Combined statistical representations with px.histogram¶

Several representations of statistical distributions are available in plotly, such as histograms, violin plots, box plots (see the complete list here). It is also possible to combine several representations in the same plot.

For example, the plotly.express function px.histogram can add a subplot with a different statistical representation than the histogram, given by the parameter marginal. Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures.

In [1]:

import plotly.express as px
df = px.data.tips()
fig = px.histogram(df, x="total_bill", y="tip", color="sex", marginal="rug",
                   hover_data=df.columns)
fig.show()

In [2]:

import plotly.express as px
df = px.data.tips()
fig = px.histogram(df, x="total_bill", y="tip", color="sex",
                   marginal="box", # or violin, rug
                   hover_data=df.columns)
fig.show()

Combined statistical representations in Dash¶

Dash is the best way to build analytical apps in Python using Plotly figures. To run the app below, run pip install dash, click "Download" to get the code and run python app.py.

Get started with the official Dash docs and learn how to effortlessly style & deploy apps like this with Dash Enterprise.

Out[3]:

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Combined statistical representations with distplot figure factory¶

The distplot figure factory displays a combination of statistical representations of numerical data, such as histogram, kernel density estimation or normal curve, and rug plot.

Basic Distplot¶

A histogram, a kde plot and a rug plot are displayed.

In [4]:

import plotly.figure_factory as ff
import numpy as np
np.random.seed(1)

x = np.random.randn(1000)
hist_data = [x]
group_labels = ['distplot'] # name of the dataset

fig = ff.create_distplot(hist_data, group_labels)
fig.show()

Plot Multiple Datasets¶

In [5]:

import plotly.figure_factory as ff
import numpy as np

# Add histogram data
x1 = np.random.randn(200) - 2
x2 = np.random.randn(200)
x3 = np.random.randn(200) + 2
x4 = np.random.randn(200) + 4

# Group data together
hist_data = [x1, x2, x3, x4]

group_labels = ['Group 1', 'Group 2', 'Group 3', 'Group 4']

# Create distplot with custom bin_size
fig = ff.create_distplot(hist_data, group_labels, bin_size=.2)
fig.show()

Use Multiple Bin Sizes¶

Different bin sizes are used for the different datasets with the bin_size argument.

In [6]:

import plotly.figure_factory as ff
import numpy as np

# Add histogram data
x1 = np.random.randn(200)-2
x2 = np.random.randn(200)
x3 = np.random.randn(200)+2
x4 = np.random.randn(200)+4

# Group data together
hist_data = [x1, x2, x3, x4]

group_labels = ['Group 1', 'Group 2', 'Group 3', 'Group 4']

# Create distplot with custom bin_size
fig = ff.create_distplot(hist_data, group_labels, bin_size=[.1, .25, .5, 1])
fig.show()

Customize Rug Text, Colors & Title¶

In [7]:

import plotly.figure_factory as ff
import numpy as np

x1 = np.random.randn(26)
x2 = np.random.randn(26) + .5

group_labels = ['2014', '2015']

rug_text_one = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j',
                'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't',
                'u', 'v', 'w', 'x', 'y', 'z']

rug_text_two = ['aa', 'bb', 'cc', 'dd', 'ee', 'ff', 'gg', 'hh', 'ii', 'jj',
                'kk', 'll', 'mm', 'nn', 'oo', 'pp', 'qq', 'rr', 'ss', 'tt',
                'uu', 'vv', 'ww', 'xx', 'yy', 'zz']

rug_text = [rug_text_one, rug_text_two] # for hover in rug plot
colors = ['rgb(0, 0, 100)', 'rgb(0, 200, 200)']

# Create distplot with custom bin_size
fig = ff.create_distplot(
    [x1, x2], group_labels, bin_size=.2,
    rug_text=rug_text, colors=colors)

fig.update_layout(title_text='Customized Distplot')
fig.show()

Plot Normal Curve¶

In [8]:

import plotly.figure_factory as ff
import numpy as np

x1 = np.random.randn(200)
x2 = np.random.randn(200) + 2

group_labels = ['Group 1', 'Group 2']

colors = ['slategray', 'magenta']

# Create distplot with curve_type set to 'normal'
fig = ff.create_distplot([x1, x2], group_labels, bin_size=.5,
                         curve_type='normal', # override default 'kde'
                         colors=colors)

# Add title
fig.update_layout(title_text='Distplot with Normal Distribution')
fig.show()

Plot Only Curve and Rug¶

In [9]:

import plotly.figure_factory as ff
import numpy as np

x1 = np.random.randn(200) - 1
x2 = np.random.randn(200)
x3 = np.random.randn(200) + 1

hist_data = [x1, x2, x3]

group_labels = ['Group 1', 'Group 2', 'Group 3']
colors = ['#333F44', '#37AA9C', '#94F3E4']

# Create distplot with curve_type set to 'normal'
fig = ff.create_distplot(hist_data, group_labels, show_hist=False, colors=colors)

# Add title
fig.update_layout(title_text='Curve and Rug Plot')
fig.show()

Plot Only Hist and Rug¶

In [10]:

import plotly.figure_factory as ff
import numpy as np

x1 = np.random.randn(200) - 1
x2 = np.random.randn(200)
x3 = np.random.randn(200) + 1

hist_data = [x1, x2, x3]

group_labels = ['Group 1', 'Group 2', 'Group 3']
colors = ['#835AF1', '#7FA6EE', '#B8F7D4']

# Create distplot with curve_type set to 'normal'
fig = ff.create_distplot(hist_data, group_labels, colors=colors, bin_size=.25,
                         show_curve=False)

# Add title
fig.update_layout(title_text='Hist and Rug Plot')
fig.show()

Plot Hist and Rug with Different Bin Sizes¶

In [11]:

import plotly.figure_factory as ff
import numpy as np

x1 = np.random.randn(200) - 2
x2 = np.random.randn(200)
x3 = np.random.randn(200) + 2

hist_data = [x1, x2, x3]

group_labels = ['Group 1', 'Group 2', 'Group 3']
colors = ['#393E46', '#2BCDC1', '#F66095']

fig = ff.create_distplot(hist_data, group_labels, colors=colors,
                         bin_size=[0.3, 0.2, 0.1], show_curve=False)

# Add title
fig.update(layout_title_text='Hist and Rug Plot')
fig.show()

Plot Only Hist and Curve¶

In [12]:

import plotly.figure_factory as ff
import numpy as np

x1 = np.random.randn(200) - 2
x2 = np.random.randn(200)
x3 = np.random.randn(200) + 2

hist_data = [x1, x2, x3]

group_labels = ['Group 1', 'Group 2', 'Group 3']
colors = ['#A56CC1', '#A6ACEC', '#63F5EF']

# Create distplot with curve_type set to 'normal'
fig = ff.create_distplot(hist_data, group_labels, colors=colors,
                         bin_size=.2, show_rug=False)

# Add title
fig.update_layout(title_text='Hist and Curve Plot')
fig.show()

Distplot with Pandas¶

In [13]:

import plotly.figure_factory as ff
import numpy as np
import pandas as pd

df = pd.DataFrame({'2012': np.random.randn(200),
                   '2013': np.random.randn(200)+1})
fig = ff.create_distplot([df[c] for c in df.columns], df.columns, bin_size=.25)
fig.show()

Reference¶

For more info on ff.create_distplot(), see the full function reference

What About Dash?¶

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.add_trace( ... )
# fig.update_layout( ... )

from dash import Dash, dcc, html

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

app.run_server(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter