Update pandas dataframe columns based on index

Question

In pandas have a dataframe:

df1 = pd.DataFrame({'Type':['Application','Application','Hardware'],
                    'Category': ['None','None','Hardware']})

I have the following index to retrieve rows where type contains "application" and Category contains 'None'.

df1[df1['Type'].str.contains('Application') & df1['Category'].str.contains('None')]

Category    Type
0   None    Application
1   None    Application

I would like to update the column Category such that the value is 'some new value' for each row.

I have also tried the same with the following loc index with no success

df1[df1.loc[:,'Type'].str.contains('Application') \
  & df1.loc[:,'Category'].str.contains('None')]

Please include an example of the expected output. Also, confirm that "None" is a string, not the Python None. — DYZ
– DYZ, Commented Aug 17, 2017 at 22:52

Vaishali · Accepted Answer · 2017-08-17 22:44:24Z

1

Are you looking for this?

df1.loc[(df1['Type'] == 'Application') & (df1['Category'] == 'None'), 'Category'] = 'New category'


    Category        Type
0   New category    Application
1   New category    Application
2   Hardware        Hardware

answered Aug 17, 2017 at 22:44

Vaishali

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1 Comment

BENY Over a year ago

kindly using df.loc[(df['Type'].str.contains('Application')) & (df['Category'].str.contains('None')), 'Category'] = 'New category' maybe the column not exactly same. ~

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