LINE CHART, PIE CHART AND BAR CHART 12th Computer Chapter - - • It makes complex data understandable and usable. • Various Charts in Data Visualization helps to show relationship in the data for one or more variables. Infographics → An infographic (information graphic) is the representation of information in a graphic format. Dashboard → A dashboard is a collection of resources assembled to create a single unified visual display.
Data visualizations and dashboards translate complex ideas and concepts into a simple visual format. Patterns and relationships that are undetectable in text are detectable at a glance using dashboard. Introduction to Matplotlib — Data Visualization in Python Matplotlib is the most popular data visualization library in Python. It allows you to create two dimension (2D) charts in few lines of code.
Types of Visualizations in Matplotlib There are many types of Visualizations under Matplotlib. Some of them are: • Line plot • Scatter plot • Histogram • Box plot • Bar chart and • Pie chart Scatter plot: A scatter plot is a type of plot that shows the data as a collection of points. The position of a point depends on its two-dimensional value, where each value is a position on either the horizontal or vertical dimension. Box plot: The box plot is a standardized way of displaying the distribution of data based on the five number summary: minimum, first quartile, median, third quartile, and maximum.
Installing Matplotlib You can install matplotlib using pip. Pip is a Package manager software for installing python packages. 12th Computer Chapter - - Data Visualization using Pyplot Detailed installation procedures given in Annexure - II Note Getting Started . After installing Matplotlib, we will begin coding by importing Matplotlib using the command: import matplotlib.pyplot as plt Now you have imported Matplotlib in your workspace.
You need to display the plots. Using Matplotlib from within a Python script, you have to add plt.show() function inside the file to display your plot. import matplotlib.pyplot as plt plt.plot([ , , , ]) plt.show() Output This window is a matplotlib window, which allows