Название: Python Programming: Pandas Across Examples
Автор: Cesar Perez Lopez
Издательство: Independently published
Год: 2023
Страниц: 396
Язык: английский
Формат: epub (true)
Размер: 10.2 MB

Pandas is a Python package that provides fast and flexible data structures designed to make working with "relational" or "labeled" data easy and intuitive. Its goal is to be the fundamental, high-level building block for doing practical analysis of real-world data in Python. Furthermore, it has the larger goal of becoming the most powerful, flexible, and available in any language open source data manipulation/analysis tool. The two main data structures in Pandas are: Series for one-dimensional data and DataFrames for two-dimensional data. Both frameworks handle the vast majority of typical use cases in finance, statistics, social sciences, and many areas of engineering. For R users, the DataFrame provides everything that R data.frame offers, and much more. pandas is based on NumPy and is designed to integrate well into a scientific computing environment with many other third-party libraries. Pandas facilitates the work in Data Science. For data scientists, working with data is typically divided into several stages: collecting and cleaning data, analyzing/modeling it, and then organizing the analysis results in a form suitable for graphing or displaying in tabular form. pandas is a help tool for all these tasks. Also Pandas has been widely used in the production of financial applications. Also Pandas works with Big Data.

Pandas allows you to work with many different types of dаta:

• Tabular data with columns of heterogeneous types, as in an SQL table or an Excel spreadsheet
• Ordered and unordered time series data (not necessarily fixed frequency).
• Arbitrary matrix data (homogeneous or heterogeneous type) with row and column labels
• Any other form of statistical data sets. It is not necessary to tag data in order to place it in a pandas data structure.

Pandas makes Data Science work easier. For data scientists, working with data is usually divided into several stages: collecting and cleaning data, analysing/modelling it and then organising the results of the analysis in a form suitable for graphing or displaying in tabular form. pandas is a helpful tool for all these tasks. Pandas has also been widely used in the production of financial applications.