Bokeh is a powerful framework for data visualization in Python. Here I take a look at straightforward plotting and visualization using this powerful library.
Credit to PythonHow. You can check him out on Youtube
bokeh.models #It is a low level interface which involves a lot of work
bokeh.plotting # It is a middle level interface
bokeh.chart # It is a high level interface
Lets try to create scatter plot
from bokeh.charts import Scatter, output_file, show
import pandas as pd
lets create a dataframe and add some values to it for our plot
df = pd.DataFrame(columns=['X','Y'])
lets add some values to the X and Y axis
df['X'] = ['1','2','3','4','5']#
df['Y'] = ['5','6','4','5','3']
lets store the scatterplot in a variable
p = Scatter(df, x='X', y='Y', title='Temperature observation', xlabel='Day of Observation', ylabel='Temperature', logo=None)
we can save our file as html using the output_file method and we can also show our plot using the show method
output_file('scatterplot.html')
show(p)
lets now plot using the bokeh.plotting interface
lets import the figure object from the plotting interface as that is the main object we will be using to plot. the idea is that you make an empty figure and you add objects to it
from bokeh.plotting import figure, output_file, show
lets store the figure object in a variable. we can set the with and height of the figure or we can leave it blank to use defaut with
and height
p = figure(plot_width=500, plot_height= 500, title='EarthQuakes',logo=None) # logo = None removes the bokeh.logo
you can also customise the plot¶
p.title_text_color = 'orange'
p.title_text_font_style = 'italic'
p.title_text_font = 'times'
p.yaxis.axis_label = 'Values'
p.xaxis.axis_label = 'Time'
let’s use the circle method to plot our scatter plots. the circle method takes array of X and Y values. You can also set the size of the scatter circles
p.circle(df['X'],df['Y'], size = [i*2 for i in ['2','4','3','6','9']] , color ='green', alpha=0.5) #alpha controls opacity
#p.triangle(df['X'],df['Y'], size = 8 , color ='green', alpha=0.5) #alpha controls opacity
You can use a list to determine the size each X,Y pair of plot
output_file('scatterplotting.html')
show(p)
you can get a full HELP with the p variable by typing help(p)
help(p)
lets plot some time series data
df = pd.read_csv('stock.csv',parse_dates=['Date'])
p = figure(width=500, height=300, x_axis_type='datetime', responsive=False) # declare the x-axis as a time series axis
responsive = True enlarges the whole graph to cover the screen
lets plot with a line graph
p.line(df['Date'],df['Close'], alpha=0.5)
p.line(df['Date'],df['Open'], alpha=0.5, color='red')
p.circle(df['Date'],df['Adj Close'], alpha=0.5, color='green')
lets save the file and also show it
output_file('linegraph.html')
show(p)
People Who Read The Above Post Also Read This:
Python Pandas Pivot Table Index location Percentage calculation on Two columns – XlsxWriter pt2
Python Bokeh plotting Data Exploration Visualization And Pivot Tables Analysis
Save Multiple Pandas DataFrames to One Single Excel Sheet Side by Side or Dowwards – XlsxWriter
Matplotlib Pyplot Plt Python Pandas Data Visualization Plotting
