Filter by specific grouped values
Method 1: Filter rows using manually giving index value
Here, we select the rows with specific grouped values in a particular column. The Age column in Dataframe is selected with a value less than 30 to filter rows.
Python3
# select the rows with specific grouped # values in a particular columnprint(data[data.Age<30]) |
Output:
Method 2: Filter rows using loc
Here, we select the rows with specific grouped values in a particular column. The ID and Age column in Dataframe is selected with a value less than equal to 103 and Age equal to 23 to filter rows.
Python3
# Chaining loc[] operator to filter rowsdf2 = data.loc[lambda x: x['ID'] <= 103].loc[lambda x: x['Age'] == 23]print(df2) |
Output:
ID Name Age Country 1 102 Jack Wills 23 Uk
Method 3: Filter rows using a mask
Here, we select the rows with specific grouped values in a particular column. The Age column in Dataframe is selected with a value greater than equal to 39 to filter rows.
Python3
# Using mask and lambda function to filterdf2 = data.mask(lambda x: x['Age'] <= 39)df2 = df2.dropna()print(df2) |
Output:
ID Name Age Country 0 105.0 Ram Kumar 40.0 India 5 104.0 Yash Raj 56.0 India
How to Filter rows using Pandas Chaining?
In this article, we will learn how to filter rows using Pandas chaining. For this first we have to look into some previous terms which are given below :
- Pandas DataFrame: It is a two-dimensional data structure, i.e. the data is tabularly aligned in rows and columns. The Pandas DataFrame has three main components i.e. data, rows, and columns.
- Pandas Chaining: Method chaining, in which methods are called on an object sequentially, one after the another. It has always been a programming style that’s been possible with pandas, and over the past few releases, many methods have been introduced that allow even more chaining.