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# Histograms in Python

How to make Histograms in Python with Plotly.

## Histogram with plotly express¶

In statistics, a histogram is representation of the distribution of numerical data, where the data are binned and the count for each bin is represented. More generally, in plotly a histogram is an aggregated bar chart, with several possible aggregation functions (e.g. sum, average, count...). Also, the data to be binned can be numerical data but also categorical or date data.

Plotly Express functions take as a first argument a tidy pandas.DataFrame.

In [1]:
import plotly.express as px
tips = px.data.tips()
fig = px.histogram(tips, x="total_bill")
fig.show()

In [2]:
import plotly.express as px
tips = px.data.tips()
# Here we use a column with categorical data
fig = px.histogram(tips, x="day")
fig.show()


#### Choosing the number of bins¶

By default, the number of bins is chosen so that this number is comparable to the typical number of samples in a bin. This number can be customized, as well as the range of values.

In [3]:
import plotly.express as px
tips = px.data.tips()
fig = px.histogram(tips, x="total_bill", nbins=20)
fig.show()


#### Type of normalization¶

The default mode is to represent the count of samples in each bin. With the histnorm argument, it is also possible to represent the percentage or fraction of samples in each bin (histnorm='percent' or probability), or a density histogram (the sum of bars is equal to 100, density), or a probability density histogram (sum equal to 1, probability density).

In [4]:
import plotly.express as px
tips = px.data.tips()
fig = px.histogram(tips, x="total_bill", histnorm='probability density')
fig.show()


#### Aspect of the histogram plot¶

In [5]:
import plotly.express as px
tips = px.data.tips()
fig = px.histogram(tips, x="total_bill",
title='Histogram of bills',
labels={'total_bill':'total bill'}, # can specify one label per df column
opacity=0.8,
log_y=True, # represent bars with log scale
color_discrete_sequence=['indianred'] # color of histogram bars
)
fig.show()


#### Several histograms for the different values of one column¶

In [6]:
import plotly.express as px
tips = px.data.tips()
fig = px.histogram(tips, x="total_bill", color="sex")
fig.show()


#### Using histfunc¶

For each bin of x, one can compute a function of data using histfunc. The argument of histfunc is the dataframe column given as the y argument. Below the plot shows that the average tip increases with the total bill.

In [7]:
import plotly.express as px
tips = px.data.tips()
fig = px.histogram(tips, x="total_bill", y="tip", histfunc='avg')
fig.show()


#### Visualizing the distribution¶

With the marginal keyword, a subplot is drawn alongside the histogram, visualizing the distribution. See the distplot pagefor more examples of combined statistical representations.

In [8]:
import plotly.express as px
tips = px.data.tips()
fig = px.histogram(tips, x="total_bill", color="sex", marginal="rug", # can be box, violin
hover_data=tips.columns)
fig.show()


## Histograms with go.Histogram¶

When data are not available as tidy dataframes, it is also possible to use the more generic go.Histogram from plotly.graph_objects. All of the available histogram options are described in the histogram section of the reference page: https://plot.ly/python/reference#histogram.

### Basic Histogram¶

In [9]:
import plotly.graph_objects as go

import numpy as np

x = np.random.randn(500)

fig = go.Figure(data=[go.Histogram(x=x)])
fig.show()


### Normalized Histogram¶

In [10]:
import plotly.graph_objects as go

import numpy as np

x = np.random.randn(500)
fig = go.Figure(data=[go.Histogram(x=x, histnorm='probability')])

fig.show()


### Horizontal Histogram¶

In [11]:
import plotly.graph_objects as go

import numpy as np

y = np.random.randn(500)
# Use y argument instead of x for horizontal histogram

fig = go.Figure(data=[go.Histogram(y=y)])
fig.show()


### Overlaid Histogram¶

In [12]:
import plotly.graph_objects as go

import numpy as np

x0 = np.random.randn(500)
# Add 1 to shift the mean of the Gaussian distribution
x1 = np.random.randn(500) + 1

fig = go.Figure()
fig.add_trace(go.Histogram(x=x0))
fig.add_trace(go.Histogram(x=x1))

# Overlay both histograms
fig.update_layout(barmode='overlay')
# Reduce opacity to see both histograms
fig.update_traces(opacity=0.75)
fig.show()


### Stacked Histograms¶

In [13]:
import plotly.graph_objects as go

import numpy as np

x0 = np.random.randn(2000)
x1 = np.random.randn(2000) + 1

fig = go.Figure()
fig.add_trace(go.Histogram(x=x0))
fig.add_trace(go.Histogram(x=x1))

# The two histograms are drawn on top of another
fig.update_layout(barmode='stack')
fig.show()


### Styled Histogram¶

In [14]:
import plotly.graph_objects as go

import numpy as np
x0 = np.random.randn(500)
x1 = np.random.randn(500) + 1

fig = go.Figure()
fig.add_trace(go.Histogram(
x=x0,
histnorm='percent',
name='control', # name used in legend and hover labels
xbins=dict( # bins used for histogram
start=-4.0,
end=3.0,
size=0.5
),
marker_color='#EB89B5',
opacity=0.75
))
fig.add_trace(go.Histogram(
x=x1,
histnorm='percent',
name='experimental',
xbins=dict(
start=-3.0,
end=4,
size=0.5
),
marker_color='#330C73',
opacity=0.75
))

fig.update_layout(
title_text='Sampled Results', # title of plot
xaxis_title_text='Value', # xaxis label
yaxis_title_text='Count', # yaxis label
bargap=0.2, # gap between bars of adjacent location coordinates
bargroupgap=0.1 # gap between bars of the same location coordinates
)

fig.show()


### Cumulative Histogram¶

In [15]:
import plotly.graph_objects as go

import numpy as np

x = np.random.randn(500)
fig = go.Figure(data=[go.Histogram(x=x, cumulative_enabled=True)])

fig.show()


### Specify Aggregation Function¶

In [16]:
import plotly.graph_objects as go

x = ["Apples","Apples","Apples","Oranges", "Bananas"]
y = ["5","10","3","10","5"]

fig = go.Figure()
fig.add_trace(go.Histogram(histfunc="count", y=y, x=x, name="count"))
fig.add_trace(go.Histogram(histfunc="sum", y=y, x=x, name="sum"))

fig.show()


### Custom Binning¶

For custom binning along x-axis, use the attribute nbinsx. Please note that the autobin algorithm will choose a 'nice' round bin size that may result in somewhat fewer than nbinsx total bins. Alternatively, you can set the exact values for xbins along with autobinx = False.

In [17]:
import plotly.graph_objects as go
from plotly.subplots import make_subplots

x = ['1970-01-01', '1970-01-01', '1970-02-01', '1970-04-01', '1970-01-02',
'1972-01-31', '1970-02-13', '1971-04-19']

fig = make_subplots(rows=3, cols=2)

trace0 = go.Histogram(x=x, nbinsx=4)
trace1 = go.Histogram(x=x, nbinsx = 8)
trace2 = go.Histogram(x=x, nbinsx=10)
trace3 = go.Histogram(x=x,
xbins=dict(
start='1969-11-15',
end='1972-03-31',
size='M18'), # M18 stands for 18 months
autobinx=False
)
trace4 = go.Histogram(x=x,
xbins=dict(
start='1969-11-15',
end='1972-03-31',
size='M4'), # 4 months bin size
autobinx=False
)
trace5 = go.Histogram(x=x,
xbins=dict(
start='1969-11-15',
end='1972-03-31',
size= 'M2'), # 2 months
autobinx = False
)

fig.append_trace(trace0, 1, 1)
fig.append_trace(trace1, 1, 2)
fig.append_trace(trace2, 2, 1)
fig.append_trace(trace3, 2, 2)
fig.append_trace(trace4, 3, 1)
fig.append_trace(trace5, 3, 2)

fig.show()


### 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 [18]:
from IPython.display import IFrame
IFrame(src= "https://dash-simple-apps.plotly.host/dash-histogramplot/", width="100%", height="650px", frameBorder="0")

Out[18]:
In [19]:
from IPython.display import IFrame
IFrame(src= "https://dash-simple-apps.plotly.host/dash-histogramplot/code", width="100%", height=500, frameBorder="0")

Out[19]:

#### Reference¶

See https://plot.ly/python/reference/#histogram for more information and chart attribute options!