Introduction:

Here are the steps for filtering data in Pandas:

Step 1: Import Pandas

First, we need to import the Pandas library using the import statement:

Step 2: Load Data

to filter it. Here's an example:DataFrameWe need to load some data into a Pandas

Step 3: Filter Rows by Column Value

We can filter rows in a DataFrame by column value using the comparison operators ==, <, >, <=, and >=. Here's an example:

Step 4: Filter Rows by Multiple Column Values

We can also filter rows in a DataFrame by multiple column values using the & (and) and | (or) operators. Here's an example:

Step 5: Filter Rows by String Value

We can filter rows in a DataFrame by string value using the str.contains() function. Here's an example:

Filtering data in Pandas is a common task in data analysis. Here are some use cases for filtering data in Pandas using Python:

1. Financial analysis: Filtering financial data, such as stock prices and trading volumes, can help identify trends and make investment decisions.
2. Customer analysis: Filtering customer data, such as demographics and purchase history, can help identify customer segments and target marketing campaigns.
3. Healthcare analysis: Filtering healthcare data, such as patient records and medical imaging, can help identify patterns and improve healthcare outcomes.

Pandas provides a flexible and easy-to-use interface for filtering data in tables, also known as DataFrames, in Python. In this guide, we have explored the steps for filtering data in Pandas using Python, including filtering rows by column value, multiple column values, and string value. We have also discussed some use cases for filtering data in Pandas, including financial analysis, customer analysis, and healthcare analysis. With Pandas, we can easily manipulate and analyze data in tables to extract insights and make informed decisions.