Shulou(Shulou.com)06/01 Report--

This article introduces the relevant knowledge of "what are the tools for data analysis with Python". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

1. Numpy

Official website: http://www.numpy.org/

Numpy library is the cornerstone of Python numerical calculation. It provides a variety of data structures, algorithms and most of the interfaces needed for Python numerical computation. It mainly includes the following contents:

Fast and efficient Multi-dimensional Array object ndarray

Array calculation based on elements or mathematical operation functions between arrays

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

Linear Algebraic Operation, Fourier transform and Random number Generation

Mature C language API, expand the code

2. Scipy

Official website: https://www.scipy.org/

This library is a collection of packages for different standard problem domains in the field of Python scientific computing, and mainly includes the following:

Integrate: numerical integration routines and differential equation solvers

Linalg: linear Algebra routines and Matrix decomposition based on numpy.linalg

Optimize: function optimizer and root algorithm

Signal: a signal processing tool

Sparse: sparse matrix and sparse linear system solver

The packaging of special:SPECFUN

Stats: standard continuous and discrete probability distributions

Together with Numpy, Scipy provides a reasonable, complete and mature scientific computing foundation for many traditional scientific computing applications.

3. Pandas

Official website: http://pandas.pydata.org/

Pandas provides advanced data structures and functions that make the work of using structured and tabular data fast, simple, and expressive. Pandas combines the flexible data manipulation capabilities of tabular and relational databases with Numpy's understanding of high-performance array computing. Provide complex index functions to make data reorganization, slicing, slicing, aggregation, subset selection easier. Pandas is the most frequently used tool in data analysis and processing, and it is also the main introduction of this tutorial.

4. Matplotlib

Official website: https://matplotlib.org/

Matplotlib is the most popular Python library for mapping and other data visualization. In the work of Python-based data visualization, this library is the industry's default choice. Although there are other visualization libraries, matplotlib is still the most widely used and well integrated with other libraries of the ecosystem.

This tool is one of the main contents of this textbook, in fact, learned this tool, other visualization libraries, and even Matlab drawing, the basic routines are similar, you can do everything in one way.

5. Jupyter notebook

Official website: https://jupyter.org/

Interactive programming environments based on Python include IPython, IPython notebook and Jupyter notebook. But for data analysis, processing, machine learning and other related work, I highly recommend web-based Jupyter notebook.

This code testing, development, editing, text tools, really who uses who knows, and is also one of the main contents of this tutorial, hematemesis recommended!

6. Scikit-learn

Official website: https://scikit-learn.org/stable/

If we say that Python-based machine learning, then the first push must be the scikit-learn library, a must-learn tool! It mainly includes the following sub-modules:

Classification: SVM, nearest neighbor, random forest, logical regression, etc.

Regression: Lasso, Ridge regression, etc.

Clustering: k-means, spectral clustering, etc.

Dimensionality reduction: PCA, feature selection, matrix decomposition, etc.

Model selection: grid search, cross-validation, index matrix, etc.

Preprocessing: feature extraction, normalization

Other useful tools and datasets

This is the end of the content of "what are the tools for data analysis with Python". Thank you for your reading. If you want to know more about the industry, you can follow the website, the editor will output more high-quality practical articles for you!

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.