Share 8 tools commonly used for Python data analysis

Python is a common tool for data processing. It can process data ranging from several K to several T. It has high development efficiency and maintainability, and also has strong versatility and cross-platform performance. Here is Let’s share some good data analysis tools. Friends who need them can refer to them

Python is a commonly used tool for data processing. It can handle data ranging from several K to several T. It has high development efficiency and It is maintainable and has strong versatility and cross-platform performance. Python can be used for data analysis, but it still has certain limitations in relying solely on Python's own libraries for data analysis. It is necessary to install third-party extension libraries to enhance analysis and mining capabilities.

The third-party extension libraries that need to be installed for Python data analysis include: Numpy, Pandas, SciPy, Matplotlib, Scikit-Learn, Keras, Gensim, Scrapy, etc. The following are Qianfeng Wuhan Python training teacher’s brief introduction to this third-party extension library:

1. Pandas

Pandas is a powerful and flexible data analysis and exploration tool for Python, including Series , DataFrame and other advanced data structures and tools. Installing Pandas can make processing data in Python very fast and simple.

Pandas is a data analysis package for Python. Pandas was originally developed as a financial data analysis tool, so Pandas provides good support for time series analysis.

Pandas was created to solve data analysis tasks. Pandas incorporates a large number of libraries and some standard data models to provide the tools needed to efficiently operate large data sets. Pandas provides a large number of functions and methods for us to process data quickly and conveniently. Pandas includes advanced data structures and tools that make data analysis fast and easy. It is built on Numpy, making Numpy applications easy.

Data structure with coordinate axes, supporting automatic or explicit data alignment. This prevents common errors caused by misaligned data structures and processing data from different sources with different indexes.

Handling missing data is easier with Pandas.
Merge popular databases (e.g. SQL-based databases)
Pandas is the best tool for data clarity/organization.

2. Numpy

Python does not provide array functions. Numpy can provide array support and corresponding efficient processing functions. It is the basis for Python data analysis, as well as SciPy and Pandas. It is the most basic function library for data processing and scientific computing libraries, and its data types are very useful for Python data analysis.

Numpy provides two basic objects: ndarray and ufunc. ndarray is a multi-dimensional array that stores a single data type, and ufunc is a function that can process arrays. Functions of Numpy:

• N-dimensional array, a multi-dimensional array that uses memory quickly and efficiently, provides vectorized mathematical operations.

• You can perform standard mathematical operations on the data in the entire array without using loops.

• It is very convenient to transfer data to external libraries written in low-level languages ​​​​(C\C), and it is also convenient for external libraries to return data in the form of Numpy arrays.

Numpy does not provide advanced data analysis functions, but it can provide a deeper understanding of Numpy arrays and array-oriented calculations.

3. Matplotlib

Matplotlib is a powerful data visualization tool and drawing library. It is a Python library mainly used for drawing data charts and provides various visualization graphics. The command font library and simple interface make it easy for users to master graphic formats and draw various visual graphics.

Matplotlib is a visualization module of Python. It can easily make line graphs, pie charts, histograms and other professional graphics.
Using Matplotlib, you can customize any aspect of the chart you make. It supports different GUI backends under all operating systems, and can output graphics into common vector graphics and graphics tests, such as PDF SVG JPG PNG BMP GIF. Through data drawing, we can transform boring numbers into something that people can easily accept. chart.
Matplotlib is a set of Python packages based on Numpy. This package provides commanded data drawing tools, mainly used to draw some statistical graphics.
Matplotlib has a set of default settings that allow customization of various properties. You can control every default property in Matplotlib: image size, dots per inch, line width, color and style, subplots, axes, mesh properties, text and text attributes.

4. SciPy

SciPy is a collection of packages specifically designed to solve various standard problem areas in scientific computing. It includes functions such as optimization, linear algebra, Integration, interpolation, fitting, special functions, fast Fourier transform, signal processing and image processing, solving ordinary differential equations and other calculations commonly used in science and engineering are very useful for data analysis and mining.

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

Python has Numpy, a numerical calculation toolkit as powerful as Matlab; it has Matplotlib, a drawing toolkit; and Scipy, a scientific computing toolkit.
Python can process data directly, while Pandas can control data almost like SQL. Matplotlib can visualize data and demerits to quickly understand the data. Scikit-Learn provides support for machine learning algorithms, and Theano provides a progression learning framework (CPU acceleration can also be used).

5. Keras

Keras is a deep learning library, artificial neural network and deep learning model. It is based on Theano and relies on Numpy and Scipy. It can be used to build Ordinary neural networks and various deep learning models, such as language processing, image recognition, autoencoders, recurrent neural networks, recursive audit networks, convolutional neural networks, etc.

6. Scikit-Learn

Scikit-Learn is a commonly used machine learning toolkit for Python. It provides a complete machine learning toolbox and supports data preprocessing and classification. , regression, clustering, prediction and model analysis and other powerful machine learning libraries, which rely on Numpy, Scipy and Matplotlib, etc.

Scikit-Learn is a Python machine learning module based on the BSD open source license.
The installation of Scikit-Learn requires modules such as Numpy Scopy Matplotlib. The main functions of Scikit-Learn are divided into six parts, classification, regression, clustering, data dimensionality reduction, model selection, and data preprocessing.

Scikit-Learn comes with some classic data sets, such as the iris and digits data sets for classification, and the boston house prices data set for regression analysis. The data set is a dictionary structure, with data stored in the .data member and output labels stored in the .target member. Scikit-Learn is built on Scipy and provides a set of commonly used machine learning algorithms through a unified interface. Scikit-Learn helps implement popular algorithms on data sets.
Scikit-Learn also has some libraries, such as: Nltk for natural language processing, Scrappy for website data scraping, Pattern for web mining, Theano for deep learning, etc.

7. Scrapy

Scrapy is a tool specially designed for crawlers. It has functions such as URL reading, HTML parsing, and data storage. You can use the Twisted asynchronous network library To handle network communication, the architecture is clear and contains various middleware interfaces, which can flexibly fulfill various needs.

8. Gensim

Gensim is a library used to make text topic models. It is often used to handle language tasks and supports TF-IDF, LSA, LDA and Word2Vec. It supports a variety of topic model algorithms, including streaming training, and provides API interfaces for some common tasks such as similarity calculation and information retrieval.

The above is a brief introduction to commonly used tools for Python data analysis. If you are interested, you can study the related usage in depth!

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