Develop Reference

How to use and block a specific color for a location in plotly chloropeth map (python)

I'm trying to set a map of European country with their result in Eurovision.
I have a button to choose the different countries ( Italy, France, Portugal, UK , etc ...)
For Example, if I choose to see the Result of Sweden, I want to see on the map the numbers of points given by the others according to a color scale. I success to do it !
But I want to visualize Sweden, for example in black on the map, to better see where it is, and the "neighborhood effect of notation" .
fig3 = go.Figure(data=go.Choropleth(
locations=Euro_tr['Country_code'], # Spatial coordinates
z = Euro_tr['Italy'], # Data to be color-coded
locationmode = "ISO-3",
colorbar_title = "Points donnés",
text=Euro_tr['Country'],
))
fig3.update_layout(
title_text = 'Score Eurovision',
margin={"r":55,"t":55,"l":55,"b":55},
height=500,
geo_scope="europe" ,
)
#Make a button for each country
button=[]
for country in Euro_tr.columns[1:-1] :
dico=dict (
label=country,
method="update",
args = [{'z': [ Euro_tr[country] ] }],)
button.append(dico)
fig3.update_layout(
updatemenus=[
dict(
buttons=button,
y=0.9,
x=0,
xanchor='right',
yanchor='top',
active=0,
),
])
As you see in this example showing the points given to Sweden, I want Sweden to be in a specific color, independently to others countries, the ones that have given points and the ones that have given no points.
Thanks for your help !

I followed the answers from #vestland, and I succeed to put my country of interest in one color , independently to others by using fig.add_traces(go.Choropleth)
To have the possibility to change the data and the trace according to my country of interest, I use streamlit and buttons.
import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Creation of graphes for each country
Graphes=[]
for country in Euro_tr.columns[1:-1] : #To pass each country
Graphe=go.Figure(data=go.Choropleth(
locations=Euro_tr['Country_code'], # Spatial coordinates
z = Euro_tr[country], # Data to be color-coded
locationmode = "ISO-3",
colorbar_title = "Points donnés",
autocolorscale= False,
colorscale="viridis",
text=Euro_tr['Country'],
))
# customisation : title according to the country and its points
Graphe.update_layout(
title_text = "Total :Points donnés à {fcountry} qui a remporté {fpoints} points".format(fcountry = country, fpoints = Eurovision_tot['Result_tot'][Eurovision_tot["Country"]==country].values[0]),
margin={"r":55,"t":55,"l":55,"b":55},
height=500,
)
)
# block a specific zoom on the map ( scope "europe" isn't complete for eurovision countries xD!)
Graphe.update_geos(
center=dict(lon= 23, lat= 54),
lataxis_range=[31.0529,-40.4296], lonaxis_range=[-24, 88.2421],
projection_scale=3
)
# add trace for the specific country.
Graphe.add_traces(go.Choropleth(locations=Country_df['Country_code'][Country_df["Country"]==country],
z = [1],
colorscale = [[0, col_swe],[1, col_swe]],
colorbar=None,
showscale = False))
Graphes.append(Graphe)
#creation selectbox to select country
col12, col22 = st.beta_columns([0.2,0.8]) # I use columns to put the selector on a side and the graph on other side
Pays=list(Euro_tr.columns[1:-1]) # List containing country's name
Selection_Pays = col12.selectbox('',(Pays)) #create a multiple selector with the different countries as possible choice
# define action according to the selection.
for country in Pays :
if Selection_Pays== country : #if country is selected
col22.plotly_chart(Graphes[Pays.index(country)]) # plot the corresponding map.

Even though you've built your choropleth map using px.express you can always add a trace using plotly.graph_objects with fig.add_traces(go.Choropleth) such as this:
col_swe = 'Black'
fig.add_traces(go.Choropleth(locations=df_swe['iso_alpha'],
z = [1],
colorscale = [[0, col_swe],[1, col_swe]],
colorbar=None,
showscale = False)
)
To my knowledge it's not possible to define a single color for a single country directly, and thats why I've assigned z=[1] as a value, and a custom scale colorscale = [[0, col_swe],[1, col_swe]] to make sure that Sweden always is illustrated in 'Black'.
Code:
import plotly.express as px
import plotly.graph_objects as go
df = px.data.gapminder().query("year==2007")
fig = px.choropleth(df, locations="iso_alpha",
color="lifeExp", # lifeExp is a column of gapminder
hover_name="country", # column to add to hover information
color_continuous_scale=px.colors.sequential.Plasma)
df_swe = df[df['country']=='Sweden']
col_swe = 'Black'
fig.add_traces(go.Choropleth(locations=df_swe['iso_alpha'],
z = [1],
colorscale = [[0, col_swe],[1, col_swe]],
colorbar=None,
showscale = False)
)
f = fig.full_figure_for_development(warn=False)
fig.show()

Bokeh scatter plot: is it possible to overlay a line colored by category?

I have a dataframe that details sales of various product categories vs. time. I'd like to make a "line and marker" plot of sales vs. time, per category. To my surprise, this appears to be very difficult in Bokeh.
The scatter plot is easy. But then trying to overplot a line of sales vs. date with the same source (so I can update both scatter and line plots in one go when the source updates) and in such a way that the colors of the line match the colors of the scatter plot markers proves near impossible.
Minimal reproducible example with contrived data:
import pandas as pd
df = pd.DataFrame({'Date':['2020-01-01','2020-01-02','2020-01-01','2020-01-02'],\
'Product Category':['shoes','shoes','grocery','grocery'],\
'Sales':[100,180,21,22],'Colors':['red','red','green','green']})
df['Date'] = pd.to_datetime(df['Date'])
from bokeh.io import output_notebook
output_notebook()
from bokeh.io import output_file, show
from bokeh.plotting import figure
source = ColumnDataSource(df)
plot = figure(x_axis_type="datetime", plot_width=800, toolbar_location=None)
plot.scatter(x="Date",y="Sales",size=15, source=source, fill_color="Colors", fill_alpha=0.5, \
line_color="Colors",legend="Product Category")
for cat in list(set(source.data['Product Category'])):
tmp = source.to_df()
col = tmp[tmp['Product Category']==cat]['Colors'].values[0]
plot.line(x="Date",y="Sales",source=source, line_color=col)
show(plot)
Here's what it looks like, which is clearly wrong:
Here's what I want and don't know how to make:
Can Bokeh not make such plots, where scatter markers and lines have the same color per category, with a legend?

With bokeh it is often helpful to first think about the visualisation you want and then structuring the data source appropriately. You want two lines, on per category, the x axis is time and y axis is the sales. Then a natural way to structure your data source is the following:
df = pd.DataFrame({'Date':['2020-01-01','2020-01-02'],
'Shoe Sales':[100, 180],
'Grocery Sales': [21, 22]
})
from bokeh.io import output_notebook
output_notebook()
from bokeh.io import output_file, show
from bokeh.plotting import figure
source = ColumnDataSource(df)
plot = figure(x_axis_type="datetime", plot_width=800, toolbar_location=None)
categories = ["Shoe Sales", "Grocery Sales"]
colors = {"Shoe Sales": "red", "Grocery Sales": "green"}
for category in categories:
plot.scatter(x="Date",y=category,size=15, source=source, fill_color=colors[category], legend=category)
plot.line(x="Date",y=category,source=source, line_color=colors[category])
show(plot)

The solutions is to group your data. Then you can plot lines for each group.
Minimal Example
import pandas as pd
from bokeh.plotting import figure, show, output_notebook
output_notebook()
df = pd.DataFrame({'Date':['2020-01-01','2020-01-02','2020-01-01','2020-01-02'],
'Product Category':['shoes','shoes','grocery','grocery'],
'Sales':[100,180,21,22],'Colors':['red','red','green','green']})
df['Date'] = pd.to_datetime(df['Date'])
plot = figure(x_axis_type="datetime",
plot_width=400,
plot_height=400,
toolbar_location=None
)
plot.scatter(x="Date",
y="Sales",
size=15,
source=df,
fill_color="Colors",
fill_alpha=0.5,
line_color="Colors",
legend_field="Product Category"
)
for color in df['Colors'].unique():
plot.line(x="Date", y="Sales", source=df[df['Colors']==color], line_color=color)
show(plot)
Output

How to set initial zoom of bokeh box chart of pandas group with a large number of categories

I'm plotting covid-19 data for countries grouped by World Bank regions using pandas and Bokeh.
from bokeh.io import output_file, show
from bokeh.palettes import Spectral5
from bokeh.plotting import figure
from bokeh.transform import factor_cmap
group = data.groupby(["region", "CountryName"])
index_cmap = factor_cmap(
'region_CountryName',
palette=Spectral5,
factors=sorted(data.region.unique()),
end=1
)
p = figure(plot_width=800, plot_height=600, title="Confirmed cases per 100k people by country",
x_range=group, toolbar_location="left")
p.vbar(x='region_CountryName', top='ConfirmedPer100k_max', width=1, source=group,
line_color="white", fill_color=index_cmap, )
p.y_range.start = 0
p.xgrid.grid_line_color = None
p.xaxis.major_label_orientation = 3.14159/2
p.xaxis.group_label_orientation = 3.14159/2
p.outline_line_color = None
show(p)
And I get a
I would like to set some sort of initial zoom into the x-axis to get a more manageable image
, which I got by manually zooming in.
Any suggestions?

