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.
1. Pandas (Recommended learning: Python video tutorial)
Pandas is a powerful and flexible version of Python Data analysis and exploration tools, including Series, DataFrame and other advanced data structures and tools. Installing Pandas can make data processing 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.
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.
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.
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.
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.
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 Twisted The asynchronous network library handles network communication, has a clear structure, 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.
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