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Pandas is one library that is a very integral part of machine learning. Because of this, it is vital that the python enthusiast learn the dynamics of this library so that he can progress in the field. In this blog post I will endeavour to cover some important topics that surround pandas to hopefully improve a python developer’s skill.
Pandas is an open-source data analysis and manipulation library for the Python programming language. It was initially released in 2011 and was written by Wes McKinney. This library has since gained widespread adoption among data analysts, data scientists, and developers.
Pandas was written in the Python programming language. It is a Python library for data manipulation, analysis and cleaning, built on top of other popular libraries in the scientific Python ecosystem such as numpy and matplotlib. It provides data structures for efficiently storing and manipulating large and complex datasets, and a wide range of functions for data cleaning, exploration, and transformation.
The two main data structures in Pandas are Series and DataFrame. A Series is a one-dimensional labelled array that can hold any data type, including numeric, string, and object data. A DataFrame is a two-dimensional table with rows and columns that can contain a mix of different data types. DataFrames can be thought of as a spreadsheet, where each row…
