**apply**function returns some value after passing each row/column of a data frame with some function. The function can be default or user-defined or lambda. We will create a user defined function which calculates missing values and returns the count. First we will call this function for all columns and then for all rows using apply function.

Consider a Load Prediction dataset. We will try to find out count of missing values in each row and column using apply function.

**Step 1: Import the required libraries**

import pandas as pd

import numpy as np

**Step 2: Load the dataset**

dataset = pd.read_csv("C:/train_loan_prediction.csv")

**Step 3: Create a function which returns count of missing values**

def num_missing(x):

return sum(x.isnull())

**Step 4: Find out number of missing values in each column**

print("Missing values per column:")

print(dataset.

**apply**(num_missing,

**axis=0**))

**axis=0**defines that function is to be applied on each column.

**Step 5: Find out number of missing values in each row**

print("Missing values per row:")

print(dataset.

**apply**(num_missing,

**axis=1**).head())

**axis=1**defines that function is to be applied on each row.

You can also use lambda function with apply. Here is an example.

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