Getting Started with Data Visualization in Python

Data visualization is one of the sectors in IT that is gaining importance each day. A myriad of data visualization tools are available in the market today. Not surprisingly, many of these tools are built in Python. If you want to implement data visualization, one of the best ways is to hire a Python developer or contract a Python development company and get started with it.

Understanding data visualization

Data visualization is used to represent data in a presentable and organized manner. It comes into the picture usually when you are dealing with huge amounts of data. It becomes difficult to analyze data in its raw format. That is where data visualization is useful as it helps analyze the trends and patterns in the data if any.

Data visualization using Python

So many Python tools like Matplotlib, Seaborn, Plotly, and so on are available for data visualization. Hire a Python programmer and you get the benefits of implementing data visualization along with them. These visualization tools can be used along with Python web development services very easily. Let us have a look at some of the best Python data visualization tools to understand this better.

Best Python libraries to use

The list of tools here is by no means exhaustive. There are more tools available for data visualization. However, we will go over the ones that are most popular and easy to implement.

Matplotlib

Matplotlib is one of the first data visualization libraries that were introduced. It is old and a bit complex to use. However, it is still pretty popular, thanks to its ability to provide graphs and charts in a variety of formats across multiple platforms. It is still considered one of the most powerful visualization tools. It can be used in iPython shells, Jupyter notebooks, and Python scripts as well. Some of the benefits of Matplotlib are as follows:

1. Includes a lot of toolkits like 3D plotting
2. It is open source with an active developer community
3. Has an interface similar to MATLAB
4. Can be used to generate bar graphs, pie charts, plots, and many more
5. Has a set of functions to control line styles, axes and font properties, and so on

Seaborn

Seaborn library is based on Matplotlib. The graphs and plots created using Seaborn are a little more sophisticated and look better. It uses a high-level interface to create attractive and informative graphs. Seaborn is mainly used for statistical visualizations. The best part of Seaborn is that it can generate complex visualizations with fewer commands compared to Matplotlib. Let’s take a look at its benefits:

1. A big range of settings to process the graphs and charts
2. Sophisticated color palette
3. It can show information from matrices and Dataframes
4. It can display data relationships and distributions easily
5. Easy to display data relationships and distributions
6. Can also show some complex visualizations like violin plots and time series

Folium

Folium specializes in visualizing geospatial data. Folium is useful while working with data concerning cities, countries, and so on. If you want to understand the relationship between the location and geographical context, Folium is a go-to tool. Here are some of the important benefits of Folium:

1. It visualizes data in the form of maps
2. This library has highly interactive and is very easy to learn
3. It has various sets within the library like Mapbox, Cloudmade, Chloropleth, and so on for more specific plots

Bokeh

Bokeh is another tool that is capable of providing highly interactive plots with diverse graphics. It can scale this capability on very large data sets. It has a huge range of charts, styling options, widgets, and a lot of other options. It also allows you to work on geospatial data. Some of the important advantages of Bokeh are listed here:

1. Capable of creating network graph visualizations
2. It helps create sophisticated interactions with the help of widgets and planning
3. It can handle categorical data with techniques like heatmaps
4. It can display interactive plots inside Jupyter notebooks
5. Adding new features with custom extensions is possible in Bokeh
6. It has options to use interactive tools like zoom and select the plots

Plotly

Plotly is probably the most popular library used when we talk about interactive plots. You can choose from a wide range of line plots, scatter plots, error bars, bar charts, 3D visualization, polar charts, bubble charts, and more! Here are some exciting benefits of Plotly:

1. It can be used in online or offline mode on Jupyter
2. It supports multiple linked views and animation
3. It is able to graph and visualize almost all types of data
4. It has a variety of interactive graphs
5. It has a range of charts like financial charts, multiple axes, subplots, and more

Altair

Altair is a statistical visualization tool based on Vega and Vega-lite. This tool is very user-friendly as it does not require a lot of coding. You essentially need to declare the links between data columns and the visual encoding channels. The rest of the procedure will be automatically handled by Altair. Let’s take a look at the benefits of Altair:

• The plots can be pretty interactive
• It transforms or filters data in the process of visualizing the data
• It can automatically choose appropriate plot settings configurations. This helps you focus on data more than plotting mechanics
• A big set of charts is available like histograms, area charts, interactive charts, and so on

How to choose a library

Although there are a lot of similar kinds of charts available in all these libraries, they have their unique features too. Which tool to use will mainly depend on your desired outcome. For example, if you want interactive maps, it’s best to use Folium. If you require charts with heavy interactivity, you can use Bokeh.

Your decision has to be in alignment with the benefits that the library has to offer. Also, do not forget, there are more tools like ggplot, Pygal, Pydot, and more which have their own set of features to help you visualize the data. You can make an informed decision after gaining basic information about the libraries and the features they provide.

Read the top benefits and challenges in implementing data visualization.

Conclusion

We can see how heavily Python is used in a lot of data visualization tools and how user-friendly it is. If you don’t possess the skills you can hire a python developer to meet data visualization needs for your company processes. You can also get in touch with a Python development company, to get customized and trendy software solutions developed in Python for your specific needs. VLink offers Python web development services as well to their clients. If you don’t have a permanent requirement and don’t want to hire a Python programmer, hire python developers through VLink!