This Pandas article will discuss how you can determine the number of NaN values in a Pandas DataFrame.
Pandas isnull() function
The isnull() function in Pandas allows us to determine missing values in a dataset. For example, we can use this function to get the number of NaN elements in a Pandas DataFrame.
Consider the example DataFrame shown below:
1 2 3 4 5 | # import pandas and numpy import pandas as pd import numpy as np df = pd.DataFrame([[1,2,np.nan, 3, 4, np.nan, 5, np.nan]]) df |
The above creates a simple DataFrame containing NaN values.
Pandas Count NaN in Column
To count the number of NaN values in a single column, we can do:
1 | print(f"null: {df[2].isnull().sum()}") |
In the above example, we use the isnull() and sum() functions to determine the number of elements in column number 2.
The code above should return:
1 | null: 1 |
Pandas Count NaN in DataFrame
To get the number of NaN values in the entire DataFrame, we can do:
1 | print(f"NaN: {df.isnull().sum().sum()}") |
This returns the number of NaN values in the specified DataFrame.
1 | NaN: 3 |
Pandas Count NaN in Row
To find the number of NaN values in a row, we can use the loc and sum functions as shown in the example below:
1 | print(f"NaN in row(0): {df.loc[0].isnull().sum()}") |
The above should return the number of NaN values in the row at index 0.
1 | NaN in row(0): 3 |
Conclusion
Using this guide, you learned how to determine the number of NaN values in a DataFrame column, entire DataFrame, and in a single row.
Thanks for reading!!
