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This article mainly introduces "what are the common data analysis libraries of Python". In the daily operation, I believe that many people have doubts about the common data analysis libraries of Python. The editor consulted all kinds of materials and sorted out simple and easy-to-use operation methods. I hope it will be helpful to answer the doubts of "what are the common data analysis libraries of Python?" Next, please follow the editor to study!

Common Python data Analysis Library

Pandas

Pandas is an open source Python library that uses powerful data structures to provide high-performance data manipulation and analysis tools. Its name: Pandas is from Panel Data-multidimensional data econometrics (an Econometrics from Multidimensional data).

Before Pandas, Python was mainly used for data migration and preparation. It contributes even less to data analysis. Pandas solves this problem. Using Pandas, you can complete five typical steps of data processing and analysis, regardless of the source of the data-loading, preparation, operation, model, and analysis. Python Pandas is used in a wide range of fields, including finance, economics, statistics, analysis and other academic and business fields.

The main functions of Pandas are:

Fast and efficient DataFrame object with default and custom indexes

Tools for loading data from different file formats into in-memory data objects

Data alignment and comprehensive processing of lost data. Reorganize and wobble date set

Tag-based slicing, indexing and a subset of big data sets

Columns from data structures can be deleted or inserted

Aggregate and transform by data packet

High performance merging and data joining

Time series function

Generally speaking, Pandas is more suitable for data preprocessing and data structure processing.

NumPy

NumPy is a Python package. It stands for "Numeric Python". It is a library of multidimensional array objects and a collection of routines used to process arrays.

The main functions of NumPy are:

Fast and efficient Multi-dimensional Array object ndarray

Functions that perform element-level calculations on arrays and perform mathematical operations on arrays directly

A tool for reading and writing array-based datasets on a hard disk

Linear algebraic operations, Fourier transform, and random number generation

Tools for integrating C, C++, Fortran code into python

Operations related to linear algebra

NumPy has built-in functions for linear algebra and random number generation.

Generally speaking, NumPy is suitable for large-scale computing projects such as scientific computing and machine learning, and even becomes an excellent substitute for MatLab.

SciPy

SciPy is an open source BSD licensed math, science and engineering library. The SciPy library relies on NumPy, which provides convenient and fast N-dimensional array operations. The main reason for building the SciPy library is that it works with NumPy arrays and provides many user-friendly and efficient digital practices, such as numerical integration and optimized routines.

Matplotlib

Matplotlib is an Python 2D drawing library that can generate publication quality data in a variety of hard copy formats and cross-platform interactive environments. Matplotlib can be used for Python scripts, Python and IPython shell,Jupyter notebooks, Web application servers, and four graphical user interface toolkits.

Matplotlib is mainly used for the final data visualization of data analysis. Of course, there are many Matplotlib replacements, such as Pychart and echarts.

At this point, the study of "what are the common data analysis libraries of Python" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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