How to count nan values in a pandas DataFrame?

Question

What is the best way to account for (not a number) nan values in a pandas DataFrame?

The following code:

import numpy as np
import pandas as pd
dfd =         


        

Answer 1

A good clean way to count all NaN's in all columns of your dataframe would be ...

import pandas as pd 
import numpy as np


df = pd.DataFrame({'a':[1,2,np.nan], 'b':[np.nan,1,np.nan]})
print(df.isna().sum().sum())

Using a single sum, you get the count of NaN's for each column. The second sum, sums those column sums.

Answer 2

If you want to count only NaN values in column 'a' of a DataFrame df, use:

len(df) - df['a'].count()

Here count() tells us the number of non-NaN values, and this is subtracted from the total number of values (given by len(df)).

To count NaN values in every column of df, use:

len(df) - df.count()

---

If you want to use value_counts, tell it not to drop NaN values by setting dropna=False (added in 0.14.1):

dfv = dfd['a'].value_counts(dropna=False)

This allows the missing values in the column to be counted too:

 3     3
NaN    2
 1     1
Name: a, dtype: int64

The rest of your code should then work as you expect (note that it's not necessary to call sum; just print("nan: %d" % dfv[np.nan]) suffices).

Answer 3

dfd['a'].isnull().value_counts()

return :

• (True 695
• False 60,
• Name: a, dtype: int64)

• True : represents the null values count
• False : represent the non-null values count

Answer 4

if you only want the summary of null value for each column, using the following code df.isnull().sum() if you want to know how many null values in the data frame using following code df.isnull().sum().sum() # calculate total

Answer 5

To count just null values, you can use isnull():

In [11]:
dfd.isnull().sum()

Out[11]:
a    2
dtype: int64

Here a is the column name, and there are 2 occurrences of the null value in the column.

Answer 6

Yet another way to count all the nans in a df:

num_nans = df.size - df.count().sum()

Timings:

import timeit

import numpy as np
import pandas as pd

df_scale = 100000
df = pd.DataFrame(
    [[1, np.nan, 100, 63], [2, np.nan, 101, 63], [2, 12, 102, 63],
     [2, 14, 102, 63], [2, 14, 102, 64], [1, np.nan, 200, 63]] * df_scale,
    columns=['group', 'value', 'value2', 'dummy'])

repeat = 3
numbers = 100

setup = """import pandas as pd
from __main__ import df
"""

def timer(statement, _setup=None):
    print (min(
        timeit.Timer(statement, setup=_setup or setup).repeat(
            repeat, numbers)))

timer('df.size - df.count().sum()')
timer('df.isna().sum().sum()')
timer('df.isnull().sum().sum()')

prints:

3.998805362999999
3.7503365439999996
3.689461442999999

so pretty much equivalent