pandas_get_dummies_how_to_create_dummy_variables.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to Make Dummy Variables in Python using Pandas get_dummies()\n",
    "This is the Jupyter notebook for the blog post [https://www.marsja.se/how-to-use-pandas-get_dummies-to-create-dummy-variables-in-python/]/(https://www.marsja.se/how-to-use-pandas-get_dummies-to-create-dummy-variables-in-python/). In that post, you will learn how to create dummy variables for categorical (2 and 3 levels) variables.\n",
    "\n",
    "### Import Data from CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>yrs.since.phd</th>\n",
       "      <th>yrs.service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>139750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>Male</td>\n",
       "      <td>173200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>79750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>45</td>\n",
       "      <td>39</td>\n",
       "      <td>Male</td>\n",
       "      <td>115000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>40</td>\n",
       "      <td>41</td>\n",
       "      <td>Male</td>\n",
       "      <td>141500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       rank discipline  yrs.since.phd  yrs.service   sex  salary\n",
       "1      Prof          B             19           18  Male  139750\n",
       "2      Prof          B             20           16  Male  173200\n",
       "3  AsstProf          B              4            3  Male   79750\n",
       "4      Prof          B             45           39  Male  115000\n",
       "5      Prof          B             40           41  Male  141500"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_url = 'http://vincentarelbundock.github.io/Rdatasets/csv/carData/Salaries.csv'\n",
    "df = pd.read_csv(data_url, index_col=0)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making Dummy Variables using Pandas get_dummies()\n",
    "In the first, very simple example of making dummy variables in Python. Here, we use pd.get_dummies and put in a Series object. We can se in the output that we get two new columns for the indicator (dummy) variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Female  Male\n",
       "1       0     1\n",
       "2       0     1\n",
       "3       0     1\n",
       "4       0     1\n",
       "5       0     1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(df['sex']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we, on the other hand, want to save data as a new dataframe we can add the \"columns\" argument with the parameter list containing 'sex' (a list with column names)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>yrs.since.phd</th>\n",
       "      <th>yrs.service</th>\n",
       "      <th>salary</th>\n",
       "      <th>sex_Female</th>\n",
       "      <th>sex_Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>139750</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>173200</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>79750</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>45</td>\n",
       "      <td>39</td>\n",
       "      <td>115000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>40</td>\n",
       "      <td>41</td>\n",
       "      <td>141500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       rank discipline  yrs.since.phd  yrs.service  salary  sex_Female  \\\n",
       "1      Prof          B             19           18  139750           0   \n",
       "2      Prof          B             20           16  173200           0   \n",
       "3  AsstProf          B              4            3   79750           0   \n",
       "4      Prof          B             45           39  115000           0   \n",
       "5      Prof          B             40           41  141500           0   \n",
       "\n",
       "   sex_Male  \n",
       "1         1  \n",
       "2         1  \n",
       "3         1  \n",
       "4         1  \n",
       "5         1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummies = pd.get_dummies(df, columns=['sex'])\n",
    "df_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can, further, use the prefix and prefix_sep arguments to change the column names of the dummy variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>discipline</th>\n",
       "      <th>yrs.since.phd</th>\n",
       "      <th>yrs.service</th>\n",
       "      <th>salary</th>\n",
       "      <th>Gender.Female</th>\n",
       "      <th>Gender.Male</th>\n",
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       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>79750</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>45</td>\n",
       "      <td>39</td>\n",
       "      <td>115000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>40</td>\n",
       "      <td>41</td>\n",
       "      <td>141500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       rank discipline  yrs.since.phd  yrs.service  salary  Gender.Female  \\\n",
       "1      Prof          B             19           18  139750              0   \n",
       "2      Prof          B             20           16  173200              0   \n",
       "3  AsstProf          B              4            3   79750              0   \n",
       "4      Prof          B             45           39  115000              0   \n",
       "5      Prof          B             40           41  141500              0   \n",
