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Python pandas: check if any value is NaN in DataFrame

Posted by: admin November 1, 2017 Leave a comment


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.


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:


For example:

In [2]: df = pd.DataFrame(np.random.randn(1000,1000))

In [3]: df[df > 0.9] = pd.np.nan

In [4]: %timeit df.isnull().any().any()
100 loops, best of 3: 14.7 ms per loop

In [5]: %timeit df.isnull().values.sum()
100 loops, best of 3: 2.15 ms per loop

In [6]: %timeit df.isnull().sum().sum()
100 loops, best of 3: 18 ms per loop

In [7]: %timeit df.isnull().values.any()
1000 loops, best of 3: 948 µs per loop

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


You have a couple 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()
  • 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:

0    0
1    2
2    0
3    1
4    0
5    2
dtype: int64

Then to get the total:



If you need to know how many “1 or more” rows have NaNs:


Or if you need to pull out these rows and examine them:

nan_rows = df[df.isnull().T.any().T]


df.isnull().any().any() should do it.


Since none have mentioned, there is just another variable called hasnans.

df[i].hasnans will output to True if one or more of the values in the pandas Series is NaN, False if not. Note that its not a function.

pandas version ‘0.19.2’ and ‘0.20.2’


Since pandas has to find this out for DataFrame.dropna(), I took a look to see how they implement it and discovered that they made use of DataFrame.count(), which counts all non-null values in the DataFrame. Cf. pandas source code. I haven’t benchmarked this technique, but I figure the authors of the library are likely to have made a wise choice for how to do it.


Adding to Hobs brilliant answer, I am very new to Python and Pandas so please point out if I am wrong.

To find out which rows have NaNs:

nan_rows = df[df.isnull().any(1)]

would perform the same operation without the need for transposing by specifying the axis of any() as 1 to check if ‘True’ is present in rows.


Depending on the type of data you’re dealing with, you could also just get the value counts of each column while performing your EDA by setting dropna to False.

for col in df:
   print df[col].value_counts(dropna=False)

Works well for categorical variables, not so much when you have many unique values.