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 pdcon = pd.Series(list('abcba'))print(pd.get_dummies(con)) |
Output:
Output
Example 2:
Python
import pandas as pdimport numpy as np# listli = ['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 pdimport numpy as np# listli = ['s', 'a', 't', np.nan]print(pd.get_dummies(li, dummy_na=True)) |
Output:
Example 4:
Python3
import pandas as pdimport numpy as np# dictionarydiff = pd.DataFrame({'R': ['a', 'c', 'd'], 'T': ['d', 'a', 'c'], 'S_': [1, 2, 3]})print(pd.get_dummies(diff, prefix=['column1', 'column2'])) |
Output:
