# One Hot Encoding | Dummies

One Hot encoding means splitting categorical variable into multiple binary variables.

“One hot” means at a time only one feature is Hot/Active out of all the split features.

In one hot encoding a categorical feature is split into as many features as there are categories in that feature.

In below example, COLOR is a categorical feature with 3 categories namely red, blue and green,

so new features that are formed from COLOR are COLOR_RED, COLOR_GREEN and COLOR_BLUE.

Now, if the value of color for an entry was red then COLOR_RED will be assigned value 1 and

COLOR_GREEN, COLOR_BLUE will be assigned value 0.

In Pandas categorical features can be one hot encoded by ‘get_dummies’ method.

```
# Imports
import pandas as pd
# Let's create a dataframe
data = {'Color': ['Red', 'Red', 'Green', 'Blue', 'Red', 'Green'],
'Shape': ['Circle', 'Square', 'Square', 'Triangle', 'Circle', 'Triangle'],
'Value': [1, 1, 2, 1, 3, 3]}
df = pd.DataFrame(data)
df
```

Color | Shape | Value | |
---|---|---|---|

0 | Red | Circle | 1 |

1 | Red | Square | 1 |

2 | Green | Square | 2 |

3 | Blue | Triangle | 1 |

4 | Red | Circle | 3 |

5 | Green | Triangle | 3 |

```
# Color is a categorical feature having Red, Blue and Green as categories
# Shape is a categorical feature having Circle, Square, Triangle as categories
# Let's get one hot encoded features from Color and Shape
pd.get_dummies(df)
```

Value | Color_Blue | Color_Green | Color_Red | Shape_Circle | Shape_Square | Shape_Triangle | |
---|---|---|---|---|---|---|---|

0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 |

1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 |

2 | 2 | 0 | 1 | 0 | 0 | 1 | 0 |

3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 |

4 | 3 | 0 | 0 | 1 | 1 | 0 | 0 |

5 | 3 | 0 | 1 | 0 | 0 | 0 | 1 |

## That's how we do One Hot Encoding | Dummies

That’s all for this mini tutorial. To sum it up, we learned how to do One Hot Encoding | Dummies.

Hope it was easy, cool and simple to follow. Now it’s on you.

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