Aggregations in Python/v3

How to use aggregates in Python with Plotly.

Note: this page is part of the documentation for version 3 of Plotly.py, which is not the most recent version.
See our Version 4 Migration Guide for information about how to upgrade.
The version 4 version of this page is here.

New to Plotly?¶

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We also have a quick-reference cheatsheet (new!) to help you get started!

Version Check¶

Plotly's python package is updated frequently. Run pip install plotly --upgrade to use the latest version.

In [1]:

import plotly
plotly.__version__

Out[1]:

'2.2.3'

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 [2]:

import plotly.offline as off

off.init_notebook_mode(connected=False)

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),
    ]
  )]
)]


off.iplot({'data': data}, validate=False)

Aggregate Functions¶

In [3]:

import plotly.offline as off

off.init_notebook_mode(connected=False)

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
  )]
)

off.iplot({'data': data,'layout': layout}, validate=False)

Histogram Binning¶

In [4]:

import plotly.offline as off

import pandas as pd

off.init_notebook_mode(connected=False)

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',
        )]
  )]
)

off.iplot({'data': data,'layout': layout}, validate=False)

Mapping with Aggregates¶

In [5]:

import plotly.offline as off

import pandas as pd

off.init_notebook_mode(connected=False)

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
  )]
)

off.iplot({'data': data,'layout': layout}, validate=False)

Reference¶

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