dataframe find nan rows
df[df.isnull().any(axis=1)]dataframe find nan rows
df[df.isnull().any(axis=1)]count nan pandas
#Python, pandas
#Count missing values for each column of the dataframe df
df.isnull().sum()df count missing values
In [5]: df = pd.DataFrame({'a':[1,2,np.nan], 'b':[np.nan,1,np.nan]})
In [6]: df.isna().sum()
Out[6]:
a 1
b 2
dtype: int64check for missing/ nan values in pandas dataframe
In [27]: df
Out[27]:
A B C
1 NaN -2.027325 1.533582
2 NaN NaN 0.461821
3 -0.788073 NaN NaN
4 -0.916080 -0.612343 NaN
5 -0.887858 1.033826 NaN
In [28]: df.isnull().sum() # Returns the sum of NaN values in each column.
Out[28]:
A 2
B 2
C 3
In [29]: df.isnull().sum().sum # Returns the total NaN values in the dataframe
Out[29]:
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