Notebook

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Basic Box Plot¶

In [1]:

import plotly.plotly as py
import plotly.graph_objs as go

import numpy as np

y0 = np.random.randn(50)-1
y1 = np.random.randn(50)+1

trace0 = go.Box(
    y=y0
)
trace1 = go.Box(
    y=y1
)
data = [trace0, trace1]
py.iplot(data)

Out[1]:

Basic Horizontal Box Plot¶

In [2]:

import plotly.plotly as py
import plotly.graph_objs as go

import numpy as np

x0 = np.random.randn(50)
x1 = np.random.randn(50) + 2

trace0 = go.Box(x=x0)
trace1 = go.Box(x=x1)
data = [trace0, trace1]
py.iplot(data)

Out[2]:

Box Plot That Displays the Underlying Data¶

In [3]:

import plotly.plotly as py
import plotly.graph_objs as go

data = [
    go.Box(
        y=[0, 1, 1, 2, 3, 5, 8, 13, 21],
        boxpoints='all',
        jitter=0.3,
        pointpos=-1.8
    )
]
py.iplot(data)

Out[3]:

Colored Box Plot¶

In [4]:

import plotly.plotly as py
import plotly.graph_objs as go

import numpy as np

y0 = np.random.randn(50)
y1 = np.random.randn(50)+1

trace0 = go.Box(
    y=y0,
    name = 'Sample A',
    marker = dict(
        color = 'rgb(214, 12, 140)',
    )
)
trace1 = go.Box(
    y=y1,
    name = 'Sample B',
    marker = dict(
        color = 'rgb(0, 128, 128)',
    )
)
data = [trace0, trace1]
py.iplot(data)

Out[4]:

Box Plot Styling Mean & Standard Deviation¶

In [5]:

import plotly.plotly as py
import plotly.graph_objs as go

trace0 = go.Box(
    y=[2.37, 2.16, 4.82, 1.73, 1.04, 0.23, 1.32, 2.91, 0.11, 4.51, 0.51, 3.75, 1.35, 2.98, 4.50, 0.18, 4.66, 1.30, 2.06, 1.19],
    name='Only Mean',
    marker=dict(
        color='rgb(8, 81, 156)',
    ),
    boxmean=True
)
trace1 = go.Box(
    y=[2.37, 2.16, 4.82, 1.73, 1.04, 0.23, 1.32, 2.91, 0.11, 4.51, 0.51, 3.75, 1.35, 2.98, 4.50, 0.18, 4.66, 1.30, 2.06, 1.19],
    name='Mean & SD',
    marker=dict(
        color='rgb(10, 140, 208)',
    ),
    boxmean='sd'
)
data = [trace0, trace1]
py.iplot(data)

Out[5]:

Styling Outliers¶

In [6]:

import plotly.plotly as py
import plotly.graph_objs as go

trace0 = go.Box(
    y = [0.75, 5.25, 5.5, 6, 6.2, 6.6, 6.80, 7.0, 7.2, 7.5, 7.5, 7.75, 8.15, 
       8.15, 8.65, 8.93, 9.2, 9.5, 10, 10.25, 11.5, 12, 16, 20.90, 22.3, 23.25],
    name = "All Points",
    jitter = 0.3,
    pointpos = -1.8,
    boxpoints = 'all',
    marker = dict(
        color = 'rgb(7,40,89)'),
    line = dict(
        color = 'rgb(7,40,89)')
)

trace1 = go.Box(
    y = [0.75, 5.25, 5.5, 6, 6.2, 6.6, 6.80, 7.0, 7.2, 7.5, 7.5, 7.75, 8.15, 
        8.15, 8.65, 8.93, 9.2, 9.5, 10, 10.25, 11.5, 12, 16, 20.90, 22.3, 23.25],
    name = "Only Whiskers",
    boxpoints = False,
    marker = dict(
        color = 'rgb(9,56,125)'),
    line = dict(
        color = 'rgb(9,56,125)')
)

trace2 = go.Box(
    y = [0.75, 5.25, 5.5, 6, 6.2, 6.6, 6.80, 7.0, 7.2, 7.5, 7.5, 7.75, 8.15, 
        8.15, 8.65, 8.93, 9.2, 9.5, 10, 10.25, 11.5, 12, 16, 20.90, 22.3, 23.25],
    name = "Suspected Outliers",
    boxpoints = 'suspectedoutliers',
    marker = dict(
        color = 'rgb(8,81,156)',
        outliercolor = 'rgba(219, 64, 82, 0.6)',
        line = dict(
            outliercolor = 'rgba(219, 64, 82, 0.6)',
            outlierwidth = 2)),
    line = dict(
        color = 'rgb(8,81,156)')
)

