04_Sort a DataFrame.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sort a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create an example DataFrame of normally distributed random numbers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.624345</td>\n",
       "      <td>-0.611756</td>\n",
       "      <td>-0.528172</td>\n",
       "      <td>-1.072969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.865408</td>\n",
       "      <td>-2.301539</td>\n",
       "      <td>1.744812</td>\n",
       "      <td>-0.761207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.319039</td>\n",
       "      <td>-0.249370</td>\n",
       "      <td>1.462108</td>\n",
       "      <td>-2.060141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.322417</td>\n",
       "      <td>-0.384054</td>\n",
       "      <td>1.133769</td>\n",
       "      <td>-1.099891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.172428</td>\n",
       "      <td>-0.877858</td>\n",
       "      <td>0.042214</td>\n",
       "      <td>0.582815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.100619</td>\n",
       "      <td>1.144724</td>\n",
       "      <td>0.901591</td>\n",
       "      <td>0.502494</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  1.624345 -0.611756 -0.528172 -1.072969\n",
       "1  0.865408 -2.301539  1.744812 -0.761207\n",
       "2  0.319039 -0.249370  1.462108 -2.060141\n",
       "3 -0.322417 -0.384054  1.133769 -1.099891\n",
       "4 -0.172428 -0.877858  0.042214  0.582815\n",
       "5 -1.100619  1.144724  0.901591  0.502494"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use a random seed to make reproducible data.\n",
    "np.random.seed(1)\n",
    "df = pd.DataFrame(np.random.randn(6,4), columns=list(\"ABCD\"))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sort the DataFrame by a specified column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.865408</td>\n",
       "      <td>-2.301539</td>\n",
       "      <td>1.744812</td>\n",
       "      <td>-0.761207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.172428</td>\n",
       "      <td>-0.877858</td>\n",
       "      <td>0.042214</td>\n",
       "      <td>0.582815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.624345</td>\n",
       "      <td>-0.611756</td>\n",
       "      <td>-0.528172</td>\n",
       "      <td>-1.072969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.322417</td>\n",
       "      <td>-0.384054</td>\n",
       "      <td>1.133769</td>\n",
       "      <td>-1.099891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.319039</td>\n",
       "      <td>-0.249370</td>\n",
       "      <td>1.462108</td>\n",
       "      <td>-2.060141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.100619</td>\n",
       "      <td>1.144724</td>\n",
       "      <td>0.901591</td>\n",
       "      <td>0.502494</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "          A         B         C         D\n",
       "1  0.865408 -2.301539  1.744812 -0.761207\n",
       "4 -0.172428 -0.877858  0.042214  0.582815\n",
       "0  1.624345 -0.611756 -0.528172 -1.072969\n",
       "3 -0.322417 -0.384054  1.133769 -1.099891\n",
       "2  0.319039 -0.249370  1.462108 -2.060141\n",
       "5 -1.100619  1.144724  0.901591  0.502494"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=\"B\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note this returns a new DataFrame and doesn't alter the original:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.624345</td>\n",
       "      <td>-0.611756</td>\n",
       "      <td>-0.528172</td>\n",
       "      <td>-1.072969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.865408</td>\n",
       "      <td>-2.301539</td>\n",
       "      <td>1.744812</td>\n",
       "      <td>-0.761207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.319039</td>\n",
       "      <td>-0.249370</td>\n",
       "      <td>1.462108</td>\n",
       "      <td>-2.060141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.322417</td>\n",
       "      <td>-0.384054</td>\n",
       "      <td>1.133769</td>\n",
       "      <td>-1.099891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.172428</td>\n",
       "      <td>-0.877858</td>\n",
       "      <td>0.042214</td>\n",
       "      <td>0.582815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.100619</td>\n",
       "      <td>1.144724</td>\n",
       "      <td>0.901591</td>\n",
       "      <td>0.502494</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "          A         B         C         D\n",
       "0  1.624345 -0.611756 -0.528172 -1.072969\n",
       "1  0.865408 -2.301539  1.744812 -0.761207\n",
       "2  0.319039 -0.249370  1.462108 -2.060141\n",
       "3 -0.322417 -0.384054  1.133769 -1.099891\n",
       "4 -0.172428 -0.877858  0.042214  0.582815\n",
       "5 -1.100619  1.144724  0.901591  0.502494"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use <code>inplace=True</code> to alter the original DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.865408</td>\n",
       "      <td>-2.301539</td>\n",
       "      <td>1.744812</td>\n",
       "      <td>-0.761207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.172428</td>\n",
       "      <td>-0.877858</td>\n",
       "      <td>0.042214</td>\n",
       "      <td>0.582815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.624345</td>\n",
       "      <td>-0.611756</td>\n",
       "      <td>-0.528172</td>\n",
       "      <td>-1.072969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.322417</td>\n",
       "      <td>-0.384054</td>\n",
       "      <td>1.133769</td>\n",
       "      <td>-1.099891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.319039</td>\n",
       "      <td>-0.249370</td>\n",
       "      <td>1.462108</td>\n",
       "      <td>-2.060141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.100619</td>\n",
       "      <td>1.144724</td>\n",
       "      <td>0.901591</td>\n",
       "      <td>0.502494</td>\n",
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      "text/plain": [
       "          A         B         C         D\n",
       "1  0.865408 -2.301539  1.744812 -0.761207\n",
       "4 -0.172428 -0.877858  0.042214  0.582815\n",
       "0  1.624345 -0.611756 -0.528172 -1.072969\n",
       "3 -0.322417 -0.384054  1.133769 -1.099891\n",
       "2  0.319039 -0.249370  1.462108 -2.060141\n",
       "5 -1.100619  1.144724  0.901591  0.502494"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=\"B\", inplace=True)\n",
    "df"
   ]
  }
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