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I am trying to fill none values in a Pandas dataframe with 0's for only some subset of columns.
When I do:
import pandas as pd df = pd.DataFrame(data={'a':[1,2,3,None],'b':[4,5,None,6],'c':[None,None,7,8]}) print df df.fillna(value=0, inplace=True) print df The output:
a b c 0 1.0 4.0 NaN 1 2.0 5.0 NaN 2 3.0 NaN 7.0 3 NaN 6.0 8.0 a b c 0 1.0 4.0 0.0 1 2.0 5.0 0.0 2 3.0 0.0 7.0 3 0.0 6.0 8.0 It replaces every None with 0's. What I want to do is, only replace Nones in columns a and b, but not c.
What is the best way of doing this?
265
The fillna() method replaces the NULL values with a specified value. The fillna() method returns a new DataFrame object unless the inplace parameter is set to True , in that case the fillna() method does the replacing in the original DataFrame instead.
We can use fillna() function to impute the missing values of a data frame to every column defined by a dictionary of values. The limitation of this method is that we can only use constant values to be filled.
fillna() method is used to fill NaN/NA values on a specified column or on an entire DataaFrame with any given value. You can specify modify using inplace, or limit how many filling to perform or choose an axis whether to fill on rows/column etc. The Below example fills all NaN values with None value.
You can select your desired columns and do it by assignment:
df[['a', 'b']] = df[['a','b']].fillna(value=0) The resulting output is as expected:
a b c 0 1.0 4.0 NaN 1 2.0 5.0 NaN 2 3.0 0.0 7.0 3 0.0 6.0 8.0 You can using dict , fillna with different value for different column
df.fillna({'a':0,'b':0}) Out[829]: a b c 0 1.0 4.0 NaN 1 2.0 5.0 NaN 2 3.0 0.0 7.0 3 0.0 6.0 8.0 After assign it back
df=df.fillna({'a':0,'b':0}) df Out[831]: a b c 0 1.0 4.0 NaN 1 2.0 5.0 NaN 2 3.0 0.0 7.0 3 0.0 6.0 8.0 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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