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.
Code #2: Single line
To create a single line, line() method is used.
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.
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.
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.
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.
- Data Analysis and Visualization with Python | Set 2
- Data visualization with different Charts in Python
- Data analysis and Visualization with Python
- Pandas Built-in Data Visualization | ML
- Mandelbrot Fractal Set visualization in Python
- Understanding different Box Plot with visualization
- Box plot visualization with Pandas and Seaborn
- KDE Plot Visualization with Pandas and Seaborn
- Python for Data Science
- Inbuilt Data Structures in Python
- Python | Data analysis using Pandas
- Data Classes in Python | An Introduction
- Python | Pandas Index.data
- Exploratory Data Analysis in Python
- Python IDEs For Data Science
If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to email@example.com. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.