NumPy

NumPy is a fundamental library for scientific computing in Python. It provides support for multidimensional arrays, matrices, and a collection of mathematical functions for operating on this data. It is the foundation of many other data analysis and machine learning libraries.

★★★★★

5.0(2 ratings)

• np.array(). Creates a multidimensional array from a list or tuple. Arrays are the core data structure in NumPy, enabling vectorized operations and efficient storage of numerical data.
• np.zeros(). Generates an array filled with zeros in a specified shape. Useful for initializing data structures before processing.
• np.ones(). Similar to np.zeros(), but fills the array with ones. Commonly used for matrix initialization and linear algebra operations.
• np.arange(). Creates an array of evenly spaced values within a given interval. Similar to Python's range(), but supports floating-point steps.
• np.linspace(). Generates an array with a specified number of evenly spaced values between two endpoints. Ideal for creating axes for visualizations and numerical calculations.
• np.reshape(). Changes the shape of an array without altering its data. Allows multidimensional structures to be rearranged for specific operations.
• np.dot(). Performs matrix multiplication or the dot product of arrays. Fundamental for linear algebra operations and mathematical transformations.
• np.sum(). Calculates the sum of array elements along a specified axis. Supports aggregate operations on multidimensional arrays.
• np.mean(). Computes the arithmetic mean of array elements. Includes parameters for calculating means along specific axes.
• np.random.rand(). Generates random numbers from a uniform distribution over the interval [0, 1). Used in simulations and test data generation.
• np.linalg.inv(). Computes the inverse of a square matrix. Part of NumPy's linear algebra module for advanced matrix operations.
• np.fft.fft(). Implements the Fast Fourier Transform (FFT). Essential for signal processing and frequency analysis.

NumPy was created in 2005 by Travis Oliphant as an evolution of Numeric and Numarray, two earlier numerical libraries for Python. It is written mainly in C and Python, which lets it combine high performance with an accessible interface. NumPy is developed and maintained by a community of contributors, including scientists, engineers, and programmers. Its design focuses on computational efficiency and integration with other scientific tools in Python.

Alternatives to NumPy:

For NumPy there are 5 alternative programs. Free: 5, paid: 0.

R for Windows

R for Windows provides a complete environment for data manipulation, statistical calculation, and visualization generation.

Price: Free   Size: 87.5 MB   Version: 4.6.0   OS: Windows, Linux, MacOS

Agena

Agena is a procedural programming language derived from Lua, Algol 68 and Maple, focused on scientific computing, data analysis and text processing.

Price: Free   Size: 12.2 MB   Version: 7.8.6   OS: Windows, Linux, MacOS

OpenModelica

OpenModelica is an open-source environment for modeling, simulation, optimization, and analysis of complex multi-domain systems using the Modelica language.

Price: Free   Size: 2000 MB   Version: 1.25.7   OS: Windows, Linux, MacOS

Spyder

Spyder is an integrated development environment for the Python language.

Price: Free   Size: 506 MB   Version: 6.1.1   OS: Windows, Linux, MacOS

MedCalc

MedCalc is a statistical software package designed for biomedical research.

Price: Free   Size: 85.37 MB   Version: 23.6.1   OS: Windows