Check if all values in column are NaN in Pandas

This article will discuss checking if all values in a DataFrame column are NaN.

First of all, we will create a DataFrame from a list of tuples,

import pandas as pd
import numpy as np

# List of Tuples
empoyees = [('Jack', np.NaN, 34, 'Sydney', np.NaN, 5),
            ('Riti', np.NaN, 31, 'Delhi' , np.NaN, 7),
            ('Aadi', np.NaN, 16, 'London', np.NaN, np.NaN),
            ('Mark', np.NaN, 41, 'Delhi' , np.NaN, np.NaN)]

# Create a DataFrame object
df = pd.DataFrame(  empoyees,
                    columns=['A', 'B', 'C', 'D', 'E', 'F'])

# Display the DataFrame
print(df)

Output:

      A   B   C       D   E    F
0  Jack NaN  34  Sydney NaN  5.0
1  Riti NaN  31   Delhi NaN  7.0
2  Aadi NaN  16  London NaN  NaN
3  Mark NaN  41   Delhi NaN  NaN

This DataFrame has four rows and six columns, out of which two columns (‘B’ & ‘E’) have all NaN values. Let’s see how we can verify if a column contains all NaN values or not in a DataFrame.

Check if all values are NaN in a column

Select the column as a Series object and then use isnull() and all() methods of the Series to verify if all values are NaN or not. The steps are as follows,

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  • Select the column by name using subscript operator of DataFrame i.e. df[‘column_name’]. It gives the column contents as a Pandas Series object.
  • Call the isnull() function of the Series object. It returns a boolean Series of the same size. Each True value in this boolean Series indicates that the corresponding value in the Original Series (selected column) is NaN.
  • Check if all values in the boolean Series are True or not. If yes, then it means all values in that column are NaN.

For example, let’s check if all values are NaN in column ‘B’ from the above created DataFrame,

# Check if all values in column 'B' are NaN
if df['B'].isnull().all():
    print("All values in the column 'B' are NaN")
else:
    print("All values in the column 'B' are not NaN")

Output:

All values in the column 'B' are NaN

We selected the column and then got a boolean series using the isnull() method. Then using the all() function, we checked if all the values in Boolean Series are True or not. If all values are True, then it means that all elements in the column are NaN.

In this example, the ‘B’ column had all values; therefore, the returned boolean Series had all True values, and the Series.all() function returned True in this case. Let’s check out a negative example,

Let’s check if all values are NaN in column ‘F’ in the above created DataFrame,

# Check if all values in column 'F' are NaN
if df['F'].isnull().all():
    print("All values in the column 'F' are NaN")
else:
    print("All values in the column 'F' are not NaN")

Output:

All values in the column 'F' are not NaN

In this example, all values in column ‘F’ are not NaN; therefore, the returned boolean Series had some True and few False values, and the Series.all() function returned False in this case. It proved that all elements in column ‘F’ are not NaN.

The complete working example is as follows,

import pandas as pd
import numpy as np

# List of Tuples
empoyees = [('Jack', np.NaN, 34, 'Sydney', np.NaN, 5),
            ('Riti', np.NaN, 31, 'Delhi' , np.NaN, 7),
            ('Aadi', np.NaN, 16, 'London', np.NaN, np.NaN),
            ('Mark', np.NaN, 41, 'Delhi' , np.NaN, np.NaN)]

# Create a DataFrame object
df = pd.DataFrame(  empoyees,
                    columns=['A', 'B', 'C', 'D', 'E', 'F'])

# Display the DataFrame
print(df)

# Check if all values in column 'B' are NaN
if df['B'].isnull().all():
    print("All values in the column 'B' are NaN")
else:
    print("All values in the column 'B' are not NaN")

Output:

      A   B   C       D   E    F
0  Jack NaN  34  Sydney NaN  5.0
1  Riti NaN  31   Delhi NaN  7.0
2  Aadi NaN  16  London NaN  NaN
3  Mark NaN  41   Delhi NaN  NaN


All values in the column 'B' are NaN

Summary

We learned how to check if all values in a DataFrame column are NaN.

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