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Python Libraries That Are Essential For Data Visualization
Data Visualization is a crucial task in the data modeling process of Data Science. By using advanced Data visualization tools like Tableau, Data Scientists will be able to effectively communicate their findings from Big Data to the business stakeholders.
Speaking of Data Visualization, there are plenty of libraries in Python than support Data Visualization operations. These libraries are so overwhelming that one would often get confused about what to use. Work towards gaining hands-on expertise in handling Data Visualization in Data Science by joining for the best Data Science Training In Hyderabad program by Kelly Technologies.
Now, let’s have a look at the list of most extensively used Python libraries for Data Visualization operations.
PANDAS-
This is an advanced library in Python that supports advanced analytics operations like Data Analysis, Data Manipulation & Data Visualization operations. Working on Pandas is very easy. Most of the Data Scientists are very much comfortable to work with Pandas for Data Visualization operations as it is having in-built options for plotting visualization in two dimension tabular style.
Using Pandas, we can present attractive visuals which are easy to read. One of its major drawbacks is that it doesn’t support customization of graph.
MATPLOTLIB
MATPLOTLIB is a powerful Python library in Python that supports visualization algorithms. Data Scientists will find it ideal to effectively present their findings from the data in the form of attractive visuals. One of the major benefits of using MATPLOTLIB is that it can be easily handled by beginners as it is having extensive documentation.
With MATPLOTLIB, visualizations can be customized in the aspects like arbitrary colors, shapes, line type or marker, transparency level, and many more. If you are a beginner in Data Science, then working in MATPLOTLIB for Data Visualization operations will be the ideal choice.
SEABORN
One of the major drawbacks of using Matplotlib library makes it difficult for users with low-level API. Data Scientists need to write several lines of generic code in order to present the visuals in attractive infographic format. This is where SEABORN library is gaining an edge over MATPLOTLIB. Using SEABORN, it becomes possible to create highly attractive visuals which can be customized.
Get to know more about the other Python prominent libraries in Python that support Data Visualization.