Bokeh is a data visualization library in Python that provides high-performance interactive charts and plots. Bokeh output can be obtained in various mediums like notebook, html and server. It is possible to embed bokeh plots in Django and flask apps.
Bokeh provides two visualization interfaces to users:
bokeh.models : A low level interface that provides high flexibility to application developers.
bokeh.plotting : A high level interface for creating visual glyphs.
To install bokeh package, run the following command in the terminal:
pip install bokeh
The dataset used for generating bokeh graphs is collected from Kaggle.
Code #1: Scatter Markers
To create scatter circle markers, circle() method is used.
Output :
👁 Image
Code #2: Single line
To create a single line, line() method is used.
Output :
👁 Image
Code #3: Bar Chart
Bar chart presents categorical data with rectangular bars. The length of the bar is proportional to the values that are represented.
Output :
👁 Image
Code #4: Box Plot
Box plot is used to represent statistical data on a plot. It helps to summarize statistical properties of various data groups present in the data.
Output :
👁 Image
Code #5: Histogram
Histogram is used to represent distribution of numerical data. The height of a rectangle in a histogram is proportional to the frequency of values in a class interval.
Output :
👁 Image
Code #6: Scatter plot
Scatter plot is used to plot values of two variables in a dataset. It helps to find correlation among the two variables that are selected.
Output :
👁 Image
