aggregations.md

jupyter

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 aggregates in Python with Plotly. transforms python base Aggregations 3 example_index python/aggregations/ thumbnail/aggregations.jpg

Introduction

Aggregates are a type of transform that can be applied to values in a given expression. Available aggregations are:

Function	Description
count	Returns the quantity of items for each group.
sum	Returns the summation of all numeric values.
avg	Returns the average of all numeric values.
median	Returns the median of all numeric values.
mode	Returns the mode of all numeric values.
rms	Returns the rms of all numeric values.
stddev	Returns the standard deviation of all numeric values.
min	Returns the minimum numeric value for each group.
max	Returns the maximum numeric value for each group.
first	Returns the first numeric value for each group.
last	Returns the last numeric value for each group.

Basic Example

import plotly.io as pio

subject = ['Moe','Larry','Curly','Moe','Larry','Curly','Moe','Larry','Curly','Moe','Larry','Curly']
score = [1,6,2,8,2,9,4,5,1,5,2,8]

data = [dict(
  type = 'scatter',
  x = subject,
  y = score,
  mode = 'markers',
  transforms = [dict(
    type = 'aggregate',
    groups = subject,
    aggregations = [dict(
        target = 'y', func = 'sum', enabled = True),
    ]
  )]
)]

fig_dict = dict(data=data)

pio.show(fig_dict, validate=False)

Aggregate Functions

import plotly.io as pio

subject = ['Moe','Larry','Curly','Moe','Larry','Curly','Moe','Larry','Curly','Moe','Larry','Curly']
score = [1,6,2,8,2,9,4,5,1,5,2,8]

aggs = ["count","sum","avg","median","mode","rms","stddev","min","max","first","last"]

agg = []
agg_func = []
for i in range(0, len(aggs)):
    agg = dict(
        args=['transforms[0].aggregations[0].func', aggs[i]],
        label=aggs[i],
        method='restyle'
    )
    agg_func.append(agg)


data = [dict(
  type = 'scatter',
  x = subject,
  y = score,
  mode = 'markers',
  transforms = [dict(
    type = 'aggregate',
    groups = subject,
    aggregations = [dict(
        target = 'y', func = 'sum', enabled = True)
    ]
  )]
)]

layout = dict(
  title = '<b>Plotly Aggregations</b><br>use dropdown to change aggregation',
  xaxis = dict(title = 'Subject'),
  yaxis = dict(title = 'Score', range = [0,22]),
  updatemenus = [dict(
        x = 0.85,
        y = 1.15,
        xref = 'paper',
        yref = 'paper',
        yanchor = 'top',
        active = 1,
        showactive = False,
        buttons = agg_func
  )]
)

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

pio.show(fig_dict, validate=False)

Histogram Binning

import plotly.io as pio

import pandas as pd

df = pd.read_csv("https://plotly.com/~public.health/17.csv")

data = [dict(
  x = df['date'],
  autobinx = False,
  autobiny = True,
  marker = dict(color = 'rgb(68, 68, 68)'),
  name = 'date',
  type = 'histogram',
  xbins = dict(
    end = '2016-12-31 12:00',
    size = 'M1',
    start = '1983-12-31 12:00'
  )
)]

layout = dict(
  paper_bgcolor = 'rgb(240, 240, 240)',
  plot_bgcolor = 'rgb(240, 240, 240)',
  title = '<b>Shooting Incidents</b>',
  xaxis = dict(
    title = '',
    type = 'date'
  ),
  yaxis = dict(
    title = 'Shootings Incidents',
    type = 'linear'
  ),
  updatemenus = [dict(
        x = 0.1,
        y = 1.15,
        xref = 'paper',
        yref = 'paper',
        yanchor = 'top',
        active = 1,
        showactive = True,
        buttons = [
        dict(
            args = ['xbins.size', 'D1'],
            label = 'Day',
            method = 'restyle',
        ), dict(
            args = ['xbins.size', 'M1'],
            label = 'Month',
            method = 'restyle',
        ), dict(
            args = ['xbins.size', 'M3'],
            label = 'Quater',
            method = 'restyle',
        ), dict(
            args = ['xbins.size', 'M6'],
            label = 'Half Year',
            method = 'restyle',
        ), dict(
            args = ['xbins.size', 'M12'],
            label = 'Year',
            method = 'restyle',
        )]
  )]
)

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

pio.show(fig_dict, validate=False)

Mapping with Aggregates

import plotly.io as pio

import pandas as pd

df = pd.read_csv("https://raw-hub.myxuebi.top/bcdunbar/datasets/master/worldhappiness.csv")

aggs = ["count","sum","avg","median","mode","rms","stddev","min","max","first","last"]

agg = []
agg_func = []
for i in range(0, len(aggs)):
    agg = dict(
        args=['transforms[0].aggregations[0].func', aggs[i]],
        label=aggs[i],
        method='restyle'
    )
    agg_func.append(agg)

data = [dict(
  type = 'choropleth',
  locationmode = 'country names',
  locations = df['Country'],
  z = df['HappinessScore'],
  autocolorscale = False,
  colorscale = 'Portland',
  reversescale = True,
  transforms = [dict(
    type = 'aggregate',
    groups = df['Country'],
    aggregations = [dict(
        target = 'z', func = 'sum', enabled = True)
    ]
  )]
)]

layout = dict(
  title = '<b>Plotly Aggregations</b><br>use dropdown to change aggregation',
  xaxis = dict(title = 'Subject'),
  yaxis = dict(title = 'Score', range = [0,22]),
  height = 600,
  width = 900,
  updatemenus = [dict(
        x = 0.85,
        y = 1.15,
        xref = 'paper',
        yref = 'paper',
        yanchor = 'top',
        active = 1,
        showactive = False,
        buttons = agg_func
  )]
)

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!