Pandas is a Python extension library that provides definitions for operations that manipulate data sets and structures. One of the most practical uses of Pandas is the ability to import data from external files, like CSV, JSON, SQL and Excel files. After importing, Pandas can convert the raw data into a usable data frame. Pandas is a very useful tool for programmers using Python to work with data analytics. Pandas makes importing, manipulating, merging, cleaning and re-exporting data easy to do in a virtual Python environment. Click here to learn more.
Uses: Pandas is an essential tool for data analysis in Python. Because most data comes in CSV and Excel formats, these data files must be converted into native Python in order to be readable. Pandas performs this task with ease and simplicity, creating data frames that logically and numerically organize data into rows and columns. Pandas also allows for the cleaning of data, such as the removal of unwanted columns or rows, and the merging of data, such as combining data from two different files.
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