Frequency counts

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Imports

The tutorial below imports numpy, pandas, and scipy

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 distribution

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

where $U$ is drawn from the Uniform Distribution.

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

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

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')

Larger Bins

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

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')

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(
    'python-Frequency-Counts.ipynb', 'python/frequency-counts/', 'Frequency Counts | plotly',
    'Learn how to perform frequency counts using Python.',
    title='Frequency Counts in Python. | plotly',
    name='Frequency Counts',
    language='python',
    page_type='example_index', has_thumbnail='false', display_as='statistics', order=2,
    ipynb= '~notebook_demo/111')
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