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In recent years, Python has become one of the most widely used programming languages today, especially in data science.

Python is a high-performance language that is easy to learn and debug, and has extensive library support. Each of these libraries has its own unique features, some focusing on data mining, others on data visualization and neural networks. Data enthusiasts, analysts, engineers, and scientists are harnessing the power of Python for statistical analysis and predictive modeling as they solve data science tasks and challenges.

My previous articles have also taken a brief look at Python’s third-party libraries. For those new to Python and unfamiliar with its data ecosystem, I’ll focus on some of the most important data science libraries. The libraries include IPython, NumPy, SciPy, Pandas, StatsModels, and sciKit-learn.

 

IPython IPython is a Python interactive shell that is much easier to use than the default Python shell. It supports automatic variable completion, automatic indentation, bash shell commands, and many useful functions. Learning Ipython will allow us to use Python in a much more efficient way. It is also a great platform for scientific computation and interactive visualization using Python.

IPython is open source based on BSD.

IPython provides a rich architecture for interactive computing, including:

  • Powerful interactive shell
  • Jupyter kernel
  • Interactive data visualization tool
  • Flexible, embeddable interpreter
  • Easy to use, high performance parallel computing tool

Although it does not provide any computing or data analysis tools per se, it is designed to maximize productivity in both interactive computing and software development. It uses an execution-explore workflow to replace the edit-compile-run workflow typical in other languages. It also provides an easy-to-use interface to the operating system command line and file system. Because data analysis coding involves a lot of exploration, trial and error, and traversal, IPython allows you to get things done faster.

Liverpoolfc.tv: ipython.org/

 

例 句 : NumPy is short for Numerical Python, the cornerstone of Numerical computation in Python. NumPy is one of the main software packages of the science application library for handling large multidimensional arrays and matrices, and its extensive collection of high-level mathematical functions and implementations make it possible for these objects to perform operations. It provides a variety of data structures, algorithms, and most of the interfaces needed for numerical computation involving Python. NumPy is a very fast mathematical library for array computation. It contains:

  • A powerful N – dimensional array object nDARray
  • Broadcast function
  • Tools to integrate C/C++/Fortran code
  • Linear algebra, Fourier transform, random number generation, etc

NumPy is commonly used with SciPy (Scientific Python) and Matplotlib (Graph Library). This combination is widely used as an alternative to MatLab and is a powerful Scientific computing environment for learning data science or machine learning through Python.

Website: www.numpy.org/

 

 

SciPy, another core scientific computing library, is based on and extends NumPy. SciPy’s main data structure is multi-dimensional arrays, implemented using Numpy. The library provides tools for solving tasks such as linear algebra, probability theory, integral computation, and more. Its main functionality is based on the Numpy library, so its array operations make heavy use of the Numpy library. SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, fast Fourier transform, signal processing and image processing, solving ordinary differential equations, and other calculations commonly used in science and engineering.

  • The Python SciPy library supports integration, gradient optimization, ordinary differential equation solvers, parallel programming tools,
  • Interactive sessions with SciPy are data processing and system prototyping environments similar to MATLAB, Octave, Scilab or R-Lab.
  • SciPy provides high-level commands and classes for data science. This greatly improves the power of interactive Python sessions
  • In addition to mathematical algorithms, SciPy includes everything from classes to parallel programming. This makes it easier for programmers to develop complex professional applications.
  • SciPy is an open source project. Therefore, it has good community support.
  • SciPy together with NumPy provides a reasonable, complete and mature computing foundation for many traditional scientific computing applications.

Liverpoolfc.tv: scipy.org/scipylib/

 

 

4.Pandas is an extension library of the Python language for data analysis.

Pandas is an open source, BSD-licensed library that provides high-performance, easy-to-use data structures and data analysis tools. Pandas The name is derived from the terms “panel data” and “Python data analysis”. A powerful tool set for analyzing structured data, based on Numpy (providing high-performance matrix operations); You can import data from a variety of file formats such as CSV, JSON, SQL, and Microsoft Excel. It can perform operations on a variety of data, such as merging, reshaping, and selecting, as well as data cleaning and data processing features; Widely used in academic, financial, statistics and other data analysis fields.

  • Data structures with label axes that support automated or explicit data alignment — this prevents common errors caused by unaligned data and different index data from different data sources
  • Integrate time series functions
  • A unified data structure capable of processing both time series data and non-time series data
  • Can save metadata arithmetic operations and simplification
  • Flexible handling of missing data
  • Relational operations such as merges in popular databases, such as SQL-based databases

Pandas’ primary data structures are Series (one-dimensional data) and DataFrame (two-dimensional data), which are sufficient to handle most typical use cases in finance, statistics, social sciences, engineering, and other fields.

Website: pandas.pydata.org

 

StatsModels is a Python module that provides many possibilities for statistical analysis, such as statistical model estimation, running statistical tests, and so on. You can use it to implement many machine learning methods and explore different drawing possibilities. Some of the models included in Statsmodels:

  • Linear models, generalized linear models and robust linear models
  • Linear mixed effects model
  • Analysis of variance (ANOVA) method
  • Time series processes and state space models
  • Generalized method of moments

Statsmodels focuses more on statistical reasoning, providing uncertainty evaluation and p-value parameters.

Website: statsmodels.org

 

Scikit-learn (formerly sciKits. Learn, also known as SkLearn) is a free software machine learning library for the Python programming language. It features a variety of classification, regression, and clustering algorithms, including support vector machines, random forests, gradient lifting, K-means, and DBSCAN, and is intended for use in conjunction with Python’s numerical science libraries NumPy and SciPy. Contains submodules.

  • Classification: SVM, nearest neighbor, random forest, logistic regression, etc
  • Regression: Lasso, Ridge regression, etc
  • Clustering: K-means, spectral clustering, etc
  • Dimension reduction: PCA, feature selection, matrix decomposition, etc
  • Model selection: grid search, cross validation, index matrix
  • Pretreatment: feature extraction and normalization

Scikit-learn, along with pandas, Statsmodels, and IPython, makes Python an efficient data science programming language.

Website: scikit-learn.org

 

If you want to learn more about the Python science database, you can go to the Python website, or find books on it. Like this sharing partners, remember one key three even oh! Ink ~

 

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