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Aggregations in Python

How to use aggregates in Python with Plotly.

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

In [1]:
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

In [2]:
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

In [3]:
import plotly.io as pio

import pandas as pd

df = pd.read_csv("https://plot.ly/~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

In [4]:
import plotly.io as pio

import pandas as pd

df = pd.read_csv("https://raw.githubusercontent.com/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://plot.ly/python/reference/ for more information and chart attribute options!