SciPy is a free and open-source Python library used for scientific computing and technical computing. SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering
Curriculum
- 11 Sections
- 37 Lessons
- 10 Weeks
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- Installation of SciPyIf you have Python and PIP already installed on a system, then installation of SciPy is very easy. Install it using this command:1
- SciPy ConstantsAs SciPy is more focused on scientific implementations, it provides many built-in scientific constants. These constants can be helpful when you are working with Data Science.1
- SciPy OptimizersOptimizers are a set of procedures defined in SciPy that either find the minimum value of a function, or the root of an equation.3
- SciPy Sparse DataSparse data is data that has mostly unused elements (elements that don't carry any information ). It can be an array like this one: [1, 0, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0]4
- SciPy GraphsGraphs are an essential data structure. SciPy provides us with the module scipy.sparse.csgraph for working with such data structures.2
- SciPy Spatial DataSpatial data refers to data that is represented in a geometric space. E.g. points on a coordinate system.2
- SciPy Matlab ArraysWe know that NumPy provides us with methods to persist the data in readable formats for Python. But SciPy provides us with interoperability with Matlab as well. SciPy provides us with the module scipy.io, which has functions for working with Matlab arrays.3
- SciPy InterpolationInterpolation is a method for generating points between given points. For example: for points 1 and 2, we may interpolate and find points 1.33 and 1.66.3
- SciPy Statistical Significance TestsIn statistics, statistical significance means that the result that was produced has a reason behind it, it was not produced randomly, or by chance.10
- Statistical Description of DataIn order to see a summary of values in an array, we can use the describe() function6
- Normality Tests (Skewness and Kurtosis)Normality tests are based on the skewness and kurtosis. The normaltest() function returns p value for the null hypothesis: "x comes from a normal distribution".2
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FAQs
SciPy is a scientific computation library that uses NumPy underneath.
SciPy stands for Scientific Python.
It provides more utility functions for optimization, stats and signal processing.
Like NumPy, SciPy is open source so we can use it freely.
SciPy was created by NumPy's creator Travis Olliphant.
If SciPy uses NumPy underneath, why can we not just use NumPy?
SciPy has optimized and added functions that are frequently used in NumPy and Data Science.
SciPy is predominantly written in Python, but a few segments are written in C.
The source code for SciPy is located at this github repository https://github.com/scipy/scipy
