Category: SciPy

What is Statistical Significance Test? In statistics, statistical significance means that the result that was produced has a reason behind it, it was not produced randomly, or by chance. SciPy provides us with a module called scipy.stats, which has functions for performing statistical significance tests. Here are some techniques and keywords that are important when performing…

What is Interpolation? Interpolation 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. Interpolation has many usage, in Machine Learning we often deal with missing data in a dataset, interpolation is often used to substitute those values. This method…

Working With Matlab Arrays We 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. Exporting Data in Matlab Format The savemat() function allows us to export…

Working with Spatial Data Spatial data refers to data that is represented in a geometric space. E.g. points on a coordinate system. We deal with spatial data problems on many tasks. E.g. finding if a point is inside a boundary or not. SciPy provides us with the module scipy.spatial, which has functions for working with spatial…

Working with Graphs Graphs are an essential data structure. SciPy provides us with the module scipy.sparse.csgraph for working with such data structures. Adjacency Matrix Adjacency matrix is a nxn matrix where n is the number of elements in a graph. And the values represents the connection between the elements. Example: For a graph like this, with elements A, B and C,…

What is Sparse Data Sparse data is data that has mostly unused elements (elements that don’t carry any information ). Sparse Data: is a data set where most of the item values are zero. Dense Array: is the opposite of a sparse array: most of the values are not zero. In scientific computing, when we are dealing with partial…

Optimizers in SciPy Optimizers are a set of procedures defined in SciPy that either find the minimum value of a function, or the root of an equation. Optimizing Functions Essentially, all of the algorithms in Machine Learning are nothing more than a complex equation that needs to be minimized with the help of given data.…

Constants in SciPy As 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. PI is an example of a scientific constant. Example Print the constant value of PI: Try it Yourself » Constant Units A list of all units…

Installation of SciPy If you have Python and PIP already installed on a system, then installation of SciPy is very easy. Install it using this command: If this command fails, then use a Python distribution that already has SciPy installed like, Anaconda, Spyder etc. Import SciPy Once SciPy is installed, import the SciPy module(s) you want to use in…

What is SciPy? 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. Why Use SciPy? If SciPy uses NumPy underneath,…