Python Pandas DataFrame get_dummies() to convert categorical variable into dummy/indicator variables

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

From categorical variables to dummy / indicator vaiables by using get_dummies().

`self`	array, DataFrame
`prefix`	str (optional ), string to append to column names.
`prefix_sep`	str ( Optional ),default is '_', Separator to be used in column names
`dummy_na`	Bool ( Optional ),default is False, Column is used to indicate NaN values.
`columns`	list ( Optional ),default is None, columns to be encoded.
`sparse`	dummy columns to be sparse or not
`drop_first`	Bool ( default False ), to remove first level of categorical levels

import pandas as pd 
my_dict={'ID':[1,2,3,4,5,6],
         'MATH':[80,40,70,70,70,30],
         'CLASS1':['Four','Three','Three','Four','Five','Three']}
my_data = pd.DataFrame(data=my_dict)
print(pd.get_dummies(my_data))

Output

   ID  MATH  CLASS1_Five  CLASS1_Four  CLASS1_Three
0   1    80            0            1             0
1   2    40            0            0             1
2   3    70            0            0             1
3   4    70            0            1             0
4   5    70            1            0             0
5   6    30            0            0             1

Output

   ID  MATH  CLASS1_Five  CLASS1_Four  CLASS1_Three
0   1    80            0            1             0
1   2    40            0            0             1
2   3    70            0            0             1
3   4    70            0            1             0
4   5    70            1            0             0
5   6    30            0            0             1

prefix

String to be added before column name.

print(pd.get_dummies(my_data,prefix='my'))

Output

   ID  MATH  my_Five  my_Four  my_Three
0   1    80        0        1         0
1   2    40        0        0         1
2   3    70        0        0         1
3   4    70        0        1         0
4   5    70        1        0         0
5   6    30        0        0         1

prefex_sep

Separator to be used between column name and prefix. Default value is #

print(pd.get_dummies(my_data,prefix='my',prefix_sep='-'))

Output

   ID  MATH  my-Five  my-Four  my-Three
0   1    80        0        1         0
1   2    40        0        0         1
2   3    70        0        0         1
3   4    70        0        1         0
4   5    70        1        0         0
5   6    30        0        0         1

columns

List of Column names on which get_dummies() will be applied. By default categorical column is used.

print(pd.get_dummies(my_data,prefix='my',columns=['CLASS1','MATH']))

Output

   ID  my_Five  my_Four  my_Three  my_30  my_40  my_70  my_80
0   1        0        1         0      0      0      0      1
1   2        0        0         1      0      1      0      0
2   3        0        0         1      0      0      1      0
3   4        0        1         0      0      0      1      0
4   5        1        0         0      0      0      1      0
5   6        0        0         1      1      0      0      0

spares

Default value is False ( boolean ).

print(pd.get_dummies(my_data,prefix='my',sparse=True))

drop_first

Boolean , default value is False. If it is set to True then first level is removed.

print(pd.get_dummies(my_data,prefix='my',drop_first=True))

Output

   ID  MATH  my_Four  my_Three
0   1    80        1         0
1   2    40        0         1
2   3    70        0         1
3   4    70        1         0
4   5    70        0         0
5   6    30        0         1

Pandas Plotting graphs mean min sum len Filtering of Data

Numpy arrays Python & MySQL Python- Tutorials

Subhendu Mohapatra

Author & Instructor at plus2net

I write and maintain practical tutorials on Python, PHP, SQL, JavaScript, HTML, jQuery, and web development at plus2net. The tutorials focus on clear explanations, working examples, and code that readers can test and adapt while learning.

About the author Watch on YouTube

Connect:

Subscribe to our YouTube Channel here