7.8 Sorting Series and Dataframe.ipynb

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    "![title](img/cover4.png)"
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   "metadata": {},
   "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",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.8 Sorting Series and Dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we import numpy and pandas libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we create a series using the function **pd.Series()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
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       "j    37\n",
       "s    40\n",
       "g    26\n",
       "j    36\n",
       "h    42\n",
       "f    45\n",
       "s    42\n",
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     "execution_count": 2,
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   ],
   "source": [
    "ser1 = pd.Series(np.random.randint(50, size =10), index = list('jsgjhfsagb'))\n",
    "ser1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can sort the index of this series using the function **sort_index()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hide_input": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     7\n",
       "b    38\n",
       "f    45\n",
       "g    26\n",
       "g    37\n",
       "h    42\n",
       "j    37\n",
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "ser1.sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But notice that the original series is not changes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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     "data": {
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       "j    37\n",
       "s    40\n",
       "g    26\n",
       "j    36\n",
       "h    42\n",
       "f    45\n",
       "s    42\n",
       "a     7\n",
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       "dtype: int32"
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     "execution_count": 4,
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   ],
   "source": [
    "ser1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make the sorting permenant, we use the argument **inplace = True**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     7\n",
       "b    38\n",
       "f    45\n",
       "g    26\n",
       "g    37\n",
       "h    42\n",
       "j    37\n",
       "j    36\n",
       "s    40\n",
       "s    42\n",
       "dtype: int32"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "ser1.sort_index(inplace = True)\n",
    "ser1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also sort the data of the series using the function **sort_values()**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hide_input": false
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     "data": {
      "text/plain": [
       "a     7\n",
       "g    26\n",
       "j    36\n",
       "g    37\n",
       "j    37\n",
       "b    38\n",
       "s    40\n",
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     "execution_count": 6,
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   "source": [
    "ser1.sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we read a dataframe from a csv file using the function **pd.read_csv()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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      "text/plain": [
       "      age        wage\n",
       "year                 \n",
       "2006   18   75.043154\n",
       "2004   24   70.476020\n",
       "2003   45  130.982177\n",
       "2003   43  154.685293\n",
       "2005   50   75.043154\n",
       "2008   54  127.115744\n",
       "2009   44  169.528538\n",
       "2008   30  111.720849\n",
       "2006   41  118.884359\n",
       "2004   52  128.680488\n",
       "2007   45  117.146817\n",
       "2007   34   81.283253\n",
       "2005   35   89.492480\n",
       "2003   39  134.705375\n",
       "2009   54  134.705375\n",
       "2009   51   90.481913\n",
       "2003   37   82.679637\n",
       "2006   50  212.842352\n",
       "2007   56  129.156693\n",
       "2003   37   98.599344"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "wage = pd.read_csv('data/wage.csv', index_col = 'year')\n",
    "wage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can sort the index of this dataframe using the function **sort_index()**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hide_input": false
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       "      <th>2006</th>\n",
       "      <td>18</td>\n",
       "      <td>75.043154</td>\n",
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       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>56</td>\n",
       "      <td>129.156693</td>\n",
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       "      <th>2007</th>\n",
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       "      <td>134.705375</td>\n",
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      "text/plain": [
       "      age        wage\n",
       "year                 \n",
       "2003   37   98.599344\n",
       "2003   37   82.679637\n",
       "2003   45  130.982177\n",
       "2003   43  154.685293\n",
       "2003   39  134.705375\n",
       "2004   52  128.680488\n",
       "2004   24   70.476020\n",
       "2005   35   89.492480\n",
       "2005   50   75.043154\n",
       "2006   41  118.884359\n",
       "2006   50  212.842352\n",
       "2006   18   75.043154\n",
       "2007   56  129.156693\n",
       "2007   45  117.146817\n",
       "2007   34   81.283253\n",
       "2008   30  111.720849\n",
       "2008   54  127.115744\n",
       "2009   44  169.528538\n",
       "2009   54  134.705375\n",
       "2009   51   90.481913"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wage.sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also sort the labels of the columns by using the argument **axis = 1**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hide_input": false
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   "outputs": [
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       "      <th>2007</th>\n",
       "      <td>34</td>\n",
       "      <td>81.283253</td>\n",
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       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>35</td>\n",
       "      <td>89.492480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>39</td>\n",
       "      <td>134.705375</td>\n",
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       "      <th>2009</th>\n",
       "      <td>54</td>\n",
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       "      <td>56</td>\n",
       "      <td>129.156693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>37</td>\n",
       "      <td>98.599344</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age        wage\n",
       "year                 \n",
       "2006   18   75.043154\n",
       "2004   24   70.476020\n",
       "2003   45  130.982177\n",
       "2003   43  154.685293\n",
       "2005   50   75.043154\n",
       "2008   54  127.115744\n",
       "2009   44  169.528538\n",
       "2008   30  111.720849\n",
       "2006   41  118.884359\n",
       "2004   52  128.680488\n",
       "2007   45  117.146817\n",
       "2007   34   81.283253\n",
       "2005   35   89.492480\n",
       "2003   39  134.705375\n",
       "2009   54  134.705375\n",
       "2009   51   90.481913\n",
       "2003   37   82.679637\n",
       "2006   50  212.842352\n",
       "2007   56  129.156693\n",
       "2003   37   98.599344"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wage.sort_index(axis =1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For descending ordering of the index, we use the argument **ascending = False**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
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       "      <td>43</td>\n",
       "      <td>154.685293</td>\n",
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       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>45</td>\n",
       "      <td>130.982177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>37</td>\n",
       "      <td>98.599344</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age        wage\n",
       "year                 \n",
       "2009   51   90.481913\n",
       "2009   44  169.528538\n",
       "2009   54  134.705375\n",
       "2008   54  127.115744\n",
       "2008   30  111.720849\n",
       "2007   45  117.146817\n",
       "2007   34   81.283253\n",
       "2007   56  129.156693\n",
       "2006   50  212.842352\n",
       "2006   18   75.043154\n",
       "2006   41  118.884359\n",
       "2005   35   89.492480\n",
       "2005   50   75.043154\n",
       "2004   24   70.476020\n",
       "2004   52  128.680488\n",
       "2003   39  134.705375\n",
       "2003   37   82.679637\n",
       "2003   43  154.685293\n",
       "2003   45  130.982177\n",
       "2003   37   98.599344"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wage.sort_index(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also sort the dataframe by a column, using the function **sort_values**: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>2007</th>\n",
       "      <td>45</td>\n",
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       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>54</td>\n",
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       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>54</td>\n",
       "      <td>134.705375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>56</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      age        wage\n",
       "year                 \n",
       "2006   18   75.043154\n",
       "2004   24   70.476020\n",
       "2008   30  111.720849\n",
       "2007   34   81.283253\n",
       "2005   35   89.492480\n",
       "2003   37   82.679637\n",
       "2003   37   98.599344\n",
       "2003   39  134.705375\n",
       "2006   41  118.884359\n",
       "2003   43  154.685293\n",
       "2009   44  169.528538\n",
       "2007   45  117.146817\n",
       "2003   45  130.982177\n",
       "2005   50   75.043154\n",
       "2006   50  212.842352\n",
       "2009   51   90.481913\n",
       "2004   52  128.680488\n",
       "2008   54  127.115744\n",
       "2009   54  134.705375\n",
       "2007   56  129.156693"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wage.sort_values(by = 'age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can even sort the dataframe by multiple columns, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
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       "      <th>2007</th>\n",
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       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>44</td>\n",
       "      <td>169.528538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>45</td>\n",
       "      <td>117.146817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>45</td>\n",
       "      <td>130.982177</td>\n",
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       "      <th>2005</th>\n",
       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>50</td>\n",
       "      <td>212.842352</td>\n",
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       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>51</td>\n",
       "      <td>90.481913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>52</td>\n",
       "      <td>128.680488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>54</td>\n",
       "      <td>127.115744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>54</td>\n",
       "      <td>134.705375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>56</td>\n",
       "      <td>129.156693</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age        wage\n",
       "year                 \n",
       "2006   18   75.043154\n",
       "2004   24   70.476020\n",
       "2008   30  111.720849\n",
       "2007   34   81.283253\n",
       "2005   35   89.492480\n",
       "2003   37   82.679637\n",
       "2003   37   98.599344\n",
       "2003   39  134.705375\n",
       "2006   41  118.884359\n",
       "2003   43  154.685293\n",
       "2009   44  169.528538\n",
       "2007   45  117.146817\n",
       "2003   45  130.982177\n",
       "2005   50   75.043154\n",
       "2006   50  212.842352\n",
       "2009   51   90.481913\n",
       "2004   52  128.680488\n",
       "2008   54  127.115744\n",
       "2009   54  134.705375\n",
       "2007   56  129.156693"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wage.sort_values(by = ['age', 'wage'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "<h1 align=\"center\">Thank You</h1> \n",
    "\n",
    "***"
   ]
  }
 ],
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