how to filter out all nan values in pandas df

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Bailey
04 Jan 2021
1df = df[df['EPS'].notna()]
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Sofie
13 Aug 2017
1#return a subset of the dataframe where the column name value != NaN 
2df.loc[df['column name'].isnull() == False]
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Ilian
10 Jun 2016
1df = df[df['my_var'].notna()]
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Norbert
05 Jan 2018
1In [30]: df.dropna(subset=[1])   #Drop only if NaN in specific column (as asked in the question)
2Out[30]:
3          0         1         2
41  2.677677 -1.466923 -0.750366
52       NaN  0.798002 -0.906038
63  0.672201  0.964789       NaN
75 -1.250970  0.030561 -2.678622
86       NaN  1.036043       NaN
97  0.049896 -0.308003  0.823295
109 -0.310130  0.078891       NaN
11
Olaf
05 May 2016
1a = [[y for y in x if pd.notna(y)] for x in df.values.tolist()]
2print (a)
3[['str', 'aad', 'asd'], ['ddd'], ['xyz', 'abc'], ['btc', 'trz', 'abd']]
4
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