How to check if a particular cell in pandas DataFrame isnull?

Question

I have the following df in pandas.

0       A     B     C
1       2   NaN     8

How can I check if df.iloc[1]['B'] is NaN?

I tried using df.isnan() and I get a table like this:

0       A     B      C
1   false  true  false

but I am not sure how to index the table and if this is an efficient way of performing the job at all?

jezrael · Accepted Answer · 2017-03-21T08:41:04.100

45

Use pd.isnull, for select use loc or iloc:

print (df)
   0  A   B  C
0  1  2 NaN  8

print (df.loc[0, 'B'])
nan

a = pd.isnull(df.loc[0, 'B'])
print (a)
True

print (df['B'].iloc[0])
nan

a = pd.isnull(df['B'].iloc[0])
print (a)
True

edited Mar 21 '17 at 08:41

answered Mar 21 '17 at 08:33

jezrael

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1,334
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I tried this but I get the following error: TypeError: isnull() takes exactly 1 argument (2 given) – Newskooler Mar 21 '17 at 08:36
I had written `df.isnull(df['B'].iloc[0])` instead of `pd.isnull(df['B'].iloc[0])`. Thank you this fixed my problem! – Newskooler Mar 21 '17 at 08:40

score 3 · Answer 2 · answered Mar 21 '17 at 09:33

3

jezrael response is spot on. If you are only concern with NaN value, 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()

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

answered Mar 21 '17 at 09:33

ankur09011

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3

This does not check a particular cell. – Newskooler Nov 26 '18 at 16:23
how to check if a particular cell is nan e,g df['colname'].values[0] is empty how to check this pd.isnull - return bool or array depending on value is empty or not empty but its throwing error when used in if condition – perseverance Nov 23 '19 at 17:15
for select use loc or iloc: – ankur09011 May 25 '20 at 12:46

Loochie · Answer 3 · 2019-01-09T10:49:25.210

If you are looking for the indexes of NaN in a specific column you can use

list(df['B'].index[df['B'].apply(np.isnan)])

In case you what to get the indexes of all possible NaN values in the dataframe you may do the following

row_col_indexes = list(map(list, np.where(np.isnan(np.array(df)))))
indexes = []
for i in zip(row_col_indexes[0], row_col_indexes[1]):
    indexes.append(list(i))

And if you are looking for a one liner you can use:

list(zip(*[x for x in list(map(list, np.where(np.isnan(np.array(df)))))]))

How to check if a particular cell in pandas DataFrame isnull?

3 Answers3