You should be able to accomplish this with the x_range parameter. In this example, the plot's x range would be the first 20 countries. You can adjust as needed. You might also have to mess around a bit to get the group_cn_list correct. It's hard to say without seeing your data. If you can post a df example for reproducibility, it would help.
group_cn_list = group["CountryName"].tolist()
p = figure(plot_width=800, plot_height=600, title="Confirmed cases per 100k people by country",
x_range=group_cn_list[0:20], toolbar_location="left")

Using Bokeh or ArcPy for labeling maps

I use Bokeh to make choropleth maps and to create other geographical graphics because Python spits it out a million times faster than I could ever create it in another way.
Right now I am doing something like:
from bokeh.io import show, output_file, export_png, output_notebook, output_file, save
from bokeh.models import ColumnDataSource, HoverTool, LabelSet
from bokeh.plotting import figure
from bokeh.sampledata.us_counties import data as counties
import pandas as pd
desired_counties=['Franklin County, Ohio','Fairfield County, Ohio']
counties2 = {code: county for code, county in counties.items() if county["detailed name"] in desired_counties}
unemploymentRate = {'Location': desired_counties, 'rate': [100, 100]} #placeholders
df = pd.DataFrame(data=unemploymentRate)
county_xs = [county["lons"] for county in counties2.values()]
county_ys = [county["lats"] for county in counties2.values()]
county_names = [county['name'] for county in counties2.values()]
unemployment = [df.loc[index,'rate'] for county in counties2.values() for index, row in df.iterrows() if df.loc[index,'Location']==county['detailed name']]
source = ColumnDataSource(data=dict(x=county_xs,y=county_ys,name=county_names,rate=unemployment))
TOOLS="hover,save"
p = figure(x_axis_location=None, y_axis_location=None, plot_width=350,plot_height=250,tools=TOOLS)
p.grid.grid_line_color = None
p.outline_line_color = None
p.patches('x', 'y', source=source,
fill_color='#007a33',
fill_alpha=1, line_color="black", line_width=3.5)
hover = p.select_one(HoverTool)
hover.point_policy = "follow_mouse"
hover.tooltips = [
("Name", "#name"),
("Rate","#rate{0.0}%")
]
labels = LabelSet(x='x', y='y', text='name', level='glyph',
x_offset=0, y_offset=0, source=source, render_mode='css')
p.add_layout(labels)
p.toolbar.logo = None
p.toolbar_location = None
show(p)
Which gives:
When I hover over the image I see the data I want, but what I would rather have is the data annotated on the image instead for a print possibility. Using the LabelSet class that Bokeh has in their documentation seems perfect, except it's geared toward xy plots and so when trying to use it here it just stacks the labels in the corner.
Questions:
Does anyone know how to label on a Bokeh choropleth without using outside graphics software?
I have ArcGIS Pro, which can obviously create this type of map, but I am looking for something that does it using programming. I use Bokeh right now because I can basically tell it a central county I want, and I have code written that makes me a map of the state highlighting the county I want and its surrounding counties, creates an inset map of the county/surrounding counties, and gives hover data on the inset map of the unemployment rate in those counties (the picture/code above is a simplified version of that). Is there a way to go about this using AcrPy/ArcGIS instead?

The reason your labels are misplaced is because you tell them to use fields 'x' and 'y' from the data source, but the data source does not have these columns. You could compute the "center" of each shape, and add those as the x and y columns to source, then things would display as you want.

Use of Linear Color mapper in Bokeh

I am intending to create a scatter plot with a linear colormapper. The dataset is the popular Female Literacy and Birthrate dataset.
The plot would have the "GDP per capita" on the x axis and "Life Expectancy at Birth" on the y axis. In addition to this (and this is where i am running into the issue), is to vary the color of the points according to "Birth rate".
Current Code:
#DATA MANIPULATION
# import Pandas, Bokeh, etc
import numpy as np
import pandas as pd
from bokeh.io import show, output_file
from bokeh.models import ColumnDataSource
from bokeh.palettes import Viridis256 as palette
from bokeh.plotting import figure
from bokeh.sampledata.autompg import autompg as df
from bokeh.transform import linear_cmap
# load the data file
excel_file = '../factbook.xlsx'
#(removed url above since it is private)
factbook = pd.read_excel(excel_file)
source = ColumnDataSource(factbook)
colormapper = linear_cmap(field_name = factbook["Birth rate"], palette=palette, low=min(factbook["Birth rate"]), high=max(factbook["Birth rate"]))
p = figure(title = "UN Factbook Bubble Visualization",
x_axis_label = 'GDP per capita', y_axis_label = 'Life expectancy at birth')
p.circle(x = 'GDP per capita', y = 'Life expectancy at birth', source = source, color =colormapper)
output_file("file", title="Bubble Graph")
show(p)
the p.circle line is having an issue with consuming the colormapper. I would like help on understanding how to resolve this.

The field_name parameter should be provided with the name of a column. You are supplying the entire data column itself. Since you have not provided a complete runnable example, it is impossible to test for sure, but presumably you want:
linear_cmap(field_name="Birth rate", ...)