What are Pandas in Python programming language?

Pandas is a library in the Python programming language used to perform data analysis. Usually, a Data Scientist and Data Analyst will use pandas to support their work in processing and analyzing a given data for certain purposes needed.

Pandas is a library that has high speed (velocity), flexibility (flexibility), and ease (simplicity) in its use to manipulate the data provided. The Pandas library is built and structured based on the Python programming language, because the Python programming language is able to provide powerful and easy-to-use data structures to perform open data analysis and manipulate data according to desired needs.

History of the Pandas Library in Python

The Pandas library was first developed by Wes McKinney in 2008. McKinney began the development of the Pandas Library to solve problems in the complex data analysis needs of the financial industry. Wes McKinney wanted to create an efficient tool for transforming and analyzing financial data using the Python programming language.

The Pandas library was first released as an open source project in 2009 and quickly gained popularity in the Python development community, especially around data scientists and data analysts. The Pandas library provides an efficient and easy way to import, organize, and analyze data, which is an important part of their job duties.

Data Structures on Pandas in Python Programming Language

This Pandas library has two main data structures as its constituent components, namely the two main data structures are Series and DataFrame. Series is a form of one-dimensional array capable of storing various types of data types such as integer (int), float, boolean or True or False (bool), and string (str). While DataFrame is a two-dimensional data structure that can store various types of data types such as integer (int), float, boolean or True or False (bool), string (str), and others.

Uses and Functions of the Pandas Library in Python

1. Use of Tabular Data Structures: The Pandas library certainly provides data structures such as DataFrames that are similar to spreadsheets or tables in Structured Query Language (SQL). It allows a data scientist and data analyst to organize data in rows and columns, and perform operations such as filtering, indexing, and sorting easily and quickly.
2. Reading and Writing Data in the form of datasets: The Pandas Library has the advantage of being very useful in reading data from various sources and datasets, such as CSV files, Excel, SQL databases, JSON, and various other types of datasets. A Data Scientist and Data Analyst and other Pandas Library users can also store the modified data on datasets with various types of appropriate formats.
3. Data Filtering and Selection: A Data Scientist and Data Analyst can easily access, filter, and select data from a given dataset, of course according to the needs and desired goals by using expressions in the available Pandas Library.
4. Combining Multiple Data: The Pandas library allows a data scientist and data analyst to combine multiple DataFrames based on keys or indexes, such as using JOIN operations in Structured Query Language (SQL).
5. Data Manipulation and Alteration: A Data Scientist and Data Analyst can perform various types of data manipulation operations such as replacing a value, filling in missing values, adding or removing columns, and more. Its use can be seen when manipulating and changing data on a given dataset.
6. Data Aggregation and Analysis: A Data Scientist and Data Analyst can easily perform data aggregation processes, such as calculating sums, calculating averages, finding middle or median values, and other basic statistical analysis. The Pandas library also supports the use of advanced statistical analysis such as correlation measurement, linear regression, and so on.