NumPy is a very popular Python library that is mainly used to perform mathematical and scientific calculations. It offers **many features and tools** that can be useful for Data Science projects. Becoming familiar with NumPy is an essential step in a **Data Science training project**. Find out everything you need to know to master Numpy.

### What is NumPy?

The term NumPy is an abbreviation for “**Numerical Python**“. It is an open-source library in the **Python language**. It is used for scientific programming in Python, and in particular for programming in Data Science, engineering, mathematics, or science.

Data Science is based on highly** complex scientific calculations**. To perform these calculations, **Data Scientists** need powerful tools. This library is very useful to perform mathematical and statistical operations in Python. It works great for **multiplying matrices **or multidimensional arrays. Integration with C/C++ and Fortran is very easy.

### How does NumPy work?

This platform includes multidimensional objects in arrays and a package with integration tools for Python implementation. Simply put, NumPy is a mix between C and Python that is used as an alternative to traditional MATLAB programming.

The data, in the form of numbers, are treated as arrays for multidimensional functions and rearrangement operations. It is a **widely used tool** in the field of Data Science.

Among the many libraries in Python, **NumPy is one of the most used**. This is because many data science techniques require large tables and matrices and complex calculations to extract valuable information from data. NumPy simplifies this process with a **variety of mathematical functions**.

Although basic, it is** one of the most important Python libraries** for scientific computing. In addition, other libraries rely heavily on the NumPy arrays they use as inputs and outputs. For example, **TensorFlow** and Scikit learn to use NumPy arrays to calculate matrix multiplications.

Beyond that, NumPy also** provides functions** that allow developers to perform basic or advanced mathematical and statistical functions on arrays and multidimensional matrices with few lines of code.

The ndarray or n-dimensional array data structure is the **main feature **of NumPy. These arrays have the particularity of being homogeneous, so all elements must be of the same type.

In general, NumPy arrays are faster than Python lists. However, since it is only possible to **store data** of the same type in each column, Python lists are more flexible.

To use NumPy you must first import the library, most often it is used under its alias “np” which makes it easier to use.

Here we can see an example of an array. The square brackets are used to delimit the **lists of elements** in the array, as we can see here where we have first [0,1,2,3] which represents our first dimension.

It is also possible to create an array using the np.array() function of Numpy.

From our list L we created, we can transform it into a numpy array. Don’t forget that NumPy arrays can only take **one type of data at a time**, unlike lists which can mix numerical values and characters.

This time our array is **multidimensional**, that is, it will be composed of several comma-delimited lists. Using the shape attribute of our array, we see that we have a **4×4 array**. Arrays are comparable to matrices (2d), and a one-dimensional array to a vector (1d). It is also possible to make lists of matrices (3d), this format is notably used for **image processing**, the third dimension being the color (**RGB** for Red, Green, and Blue).

You can use the **Dot attribute** of Numpy arrays to do matrix multiplication, however, the @ operator is the way recommended by numPy to do it even though the Dot attribute gives the same result.

In general, NumPy arrays are **faster than Python lists**. However, since it is only possible to store data of the same type in each column, Python lists are more flexible.

Here we can see that NumPy is approximately **35 times faster** on average than python lists for sum operations.

### What is Numpy used for?

To summarize NumPy, here are its main features. It is a combination of C and Python, based on multidimensional and homogeneous data arrays: Ndarrays (ndimensional arrays).

As in MATLAB, the basic type is a multidimensional array, which allows to speed up the computation speed on arrays. Even if there are differences in syntax, the **behavior is similar **between NumPy and MATLAB. With the help of other Python libraries, notably **Scikit-Learn**, Numpy allows Python to be the language of choice in Data Science.

This tool is compatible with many other popular Python packages, including **pandas and Matplotlib**. Its popularity is due to the fact that it is faster than regular Python arrays, thanks to the pre-compiled and optimized C code.

In addition, **arrays and operations are vectorized** which means that there is no explicit looping or indexing in the code. Thus, the code is more readable and similar to standard mathematical notation.

One can use NumPy to create an identity matrix using the NumPy Identity function. This tool allows you to **create arrays of any dimensions**.

Within a NumPy array, the first axis is axis 0. It is possible to add elements to the arrays, allowing assembly vectors and matrices.

A wide variety of data types are supported by NumPy arrays, and all kinds of numerical calculations can be performed.

In addition, it is possible to **convert a NumPy array into a list of strings**, a list of tuples, or a list of lists. Conversely, lists can be converted to array, matrix, ndarray, string, or CSV.

In general, NumPy allows you to easily perform many mathematical operations used in **scientific computing** such as vector-vector, matrix-matrix, or matrix-vector multiplication.

This package also allows operations on vectors and matrices like addition, subtraction, multiplication, or division by a number. You can also perform comparisons, apply functions to vectors and matrices, and perform reduction and statistical operations.

### What are the advantages of NumPy?

NumPy is very useful for performing logical and mathematical calculations on arrays and matrices. This tool performs these operations **much faster and more efficiently** than Python lists.

Numpy uses less memory and storage space, which is the main advantage. In addition, NumPy offers **better performance** in terms of execution speed. However, it is easier and more convenient to use.

Moreover, it is an **open-source tool** that can be used completely free of charge. It is based on Python which is an extremely popular programming language with many high-quality libraries for any task. Finally, it is very easy to connect existing C code to the Python interpreter

### Which training to learn how to use NumPy?

Currently, Python is the most popular programming language in the computer industry. Mastering this language offers many career opportunities all over the world.

Thi**s high-level programming language **has many advantages, including its concise syntax. It is one of the best tools for dynamic scripting, web development, application development, and data science. In this favorable context, learning to handle Python and NumPy can open many doors for you. To acquire these skills, you can opt for **DataScientest training**.

Indeed, NumPy is at the heart of the Programming module of our **Data Analyst** and **Data Scientist training course**. It is also part of our **Data Management training program**, in the Introduction to Python module. These three courses allow you to access the Data Science profession.

All our courses can be done either in **Continuing Education or in BootCamp.** They adopt an innovative “**blended learning**” approach combining face-to-face and distance learning.

Among the alumni, **93% found a job immediately** after the course. Don’t waste another second, and learn how to use Python and NumPy through our various Data Science courses!

You know everything about NumPy. Discover our **complete file on the Python language**, and our **introduction to Data Science**.