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This article will explain in detail what the Python data analysis software package has, the editor thinks it is very practical, so share it with you for reference. I hope you can get something after reading this article.

The main software packages of Python data analysis:

1. Python-m pip install numpy

2. Python-m pip install pandas

3. Python-m pip install matplotlib

4. Python-m pip install scipy

5. Python-m pip install wordcloud

6. Python-m pip install scikit-learn

Introduction to the functions of the software package: 1. Numpy

Numpy provides two basic objects: ndarray and ufunc. Ndarray is a multi-dimensional array that stores a single data type, while ufunc is a function that can process arrays. Numpy functions: n-dimensional array, a fast and efficient use of memory multi-dimensional array, it provides vector mathematical operations; you can do standard mathematical operations on the data in the entire array without the use of loops. It is very easy to transfer data to external libraries written in low-level languages (C\ C++), and it is also convenient for external libraries to return data as Numpy arrays. Numpy does not provide advanced data analysis capabilities, but can have a deeper understanding of Numpy arrays and array-oriented computing.

2 、 Pandas

Pandas is a data analysis package of Python. Pandas was originally developed as a financial data analysis tool. Pandas incorporates a large number of libraries and some standard data models as well as functions and methods. Provides the tools needed to manipulate large datasets efficiently. Pandas is built on top of Numpy, which makes Numpy application simple. A data structure with axes that supports automatic or explicit data alignment (which prevents common errors due to misalignment of data structures and processing of data from different sources and indexes). Use Pandas to make it easier to handle lost data) and merge popular databases (such as SQL-based databases).

3 、 Matplotlib

Matplotlib is a visualization module of Python and a set of Python packages based on Numpy. It can easily only do line charts, pie charts, bar charts and other professional graphics. With Matplotlib, you can customize any aspect of the chart you make, and you can control every default attribute in Matplotlib: image size, dots per inch, lineweight, color and style, subgraph, axis, grid properties, text and text attributes. It supports different GUI backends under all operating systems, and can output graphics to common vector graphics and graphics tests, such as PDF SVG JPG PNG BMP GIF. Through data mapping, we can convert boring numbers into charts that people can easily accept.

4 、 Scipy

Scipy is a convenient, easy-to-use, specially designed Python package for science and engineering. It includes statistics, optimization, integration, linear algebra module, Fourier transform, signal and image processing, ordinary differential equation solver, etc. Scipy relies on Numpy and provides many user-friendly and efficient numerical routines, such as numerical integration and optimization.

Python has a numerical calculation toolkit as powerful as Matlab Numpy; has a drawing toolkit Matplotlib; and a scientific computing toolkit Scipy.

5 、 Scikit-Learn

Scikit-Learn is a Python-based machine learning module based on the BSD open source license; its installation requires modules such as Numpy Scipy Matplotlib. The main functions of Scikit-Learn are: classification, regression, clustering, data dimensionality reduction, model selection, data preprocessing.

Scikit-Learn has some classic libraries:

(1) Nltk for natural language processing

(2) Scrappy, which is used to crawl website data

(3) Pattern for web mining

(4) Theano for deep learning.

Scikit-Learn comes with some classic datasets: iris and digits datasets for classification, and boston house prices datasets for regression analysis. The dataset is a dictionary structure where the data is stored in the .data member and the output label is stored in the .target member.

Scikit-Learn is based on Scipy and provides a set of commonly used machine learning algorithms. Using a unified interface, Scikit-Learn helps to implement popular algorithms on data sets.

Python can process data directly, while Pandas can control data almost like SQL. Matplotlib can visualize the data and understand the data quickly. Scikit-Learn provides support for machine learning algorithms, and Theano provides a deep learning framework (you can also use CPU acceleration).

This is the end of this article on "what are the Python data analysis packages?". I hope the above content can be of some help to you, so that you can learn more knowledge. if you think the article is good, please share it for more people to see.

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