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From Pandas data frame, how to get index of non "NaN" values?
My data frame is
A b c 0 1 q1 1 1 2 NaN 3 2 3 q2 3 3 4 q1 NaN 4 5 q2 7 And I want the index of the rows in which column b is not NaN. (there can be NaN values in other column e.g. c )
non_nana_index = [0,2,3,4]
Using this non "NaN" index list I want to create new data frame which column b do not have "Nan"
df2=
A b c 0 1 q1 1 1 3 q2 3 2 4 q1 NaN 3 5 q2 7 
599
isna() in pandas library can be used to check if the value is null/NaN. It will return True if the value is NaN/null.
The math. isnan() method checks whether a value is NaN (Not a Number), or not. This method returns True if the specified value is a NaN, otherwise it returns False.
You can filter out rows with NAN value from pandas DataFrame column string, float, datetime e.t.c by using DataFrame. dropna() and DataFrame. notnull() methods. Python doesn't support Null hence any missing data is represented as None or NaN.
Just filter them
In [62]: df['b'].notnull() Out[62]: 0 True 1 False 2 True 3 True 4 True Name: b, dtype: bool In [63]: df[df['b'].notnull()] Out[63]: A b c 0 1 q1 1 2 3 q2 3 3 4 q1 NaN 4 5 q2 7 DataFrames have a dropna method:
import pandas import numpy d = pandas.DataFrame({'A': [1, 2, 3, numpy.nan], 'b': [1, 2, numpy.nan, 3], 'c': [1, numpy.nan, 2, 3]}) d.dropna(subset=['b']) 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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