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 modulesfrom bokeh.plotting import figure, output_notebook, show # output to notebookoutput_notebook() # create figurep = figure(plot_width = 400, plot_height = 400) # add a circle renderer with# size, color and alphap.circle([1, 2, 3, 4, 5], [4, 7, 1, 6, 3], size = 10, color = "navy", alpha = 0.5) # show the resultsshow(p) |
Output :
Code #2: Single line
To create a single line, line() method is used.
# import modulesfrom bokeh.plotting import figure, output_notebook, show # output to notebookoutput_notebook() # create figurep = figure(plot_width = 400, plot_height = 400) # add a line rendererp.line([1, 2, 3, 4, 5], [3, 1, 2, 6, 5], line_width = 2, color = "green") # show the resultsshow(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 modulesimport pandas as pdfrom bokeh.charts import Bar, output_notebook, show # output to notebookoutput_notebook() # read data in dataframedf = pd.read_csv(r"D:/kaggle/mcdonald/menu.csv") # create barp = Bar(df, "Category", values = "Calories", title = "Total Calories by Category", legend = "top_right") # show the resultsshow(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 modulesfrom bokeh.charts import BoxPlot, output_notebook, showimport pandas as pd # output to notebookoutput_notebook() # read data in dataframedf = pd.read_csv(r"D:/kaggle / mcdonald / menu.csv") # create barp = BoxPlot(df, values = "Protein", label = "Category", color = "yellow", title = "Protein Summary (grouped by category)", legend = "top_right") # show the resultsshow(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 modulesfrom bokeh.charts import Histogram, output_notebook, showimport pandas as pd # output to notebookoutput_notebook() # read data in dataframedf = pd.read_csv(r"D:/kaggle / mcdonald / menu.csv") # create histogramp = Histogram(df, values = "Total Fat", title = "Total Fat Distribution", color = "navy") # show the resultsshow(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 modulesfrom bokeh.charts import Scatter, output_notebook, showimport pandas as pd # output to notebookoutput_notebook() # read data in dataframedf = pd.read_csv(r"D:/kaggle / mcdonald / menu.csv") # create scatter plotp = Scatter(df, x = "Carbohydrates", y = "Saturated Fat", title = "Saturated Fat vs Carbohydrates", xlabel = "Carbohydrates", ylabel = "Saturated Fat", color = "orange") # show the resultsshow(p) |
Output :
References: https://bokeh.pydata.org/en/latest/
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