Frequency Counts in Python/v3

Learn how to perform frequency counts using Python.

Note: this page is part of the documentation for version 3 of Plotly.py, which is not the most recent version.
See our Version 4 Migration Guide for information about how to upgrade.

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Imports¶

The tutorial below imports numpy, pandas, and scipy

In [1]:

import plotly.plotly as py
import plotly.graph_objs as go
from plotly.tools import FigureFactory as FF

import numpy as np
import pandas as pd
import scipy

Make the Data¶

We are generating a 1D dataset from a Weibull Distribution which has the distrubution

$$ \begin{align*} X = \log(U)^{\frac{1}{a}} \end{align*} $$

where $U$ is drawn from the Uniform Distribution.

In [17]:

x=np.random.weibull(1.25, size=1000)
print(x[:10])

[ 0.86317076  0.79217698  2.07432654  0.70721605  0.24102326  1.44261213
  0.85526797  1.0158948   1.19976016  1.78112064]

Histogram¶

By using a histogram, we can properly divide a 1D dataset into bins with a particular size or width, so as to form a discrete probability distribution

In [21]:

trace = go.Histogram(x=x, xbins=dict(start=np.min(x), size=0.25, end=np.max(x)),
                   marker=dict(color='rgb(0, 0, 100)'))

layout = go.Layout(
    title="Histogram Frequency Counts"
)

fig = go.Figure(data=go.Data([trace]), layout=layout)
py.iplot(fig, filename='histogram-freq-counts')

Out[21]:

Larger Bins¶

We can experiment with our bin size and the histogram by grouping the data into larger intervals

In [20]:

trace = go.Histogram(x=x, xbins=dict(start=np.min(x), size=0.75, end=np.max(x)),
                   marker=dict(color='rgb(0, 0, 100)'))

layout = go.Layout(
    title="Histogram Frequency Counts"
)

fig = go.Figure(data=go.Data([trace]), layout=layout)
py.iplot(fig, filename='histogram-freq-counts-larger-bins')

Out[20]: