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1.1 |
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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 |
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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)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)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)See https://plot.ly/python/reference/ for more information and chart attribute options!