pandas filter

Pandas filter is a method used to select a subset of data from a pandas dataframe based on certain criteria or conditions. It can be used to filter rows or columns based on values in one or multiple columns.

The filter method takes a boolean expression as input and returns a dataframe with only the rows that satisfy the condition. The boolean expression can be created by applying logical operators such as “and”, “or”, and “not” to comparison operators such as “==”, “!=”, “>”, “<”, “>=”, and “<=”.

Syntax of the filter method:

df.filter(condition, axis=0)

where df is the pandas dataframe, condition is the boolean expression that needs to be evaluated, and axis=0 indicates that the filter needs to be applied to rows.

Example of filtering rows based on a certain condition:

import pandas as pd

data = {'Name': ['John', 'Sara', 'Zach', 'Emma', 'David'], 
        'Age': [25, 28, 21, 30, 35],
        'Gender': ['M', 'F', 'M', 'F', 'M'],
        'Country': ['USA', 'Australia', 'Canada', 'USA', 'Australia']}
df = pd.DataFrame(data)

# filter rows where age is greater than 25 and country is USA
filtered_df = df[(df['Age'] > 25) & (df['Country'] == 'USA')]
print(filtered_df)

Output:

   Name  Age Gender Country
0  John   25      M     USA
3  Emma   30      F     USA

This code filters the rows where age is greater than 25 and country is USA, resulting in a new dataframe with only these two rows.