Programming languages are an essential component of data science, with many data scientists learning multiple languages in order to work within specific fields and industries. As an open-source programming language, Python has a rich community and resources that come along with it. Python is known for its libraries and packages which offer functions and code that are helpful to data science students and professionals. Of the many Python libraries available to data scientists, NumPy has become well-known for its capabilities in creating arrays and utilizing mathematical operations. Read on if you would like to know more about all of the things that you can do with the NumPy Python library!
What is NumPy?
Created in 2005, NumPy is an open-source Python programming library that simplifies the process of numerical computing with a focus on mathematical functions and arrays. NumPy is also the foundation of an extensive ecosystem of Python products and libraries, with the NumPy library itself being based on C and Fortran. Many Python users draw upon NumPy and other programming libraries which are dependent on NumPy, such as Pandas, SciPy, and Seaborn. NumPy is highly interoperable and works with multiple programming languages, hardware, and platforms. Whether you are a data scientist in the social sciences and academic research or working within engineering and computer science, there are multiple uses for the NumPy programming library.
How is NumPy used in Data Science?
NumPy is commonly used within data science in order to work through numerical analyses and functions, such as creating and working with arrays, returning descriptive statistics, and a variety of machine learning models and mathematical formulas. You can also access the NumPy library through the GitHub platform.
Creating and Working with N-Dimensional Arrays
One of the main uses of the NumPy Python library is for the creation and deployment of arrays. Arrays, like data frames, are another type of data structure that can be used to organize a dataset. Arrays are multidimensional in their appearance and have the capacity to hold different columns and axes of data within the same structure and data type. Working with arrays is an essential component of both computer and data science because you can use them to index data and select out or input specific variables within a dataset. Arrays can also be used when creating machine learning and deep learning models.
When creating an array in NumPy, you can use the “np.array()” or “np.asarray()” functions in order to declare an array. Once you have created your array, you can work through other functions, such as changing the shape of the array, manipulating elements of an array through indexing and slicing, as well as computing mathematical functions with arrays. By using each of these functions and techniques, data scientists are able to perform complex statistical analyses on a dataset with relative ease. NumPy has also spawned other array libraries, such as xtensor and xnd, which can be used to create array expressions.
Descriptive Statistics and Data Visualization
Used by itself, or in conjunction with other Python libraries, NumPy is an excellent tool
for performing exploratory analysis on a dataset. Especially when used within a Python environment like JupyterLab and/or Notebook, you can use NumPy to work through a series of functions that are useful for making inferences and initial hypotheses. In particular, these functions can be used for returning descriptive statistics within the NumPy library. Whether you require the average of a set of values or the standard deviation, there are functions that can be used to perform calculations that return a statistical overview of the dataset under analysis.
Descriptive statistics are especially useful in the exploratory stage of data analysis, as it gives you some basic findings from the data. These statistics can also be used to test assumptions and hypotheses as you move into the data analysis process. NumPy is also the foundation of multiple Python plotting libraries, which allow data scientists to create data visualizations once a dataset is analyzed. Used in conjunction with libraries like Matplotlib, NumPy is a key component of creating visualizations with Python.
Machine Learning and Mathematical Functions
As a high-level computational library, NumPy is essential for data scientists that work with algorithms, artificial intelligence, and/or machine learning. Acting as the basis for machine learning libraries such as Scikit-learn and SciPy, as well as deep learning libraries such as PyTorch, there are multiple possibilities for data scientists that incorporate NumPy into their projects. Functions, such as randomization (“np.random.rand()”) and exponentiation (“print(np.exp(x))”), are also essential to machine learning. Plus, arrays can be used for machine learning and algorithms, making this popular NumPy function an essential component of designing recommendation systems, data forecasting, and predictive analytics.
As a primarily numerical library, there are multiple mathematical functions that can be completed with NumPy, such as algebraic equations and formulas. Whether you need to compute the sum (“np.sum([])”)or even the mean (“np.mean()”) of values or arrays, the NumPy library includes an extensive list of operators and formulas. NumPy additionally supports a considerable amount of data types to account for the different types of values that one would find in mathematics and computer science. This is important for data scientists working in the realm of statistics, engineering, and other science and technology-focused fields that require writing up the mathematical operations behind your work.
Need to learn more about NumPy?
Noble Desktop offers courses that teach NumPy because Python libraries incredibly are important data science tools. The Noble Desktop Data Science Certificate includes extensive training in Python and its libraries, including the NumPy Python library. You can also take one of the many data sciences classes or Python bootcamps that are offered in both in-person and live online formats. With both options, you will not only learn more about how to use NumPy, but multiple tools and techniques that will improve your programming and data science skills!