For every programming language, there are multiple libraries that accompany the language in order to assist users in creating their own programs based on code that was previously created and tested by other users. These libraries can also include additional resources that are useful to completing specific tasks or commands, such as templates and modules that make the process of writing code and analyzing data much faster. Most data science libraries can be found online within repositories like GitHub, and are openly accessible to users within the community for a specific programming language.

From the user-friendly syntax to the open-source software, Python is one of the most versatile programming languages, due in part to its thousands of libraries and accessible programming packages. Python’s libraries are able to assist Python users in their programming, by providing access to pre-written code and functions. Offering instructions for how to create a particular type of visualization or model, Python libraries make it easier and faster to create and run programs. To summarize: working with Python in a data science career calls for the use of specific libraries (collections of prewritten code that are designed for certain functions).

Top 10 Python Libraries

The following list includes some of the most popular Python libraries that are used in data science and machine learning. Most of the libraries listed work well with each other by allowing Python users to analyze, visualize, and model big data projects. The GitHub repositories for each of these libraries are also linked in each section so that you can learn more about the libraries and the code available when using each library.

1. Pandas

Standing for Python Data Analysis Library, the Pandas Python library can be used for multiple data analysis and machine learning-based projects, along with NumPy (the second library on this list). The Pandas library allows users to read and write data across multiple formats (Microsoft Excel, SQL, CSV, etc), organize and index big data sets, and other methods of data manipulation and restructuring.

2. NumPy

As a library that is focused on numerical computation and scientific analysis, NumPy offers multiple methods of working with Arrays. Arrays are a data structure that can be used to sort, display, and/or model different types of data structures, and NumPy allows Python users to primarily work with arrays using C code. In particular, NumPy has the processing speed and power to work well with creating images and frames, as well as statistical models.

3. SciPy

Known as one of the Python libraries that works with NumPy, SciPy is commonly used by researchers and data scientists in mathematics, science, and engineering. As a collection of open-source software and a community of users, the SciPy library can be used to work with algorithms, machine learning, network analysis, and other data structures.

4. Matplotlib

Primarily used for data visualizations, Matplotlib is another library that can be used with NumPy and allows Python users to create and plot charts, graphs, and animations. In creating these data visualizations, you can also customize and extend data to third-party packages and convert your images into multiple formats using this library. Matplotlib is also hosted on GitHub and you can find more information about these Python libraries on the GitHub platform.

5. Seaborn

Focused on statistical analysis and modeling, Seaborn is a library that can integrate with both Pandas and Matplotlib by allowing Python users to visualize their data through a variety of graphs, charts, and plots. Through working with dataframes and arrays, Seaborn users can visit the GitHub repository to begin creating compelling images that bring out the most important information underlying large stores of data.

6. Scikit-Learn

An engaging open-source Python library, scikit-learn works with NumPy, SciPy, and Matplotlib. Focused on statistical modeling and predictive data analysis, scikit-learn is useful for the visualization and analysis of multiple applications and algorithms. The versatility of this library can also be seen in the industries that use it, and scikit-learn is commonly seen within data science and other fields.

7. TensorFlow

A popular option in the realms of artificial intelligence and machine learning, TensorFlow was created by Google and is useful across online platforms. Due to its connection to Google, TensorFlow is used by multiple companies, making it an important Python library to learn for data scientists in the corporate world. Specifically, the TensorFlow library includes resources for working with machine learning models, recommendation systems, social and neurological networks, as well as decision making and other more common statistical and computational analysis models. In addition to offering libraries in Python, TensorFlow also offers JavaScript libraries for data scientists who are skilled in the programming language.

8. Keras

Offering more options for data scientists working with deep learning and neural networks, the Keras Python library is also compatible with TensorFlow. As a high-level application programming interface (API), Keras works with Jupyter notebooks and cloud computing to create different types of graphs and visualizations built on layers and models. Similar to TensorFlow, Keras is extremely popular and used within multiple technology corporations.

9. Statsmodels

Prioritizing regression models and other forms of statistical analysis and graphs, Statsmodels employs statistical packages to create formulas and arrays. Compatible with NumPy and statistical analysis software like SAS and Stata, the Statsmodels Python library also includes code for prediction and forecasting as well as statistical tests and data exploration models.

10. Plotly

Part of the Dash open source applications, Plotly is a plotting and graphing library that can be used to make statistical, financial, and scientific charts. Primarily used for data visualizations and graphics, Plotly is compatible with Jupyter notebooks, HTML, and other Python applications. Therefore, the Plotly Python library has hundreds of GitHub repositories and is an essential tool for data scientists that want to create high-quality data visualizations.

Overall, these Python libraries are essential to individuals that are pursuing a career in data science. With various models and methods of statistical and network analysis, data visualization, deep learning, machine learning, and algorithmic design, Python libraries are an important part of the data science toolkit.

Interested in learning more about the latest Python libraries?

Noble Desktop’s Data Science Certificate course includes instruction on the Pandas, NumPy, Matplotlib, and Scikit-Learn Python libraries. Additionally, you can peruse Noble Desktop’s Python classes, as well as the data science classes, to learn more about the in-person and live online course offerings on the topic. Whether you want to take an in-person Python class in your area or a variety of live online Python classes that you can fit into a busy schedule, there are many ways to take a class on this popular programming language!