Is ISNA and Isnull same?

Yes, both methods are used to detect the missing values. isnull() is just an alias of the isna() method and internally uses isna method.

Pandas Isna Vs Isnull - Differences Explained

Pandas is a two-dimensional data structure that allows you to store data in rows and columns format. It also provides a lot of API methods that can be used for easier data analysis. Two such methods are isna() and isnull()

Both isna() and isnull() functions are used to find the missing values in the pandas dataframe.

isnull() and isna() literally does the same things. isnull() is just an alias of the isna() method as shown in pandas source code.

Missing values are used to denote the values which are null or do not have any actual values. You can use the pd.NaT, np.NaN or None to denote the missing values in the dataframe.

Table of Contents

What is isna()

isna() is used to detect the missing values in the cells of the pandas dataframe.

It returns a dataframe of the same size with the values masked as True for na values and False for non-NA values.

What is isnull()

isnull() is also used to identify or detect the missing values in the dataframe. It is just an alias for isna() method.

It also returns a dataframe of the same size where the values such as None or Np.NaN are masked as True and other values are masked as False.

Why Two Methods in Different Name

Pandas dataframe are based on R dataframes. In R, the na values and null values are different types. Hence, there are two different methods to check na and null. That’s why pandas have two method names.

On the other hand, in Python pandas is built on top of NumPy which doesn’t have na or null values. It uses Np.NaN values to denote the missing values. Even None values are considered as Np.NaN.

You’ll see it in detail in the below examples.

Finding Missing Values

In this section, you’ll use the isna() and the isnull() method to find the missing values in the sample dataframe.

The dataframe contains all the different types of the missing values pd.naT, None, Np.NaN.

Sample Dataframe

import pandas as pd
import numpy as np

data = {"Product_Name":["Mouse", "Monitor", "CPU", "Speakers","Headset"],
        "Unit_Price":[200, 5000.235, 10000.550,  250.50, None],
        "No_Of_Units":[5, 10, 20,  8, pd.NaT],
        "Available_Quantity":[6,5,5, pd.NaT,np.NaN],
        "Remarks":[np.NaN,pd.NaT,pd.NaT,pd.NaT,pd.NaT]
       }


df = pd.DataFrame(data)

df

Dataframe Will Look Like

	Product_Name	Unit_Price	No_Of_Units	Available_Quantity	Remarks
0	Mouse	200.000	5	6	`NaT`
1	Monitor	5000.235	10	5	`NaT`
2	CPU	10000.550	20	5	`NaT`
3	Speakers	250.500	8	`NaT`	`NaT`
4	Headset	`NaN`	`NaT`	`NaN`	`NaT`

Detect Missing Values Using isna()

You can use the below snippet to find the missing values in the dataframe using isna().

The values None, Np.NaN and the pd.Nat will be identified as missing values when you use the isna() function.

Snippet

df.isna()

The cells that have True denote that has missing values and the cells that have False denote that have a valid value.

Dataframe Will Look Like

	Product_Name	Unit_Price	No_Of_Units	Available_Quantity	Remarks
0	False	False	False	False	`True`
1	False	False	False	False	`True`
2	False	False	False	False	`True`
3	False	False	False	`True`	`True`
4	False	`True`	`True`	`True`	`True`

Detect Missing Values Using isNull()

You can use the below snippet to find the missing values in the dataframe using isnull().

As the isnull() method is just an alias of the isna() method, it’ll also identify the values None, Np.NaN and the pd.Nat as missing values.

Snippet

df.isnull()

The cells that have True denote that have missing values and the cells that have False denote that have a valid value.

Dataframe Will look like

	Product_Name	Unit_Price	No_Of_Units	Available_Quantity	Remarks
0	False	False	False	False	`True`
1	False	False	False	False	`True`
2	False	False	False	False	`True`
3	False	False	False	`True`	`True`
4	False	`True`	`True`	`True`	`True`

Which One to Use

You can use the isna() method to identify the missing values. Because it is the original method implemented and isnull() is just an alias that internally calls the isna() method.

Conclusion

To summarize, you’ve learned the difference between isnull() and isna()methods in the pandas dataframe. You’ve also learned which method needs to be used.