Data_Visualization

DataVisualization Using pyplot

Data visualization is the process of presenting the information in form of various charts and graphs. It simplifies complex data making it easier to identify patterns, analyze trends and discover actionable insights.One of the greatest benefits of visualization is that it allows us visual access to huge amounts of data in easily digestible visuals.

Matplotlib

Matplotlib is one of the most popular uses for Python in data analysis. Naturally, data scientists want a way to visualize their data. Either they are wanting to see it for themselves to get a better grasp of the data, or they want to display the data to convey their results to someone.

Numpy

Another library that helps in the process of plotting graphs/charts using pyplot in NumPy. NumPy stands for numerical python. NumPy is the core library for scientific computing in python. It provides a highperformance multidimensional array object and tools for working with these arrays. Using NumPy, a developer can perform the following operations:

• Mathematical and logical operations on the array.
• Transforms and routines for shape manipulation.
• Operations related to linear algebra. NumPy has inbuilt functions for linear algebra and random number generation.

Basic Visualization rules

There are some basic rules we should be aware of before we proceed to plot.

1. We have to choose as appropriate plot type
2. when we choose the type of plot, one of the most important is to label the axis. if we don’t do this, the plot is not informative enough. When there are no axis labels, we can try to look at the code to see what data is used and we can understand the plot.
3. we can add a title to make out plot more informative
4. add labels for different categories when needed.
5. in some cases we can use some sizes and colors of the data to make the plot more informative.