Pandas is a software library written for the Python programming language for data manipulation and analysis). It is mainly used for data analysis and associated manipulation of tabular data in DataFrames). Pandas allows importing data from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel). Pandas allows various data manipulation operations such as merging, reshaping, selecting, as well as data cleaning, and data wrangling features). It is built on top of another package named NumPy, which provides support for multi-dimensional arrays.
Pandas is a fast, powerful, flexible, and easy-to-use open-source data analysis and manipulation tool. It has functions for analyzing, cleaning, exploring, and manipulating data. The name "Pandas" is derived from the term "panel data," an econometrics term for data sets that include observations over multiple time periods for the same individuals). Its name is also a play on the phrase "Python data analysis" itself).
Pandas is typically included in every Python distribution, from those that come with your operating system to commercial vendor distributions like ActiveState’s ActivePython. It is widely used for data science/data analysis and machine learning tasks. With Pandas, you can do everything that makes world-leading data scientists vote Pandas as the best data analysis and manipulation tool available.
Some of the things you can do with Pandas include:
- Data cleansing
- Data fill
- Data normalization
- Merges and joins
- Data visualization
- Statistical analysis
- Data inspection
- Loading and saving data
- And much more
Pandas is a powerful tool for data analysis and manipulation, and it is widely used in the data science community.
