Common Pandas Operations for Data Analysis

Pandas is a popular Python library for data analysis, and it provides a wide range of functions and methods for manipulating and analyzing data. Here are some common Pandas operations that are frequently used in data analysis:

1. Reading and writing data: Pandas provides functions for reading and writing data in various formats, such as CSV, Excel, SQL databases, and more.
2. Data selection and indexing: Pandas allows you to select and index data using various methods, such as label-based indexing, position-based indexing, and boolean indexing.
3. Data filtering: Pandas provides a variety of functions for filtering data based on certain conditions, such as df[df['column'] > 5].
4. Data aggregation: Pandas allows you to group data based on one or more variables and perform aggregation functions on each group, such as df.groupby('column').mean().
5. Data cleaning and preprocessing: Pandas provides many functions for cleaning and preprocessing data, such as filling missing values, removing duplicates, and transforming data types.