What software is used for python data analysis?

Python is a common tool for data processing, which can process data from several k to several t, and has high development efficiency and maintainability, as well as strong versatility and cross-platform. Here, I will share with you several good data analysis tools. The third-party extension libraries to be installed for Python data analysis include: Numpy, Pandas, SciPy, Matplotpb, Scikit-Learn, Keras, Gensim, Scrapy, etc. Let's briefly introduce the third-party extension library: (Recommended learning: Python video tutorial)

1. Panda

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

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

Panda was created to solve the task of data analysis. Pandas contains a large number of libraries and some standard data models, and provides the tools needed to operate large data sets efficiently. Pandas provides a large number of functions and methods, allowing us to process data quickly and conveniently. Pandas contains advanced data structures and tools, which make data analysis fast and simple. It is based on Numpy, which makes the application of Numpy simple.

A data structure with coordinate axes that supports automatic or explicit data alignment. This can prevent common errors caused by misaligned data structures and processing data from different sources with different indexes.

It's easier to deal with lost data with pandas. Merging popular databases (such as SQL-based databases) Pandas is the best tool for data clarification/arrangement.

2.Numpy

Python does not provide array functions, but Numpy can provide array support and corresponding efficient processing functions, which is the basis of Python data analysis and the most basic function library of SciPy, Pandas and other 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 multidimensional array that stores a single data type, while ufunc is a function that can handle arrays. Functions of Numpy:

N-dimensional array, fast and efficient use of multi-dimensional array of memory, providing vectorized mathematical operations. You can perform standard mathematical operations on data in an entire array without using loops. It is very convenient to transfer data to an external library written in low-level language (CC++), and it is also convenient for the external library to return data in the form of Numpy array.

Numpy does not provide advanced data analysis function, but it can understand Numpy array and array-oriented calculation more deeply.

3.Matplotpb

Matplotpb is a powerful data visualization tool and drawing library, and it is a Python library mainly used for drawing data charts. It provides a simple interface for command fonts and drawing various visual graphics, which is convenient for users to easily master the graphic format and draw various visual graphics.

Matplotpb is a visual module of Python, which can easily make only professional graphics such as line charts, pie charts and bar charts. With Matplotpb, you can customize any aspect of your chart. It supports different GUI backend under all operating systems, and can output graphics as common vector diagrams and graphics tests, such as PDF SVG JPG PNG BMP GIF. By drawing data, we can turn boring numbers into charts that people can easily accept. Matplotpb is a Python package based on Numpy, which provides imperative data drawing tools, mainly used to draw some statistical charts. Matplotpb has a set of default settings, allowing you to customize various properties, and you can control every default property in Matplotpb: image size, dots per inch, line width, color and style, subgraph, axis, network property, text and text property.

4.SciPy

SciPy is a software package dedicated to solving various standard problem domains in scientific computing, including optimization, linear algebra, integration, interpolation, fitting, special functions, fast Fourier transform, signal processing and image processing, solving ordinary differential equations and other commonly used calculations in science and engineering. These are very useful for data analysis and mining.

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

Python has a powerful numerical calculation toolkit. Numpy likes Matlab. MatplotpbScipy with drawing toolkit has a scientific calculation toolkit. Python can process data directly, while panda can control data almost like SQL. Matplotpb can visualize data and shortcomings and quickly understand data. Scikit-Learn provides support for machine learning algorithms, and Theano provides a framework for upgrading learning (which can also be accelerated by CPU).

5. Crass

Keras is a deep learning library, artificial neural network and deep learning model. Based on Theano and relying on Numpy and Scipy, it can be used to build common neural networks and various deep learning models, such as language processing, image recognition, self-encoder, recursive neural networks, recursive audit networks and convolutional neural networks.

6.sci kit- learning

Scikit-Learn is a common machine learning toolkit in Python, which provides a perfect machine learning toolkit and supports powerful machine learning libraries such as data preprocessing, classification, regression, clustering, prediction and model analysis, and it relies on Numpy, Scipy and Matplotpb.

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

Scikit-Learn comes with some classic data sets, such as iris and digits data sets for classification and Boston house price data sets for regression analysis. The data set is a dictionary structure, and the data is stored in. Data members, output labels are stored in the. Target member. Scikit-Learn provides a set of commonly used machine learning algorithms based on Scipy, which can be used through a unified interface. Scikit-Learn helps to implement popular algorithms on data sets. Scikit-Learn also has some libraries, such as Nltk for natural language processing, Scrappy for website data grabbing, Pattern for network mining, Theano for deep learning, etc.

7.Scrapy

Scrapy is a tool specially designed for reptiles. It has the functions of URL reading, HTML parsing and data storage. It can use the Twisted asynchronous network library to handle network communication. It has a clear architecture and contains various middleware interfaces, which can flexibly meet various requirements.

8. Gensim

Gensim is a library for making text theme models, which is often used to deal with language tasks. It supports TF-IDF, LSA, LDA, Word2Vec and other topic model algorithms, supports stream training, and provides API interfaces for some common tasks such as similarity calculation and information retrieval.

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