Python Pandas – get_dummies() method

pandas.get_dummies() is used for data manipulation. It converts categorical data into dummy or indicator variables.

syntax:  pandas.get_dummies(data, prefix=None, prefix_sep=’_’, dummy_na=False, columns=None, sparse=False, drop_first=False, dtype=None)

Parameters:

• data: whose data is to be manipulated.
• prefix: String to append DataFrame column names. Pass a list with length equal to the number of columns when calling get_dummies on a DataFrame. Default value is None.
• prefix_sep: Separator/delimiter to use if appending any prefix. Default is ‘_’
• dummy_na: It adds a column to indicate NaN values, default value is false, If false NaNs are ignored.
• columns: Column names in the DataFrame that needs to be encoded. Default value is None, If columns is None then all the columns with object or category dtype will be converted.
• sparse: It  specify whether the dummy-encoded columns should be backed by a SparseArray (True) or a regular NumPy array (False). default value is False.
• drop_first: Remove first level to get k-1 dummies out of k categorical levels.
• dtype: Data type for new columns. Only a single dtype is allowed. Default value is np.uint8.

Returns: Dataframe (Dummy-coded data)

Example 1:

Python3

import pandas as pd

con = pd.Series(list('abcba'))
print(pd.get_dummies(con))

Output:

Output 

Example 2:

Python

import pandas as pd
import numpy as np


# list
li = ['s', 'a', 't', np.nan]
print(pd.get_dummies(li))

Output:

Nan column is not there as dummy_na is False by default

Example 3: (To get NaN column)

Python

import pandas as pd
import numpy as np


# list
li = ['s', 'a', 't', np.nan]
print(pd.get_dummies(li, dummy_na=True))

Output:

Example 4:

Python3

import pandas as pd
import numpy as np


# dictionary
diff = pd.DataFrame({'R': ['a', 'c', 'd'],
                     'T': ['d', 'a', 'c'],
                     'S_': [1, 2, 3]})

print(pd.get_dummies(diff, prefix=['column1', 'column2']))

Output: