Easy Plotly
Project description
Easy Plotly
This is on-going research on how ploting with Plotly.py, especially ploting of hierarchical data, could be made easier.
See the outputs of the commands below - tables and plots -
in the HTML export of this notebook.
Or even, open this README.md
as a notebook and run it interactively on
Binder!
Installation
Install the easyplotly
python package with
pip install easyplotly
Sample data
Our sample data is the population and life expectancy, per country and region:
import world_bank_data as wb
import itables.interactive
# Collect countries
countries = wb.get_countries()
region_country = countries[['region', 'name']].rename(columns={'name': 'country'})
# Population & life expectancy
region_country['population'] = wb.get_series('SP.POP.TOTL', mrv=1, id_or_value='id', simplify_index=True)
region_country['life_expectancy'] = wb.get_series('SP.DYN.LE00.IN', mrv=1, id_or_value='id', simplify_index=True)
# Observations restricted to the countries
pop_and_exp = region_country.loc[countries.region != 'Aggregates'].set_index(['region', 'country']).sort_index()
pop_and_exp
Sunburst Charts
import plotly.graph_objects as go
import plotly.io as pio
import easyplotly as ep
pio.renderers.default = 'notebook_connected'
Our Sunburst
function accepts inputs of many types: pandas Series, dictionaries, and list of such objects.
If wanted, you can redefine labels
, or add other arguments like text
- use either a Series with an index
identical to that of values
, or a function that to any tuple (level0, level1, ... leveln)
associates the corresponding label or value.
sunburst = ep.Sunburst(pop_and_exp.population, text=pop_and_exp.life_expectancy, root_label='World')
layout = go.Layout(title='World Population and Life Expectancy<br>Data from the World Bank', height=800)
go.Figure(sunburst, layout)
Treemaps
The Treemap
function works like the Sunburst
one.
In this example we use a function to define the text associated to an entry in the map.
import numpy as np
def average(values, weights):
"""Same as np.average, but remove nans"""
total_obs = 0.
total_weight = 0.
if isinstance(values, np.float):
values = [values]
weights = [weights]
for x, w in zip(values, weights):
xw = x * w
if np.isnan(xw):
continue
total_obs += xw
total_weight += w
return total_obs / total_weight if total_weight != 0 else np.NaN
def text(item):
"""Return the text associated to a tuple like (), ('Europe & Central Asia') or ('East Asia & Pacific', 'China')"""
sub = pop_and_exp.loc[item] if item else pop_and_exp
pop = sub.population.sum()
if pop > 0:
life_exp = average(sub.life_expectancy, weights=sub.population)
return 'Population: {:,}<br>Life expectancy: {:.2f}'.format(int(pop) if pop > 0 else 0, life_exp)
treemap = ep.Treemap(pop_and_exp.population, text=text, root_label='World')
treemap.hoverinfo = 'label+text' # Remove value since it is already in the text
go.Figure(treemap, layout)
Sankey Plot
Plot links from a dict, or a series with a source/target multiindex:
links = {('A', 'B'): 3, ('B', 'C'): 1, ('B', 'D'): 2, ('C', 'A'): 1, ('D', 'A'): 1, ('A', 'D'): 1}
go.Figure(ep.Sankey(links))
Plot links from a DataFrame (sources as the index, targets as the columns):
import pandas as pd
links = pd.DataFrame(1, index=['Source A', 'Source B'], columns=['Target'])
go.Figure(ep.Sankey(links))
We conclude the examples with a plot in which the links are a list of pandas Series:
region_income = wb.get_countries().query("region != 'Aggregates'").copy()
region_income['population'] = wb.get_series('SP.POP.TOTL', mrv=1, id_or_value='id', simplify_index=True)
income_lending = region_income.copy()
region_income.set_index(['region', 'incomeLevel'], inplace=True)
income_lending.set_index(['incomeLevel', 'lendingType'], inplace=True)
sankey = ep.Sankey(
links=[region_income['population'], income_lending['population']],
link_labels=[region_income['name'], income_lending['name']]
)
layout = go.Layout(title='Regions income and lending type<br>Data from the World Bank')
go.Figure(sankey, layout)
Project details
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