Pandas is an open-source data analysis and manipulation tool that provides data structures and functions.
Pandas was created by Wes McKinney at AQR Capital Management in 2008 for financial analytics use cases.
It was released as an open-source project in 2009 and gained popularity in the data science community.
Since then, the library has been continuously updated with new features and improvements, with the latest stable release being version 1.3.3.
A library for numerical computing in Python. Pandas builds on top of NumPy.
A flexible parallel computing library for analytics in Python.
A SQL toolkit and Object-Relational Mapping (ORM) library for Python that allows interfacing with databases.
A two-dimensional table-like data structure with columns of potentially different types.
A one-dimensional data structure with a labelled index.
A function to read data from a CSV file into a pandas.DataFrame.
A module with functions to create common types of visualizations.
A DataFrame is a two-dimensional table-like data structure with labeled columns and rows, while a Series is a one-dimensional labeled array that can hold any data type.
Yes, pandas provides a function called 'read_excel' to read data from an Excel file into a DataFrame.
Pandas is optimized for in-memory data processing and may not be suitable for big data applications. However, it integrates well with other tools like Dask and Apache Spark that can handle big data processing.
Pandas can be used for many common data cleaning operations like removing duplicates, handling missing values, and converting data types.
Pandas performance can be slower than lower-level tools like NumPy for numerical operations. However, it provides a high-level interface that can be more efficient for data cleaning and preparation tasks relative to other data analysis tools.