# Aggregations in Python

How to use aggregates in Python with Plotly.

New to Plotly?

Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

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

#### 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 :
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 :
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.aggregations.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]),
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 :
import plotly.io as pio

import pandas as pd

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'
),
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 :
import plotly.io as pio

import pandas as pd

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.aggregations.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,
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¶

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.update_layout( ... )

import dash
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash()
app.layout = html.Div([
dcc.Graph(figure=fig)
]) 