trace3 = go.Box(
    y = [0.75, 5.25, 5.5, 6, 6.2, 6.6, 6.80, 7.0, 7.2, 7.5, 7.5, 7.75, 8.15, 
        8.15, 8.65, 8.93, 9.2, 9.5, 10, 10.25, 11.5, 12, 16, 20.90, 22.3, 23.25],
    name = "Whiskers and Outliers",
    boxpoints = 'outliers',
    marker = dict(
        color = 'rgb(107,174,214)'),
    line = dict(
        color = 'rgb(107,174,214)')
)

data = [trace0,trace1,trace2,trace3]

layout = go.Layout(
    title = "Box Plot Styling Outliers"
)

fig = go.Figure(data=data,layout=layout)
py.iplot(fig, filename = "Box Plot Styling Outliers")

Out[6]:

Grouped Box Plots¶

In [7]:

import plotly.plotly as py
import plotly.graph_objs as go

x = ['day 1', 'day 1', 'day 1', 'day 1', 'day 1', 'day 1',
     'day 2', 'day 2', 'day 2', 'day 2', 'day 2', 'day 2']

trace0 = go.Box(
    y=[0.2, 0.2, 0.6, 1.0, 0.5, 0.4, 0.2, 0.7, 0.9, 0.1, 0.5, 0.3],
    x=x,
    name='kale',
    marker=dict(
        color='#3D9970'
    )
)
trace1 = go.Box(
    y=[0.6, 0.7, 0.3, 0.6, 0.0, 0.5, 0.7, 0.9, 0.5, 0.8, 0.7, 0.2],
    x=x,
    name='radishes',
    marker=dict(
        color='#FF4136'
    )
)
trace2 = go.Box(
    y=[0.1, 0.3, 0.1, 0.9, 0.6, 0.6, 0.9, 1.0, 0.3, 0.6, 0.8, 0.5],
    x=x,
    name='carrots',
    marker=dict(
        color='#FF851B'
    )
)
data = [trace0, trace1, trace2]
layout = go.Layout(
    yaxis=dict(
        title='normalized moisture',
        zeroline=False
    ),
    boxmode='group'
)
fig = go.Figure(data=data, layout=layout)
py.iplot(fig)

Out[7]:

Grouped Horizontal Box Plot¶

In [8]:

import plotly.plotly as py
import plotly.graph_objs as go

data = [
    {
        'x': [0.2, 0.2, 0.6, 1.0, 0.5, 0.4, 0.2, 0.7, 0.9, 0.1, 0.5, 0.3],
        'y': ['day 1', 'day 1', 'day 1', 'day 1', 'day 1', 'day 1', 'day 2', 'day 2', 'day 2', 'day 2', 'day 2', 'day 2'],
        'name':'kale',
        'marker': {
            'color': '#3D9970'
        },
        'boxmean': False,
        'orientation': 'h',
        "type": "box",
    },
    {
        'x': [0.6, 0.7, 0.3, 0.6, 0.0, 0.5, 0.7, 0.9, 0.5, 0.8, 0.7, 0.2],
        'y': ['day 1', 'day 1', 'day 1', 'day 1', 'day 1', 'day 1', 'day 2', 'day 2', 'day 2', 'day 2', 'day 2', 'day 2'],
        'name': 'radishes',
        'marker':{
            'color': '#FF4136',
        },
        'boxmean': False,
        'orientation': 'h',
        "type": "box",
    },
    {
        'x': [0.1, 0.3, 0.1, 0.9, 0.6, 0.6, 0.9, 1.0, 0.3, 0.6, 0.8, 0.5],
        'y': ['day 1', 'day 1', 'day 1', 'day 1', 'day 1', 'day 1', 'day 2', 'day 2', 'day 2', 'day 2', 'day 2', 'day 2'],
        'name':'carrots',
        'marker': {
            'color': '#FF851B',
        },
        'boxmean': False,
        'orientation': 'h',
        "type": "box",
    }
]
layout = {
    'xaxis': {
        'title': 'normalized moisture',
        'zeroline': False,
    },
    'boxmode': 'group',
}
fig = go.Figure(data=data, layout=layout)

py.iplot(fig)

Out[8]:

Rainbow Box Plots¶

In [9]:

import random
import plotly.plotly as py

from numpy import * 

N = 30     # Number of boxes

# generate an array of rainbow colors by fixing the saturation and lightness of the HSL representation of colour 
# and marching around the hue. 
# Plotly accepts any CSS color format, see e.g. http://www.w3schools.com/cssref/css_colors_legal.asp.
c = ['hsl('+str(h)+',50%'+',50%)' for h in linspace(0, 360, N)]

# Each box is represented by a dict that contains the data, the type, and the colour. 
# Use list comprehension to describe N boxes, each with a different colour and with different randomly generated data:
data = [{
    'y': 3.5*sin(pi * i/N) + i/N+(1.5+0.5*cos(pi*i/N))*random.rand(10), 
    'type':'box',
    'marker':{'color': c[i]}
    } for i in range(int(N))]

