Today, every industry revolves around data, from stock market experts to health professionals predicting the extent of a viral pandemic. It is these data that facilitate the process of analysis and calculations. As a result, data collection and analysis is a key factor that is common across the board.

Sometimes, using simple tools like Excel can help to some extent. But when the calculations become complicated, using a calculator or Excel is no longer the answer. Also, when we deal with a huge amount of data, a more powerful tool is needed to manage this amount of data. This is where data science comes to our aid.

Although there are many data analysis tools such as MATLAB and R programming language, it can be said that Python programming language is the best choice for data analysis and complex mathematical calculations. Why? Because the python has provided powerful libraries for data analysis and complex calculations.

In this article, we will introduce the scipy library.

What is the SciPy library?

SciPy stands for Scientific Python, a free and open source library in the Python programming language that is used to solve scientific problems and perform complex mathematical calculations. This library contains a set of mathematical algorithms and functions. The SciPy library allows the programmer to perform data manipulation and visualization with greater precision and power.

Although SciPy is not the only Python library for mathematical calculations, it is certainly one of the most popular. The SciPy library also builds on another popular library called NumPy. As a result, if you have called the SciPy library, there is no need to call the NumPy library.

Why use the SciPy library?

  • SciPy contains a variety of packages that help solve problems related to scientific computing.
  • The SciPy library is the most widely used library in the Python programming language.
  • In addition to the fact that SciPy has high speed and computing power, it is easy to use and easy to understand for the programmer.
  • SciPy can also work on a set of NumPy libraries.

Basic SciPy functions

As mentioned at the beginning of the article, SciPy is built on top of NumPy. As a result, we can use NumPy functions in it. In this section, we will introduce some of the useful functions of the SciPy library.

help() function

If you want information or details about a specific function, you can use the help() function. This function can be used for functions with parameters and without parameters.

Scientific constants

SciPy library is mostly used in scientific cases. As a result, its constant values are also scientific, like pi.


SciPy provides several different functions for calculating integrals. For this we can use scipy.integrate command.

Calculate the double integral

We can use SciPy dblquad to calculate double integrals. As we know, the double integral consists of two variables. The dblquad() function takes inputs including the parameter, along with four other variables, the upper and lower bounds, and the derivative functions dx and dy.

To learn more about functions for calculating other types of integrals, such as the triple integral and the Ramberg integral, you can use the help() function described above.

Linear Algebra

In linear algebra, we deal with linear equations and their representation in vector space and matrices. The SciPy library is built from the ATLAS LAPACK and BLAS libraries. The advantage of these two libraries is that they are very fast in solving linear algebra problems.

In addition, the scipy.linalg and numpy.linalg functions will greatly increase the calculation speed along with the above functions.


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