How to check if any value is NaN in a Pandas DataFrame

In Python Pandas, what's the best way to check whether a DataFrame has one (or more) NaN values?

I know about the function pd.isnan, but this returns a DataFrame of booleans for each element. This post right here doesn't exactly answer my question either.

Solution 1:

jwilner's response is spot on. I was exploring to see if there's a faster option, since in my experience, summing flat arrays is (strangely) faster than counting. This code seems faster:

df.isnull().values.any()

import numpy as np
import pandas as pd
import perfplot


def setup(n):
    df = pd.DataFrame(np.random.randn(n))
    df[df > 0.9] = np.nan
    return df


def isnull_any(df):
    return df.isnull().any()


def isnull_values_sum(df):
    return df.isnull().values.sum() > 0


def isnull_sum(df):
    return df.isnull().sum() > 0


def isnull_values_any(df):
    return df.isnull().values.any()


perfplot.save(
    "out.png",
    setup=setup,
    kernels=[isnull_any, isnull_values_sum, isnull_sum, isnull_values_any],
    n_range=[2 ** k for k in range(25)],
)

df.isnull().sum().sum() is a bit slower, but of course, has additional information -- the number of NaNs.

Solution 2:

You have a couple of options.

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(10,6))
# Make a few areas have NaN values
df.iloc[1:3,1] = np.nan
df.iloc[5,3] = np.nan
df.iloc[7:9,5] = np.nan

Now the data frame looks something like this:

          0         1         2         3         4         5
0  0.520113  0.884000  1.260966 -0.236597  0.312972 -0.196281
1 -0.837552       NaN  0.143017  0.862355  0.346550  0.842952
2 -0.452595       NaN -0.420790  0.456215  1.203459  0.527425
3  0.317503 -0.917042  1.780938 -1.584102  0.432745  0.389797
4 -0.722852  1.704820 -0.113821 -1.466458  0.083002  0.011722
5 -0.622851 -0.251935 -1.498837       NaN  1.098323  0.273814
6  0.329585  0.075312 -0.690209 -3.807924  0.489317 -0.841368
7 -1.123433 -1.187496  1.868894 -2.046456 -0.949718       NaN
8  1.133880 -0.110447  0.050385 -1.158387  0.188222       NaN
9 -0.513741  1.196259  0.704537  0.982395 -0.585040 -1.693810

• Option 1: df.isnull().any().any() - This returns a boolean value

You know of the isnull() which would return a dataframe like this:

       0      1      2      3      4      5
0  False  False  False  False  False  False
1  False   True  False  False  False  False
2  False   True  False  False  False  False
3  False  False  False  False  False  False
4  False  False  False  False  False  False
5  False  False  False   True  False  False
6  False  False  False  False  False  False
7  False  False  False  False  False   True
8  False  False  False  False  False   True
9  False  False  False  False  False  False

If you make it df.isnull().any(), you can find just the columns that have NaN values:

0    False
1     True
2    False
3     True
4    False
5     True
dtype: bool

One more .any() will tell you if any of the above are True

> df.isnull().any().any()
True

• Option 2: df.isnull().sum().sum() - This returns an integer of the total number of NaN values:

This operates the same way as the .any().any() does, by first giving a summation of the number of NaN values in a column, then the summation of those values:

df.isnull().sum()
0    0
1    2
2    0
3    1
4    0
5    2
dtype: int64

Finally, to get the total number of NaN values in the DataFrame:

df.isnull().sum().sum()
5