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 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.

Here we use the concept of chaining in Pandas to filter the dataframe and get the same rows as an output. This can be easily explained with the help of examples. Here we use a dataframe which consists of some data of person as shown below :

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# import package
import pandas as pd
  
# define data
data = pd.DataFrame(
  {'ID': {0: 105, 1: 102, 2: 101, 3: 106, 4: 103, 5: 104, 6: 107},
   
   'Name': {0: 'Ram Kumar', 1: 'Jack Wills', 2: 'Deepanshu Rustagi'
          3: 'Thomas James', 4: 'Jenny Advekar', 5: 'Yash Raj'
          6: 'Raman Dutt Mishra'},
   
   'Age': {0: 40, 1: 23, 2: 20, 3: 34, 4: 18, 5: 56, 6: 35},
   
   'Country': {0: 'India', 1: 'Uk', 2: 'India', 3: 'Australia'
               4: 'Uk', 5: 'India', 6: 'India'}
  }
)
  
# view data
data

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Output:



Filter by specific value

Here, we select the rows with a specific value in a particular column. The Country column in dataframe is selected with value ‘India’ to filter rows.

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# select the rows with specific value in 
# a particular column
print(data[data.Country.eq('India')])

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Output:

Filter by specific grouped values

Here, we select the rows with specific grouped values in a particular column. The Age column in dataframe is selected with value less than 30 to filter rows.

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# select the rows with specific grouped 
# values in a particular column
print(data[data.Age<30])

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Output:



Filter by specific character or string value

Here, we select the rows with specific character or string value in a particular column. The Name column in dataframe is selected with value contains ‘am’ to filter rows.

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# select the rows with specific string
# or character value in a particular column
print(data[data.Name.str.contains('am')])

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Output:

Filter by values from specific set of values

Here, we select the rows from specific set of values in a particular column. The Country column in dataframe is selected and matched with the given set of values to filter rows.

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# define the set of values
lst=['Uk','Australia']
  
# select the rows from specific set 
# of values in a particular column
print(data[data.Country.isin(lst)])

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Output:


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