Share the 8 tools common to Python data analysis

Python is a common tool for data processing, can handle the order of magnitude from a few k to several T data, with high development efficiency and maintainability, but also has a strong commonality and cross-platform, here for you to share a few good data analysis tools, the need for friends can refer to the next

Python is a common tool for data processing, which can handle data ranging from a few k to several T, with high development efficiency and maintainability, as well as a strong versatility and cross-platform. Python can be used for data analysis, but its reliance on Python's own library for data analysis is limited, and third-party extension libraries need to be installed to enhance analysis and mining capabilities.

Python data analysis requires the installation of third-party extension libraries: Numpy, Pandas, SciPy, Matplotlib, Scikit-learn, Keras, Gensim, scrapy, etc. The following is a brief introduction to the third-party extension library by Qian Feng Wuhan python training Teacher:

1. Pandas

Pandas is a powerful, flexible data analysis and exploration tool for Python, with advanced data structures and tools such as series, Dataframe, and the installation of pandas to make data processing in Python fast and easy.

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

Pandas is created to solve data analysis tasks, pandas incorporates a large number of libraries and some standard data models, providing the tools needed to efficiently manipulate large datasets. Pandas provides a number of functions and methods for processing data quickly and easily. Pandas contains advanced data structures and tools that make data analysis quick and easy. It is built on top of NumPy, making numpy applications simple.

Data structures with axes that support automatic or explicit data alignment. This prevents common errors resulting from misaligned data structures and processing data from different sources and with different indexes.

Using pandas makes it easier to handle lost data.
Merging popular databases (e.g., SQL-based databases)
Pandas is the best tool for data clarity/collation.

2. Numpy

Python does not provide array functionality, NumPy can provide array support and corresponding efficient processing functions, is the basis of Python data analysis, scipy, pandas and other data processing and scientific computing Library of the basic functions of the Library, And its data type is useful for Python data analysis.

NumPy provides two basic objects: Ndarray and Ufunc. Ndarray is a multidimensional array that stores a single data type, whereas Ufunc is a function that can be processed on an array. Features of the NumPy:

• n-dimensional array, a fast and efficient multi-dimensional array of memory, he provides vectorization math operations.

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

• It is very easy to transfer data to external libraries written in low-level languages (c\c++), as well as to allow external libraries to return data as numpy arrays.

NumPy does not provide advanced data analysis capabilities, but it provides a deeper understanding of numpy arrays and array-oriented computations.

3. Matplotlib

Matplotlib is a powerful data visualization tool and drawing library, is mainly used for plotting data diagram of the Python library, provides a variety of visual graphics to draw the command font, simple interface, you can easily grasp the format of the graphics, drawing all kinds of visual graphics.

Matplotlib is a visual module of Python that makes it easy to do only line drawings, pie charts, histograms, and other professional graphics.
With Matplotlib, you can customize any aspect of the chart you make. He supports different GUI backend under all operating systems, and can output graphics as common vector graphics and graphic tests, such as PDF SVG JPG PNG BMP GIF. With data plotting, we can turn boring numbers into charts that people easily receive.
Matplotlib is a set of Python packages based on NumPy, which provides the data-drawing tools that are commanded to be used primarily for drawing statistical graphs.
Matplotlib has a set of default settings that allow you to customize various properties, and you can control each of the default properties in Matplotlib: Image size, dots per inch, lineweight, color and style, sub-graph, axis, net properties, text, and text attributes.

4. SciPy

SciPy is a set of packages that specialize in solving a variety of standard problem domains in scientific computing, including features such as optimization, linear algebra, integration, interpolation, fitting, special functions, fast Fourier transforms, signal processing and image processing, ordinary differential equation solving, and other commonly used calculations in science and engineering, These are useful for data analysis and mining.

SciPy is a convenient, easy-to-use, science-and engineering-designed Python package that includes statistics, optimization, integration, linear algebra modules, Fourier transforms, signal and image processing, and ordinary differential equation solvers. SCIPY relies on NumPy and provides many user-friendly and effective numerical routines, such as numerical integration and optimization.

Python has a powerful numerical computing toolkit numpy like MATLAB, a drawing toolkit matplotlib, and a Scientific computing toolkit scipy.
Python can process data directly, and pandas can control data almost as much as SQL. Matplotlib can visualize data and demerit, and quickly understand the data. Scikit-learn provides support for machine learning algorithms, and Theano provides a read-up learning Framework (which also uses CPU acceleration).

5. Keras

Keras is a deep learning library, an artificial neural network and a deep learning model based on Theano, relying on NumPy and scipy, which can be used to build common neural networks and various deep learning models such as language processing, image recognition, self-encoder, cyclic neural networks, recursive audit networks, convolutional neural networks and so on.

6. Scikit-learn

Scikit-learn is a common machine learning toolkit for Python, providing a complete machine learning toolkit that supports powerful machine learning libraries such as data preprocessing, classification, regression, clustering, forecasting, and model analysis, and relies on NumPy, scipy, and Matplotlib.

Scikit-learn is a python-based machine learning module based on the BSD open source license.
Scikit-learn installation needs NumPy Scopy Matplotlib and other modules, the main functions of Scikit-learn are divided into six parts, classification, regression, clustering, data reduction, model selection, data preprocessing.

Scikit-learn comes with some classic datasets, such as the iris and digits datasets for classification, and the Boston house prices dataset for regression analysis. The dataset is a dictionary structure, and the data is stored in the. data member, and the output label is stored in the. Target member. Built on SciPy, Scikit-learn provides a common set of machine learning algorithms that can be used with a unified interface, Scikit-learn helps implement popular algorithms on datasets.
Scikit-learn also has some libraries, such as: NLTK for natural language processing, scrappy for Web site data fetching, pattern for network mining, Theano for deep learning, and so on.

7. Scrapy

Scrapy is a special tool for reptiles, with URL reading, HTML parsing, storage data and other functions, you can use the Twisted asynchronous network library to handle network communication, the structure is clear, and contains a variety of middleware interfaces, can be flexible to complete a variety of requirements.

8. Gensim

Gensim is a library for text-themed models, often used to handle language tasks, supports multi-topic model algorithms, including TF-IDF, LSA, LDA, and Word2vec, supports streaming training, and provides API interfaces for common tasks such as similarity computing, information retrieval, and more.

The above is a simple introduction to the common tools of Python data analysis, interested in learning more about how to use the Method!