       "\n",
       "   Gender.Male  \n",
       "1            1  \n",
       "2            1  \n",
       "3            1  \n",
       "4            1  \n",
       "5            1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummies = pd.get_dummies(df, prefix='Gender', prefix_sep='.', \n",
    "                            columns=['sex'])\n",
    "df_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we are going to remove the prefix and the prefix separator (prefix_sep) by adding empty strings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>yrs.since.phd</th>\n",
       "      <th>yrs.service</th>\n",
       "      <th>salary</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>139750</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>173200</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>79750</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>45</td>\n",
       "      <td>39</td>\n",
       "      <td>115000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>40</td>\n",
       "      <td>41</td>\n",
       "      <td>141500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       rank discipline  yrs.since.phd  yrs.service  salary  Female  Male\n",
       "1      Prof          B             19           18  139750       0     1\n",
       "2      Prof          B             20           16  173200       0     1\n",
       "3  AsstProf          B              4            3   79750       0     1\n",
       "4      Prof          B             45           39  115000       0     1\n",
       "5      Prof          B             40           41  141500       0     1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummies = pd.get_dummies(df, prefix='', prefix_sep='', \n",
    "                            columns=['sex'])\n",
    "df_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Dummy Variables for 3 Factor-Levels\n",
    "In this section, we use the column rank that contain 3 levels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AssocProf</th>\n",
       "      <th>AsstProf</th>\n",
       "      <th>Prof</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   AssocProf  AsstProf  Prof\n",
       "1          0         0     1\n",
       "2          0         0     1\n",
       "3          0         1     0\n",
       "4          0         0     1\n",
       "5          0         0     1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(df['rank']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>discipline</th>\n",
       "      <th>yrs.since.phd</th>\n",
       "      <th>yrs.service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "      <th>rank_AssocProf</th>\n",
       "      <th>rank_AsstProf</th>\n",
       "      <th>rank_Prof</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>139750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>Male</td>\n",
       "      <td>173200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>79750</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>45</td>\n",
       "      <td>39</td>\n",
       "      <td>Male</td>\n",
       "      <td>115000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B</td>\n",
       "      <td>40</td>\n",
       "      <td>41</td>\n",
       "      <td>Male</td>\n",
       "      <td>141500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  discipline  yrs.since.phd  yrs.service   sex  salary  rank_AssocProf  \\\n",
       "1          B             19           18  Male  139750               0   \n",
       "2          B             20           16  Male  173200               0   \n",
       "3          B              4            3  Male   79750               0   \n",
       "4          B             45           39  Male  115000               0   \n",
       "5          B             40           41  Male  141500               0   \n",
       "\n",
       "   rank_AsstProf  rank_Prof  \n",
       "1              0          1  \n",
       "2              0          1  \n",
       "3              1          0  \n",
       "4              0          1  \n",
       "5              0          1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummies = pd.get_dummies(df, columns=['rank'])\n",
    "df_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>discipline</th>\n",
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       "      <td>79750</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B</td>\n",
       "      <td>40</td>\n",
       "      <td>41</td>\n",
       "      <td>Male</td>\n",
       "      <td>141500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  discipline  yrs.since.phd  yrs.service   sex  salary  Rank.AssocProf  \\\n",
       "1          B             19           18  Male  139750               0   \n",
       "2          B             20           16  Male  173200               0   \n",
       "3          B              4            3  Male   79750               0   \n",
       "4          B             45           39  Male  115000               0   \n",
       "5          B             40           41  Male  141500               0   \n",
       "\n",
       "   Rank.AsstProf  Rank.Prof  \n",
       "1              0          1  \n",
       "2              0          1  \n",
       "3              1          0  \n",
       "4              0          1  \n",
       "5              0          1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummies = pd.get_dummies(df, prefix='Rank', prefix_sep='.', \n",
