How to find which columns contain any NaN value in Pandas dataframe

You can use df.isnull().sum(). It shows all columns and the total NaNs of each feature.

I had a problem where I had to many columns to visually inspect on the screen so a shortlist comp that filters and returns the offending columns is

nan_cols = [i for i in df.columns if df[i].isnull().any()]

if that's helpful to anyone

Adding to that if you want to filter out columns having more nan values than a threshold, say 85% then use

nan_cols85 = [i for i in df.columns if df[i].isnull().sum() > 0.85*len(data)]

This worked for me,

1. For getting Columns having at least 1 null value. (column names)

data.columns[data.isnull().any()]

2. For getting Columns with count, with having at least 1 null value.

data[data.columns[data.isnull().any()]].isnull().sum()

[Optional] 3. For getting percentage of the null count.

data[data.columns[data.isnull().any()]].isnull().sum() * 100 / data.shape[0]

I know this is a very well-answered question but I wanted to add a slight adjustment. This answer only returns columns containing nulls, and also still shows the count of the nulls.

As 1-liner:

pd.isnull(df).sum()[pd.isnull(df).sum() > 0]

Description

1. Count nulls in each column

null_count_ser = pd.isnull(df).sum()

2. True|False series describing if that column had nulls

is_null_ser = null_count_ser > 0

3. Use the T|F series to filter out those without

null_count_ser[is_null_ser]

Example Output

name          5
phone         187
age           644

df.columns[df.isnull().any()].tolist()

it will return name of columns that contains null rows

In datasets having large number of columns its even better to see how many columns contain null values and how many don't.

print("No. of columns containing null values")
print(len(df.columns[df.isna().any()]))

print("No. of columns not containing null values")
print(len(df.columns[df.notna().all()]))

print("Total no. of columns in the dataframe")
print(len(df.columns))

For example in my dataframe it contained 82 columns, of which 19 contained at least one null value.

Further you can also automatically remove cols and rows depending on which has more null values
Here is the code which does this intelligently:

df = df.drop(df.columns[df.isna().sum()>len(df.columns)],axis = 1)
df = df.dropna(axis = 0).reset_index(drop=True)

Note: Above code removes all of your null values. If you want null values, process them before.

i use these three lines of code to print out the column names which contain at least one null value:

for column in dataframe:
    if dataframe[column].isnull().any():
       print('{0} has {1} null values'.format(column, dataframe[column].isnull().sum()))

This is one of the methods..

import pandas as pd
df = pd.DataFrame({'a':[1,2,np.nan], 'b':[np.nan,1,np.nan],'c':[np.nan,2,np.nan], 'd':[np.nan,np.nan,np.nan]})
print(pd.isnull(df).sum())

enter image description here

Both of these should work:

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

DataFrame methods isna() or isnull() are completely identical.

Note: Empty strings '' is considered as False (not considered NA)

df.isna() return True values for NaN, False for the rest. So, doing:

df.isna().any()

will return True for any column having a NaN, False for the rest

If you are looking for printing columns alongside their respective null_values:

null_cols = [i for i in df.columns if df[i].isnull().any()] 
for i in null_cols:
    print(i,df[i].isnull().sum())

To see just the columns containing NaNs and just the rows containing NaNs:

isnulldf = df.isnull()
columns_containing_nulls = isnulldf.columns[isnulldf.any()]
rows_containing_nulls = df[isnulldf[columns_containing_nulls].any(axis='columns')].index
only_nulls_df = df[columns_containing_nulls].loc[rows_containing_nulls]
print(only_nulls_df)

features_with_na=[features for features in dataframe.columns if dataframe[features].isnull().sum()>0]

for feature in features_with_na: print(feature, np.round(dataframe[feature].isnull().mean(), 4), '% missing values') print(features_with_na)

it will give % of missing value for each column in dataframe

If you want to write it as a one-liner (could be useful if functions need to be called sequentially in a pipeline), then you can do so using either pipe() or passing a callable to loc[]. pipe() can be used to get the columns with NaN values as well.

df.isna().any().pipe(lambda x: x.index[x])

df.isna().any().loc[lambda x: x].index

A working example:

df = pd.DataFrame({
    'a': [1, 2, pd.NA],
    'b': [10, 20, 30],
    'c': [pd.NA, 'B', 'C']
})


df.isna().any().pipe(lambda x: x.index[x])  # Index(['a', 'c'], dtype='object')
df.isna().any().loc[lambda x: x].index      # Index(['a', 'c'], dtype='object')


df.isna().any().pipe(lambda x: df.loc[:, x])


      a     c
0     1  <NA>
1     2     B
2  <NA>     C

If you want to opposite, i.e. columns without any NaN, then notna().all() could be used instead of isna().any().

df.notna().all().pipe(lambda x: x.index[x])  # Index(['b'], dtype='object')

The code works if you want to find columns containing NaN values and get a list of the column names.

na_names = df.isnull().any()
list(na_names.where(na_names == True).dropna().index)

If you want to find columns whose values are all NaNs, you can replace any with all.