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easyplotly-0.1.3
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مشاهده بیشترتوضیحات
Easy Plotly
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | easyplotly-0.1.3 |
| نام | easyplotly |
| نسخه کتابخانه | 0.1.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Marc Wouts |
| ایمیل نویسنده | marc.wouts@gmail.com |
| آدرس صفحه اصلی | https://github.com/mwouts/easyplotly |
| آدرس اینترنتی | https://pypi.org/project/easyplotly/ |
| مجوز | MIT |
# Easy Plotly
[](https://travis-ci.com/mwouts/easyplotly)
[](https://codecov.io/github/mwouts/easyplotly?branch=master)
[](https://lgtm.com/projects/g/mwouts/easyplotly/context:python)
[](https://pypi.python.org/pypi/easyplotly)
[](https://pypi.python.org/pypi/easyplotly)
[](
https://mybinder.org/v2/gh/mwouts/easyplotly/master?filepath=README.md)
[](https://mwouts.github.io/easyplotly)
<a class="github-button" href="https://github.com/mwouts/easyplotly" data-icon="octicon-star" data-show-count="true" aria-label="Star mwouts/easyplotly on GitHub">Star</a>
This is on-going research on how ploting with [Plotly.py](https://github.com/plotly/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](https://mwouts.github.io/easyplotly/) of this notebook.
Or even, open this `README.md` as a notebook and run it interactively on
[Binder](https://mybinder.org/v2/gh/mwouts/easyplotly/master?filepath=README.md)!
## 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:
```python
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
```python
import plotly.graph_objects as go
import plotly.io as pio
import easyplotly as ep
pio.renderers.default = 'notebook_connected'
layout = go.Layout(title='World Population and Life Expectancy<br>Data from the World Bank', height=800)
```
Our `Sunburst` function accepts inputs of many types: pandas Series, dictionaries, and list of such objects.
If you want, 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.
```python
sunburst = ep.Sunburst(pop_and_exp.population, text=pop_and_exp.life_expectancy)
go.Figure(sunburst, layout)
```
## Treemaps
The `Treemap` function works like the `Sunburst` one:
```python
treemap = ep.Treemap(pop_and_exp.population, text=pop_and_exp.life_expectancy)
go.Figure(treemap, layout)
```
Just like the `Sunburst` function, it also accepts all the arguments supported by the original
`go.Sunburst` object. You're even welcome to use the
[magic underscore notation](https://plot.ly/python/creating-and-updating-figures/#magic-underscore-notation),
as we do below when we set `marker.colors` with `marker_colors`:
```python
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 life_expectancy(item):
"""Life expectancy 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
return average(sub.life_expectancy, weights=sub.population)
def text(item):
"""Return the text associated to a tuple like (), ('Europe & Central Asia') or ('East Asia & Pacific', 'China')"""
life_exp = life_expectancy(item)
if life_exp > 0:
pop = pop_and_exp.population.loc[item].sum() if item else pop_and_exp.population.sum()
return 'Population: {:,}<br>Life expectancy: {:.2f}'.format(int(pop), life_exp)
treemap = ep.Treemap(pop_and_exp.population,
hoverinfo='label+text',
text=text,
root_label='World',
# magic underscore notation
marker_colors=life_expectancy,
marker_colorscale='RdBu')
go.Figure(treemap, layout)
```
## Sankey Plot
Plot links from a dict, or a series with a source/target multiindex:
```python
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):
```python
import pandas as pd
```
```python
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:
```python
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)
layout = go.Layout(title='Regions income and lending type<br>Data from the World Bank')
sankey = ep.Sankey(
link_value=[region_income['population'], income_lending['population']],
link_label=[region_income['name'], income_lending['name']])
go.Figure(sankey, layout)
```
نحوه نصب
نصب پکیج whl easyplotly-0.1.3:
pip install easyplotly-0.1.3.whl
نصب پکیج tar.gz easyplotly-0.1.3:
pip install easyplotly-0.1.3.tar.gz