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HomeBackend DevelopmentPython TutorialA powerful tool for data processing: NumPy explained in detail

数据处理的利器:NumPy 详解

Basic data types and arrays

NumPy's core data structure is a multidimensional array called an ndarray. ndarray stores values ​​of the same data type and can have any number of dimensions. NumPy supports a variety of basic data types, including integers, floating point numbers, Boolean values, and complex numbers.

Array operations

NumPy provides a series of array operation functions that can efficiently perform various operations, including:

  • Arithmetic operations (addition, subtraction, multiplication, division)
  • Comparison operations (equal to, not equal to, greater than, less than)
  • Logical operations (AND, OR, NOT)
  • Broadcasting (generalize scalar or low-dimensional arrays to high-dimensional arrays)

Linear Algebra

NumPy provides powerful linear algebra functions, including:

  • Matrix multiplication, inversion and determinant calculation
  • Solving a system of linear equations
  • Solve eigenvalues ​​and eigenvectors

Statistical operations

NumPy provides a set of statistical operation functions for calculating statistical values, for example:

  • Mean, median, standard deviation
  • Cumulative sum, maximum value, minimum value
  • Histogram and Frequency Distribution

Random number generation

NumPy provides various random number generators that can generate random numbers following different distributions, such as:

  • Evenly distributed
  • normal distribution
  • Binomial distribution

File input/output

NumPy can easily load and save data from files and databases. It supports a wide range of file formats, including:

  • CSV (Comma Separated Values)
  • TXT(Text)
  • HDF5 (Hierarchical Data Format)

Integrate with other libraries

NumPy is highly integrated with other popular python libraries, including:

  • Matplotlib: for data visualization
  • pandas: used for data processing and analysis
  • Scikit-learn: for machine learning

application

NumPy has applications in a wide range of fields, including:

  • Scientific Computing
  • data analysis
  • MachineLearning
  • Image Processing
  • Signal processing

Advantage

NumPy is the tool of choice for data processing due to the following advantages:

    High performance, utilizing the underlying compiled code
  • Concise syntax, easy to use
  • Extensive function set covering various operations
  • Highly integrated with other libraries to improve scalability

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