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.
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.
