multiple-transforms.md

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jupytext kernelspec language_info plotly notebook_metadata_filter text_representation all extension format_name format_version jupytext_version .md markdown 1.1 1.1.7 display_name language name Python 3 python python3 codemirror_mode file_extension mimetype name nbconvert_exporter pygments_lexer version name version ipython 3 .py text/x-python python python ipython3 3.6.5 description display_as language layout name order page_type permalink thumbnail How to use multiple transforms (filter, group by, and aggregates) in Python with Plotly. transforms python base Multiple Transforms 4 example_index python/multiple-transforms/ thumbnail/multiple-transforms.jpg

Note transforms are deprecated in plotly v5 and will be removed in a future version.

Filter and Group By

import plotly.io as pio

import pandas as pd

df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/gapminderDataFiveYear.csv")

colors = ['blue', 'orange', 'green', 'red', 'purple']

opt = []
opts = []
for i in range(0, len(colors)):
    opt = dict(
        target = df['continent'][[i]].unique(), value = dict(marker = dict(color = colors[i]))
    )
    opts.append(opt)

data = [dict(
  type = 'scatter',
  mode = 'markers',
  x = df['lifeExp'],
  y = df['gdpPercap'],
  text = df['continent'],
  hoverinfo = 'text',
  opacity = 0.8,
  marker = dict(
      size = df['pop'],
      sizemode = 'area',
      sizeref = 200000
  ),
  transforms = [
      dict(
        type = 'filter',
        target = df['year'],
        orientation = '=',
        value = 2007
      ),
      dict(
        type = 'groupby',
        groups = df['continent'],
        styles = opts
    )]
)]

layout = dict(
    yaxis = dict(
        type = 'log'
    )
)

fig_dict = dict(data=data, layout=layout)
pio.show(fig_dict, validate=False)

Filter and Aggregate

import plotly.io as pio
import pandas as pd

df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/gapminderDataFiveYear.csv")

data = [dict(
  type = 'scatter',
  mode = 'markers',
  x = df['lifeExp'],
  y = df['gdpPercap'],
  text = df['continent'],
  hoverinfo = 'text',
  opacity = 0.8,
  marker = dict(
      size = df['pop'],
      sizemode = 'area',
      sizeref = 200000
  ),
  transforms = [
      dict(
        type = 'filter',
        target = df['year'],
        orientation = '=',
        value = 2007
      ),
      dict(
        type = 'aggregate',
        groups = df['continent'],
        aggregations = [
            dict(target = 'x', func = 'avg'),
            dict(target = 'y', func = 'avg'),
            dict(target = 'marker.size', func = 'sum')
        ]
      )]
)]

layout = dict(
    yaxis = dict(
        type = 'log'
    )
)


fig_dict = dict(data=data, layout=layout)

pio.show(fig_dict, validate=False)

All Transforms

import plotly.io as pio

import pandas as pd

df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/gapminderDataFiveYear.csv")

colors = ['blue', 'orange', 'green', 'red', 'purple']

opt = []
opts = []
for i in range(0, len(colors)):
    opt = dict(
        target = df['continent'][[i]].unique(), value = dict(marker = dict(color = colors[i]))
    )
    opts.append(opt)

data = [dict(
  type = 'scatter',
  mode = 'markers',
  x = df['lifeExp'],
  y = df['gdpPercap'],
  text = df['continent'],
  hoverinfo = 'text',
  opacity = 0.8,
  marker = dict(
      size = df['pop'],
      sizemode = 'area',
      sizeref = 200000
  ),
  transforms = [
      dict(
        type = 'filter',
        target = df['year'],
        orientation = '=',
        value = 2007
      ),
      dict(
        type = 'groupby',
        groups = df['continent'],
        styles = opts
      ),
      dict(
        type = 'aggregate',
        groups = df['continent'],
        aggregations = [
            dict(target = 'x', func = 'avg'),
            dict(target = 'y', func = 'avg'),
            dict(target = 'marker.size', func = 'sum')
        ]
      )]
)]

layout = dict(
    title = '<b>Gapminder</b><br>2007 Average GDP Per Cap & Life Exp. by Continent',
    yaxis = dict(
        type = 'log'
    )
)

fig_dict = dict(data=data, layout=layout)
pio.show(fig_dict, validate=False)

Reference

See https://plotly.com/python/reference/ for more information and chart attribute options!