**This blog post covers seven popular data analysis and visualization libraries for Python. We’ll start with the SciPy stack, a set of libraries that offer powerful algorithms and data structures for doing scientific computing. In addition, we’ll cover more recent libraries geared at web designers and novice data scientists. **

Python has become the language of choice for many new data scientists. According to Iflexion company experts, the reason Python development is popular among data scientists is that the language is easy to learn and comes with a bunch of great tools. Think of Anaconda, a free and open source distribution of both Python and R that comes with an installation of the Jupyter Notebook application. This is a handy application that lets you store code, data, plots, and commentary in a single document and uses a web browser for opening code documents that can be downloaded, edited, and shared.

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**Data analysis and visualization with Python**

Let’s have a look at what is meant by data analysis and visualization. Both data analysis and visualization are important steps in the data science workflow. Data scientists collect data to answer questions and test hypotheses. After that, they need to communicate their findings to the decision-making team in an effective and engaging way. With so many visualization types available (pie charts, line plots, scatter plots), a data scientist is required to be an expert in the field and choose the visualization that communicates the message best.

The same goes for Python visualization libraries. The following is only a small sample of what’s available: the libraries that make up the SciPy stack form the basis for many other Python data science libraries and are must-know for any data scientist.

**Python libraries for data analysis**

The SciPy stack consists of five basic Python libraries that are distributed together as a tool for data analysis and visualization: NumPy, SciPy, Pandas, iPython, and Matplotlib. In this post, we’ll cover only four of them, as iPython nowadays serves as the backend of the Jupyter Notebook application and as an interactive Python shell.

**NumPy** is a library for scientific computing. As we’ll see below, Pandas uses NumPy’s functionality as the basis for its own data objects. NumPy offers a data object called NumPy array that can be used for storing and manipulating data in multidimensional arrays and matrices. This data object speeds up your calculations drastically when compared to working with Python lists.

**Pandas **takes NumPy’s functionality one step further, enabling you to work with tabular data that uses rows and columns, as well as separate rows with multiple columns. The first is stored in a DataFrame object, the second in a Series object. You can read and write many different file formats with tabular data into Pandas, including Excel spreadsheets. The amount of functionality offered by Pandas for slicing, summarizing, and presenting data is impressive. Like the Seaborn library discussed below, it is excellent for working with tabular data.

Finally, the **SciPy** extends the NumPy library, offering a set of submodules for scientific and technical computing based on NumPy arrays.

**Choosing a Python library for Data Visualization**

There are many Python libraries available for data visualization. They can be categorized using the following attributes: platform, functionality, and level of use. Platform refers to either desktop or web-based: because Python is by default a desktop environment (even though you’re using a web browser in case of a Jupyter Notebook), your visuals cannot be directly or easily embedded in an external webpage. Some more recent libraries were written with this concept in mind, enabling you to transfer them easily in a web environment, for example, using JSON.

Functionality refers to the amount and type of functionality offered by a library. The idea is that new visualization libraries extend the functionality available in older libraries, and if a certain type of visualization is not supported by one library, it can often be found in a newer one. Finally, the level of use refers to high or low-level libraries. High-level libraries are easier to use, however, they offer less flexibility than low-level libraries.

**Popular Data Visualization Libraries**

The following Python libraries are recommended for performing data visualization:

**Matplotlib** is the most popular library for plotting and is part of the SciPy stack. It was first released in 2003 and offers a wide range of graphs such as histograms, line plots, 3D plots, and more. It integrates really well with Jupyter Notebooks so you can use it inside code cells to directly show the results of your analysis. The Matplotlib homepage has a gallery that shows what you can do with it, so it’s helpful if you’re not sure yet which type of data visualizations fits your data and message best. It offers a lot of flexibility to developers.

**Seaborn** is built on top of Matplotlib. It integrates really well with Pandas and is meant to simplify doing data visualization. This is why it’s often used in data science tutorials in favor of Matplotlib. It also works well with visualizing pandas DataFrames, unlike Matplotlib.

**Bokeh** is not part of the SciPy stack and takes a different approach than the first two libraries. You can use it to create interactive plots and transform them into JSON so data can be presented to a web browser. This can also be visuals made with Matplotlib or Seaborn. With Bokeh, you can build complex statistical plots quickly using little code, but it has relatively fewer visualization options when compared to D3.js, a popular open-source JavaScript multipurpose language.

Another interesting option is **Plotly**, an online collaborative data analysis, and graphing tool. Python users can download a free Python API that gives access to all of Plotly’s functionality from Python, both online and offline.

**Conclusion**

In this blog post, we covered seven popular Python libraries for data analysis and visualization. We started with the SciPy stack, consisting of powerful libraries such as Numpy and Pandas. These are all indispensable libraries that offer mathematical and numerical algorithms, in combination with powerful data objects that optimize complex and large computations.

Because Python offers separate libraries for data analysis and visualization, many additional libraries are available for added functionality. The most popular visualization library is Matplotlib, which has been around for fifteen years now and is part of the SciPy stack. However, beginning data scientists might prefer Seaborn, a library built on top of Matplotlib that simplifies creating a great variety of data visualizations.

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