Convert A Categorical Variable Into Dummy Variables.ipynb

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  {
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   "source": [
    "import pandas as pd"
   ]
  },
  {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jason</td>\n",
       "      <td>Miller</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Molly</td>\n",
       "      <td>Jacobson</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tina</td>\n",
       "      <td>Ali</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Milner</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amy</td>\n",
       "      <td>Cooze</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  first_name last_name     sex\n",
       "0      Jason    Miller    male\n",
       "1      Molly  Jacobson  female\n",
       "2       Tina       Ali    male\n",
       "3       Jake    Milner  female\n",
       "4        Amy     Cooze  female"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe\n",
    "raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], \n",
    "        'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'], \n",
    "        'sex': ['male', 'female', 'male', 'female', 'female']}\n",
    "df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'sex'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "# Create a set of dummy variables from the sex variable\n",
    "df_sex = pd.get_dummies(df['sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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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>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "   female  male\n",
       "0       0     1\n",
       "1       1     0\n",
       "2       0     1\n",
       "3       1     0\n",
       "4       1     0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jason</td>\n",
       "      <td>Miller</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Molly</td>\n",
       "      <td>Jacobson</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tina</td>\n",
       "      <td>Ali</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Milner</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amy</td>\n",
       "      <td>Cooze</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  first_name last_name     sex  female  male\n",
       "0      Jason    Miller    male       0     1\n",
       "1      Molly  Jacobson  female       1     0\n",
       "2       Tina       Ali    male       0     1\n",
       "3       Jake    Milner  female       1     0\n",
       "4        Amy     Cooze  female       1     0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join the dummy variables to the main dataframe\n",
    "df_new = pd.concat([df, df_sex], axis=1)\n",
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new.drop(['sex','female'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jason</td>\n",
       "      <td>Miller</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Molly</td>\n",
       "      <td>Jacobson</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tina</td>\n",
       "      <td>Ali</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Milner</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amy</td>\n",
       "      <td>Cooze</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  first_name last_name  gender\n",
       "0      Jason    Miller       1\n",
       "1      Molly  Jacobson       0\n",
       "2       Tina       Ali       1\n",
       "3       Jake    Milner       0\n",
       "4        Amy     Cooze       0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.rename(columns={'male':'gender'})"
   ]
  },
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   "execution_count": null,
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