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5 Awesome Data Visualization Libraries in Python
Exploratory data analysis with visualization libraries
Data visualization is a very important aspect of exploratory data analysis. It tells a lot about the data without getting deep into numbers. And as the name suggests — data visualization visualizes the data and tells a part of the story about the data.
This article majorly describes the five data visualization libraries in terms of Features, and Advantages.
The 5 visualization libraries are:
Let’s get started.
One mostly starts data visualization with matplotlib. This is the most basic library and the most important one too.
- It uses Numpy as its base.
- It is quite like MATLAB.
- Provides the most basic kind of plot in terms of beauty.
- Installing and importing:
This is the basic syntax. There are a lot of types of plots that matplotlib supports. These are line plots, scatterplots, histograms, bar graphs, and many more.
- It also has the functionality of subplots where multiple plots can be seen on one canvas.
- Provides great control over each element of the plot or graph
- Easy to get started for simple plots
- Supports customization of labels and texts
- Supports multi-format output
- It is a great way to make subplots when all the plots need to be seen on the same canvas
- It is very handy for MATLAB users
- Great start for beginners as a base
It is a very useful library for data analysis. Matplotlib is used as its base for this library. It is known for its beautification of graphs and plots.
- It uses plots like distplot, joint plot, pairplot, boxplot, and many more for statistical analysis.
- Installing and importing:
There are different plot types that seaborn uses and depending upon the type one can use one or two columns (x or x and y) for the plots.
- With the help of set_style() one can use different themes and further beautify the plots.
- It makes clear visualizations for statistical analysis
- Helps to find the relation between a categorical variable and a quantitative variable with the help of plots like box plot and bar plot
- It makes the correlation matrix easier to follow with the help of a heatmap.
- Offers easily customizable plots like distplot which is both a density plot as well as a histogram.
Another great and alternative library that data scientists may use for statistical visualizations is Altair.
It uses Vega and Vega-lite as a base.
- Dataframe is the type of data used for it
- Installing and importing:
Chart(data) is used to enter the data in the plot, mark_plottype() mentions the plottype and encode() gives the information about the columns or/and the shape which works the same way as hue does for the seaborn library discussed above.
- One just needs to give the data and column names to the syntax. Things like axis name are taken care of by it. So, no extra declarations are required.
- Easy and concise syntax
- Almost the same code for all types of plots with great flexibility.
- Works well with geographical data as well with the help of graticulate() function.
- Works well with data visualizations for averages as well with the help of built-in syntax for aggregation which might not be that easy with other libraries.
As the name suggests the library is used for visualizing the places where the data is missing.
Everyone working in the data science domain would have come across missing values and is the most common type of data issue. Treating missing values is one of the most important prerequisites before training data on a machine learning algorithm.
- It is typically used for finding missing values
- Plots like heatmap, bar plots, matrix, scatter plots and more help visualize the missing values in the data.
- Installing and Importing:
- Quite handy at the exploratory data analysis stage
- Saves time since one knows the exact missing value concentration through data visualizations and hence treatment becomes easier
- Very simple syntax
It is one of the few libraries dedicated to visualizing geospatial data. Geospatial data is data in the form of maps and locations. This kind of data is mostly retrieved from GPS, mobile devices, satellite images, and other resources.
The analysis made on geospatial data is termed geospatial analysis. It is quite a lot in the field of weather forecasting, telecommunications, City planning, and many more.
It requires the latitude and longitude of the location, and it offers a variety of tile styles on which the map styles depend
The marker function of the library helps mark a specific location
- Simple and easy to follow the syntax
- Features like markers help to further customize the maps
- Makes geospatial analysis easy as it is a dedicated library for it
Data visualization is one of the most important steps in building a data science model. A data scientist’s job is to tell a story with the help of data. Various data visualizations are used in this process. It’s a great way of telling a story that is understandable even by a common man. I have tried to jot some most useful libraries here used for data visualizations of statistical analysis, missing values, and geospatial data. It is a great set of libraries to start with data visualizations.
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