    "                            columns=['rank'])\n",
    "df_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Male</td>\n",
       "      <td>141500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  discipline  yrs.since.phd  yrs.service   sex  salary  AssocProf  AsstProf  \\\n",
       "1          B             19           18  Male  139750          0         0   \n",
       "2          B             20           16  Male  173200          0         0   \n",
       "3          B              4            3  Male   79750          0         1   \n",
       "4          B             45           39  Male  115000          0         0   \n",
       "5          B             40           41  Male  141500          0         0   \n",
       "\n",
       "   Prof  \n",
       "1     1  \n",
       "2     1  \n",
       "3     0  \n",
       "4     1  \n",
       "5     1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummies = pd.get_dummies(df, prefix='', prefix_sep='', \n",
    "                            columns=['rank'])\n",
    "df_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
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       "      <td>115000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B</td>\n",
       "      <td>40</td>\n",
       "      <td>41</td>\n",
       "      <td>141500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  discipline  yrs.since.phd  yrs.service  salary  AssocProf  AsstProf  Prof  \\\n",
       "1          B             19           18  139750          0         0     1   \n",
       "2          B             20           16  173200          0         0     1   \n",
       "3          B              4            3   79750          0         1     0   \n",
       "4          B             45           39  115000          0         0     1   \n",
       "5          B             40           41  141500          0         0     1   \n",
       "\n",
       "   Female  Male  \n",
       "1       0     1  \n",
       "2       0     1  \n",
       "3       0     1  \n",
       "4       0     1  \n",
       "5       0     1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummies = pd.get_dummies(df, prefix='', prefix_sep='', \n",
    "                            columns=['rank', 'sex'])\n",
    "df_dummies.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Many Columns to Create Dummy Variables\n",
    "Here, as a bonus, we create 6 new dummy columns using three variables (i.e., columns) to create the new dummy variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>40</td>\n",
       "      <td>41</td>\n",
       "      <td>141500</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   yrs.since.phd  yrs.service  salary  AssocProf  AsstProf  Prof  Female  \\\n",
       "1             19           18  139750          0         0     1       0   \n",
       "2             20           16  173200          0         0     1       0   \n",
       "3              4            3   79750          0         1     0       0   \n",
       "4             45           39  115000          0         0     1       0   \n",
       "5             40           41  141500          0         0     1       0   \n",
       "\n",
       "   Male  A  B  \n",
       "1     1  0  1  \n",
       "2     1  0  1  \n",
       "3     1  0  1  \n",
       "4     1  0  1  \n",
       "5     1  0  1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_dummies = pd.get_dummies(df, prefix='', prefix_sep='', \n",
    "                            columns=['rank', 'sex', 'discipline'])\n",
    "df_dummies.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yrs.since.phd</th>\n",
       "      <th>yrs.service</th>\n",
       "      <th>salary</th>\n",
       "      <th>p1AssocProf</th>\n",
       "      <th>p1AsstProf</th>\n",
       "      <th>p1Prof</th>\n",
       "      <th>p2Female</th>\n",
       "      <th>p2Male</th>\n",
       "      <th>3A</th>\n",
       "      <th>3B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>139750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>173200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>79750</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45</td>\n",
       "      <td>39</td>\n",
       "      <td>115000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>40</td>\n",
       "      <td>41</td>\n",
       "      <td>141500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   yrs.since.phd  yrs.service  salary  p1AssocProf  p1AsstProf  p1Prof  \\\n",
       "1             19           18  139750            0           0       1   \n",
       "2             20           16  173200            0           0       1   \n",
       "3              4            3   79750            0           1       0   \n",
       "4             45           39  115000            0           0       1   \n",
       "5             40           41  141500            0           0       1   \n",
       "\n",
       "   p2Female  p2Male  3A  3B  \n",
       "1         0       1   0   1  \n",
       "2         0       1   0   1  \n",
       "3         0       1   0   1  \n",
       "4         0       1   0   1  \n",
       "5         0       1   0   1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dc = pd.get_dummies(df, \n",
    "                       columns=['ColumnToDummyCode'])"
   ]
  }
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