CODEWITHSUNDEEP
pythonpandasdataframe
GursimranSe
GursimranSe

Reputation: 109

Count all NaNs in a pandas DataFrame

I'm trying to count NaN element (data type class 'numpy.float64')in pandas series to know how many are there which data type is class 'pandas.core.series.Series'

This is for count null value in pandas series

import pandas as pd
oc=pd.read_csv(csv_file)
oc.count("NaN")

my expected output of oc,count("NaN") to be 7 but it show 'Level NaN must be same as name (None)'

Upvotes: 5

Views: 6033

Answers (5)

MananVyas
MananVyas

Reputation: 236

If your dataframe looks like this ;

aa = pd.DataFrame(np.array([[1,2,np.nan],[3,np.nan,5],[8,7,6],
                 [np.nan,np.nan,0]]), columns=['a','b','c'])
    a    b    c
0  1.0  2.0  NaN
1  3.0  NaN  5.0
2  8.0  7.0  6.0
3  NaN  NaN  0.0

To count 'nan' by cols, you can try this

aa.isnull().sum()
a    1
b    2
c    1

For total count of nan

aa.isnull().values.sum()
4

Upvotes: 1

Quang Hoang
Quang Hoang

Reputation: 150745

Just for fun, you can do either

df.isnull().sum().sum()

or

len(df)*len(df.columns) - len(df.stack())

Upvotes: 1

Andy L.
Andy L.

Reputation: 25249

oc.size : returns total element counts of dataframe including NaN
oc.count().sum(): return total element counts of dataframe excluding NaN

Therefore, another way to count number of NaN in dataframe is doing subtraction on them:

NaN_count = oc.size - oc.count().sum()

Upvotes: 0

jeschwar
jeschwar

Reputation: 1314

You can use either of the following if your Series.dtype is float64:

oc.isin([np.nan]).sum()
oc.isna().sum()

If your Series is of mixed data-type you can use the following:

oc.isin([np.nan, 'NaN']).sum()

Upvotes: 0

cs95
cs95

Reputation: 402553

The argument to count isn't what you want counted (it's actually the axis name or index).

You're looking for df.isna().values.sum() (to count NaNs across the entire DataFrame), or len(df) - df['column'].count() (to count NaNs in a specific column).

Upvotes: 2

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