# format the layout
layout = {'xaxis': {'showgrid':False,'zeroline':False, 'tickangle':60,'showticklabels':False},
          'yaxis': {'zeroline':False,'gridcolor':'white'},
          'paper_bgcolor': 'rgb(233,233,233)',
          'plot_bgcolor': 'rgb(233,233,233)',
          }

py.iplot(data)

Out[9]:

Fully Styled Box Plots¶

In [10]:

import plotly.plotly as py
import plotly.graph_objs as go

x_data = ['Carmelo Anthony', 'Dwyane Wade',
          'Deron Williams', 'Brook Lopez',
          'Damian Lillard', 'David West',]

y0 = np.random.randn(50)-1
y1 = np.random.randn(50)+1
y2 = np.random.randn(50)
y3 = np.random.randn(50)+2
y4 = np.random.randn(50)-2
y5 = np.random.randn(50)+3

y_data = [y0,y1,y2,y3,y4,y5]

colors = ['rgba(93, 164, 214, 0.5)', 'rgba(255, 144, 14, 0.5)', 'rgba(44, 160, 101, 0.5)', 'rgba(255, 65, 54, 0.5)', 'rgba(207, 114, 255, 0.5)', 'rgba(127, 96, 0, 0.5)']

traces = []

for xd, yd, cls in zip(x_data, y_data, colors):
        traces.append(go.Box(
            y=yd,
            name=xd,
            boxpoints='all',
            jitter=0.5,
            whiskerwidth=0.2,
            fillcolor=cls,
            marker=dict(
                size=2,
            ),
            line=dict(width=1),
        ))

layout = go.Layout(
    title='Points Scored by the Top 9 Scoring NBA Players in 2012',
    yaxis=dict(
        autorange=True,
        showgrid=True,
        zeroline=True,
        dtick=5,
        gridcolor='rgb(255, 255, 255)',
        gridwidth=1,
        zerolinecolor='rgb(255, 255, 255)',
        zerolinewidth=2,
    ),
    margin=dict(
        l=40,
        r=30,
        b=80,
        t=100,
    ),
    paper_bgcolor='rgb(243, 243, 243)',
    plot_bgcolor='rgb(243, 243, 243)',
    showlegend=False
)

fig = go.Figure(data=traces, layout=layout)
py.iplot(fig)

Out[10]:

Dash Example¶

Dash is an Open Source Python library which can help you convert plotly figures into a reactive, web-based application. Below is a simple example of a dashboard created using Dash. Its source code can easily be deployed to a PaaS.

In [1]:

from IPython.display import IFrame
IFrame(src= "https://dash-simple-apps.plotly.host/dash-boxplot/", width="100%", height="650px", frameBorder="0")

Out[1]:

In [1]:

from IPython.display import IFrame
IFrame(src= "https://dash-simple-apps.plotly.host/dash-boxplot/code", width="100%", height=500, frameBorder="0")

Out[1]:

Reference¶

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

In [1]:

from IPython.display import display, HTML

display(HTML('<link href="//fonts.googleapis.com/css?family=Open+Sans:600,400,300,200|Inconsolata|Ubuntu+Mono:400,700" rel="stylesheet" type="text/css" />'))
display(HTML('<link rel="stylesheet" type="text/css" href="http://help.plot.ly/documentation/all_static/css/ipython-notebook-custom.css">'))

! pip install git+https://github.com/plotly/publisher.git --upgrade
import publisher
publisher.publish(
    'box.ipynb', 'python/box-plots/', 'Box Plots | plotly',
    'How to make Box Plots in Python with Plotly.',
    title = 'Box Plots | plotly',
    name = 'Box Plots',
    has_thumbnail='true', thumbnail='thumbnail/box.jpg', 
    language='python', page_type='example_index',
    display_as='statistical', order=3,
    ipynb='~notebook_demo/20')  

Collecting git+https://github.com/plotly/publisher.git
  Cloning https://github.com/plotly/publisher.git to /tmp/pip-req-build-65mlpoiq
Building wheels for collected packages: publisher
  Running setup.py bdist_wheel for publisher ... done
  Stored in directory: /tmp/pip-ephem-wheel-cache-sgtb8hkk/wheels/99/3e/a0/fbd22ba24cca72bdbaba53dbc23c1768755fb17b3af0f33966
Successfully built publisher
Installing collected packages: publisher
  Found existing installation: publisher 0.13
    Uninstalling publisher-0.13:
      Successfully uninstalled publisher-0.13
Successfully installed publisher-0.13
You are using pip version 10.0.1, however version 19.1.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.

In [ ]: