ]
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   "execution_count": 5,
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       "    states  population  area\n",
       "0  Florida          21    65\n",
       "1  Arizona           7   113"
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    "pd.merge(data1, data2, on = 'states')"
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   "source": [
    "To include all rows from **both** dataframes we use the argument **how = 'outer'**:"
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       "      <th>0</th>\n",
       "      <td>California</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Florida</td>\n",
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       "      <td>65.0</td>\n",
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       "      <td>Arizona</td>\n",
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       "      <td>113.0</td>\n",
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       "      <td>Colorado</td>\n",
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       "      <td>104.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.0</td>\n",
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       "       states  population   area\n",
       "0  California        40.0    NaN\n",
       "1     Georgia        10.0    NaN\n",
       "2     Florida        21.0   65.0\n",
       "3     Arizona         7.0  113.0\n",
       "4    Colorado         NaN  104.0\n",
       "5     Indiana         NaN   36.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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   "source": [
    "pd.merge(data1, data2, how = 'outer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To include only rows from the **first** dataframe, we use the argument **how = 'left'**:"
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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       "      <td>Florida</td>\n",
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       "      <td>65.0</td>\n",
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       "      <td>Arizona</td>\n",
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      "text/plain": [
       "       states  population   area\n",
       "0  California          40    NaN\n",
       "1     Georgia          10    NaN\n",
       "2     Florida          21   65.0\n",
       "3     Arizona           7  113.0"
      ]
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "pd.merge(data1, data2, how = 'left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To include only rows from the **second** dataframe, we use the argument **how = 'right'**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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       "      <th>states</th>\n",
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       "      <td>Colorado</td>\n",
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       "      <td>104</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Florida</td>\n",
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       "      <td>65</td>\n",
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      "text/plain": [
       "     states  population  area\n",
       "0   Arizona         7.0   113\n",
       "1  Colorado         NaN   104\n",
       "2   Indiana         NaN    36\n",
       "3   Florida        21.0    65"
      ]
     },
     "execution_count": 8,
     "metadata": {},
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   ],
   "source": [
    "pd.merge(data1, data2, how = 'right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wehen there is no common column, like these dataframes below, we specify the common column using the arguments **left_on = ''** and **right_on = ''**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Florida</td>\n",
       "      <td>21</td>\n",
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       "      <td>Arizona</td>\n",
       "      <td>7</td>\n",
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      "text/plain": [
       "  states_name  population\n",
       "0  California          40\n",
       "1     Georgia          10\n",
       "2     Florida          21\n",
       "3     Arizona           7"
      ]
     },
     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "data3 = pd.DataFrame({'states_name': ['California', 'Georgia', 'Florida', 'Arizona'],\n",
    "                      'population': [40, 10, 21,7]})\n",
    "data3"
   ]
  },
  {
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   "execution_count": 10,
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       "      <td>Arizona</td>\n",
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       "      <td>Colorado</td>\n",
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       "      <td>Indiana</td>\n",
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      "text/plain": [
       "     states  area\n",
       "0   Arizona   113\n",
       "1  Colorado   104\n",
       "2   Indiana    36\n",
       "3   Florida    65"
      ]
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   "source": [
    "data2"
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   "execution_count": 11,
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       "  states_name  population   states  area\n",
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    "pd.merge(data3, data2, left_on = 'states_name', right_on = 'states')"
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       "       states  population  water\n",
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    "data4 = pd.DataFrame({'states': ['California', 'Georgia', 'Florida', 'Arizona'],\n",
    "                      'population': [40, 10, 21,7], 'water': [4,6,7,2]})\n",
    "data4"
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   "source": [
    "In the case where there are many common columns, we must specify the common column for merging using the argument (**on = 'common column'**), pandas will automatically change the name of the other common columns that was not used for merging:"
   ]
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  {
   "cell_type": "code",
   "execution_count": 13,
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       "     states  area  water\n",
       "0   Arizona   113      2\n",
       "1  Colorado   104      8\n",
       "2   Indiana    36      3\n",
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    "data5 = pd.DataFrame({'states': ['Arizona', 'Colorado', 'Indiana', 'Florida', ],\n",
    "                      'area': [113, 104, 36, 65], 'water': [2,8,3,7 ]})\n",
    "data5"
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      "text/plain": [
       "       states  population  water_x   area  water_y\n",
       "0  California        40.0      4.0    NaN      NaN\n",
       "1     Georgia        10.0      6.0    NaN      NaN\n",
       "2     Florida        21.0      7.0   65.0      7.0\n",
       "3     Arizona         7.0      2.0  113.0      2.0\n",
       "4    Colorado         NaN      NaN  104.0      8.0\n",
       "5     Indiana         NaN      NaN   36.0      3.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
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   "source": [
    "pd.merge(data4, data5, on = 'states', how = 'outer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also decide which suffixes to be used for the extra common column using the argument **suffixes**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
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       "      <th>3</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104.0</td>\n",
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       "      <th>5</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       states  population  water_data4   area  water_data5\n",
       "0  California        40.0          4.0    NaN          NaN\n",
       "1     Georgia        10.0          6.0    NaN          NaN\n",
       "2     Florida        21.0          7.0   65.0          7.0\n",
       "3     Arizona         7.0          2.0  113.0          2.0\n",
       "4    Colorado         NaN          NaN  104.0          8.0\n",
       "5     Indiana         NaN          NaN   36.0          3.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(data4, data5, on = 'states', how = 'outer', suffixes = ('_data4', '_data5'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "<h1 align=\"center\">Thank You</h1> \n",
    "\n",
    "***"
   ]
  }
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