Python – Data visualization using Bokeh
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.
# import modules from bokeh.plotting import figure, output_notebook, show # output to notebook output_notebook() # create figure p = figure(plot_width = 400, plot_height = 400) # add a circle renderer with # size, color and alpha p.circle([1, 2, 3, 4, 5], [4, 7, 1, 6, 3], size = 10, color = "navy", alpha = 0.5) # show the results show(p) |
Output :
Code #2: Single line
To create a single line, line() method is used.
# import modules from bokeh.plotting import figure, output_notebook, show # output to notebook output_notebook() # create figure p = figure(plot_width = 400, plot_height = 400) # add a line renderer p.line([1, 2, 3, 4, 5], [3, 1, 2, 6, 5], line_width = 2, color = "green") # show the results show(p) |
Output :
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.
# import necessary modules import pandas as pd from bokeh.charts import Bar, output_notebook, show # output to notebook output_notebook() # read data in dataframe df = pd.read_csv(r"D:/kaggle/mcdonald/menu.csv") # create bar p = Bar(df, "Category", values = "Calories", title = "Total Calories by Category", legend = "top_right") # show the results show(p) |
Output :
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.
# import necessary modules from bokeh.charts import BoxPlot, output_notebook, show import pandas as pd # output to notebook output_notebook() # read data in dataframe df = pd.read_csv(r"D:/kaggle / mcdonald / menu.csv") # create bar p = BoxPlot(df, values = "Protein", label = "Category", color = "yellow", title = "Protein Summary (grouped by category)", legend = "top_right") # show the results show(p) |
Output :
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.
# import necessary modules from bokeh.charts import Histogram, output_notebook, show import pandas as pd # output to notebook output_notebook() # read data in dataframe df = pd.read_csv(r"D:/kaggle / mcdonald / menu.csv") # create histogram p = Histogram(df, values = "Total Fat", title = "Total Fat Distribution", color = "navy") # show the results show(p) |
Output :
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.
# import necessary modules from bokeh.charts import Scatter, output_notebook, show import pandas as pd # output to notebook output_notebook() # read data in dataframe df = pd.read_csv(r"D:/kaggle / mcdonald / menu.csv") # create scatter plot p = Scatter(df, x = "Carbohydrates", y = "Saturated Fat", title = "Saturated Fat vs Carbohydrates", xlabel = "Carbohydrates", ylabel = "Saturated Fat", color = "orange") # show the results show(p) |
Output :
