Skip to main content

Easy Plotly

Project description

Easy Plotly

Build Status codecov.io Language grade: Python Pypi pyversions Jupyter Notebook GitHub.io Star

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


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for easyplotly, version 0.1.0
Filename, size File type Python version Upload date Hashes
Filename, size easyplotly-0.1.0.tar.gz (6.1 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page