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.

Creating Dataframe to Filter rows using Pandas Chaining

# 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

Output:

Filter by specific value

Method 1: Filter rows using eq

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

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

Output:

Method 2: Filter rows using pipe

Here, we select the rows with a specific value in a particular column. The Country column in Dataframe is selected with the value 'India' to filter rows using a pipe.

# Using pipe() method
df2 = data.pipe(lambda x: x['Country'] == "India")
print(df2)

Output:

0     True
1    False
2     True
3    False
4    False
5     True
6     True
Name: Country, dtype: bool

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.

# select the rows with specific grouped 
# values in a particular column
print(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.

# Chaining loc[] operator to filter rows
df2 = 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.

# Using mask and lambda function to filter
df2 = 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

Filter by specific character or a specific set of values

Method 1: Filter rows using contains

Here, we select the rows with specific characters or string values in a particular column. The Name column in Dataframe is selected with a value containing 'am' to filter rows.

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

Output:

Method 2: Filter rows using isin

Here, we select the rows from a 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.

# 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)])

Output: