def show_grid_plot(self):
# Remove rows with NaN values and then map standard states to colors
elements.dropna(
inplace=True
) # if inplace is not set to True the changes are not written to the dataframe
colormap = {'gas': 'yellow', 'liquid': 'orange', 'solid': 'red'}
elements['color'] = [colormap[x] for x in elements['standard state']]
elements['size'] = elements['van der Waals radius'] / 10
# Create three ColumnDataSources for elements of unique standard states
gas = ColumnDataSource(elements[elements['standard state'] == 'gas'])
liquid = ColumnDataSource(
elements[elements['standard state'] == 'liquid'])
solid = ColumnDataSource(
elements[elements['standard state'] == 'solid'])
# Define the output file path
output_file("elements_gridplot.html")
# Create the figure object
f1 = figure()
# adding glyphs
f1.circle(x="atomic radius",
y="boiling point",
size='size',
fill_alpha=0.2,
color="color",
legend='Gas',
source=gas)
f2 = figure()
f2.circle(x="atomic radius",
y="boiling point",
size='size',
fill_alpha=0.2,
color="color",
legend='Liquid',
source=liquid)
f3 = figure()
f3.circle(x="atomic radius",
y="boiling point",
size='size',
fill_alpha=0.2,
color="color",
legend='Solid',
source=solid)
f = gridplot([[f1, f2], [None, f3]])
# Save and show the figure
show(f)
def show_elements(self):
elements.dropna(
inplace=True
) # if inplace is not set to True the changes are not written to the dataframe
colormap = {'gas': 'yellow', 'liquid': 'orange', 'solid': 'red'}
elements['color'] = [colormap[x] for x in elements['standard state']]
elements['size'] = elements["van der Waals radius"] / 10
# Create three ColumnDataSources for elements of unique standard states
gas = ColumnDataSource(elements[elements['standard state'] == 'gas'])
liquid = ColumnDataSource(
elements[elements['standard state'] == 'liquid'])
solid = ColumnDataSource(
elements[elements['standard state'] == 'solid'])
# Define the output file path
output_file("elements.html")
# adding glyphs
self.f.circle(x="atomic radius",
y="boiling point",
size='size',
fill_alpha=0.2,
color="color",
legend='Gas',
source=gas)
self.f.circle(x="atomic radius",
y="boiling point",
size='size',
fill_alpha=0.2,
color="color",
legend='Liquid',
source=liquid)
self.f.circle(x="atomic radius",
y="boiling point",
size='size',
fill_alpha=0.2,
color="color",
legend='Solid',
source=solid)
self.f.xaxis.axis_label = "Atomic radius"
self.f.yaxis.axis_label = "Boiling point"
# Save and show the figure
self.__show__()
"""
#Plotting periodic table elements
#Importing libraries
from bokeh.plotting import figure
from bokeh.io import output_file, show
from bokeh.layouts import gridplot
from bokeh.sampledata.periodic_table import elements
from bokeh.models import Range1d, PanTool, ResetTool, HoverTool, ColumnDataSource, LabelSet
import pandas
from numpy import nan
# Drop missing elements
elements.dropna(inplace=True)
# Add color map
colormap = {'gas': 'yellow', 'liquid': 'orange', 'solid': 'red', nan: 'grey'}
# Add element color
elements['color'] = [colormap[x] for x in elements['standard state']]
# Add element size
elements['size'] = elements['van der Waals radius'] / 10
# Bokeh ColumnDataSource
gas = ColumnDataSource(elements[elements['standard state'] == 'gas'])
liquid = ColumnDataSource(elements[elements['standard state'] == 'liquid'])
solid = ColumnDataSource(elements[elements['standard state'] == 'solid'])
#Define the output file path
output_file("elements.html")
from bokeh.plotting import figure
from bokeh.io import curdoc
from bokeh.sampledata.periodic_table import elements
from bokeh.models import Range1d, PanTool, ResetTool, HoverTool, ColumnDataSource, LabelSet, Select
from bokeh.models.annotations import Span, BoxAnnotation, Label, LabelSet
from bokeh.layouts import layout
#Remove rows with NaN values and then map standard states to colors
elements.dropna(
inplace=True
) #if inplace is not set to True the changes are not written to the dataframe
