Difference between Isna() and Isnull() in Pandas
Python is the most data analysis-focused language that why it has great worth in the field of Data Science, owing to its great ecosystem of data-centric Python tools.
Pandas is a package that dramatically simplifies data import and analysis. Many blank columns are imported as null values into the Data Frame while analyzing a file, which later causes complications while using that data frame. In this example, we will specifically discuss isna() and isnull() methods and learn how they are different.
isna() vs isnull()
The functions isna() and isnull() both are used to identify missing values like NaN, NaT or None. It returns a same-sized boolean object indicating missing or null values as True and False for all other values. Let’s explore an illustration.
Explanation
In this illustration, we used None, np.NaN and pd.NaT as null values, which all mapped as True, and they found the values it is considered False. All the results are identical with both methods, so why are there two different names with similar functionality? Let’s understand it.
Why are isna() and isnull() different?
Pandas DataFrames are built on top of R data frames. In R, there are two types of values: na and null, which are considered differently. As a result, there are two distinct techniques for checking na and null. Therefore, Pandas have two different method names.
In Python, however, Pandas are built on top of NumPy, which does not support na or null values. It denotes missing values with np.NaN values, even None values, are also considered as np.NaN values.
Syntax
The syntax of using isna() method is as follows:
pd.DataFrame.isna()
The syntax of using isnull() method is as follows:
pd.DataFrame.isnull()
Let’s explore it by finding missing values using isna() and isnull() methods using a small code snippet.
import pandas as pdimport numpy as np# Creating a dataframedata = {"Fruits":["Apple", "Mango", "BlueBerry", "Strawberry"],"Unit_Price":[200, 215, None, 250],"Weight kg":[1,3,2, None],"Total amount":[None,645,pd.NaT,np.NaN]}# Setting the Dataframesdf = pd.DataFrame(data)# Calling the isna() methoddf.isna()# Calling the isnull() methoddf.isnull()# Printing the isna() mehtod resultsprint(df.isna())# Printing the isnull() method resultsprint(df.isnull())
Explanation
We used fruits as an example in this program. We add data as Fruits there Unit_price, weight and total amount and add data in these data frames and by doing that missed some information which we narrated as None, pd.NaN and NaT to identify the missing value. In this example, we used both methods by using pd.DataFrame.isnull()to use isnull() method and pd.DataFrame.isnull() to use isna() method to compare the results.
Like in the first line of the output, we didn’t have the Total amount of apples which is narrated as None and mapped as True. Similarly, we didn’t. We used pd.NaT or np.NaN in order to identify the null value in the program, and mapped all of these as the null value in both methods.
