Pandas is a popular open-source Python library used for data manipulation, analysis, and cleaning. The library provides high-performance data structures, tools for data analysis, and data cleaning functions, making it a powerful tool for working with data.

Here are some basics of pandas:

1. Data Structures: Pandas provides two main data structures: Series and DataFrame. A Series is a one-dimensional array-like object that can hold any data type, while a DataFrame is a two-dimensional table-like data structure with rows and columns.

2. Reading Data: Pandas can read data from a variety of sources, including CSV, Excel, SQL databases, and more. The read_csv() function is commonly used to read data from a CSV file.

3. Indexing and Selecting Data: Pandas provides various methods to select and index data, including indexing by labels, indexing by integer location, and Boolean indexing.

4. Data Cleaning: Pandas provides various functions to clean and transform data, such as removing duplicates, filling missing values, and changing data types.

5. Data Aggregation: Pandas provides functions for grouping and aggregating data, such as groupby() and agg(), which can be used to perform complex data manipulations and calculations.

6. Data Visualization: Pandas integrates with other Python libraries like Matplotlib and Seaborn to create visualizations such as line plots, scatter plots, histograms, and more.

Overall, pandas is a powerful tool for data manipulation and analysis, and it's a must-know for anyone working with data in Python.