colormap = {'gas': 'green', 'liquid': 'blue', 'solid': 'red'}
elements['color'] = [colormap[x] for x in elements['standard state']]
elements['size'] = elements["van der Waals radius"] / 10
#Create three ColumnDataSources for elements of unique standard states
gas = ColumnDataSource(elements[elements['standard state'] == 'gas'])
liquid = ColumnDataSource(elements[elements['standard state'] == 'liquid'])
solid = ColumnDataSource(elements[elements['standard state'] == 'solid'])
#Create the figure object
f = figure()
#adding glyphs
f.circle(x="atomic radius",
y="boiling point",
size='size',
fill_alpha=0.2,
color="color",
legend='Gas',
source=gas)
from bokeh.sampledata.periodic_table import elements
from bokeh.io import output_file, show
from bokeh.models import Range1d, PanTool, ResetTool, HoverTool, ColumnDataSource
from bokeh.plotting import figure
output_file(
"C:\Users\labuser\Documents\Python_Data_Analysis\Learning_Bokeh\Output\elements.html"
)
f = figure()
elements = elements.dropna()
colormap = {'gas': 'yellow', 'liquid': 'orange', 'solid': 'red'}
elements['color'] = [colormap[x] for x in elements['standard state']]
gas = ColumnDataSource(elements[elements['standard state'] == 'gas'])
liquid = ColumnDataSource(elements[elements['standard state'] == 'liquid'])
solid = ColumnDataSource(elements[elements['standard state'] == 'solid'])
f.circle(x="atomic radius",
y="boiling point",
size=[i / 10 for i in gas.data["van der Waals radius"]],
fill_alpha=0.2,
color="color",
line_dash=[5, 3],
legend='Gas',
source=gas)
f.circle(x="atomic radius",
y="boiling point",
size=[i / 10 for i in liquid.data["van der Waals radius"]],
def ex_7_create_label_annotations_for_span(self):
# Remove rows with NaN values and then map standard states to colors
elements.dropna(
inplace=True
) # if inplace is not set to True the changes are not written to the dataframe
colormap = {'gas': 'yellow', 'liquid': 'orange', 'solid': 'red'}
elements['color'] = [colormap[x] for x in elements['standard state']]
elements['size'] = elements['van der Waals radius'] / 10
# Create three ColumnDataSources for elements of unique standard states
gas = ColumnDataSource(elements[elements['standard state'] == 'gas'])
liquid = ColumnDataSource(
elements[elements['standard state'] == 'liquid'])
solid = ColumnDataSource(
elements[elements['standard state'] == 'solid'])
# Define the output file path
output_file("elements_annotations.html")
# Create the figure object
f = figure()
# adding glyphs
f.circle(x="atomic radius",
y="boiling point",
size='size',
fill_alpha=0.2,
color="color",
legend='Gas',
source=gas)
f.circle(x="atomic radius",
y="boiling point",
size='size',
fill_alpha=0.2,
color="color",
legend='Liquid',
source=liquid)
f.circle(x="atomic radius",
y="boiling point",
size='size',
fill_alpha=0.2,
color="color",
legend='Solid',
source=solid)
# Add axis labels
f.xaxis.axis_label = "Atomic radius"
f.yaxis.axis_label = "Boiling point"
# Calculate the average boiling point for all three groups by dividing the sum by the number of values
gas_average_boil = sum(gas.data['boiling point']) / len(
gas.data['boiling point'])
liquid_average_boil = sum(liquid.data['boiling point']) / len(
liquid.data['boiling point'])
solid_average_boil = sum(solid.data['boiling point']) / len(
solid.data['boiling point'])
# Create three spans
span_gas_average_boil = Span(location=gas_average_boil,
dimension='width',
line_color='yellow',
line_width=2)
span_liquid_average_boil = Span(location=liquid_average_boil,
dimension='width',
line_color='orange',
line_width=2)
span_solid_average_boil = Span(location=solid_average_boil,
dimension='width',
line_color='red',
line_width=2)
# Add spans to the figure
f.add_layout(span_gas_average_boil)
f.add_layout(span_liquid_average_boil)
f.add_layout(span_solid_average_boil)
# Add labels to spans
label_span_gas_average_boil = Label(x=80,
