Pandas Tutorial

Pandas is an extension library for the Python language, used for data analysis.

Pandas is an open-source, BSD-licensed library that provides high-performance, easy-to-use data structures, and data analysis tools.

The name "Pandas" is derived from the term "panel data" and "Python data analysis."

Pandas is a powerful toolset for analyzing structured data, based on Numpy (which provides high-performance matrix operations).

Pandas can import data from various file formats such as CSV, JSON, SQL, Microsoft Excel.

Pandas can perform various operations on data, such as merging, reshaping, selecting, as well as data cleaning and data processing features.

Pandas is widely used in various fields of data analysis, including academia, finance, statistics, and engineering.

Prerequisites

Before starting the Pandas tutorial, you need to have a basic understanding of Python. If you are not familiar with Python, you can read our tutorials:

Pandas Applications

The main data structures in Pandas are Series (one-dimensional) and DataFrame (two-dimensional), which are sufficient to handle most typical use cases in fields such as finance, statistics, social sciences, and engineering.

Data Structures

Series is an object similar to a one-dimensional array, consisting of a set of data (various Numpy data types) and a set of associated data labels (indexes).

DataFrame is a tabular data structure that contains an ordered set of columns, each of which can be a different value type (numeric, string, boolean). DataFrame has both row and column indexes and can be seen as a dictionary of Series (sharing the same index).

Pandas Tutorial

Prerequisites

Pandas Applications

Data Structures

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