The correct answer is: D. Pandas
Pandas is a Python library that provides fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language.
Pandas is well suited for many different kinds of data analysis, including:
- Cleaning and munging data
- Exploring and analyzing data
- Preparing data for modeling
- Visualizing data
Pandas is a powerful tool that can be used to perform a variety of data analysis tasks. It is easy to use and can be extended to meet the needs of specific applications.
Here are some of the features of Pandas:
- DataFrames: A 2-dimensional data structure with labeled axes (rows and columns). DataFrames can be used to store and manipulate data in a variety of formats, including CSV, JSON, and SQL.
- Series: A 1-dimensional data structure with a single labeled axis. Series can be used to store and manipulate data in a variety of formats, including CSV, JSON, and SQL.
- Indexing: Pandas provides a powerful indexing system that allows you to select rows and columns from DataFrames and Series.
- Slicing: Pandas provides a powerful slicing system that allows you to select subsets of data from DataFrames and Series.
- Merging: Pandas provides a powerful merging system that allows you to combine multiple DataFrames and Series.
- Pivoting: Pandas provides a powerful pivoting system that allows you to rearrange the columns and rows of a DataFrame.
- GroupBy: Pandas provides a powerful groupby system that allows you to group data by common values.
- Aggregation: Pandas provides a powerful aggregation system that allows you to summarize data.
- Visualization: Pandas provides a variety of visualization tools that allow you to create charts and graphs.
Pandas is a powerful tool that can be used to perform a variety of data analysis tasks. It is easy to use and can be extended to meet the needs of specific applications.