y=gas_average_boil,
text="Gas average boiling point",
render_mode="css",
text_font_size="10px")
label_span_liquid_average_boil = Label(
x=80,
y=liquid_average_boil,
text="Liquid average boiling point",
render_mode="css",
text_font_size="10px")
label_span_solid_average_boil = Label(
x=80,
y=solid_average_boil,
text="Solid average boiling point",
render_mode="css",
text_font_size="10px")
# Add labels to figure
f.add_layout(label_span_gas_average_boil)
f.add_layout(label_span_liquid_average_boil)
f.add_layout(label_span_solid_average_boil)
# Save and show the figure
show(f)
def exercise_8_creating_span_annotations_depicting_averages(self):
# start Bokeh server by: bokeh serve server.py
# Remove rows with NaN values and then map standard states to colors
elements.dropna(
inplace=True
) # if inplace is not set to True the changes are not written to the dataframe
colormap = {'gas': 'yellow', 'liquid': 'orange', 'solid': 'red'}
elements['color'] = [colormap[x] for x in elements['standard state']]
elements[
'van der Waals radius'] = elements['van der Waals radius'] / 10
# Create three ColumnDataSources for elements of unique standard states
gas = ColumnDataSource(elements[elements['standard state'] == 'gas'])
liquid = ColumnDataSource(
elements[elements['standard state'] == 'liquid'])
solid = ColumnDataSource(
elements[elements['standard state'] == 'solid'])
# Create the figure object
f = figure()
# adding glyphs
size_list = [i / 10 for i in gas.data["van der Waals radius"]]
f.circle(x="atomic radius",
y="boiling point",
size="van der Waals radius",
fill_alpha=0.2,
color="color",
legend='Gas',
source=gas)
f.circle(x="atomic radius",
y="boiling point",
size="van der Waals radius",
fill_alpha=0.2,
color="color",
legend='Liquid',
source=liquid)
f.circle(x="atomic radius",
y="boiling point",
size="van der Waals radius",
fill_alpha=0.2,
color="color",
legend='Solid',
source=solid)
# Add axis labels
f.xaxis.axis_label = "Atomic radius"
f.yaxis.axis_label = "Boiling point"
# Calculate the average boiling point for all three groups by dividing the sum by the number of values
gas_average_boil = sum(solid.data['boiling point']) / len(
solid.data['boiling point'])
liquid_average_boil = sum(liquid.data['boiling point']) / len(
liquid.data['boiling point'])
solid_average_boil = sum(solid.data['boiling point']) / len(
solid.data['boiling point'])
solid_min_boil = min(solid.data['boiling point'])
solid_max_boil = max(solid.data['boiling point'])
# Create three spans
span_gas_average_boil = Span(location=gas_average_boil,
dimension='width',
line_color='yellow',
line_width=2)
span_liquid_average_boil = Span(location=liquid_average_boil,
dimension='width',
line_color='orange',
line_width=2)
span_solid_boil = Span(
location=solid_average_boil,
dimension='width',
line_color='red',
line_width=2
) # Location for this span will be updated when the Select widget is changed by the user
# Add spans to the figure
f.add_layout(span_gas_average_boil)
f.add_layout(span_liquid_average_boil)
f.add_layout(span_solid_boil)
# Create a function that updates the location attribute value for span_solid_boil span
# Also note that select.value returns values as strings so we need to convert the returned value to float
def update_span(attr, old, new):
span_solid_boil.location = float(select.value)
# Select widgets expect a list of tuples of strings, so List[Tuple(String, String)], that's why you should convert average, max, and min to strings
options = [(str(solid_average_boil), "Solid Average Boiling Point"),
(str(solid_min_boil), "Solid Minimum Boiling Point"),
(str(solid_max_boil), "Solid Maximum Boiling Point")]
# Create the Select widget
select = Select(title="Select span value", options=options)
select.on_change("value", update_span)
# Add Select widget to layout and then the layout to curdoc
lay_out = layout([[select]])
curdoc().add_root(f)
curdoc().add_root(lay_out)
