The **`get_dummies`** function is used to convert categorical variables into dummy or indicator variables.

> A dummy or indicator variable can have a value of 0 or 1.

# How `get_dummies` works

The `get_dummies` function works as follows:

* It takes a data frame, series, or list. 
* Then, it converts each unique element present in the object to a column heading.
* The function iterates over the object that is passed and checks if the element at the particular index matches the column heading.
* If it does, it encodes it as a 1. 
* Otherwise, it assigns it a 0.
 
# Illustration

The illustration below gives an example of how the `get_dummies` function works:


# Syntax

The syntax of the `get_dummies` function is as follows:

```python
pandas.get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False, dtype=None)
```

> Only the first parameter is compulsory. The rest are optional.

# Parameters

The table below describes the parameters:

| Parameter  | Description  |
| - | - |
| `data`  | Refers to a data frame, series, or list.  |
| `prefix`  | String to append column names of the data frame that is returned. It is `None` by default. |
| `prefix_sep`  | The separator or delimiter to be used if a prefix is added. It is `_` by default.  |
| `dummy_na`  | Adding a column to represent `NAN` values. It is `False` by default.  |
| `columns`  | Column names in the data frame to be encoded. It is `None` by default.  |
| `sparse`  | Whether a SparseArray should back the dummy-encoded columns. It is `False` by default.   |
| `drop_first`  | To remove the first column. It is `False` by default.  |
| `dtype`  | Data type for the new column.  |

# Return value

The `get_dummies` function returns a data frame with categorical encodings (0s and 1s).

# Example

The code snippet below shows how the `get_dummies` function is used in Pandas:


import pandas as pd
import numpy as np

# Creating a series from a list
s = pd.Series(list('abcb'))
print(s)
print('\n')
# Encoding using the function
print(pd.get_dummies(s))
print('\n')

# With dummy_na = True
s1 = ['a', 'b', np.nan]
print("With NA column as well")
print(pd.get_dummies(s1, dummy_na=True))
print('\n')

# On a dataframe with column prefixes
df = pd.DataFrame({'A': ['a', 'b', 'a'], 
     'B': ['b', 'a', 'c'],
     'C': [1, 2, 3]})

print(pd.get_dummies(df, prefix=['col1', 'col2']))
print('\n')

# With drop_first = True
print(pd.get_dummies(pd.Series(list('abcaa')), drop_first=True))
print('\n')

What is the get_dummies function in Pandas?

The get_dummies function is used to convert categorical variables into dummy or indicator variables.  A dummy or indicator variable can have a value of 0 or 1.  How get_dummies works The get_dummies function works as follows:  It takes a data frame, series, or list. Then, it converts each unique element present in the object to a column heading. The function iterates over the object that is passed and checks if the element at the particular index matches the column heading. If it does, it encodes it as a 1. Otherwise, it assigns it a 0.  Illustration The illustration below gives an example of how the get_dummies function works: 

Parameter	Description
`data`	Refers to a data frame, series, or list.
`prefix`	String to append column names of the data frame that is returned. It is `None` by default.
`prefix_sep`	The separator or delimiter to be used if a prefix is added. It is `_` by default.
`dummy_na`	Adding a column to represent `NAN` values. It is `False` by default.
`columns`	Column names in the data frame to be encoded. It is `None` by default.
`sparse`	Whether a SparseArray should back the dummy-encoded columns. It is `False` by default.
`drop_first`	To remove the first column. It is `False` by default.
`dtype`	Data type for the new column.