Different python libraries which can be used for data visualization

When we enter the world of data science, we see that it is filled with datasets, machine learning algorithms and data visualization and so on. We see that data is really important to make decisions and predict the future trends with the help of machine learning and deep learning.

When we are dealing with data, it is also equally important to know our data before giving it to the machine learning algorithms. Sometimes, the data might contain bias which might not represent certain cases of interest. In addition, there might be instances where some features from the data might not be that useful compared to the others. Hence, we might have to delete a few features to avoid the curse of dimensionally. Moreover, we might get a very good insight just by looking at the data and understanding it before we do any sort of analysis with the machine learning models and their predictions. Therefore, it is important that we analyze and understand our data before we give it to the machine learning models for predictions respectively.

One of the easiest ways at which data could be understood is with the help of visualization. Human beings are visual creatures and they are able to easily interpret the data with the help of visual plots with little effort. When we look at the tabular data, however, we cannot get a good understanding of the different features and compare them. Thus, when we see the raw data with just the numbers and nothing to give a good insight, we might be a bit confused and overwhelmed by the size of the data under consideration. When we use data visualization, however, we see that there is a great reduction in the effort that must be put by a data scientist or a machine learning engineer to understand it. Thus, we would be working with data visualization and the different libraries in python so that we can get a good understanding of the data visualization techniques that could be used. Learning these libraries and implementing them in real-life would save a lot of time and effort and ensures that one gets a good understanding of the data at hand. Let us now look at different data visualization libraries in python.

Data Visualization Libraries in Python

We have different libraries in python that could be used for data visualization. There might be times when some of the features in the data might not be very important and this could be found with the help of data visualization respectively. Below are a few data visualization libraries that we would be focusing to get a good understanding of them.

1. Matplotlib
2. Plotly
3. Seaborn
4. Pygal
5. Altair
6. Bokeh
7. Ggplot
8. Geoplotlib

Let us go through each of the following libraries so that we get a good understanding of the machine learning libraries that could be used for data visualization respectively.

1. Matplotlib

Matplotlib can be used for plotting 2D plots and 3D plots in python respectively. One could be able to plot different plots such as scatterplot, histograms and line plots respectively. One of the drawbacks while using the Matplotlib is that it is low level code. In other words, we have to write everything from scratch so that we get the desired results. Using other libraries such as Seaborn ensures that the code is high level and we are able to get the best results with less code.

Matplotlib figure from towardsdatascience.com

2. Plotly

Plotly is a good interactive library that could be used in the analysis of our datasets respectively. In addition, Plotly could also be used in visualizing the browser based data so that we get a good understanding. The library could be used for plots that could be made available for publishing and it provides a good interaction between the user and the plots. Sometimes, it might take some time to load a high dimensional and high examples dataset. However, it could be used for small-scale visualization where the number of features of our dataset are not many and where there are just a few examples in our data respectively.

Plotly from Statworx.com

3. Seaborn

Seaborn could be used to plot and visualize the data. It is a high level library that is built on top of Matplotlib and can be used to perform good visualization. We see many types of Seaborn plots such as scatterplots, bar plots and violin plots respectively.

Seaborn plots from medium.com

4. Pygal

Pygal is a good data visualization library that could be used for creating scalable vector graphics (SVG) files. In addition to this, it could also be used in creating PNG format files when we are dealing with data that is large. Therefore, we can be using this library for different machine learning visualization plots and when we want to work with SVG file format.

Pygal figure from https://dev.to/dev0928/explore-python-libraries-pygal-with-covid-data-30c3

5. Altair

We have a few visualization libraries such as Vega and Vega-lite. Altair is built on top of Vega and Vega-lite which means that we would be able to perform high level operations with the help of Altair using Vega and Vega-lite. Furthermore, Altair could be used for creating interactive web application visualizations so that they could be uploaded on the internet. Therefore, it would be very easy to use the features of Vega and Vega-lite with the help of Altair respectively.

Altair figure from https://medium.com/analytics-vidhya/exploratory-data-visualisation-with-altair-b8d85494795c

6. Bokeh

Sometimes in the process of data visualization, there might be issues such as performance where the data visualization can take a long time to get it generated. As a result, there could be a lag in the data visualization when we are using certain libraries for visualization. When we use Bokeh, on the other hand, we see that it can perform the visualization of large datasets with a short span of time, meaning that it would have high performance respectively.

Bokeh figure from https://towardsdatascience.com/interactive-plotting-with-bokeh-ea40ab10870

7. Ggplot

There is a specific library that could be used to create the data visualization that is similar to the visualization from R where we use Ggplot2 respectively. Therefore, we would be using a library that is known as 'plotnine' which is very much similar to Ggplot2 library in python. We would take advantage of plotnine library to create good plots as can be seen in the figure below.

Ggplot from https://towardsdatascience.com/ggplot-grammar-of-graphics-in-python-with-plotnine-2e97edd4dacf

8. Geoplotlib

Geoplotlib library could be used in plotting the geographical plots so that we would be able to understand and see the graph data that is usually not possible with the other libraries mentioned above. Therefore, we can use the Geoplotlib for understanding the map and understand various trends depending on the data that we have taken into consideration.

Geoplotlib figure from https://deepai.org/publication/geoplotlib-a-python-toolbox-for-visualizing-geographical-data

Conclusion

We have seen different libraries that could be used for data visualization in python. There are a few libraries that could be used which are similar to R libraries such as Plotnine. In addition to this, we also understand the importance of data visualization in understanding our data. Hope this article helps. Feel free to share your thoughts. Thanks.