discrete-frequency.md

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jupytext kernelspec plotly notebook_metadata_filter text_representation all extension format_name format_version jupytext_version .md markdown 1.1 1.1.1 display_name language name Python 2 python python2 description display_as has_thumbnail language layout name order page_type permalink thumbnail Learn how to perform discrete frequency analysis using Python. statistics false python base Discrete Frequency 3 example_index python/discrete-frequency/ /images/static-image

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Imports

The tutorial below imports Numpy, Pandas, and SciPy.

importplotly.plotlyaspyimportplotly.graph_objsasgofromplotly.toolsimportFigureFactoryasFFimportnumpyasnpimportpandasaspdimportscipy

Import Data

We will import a dataset to perform our discrete frequency analysis on. We will look at the consumption of alcohol by country in 2010.

data=pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2010_alcohol_consumption_by_country.csv') df=data[0:10] table=FF.create_table(df) py.iplot(table, filename='alcohol-data-sample')

Probability Distribution

We can produce a histogram plot of the data with the y-axis representing the probability distribution of the data.

x=data['alcohol'].values.tolist() trace=go.Histogram(x=x, histnorm='probability', xbins=dict(start=np.min(x), size=0.25, end=np.max(x)), marker=dict(color='rgb(25, 25, 100)')) layout=go.Layout( title="Histogram with Probability Distribution" ) fig=go.Figure(data=go.Data([trace]), layout=layout) py.iplot(fig, filename='histogram-prob-dist')

Frequency Counts

trace=go.Histogram(x=x, xbins=dict(start=np.min(x), size=0.25, end=np.max(x)), marker=dict(color='rgb(25, 25, 100)')) layout=go.Layout( title="Histogram with Frequency Count" ) fig=go.Figure(data=go.Data([trace]), layout=layout) py.iplot(fig, filename='histogram-discrete-freq-count')

Percentage

trace=go.Histogram(x=x, histnorm='percent', xbins=dict(start=np.min(x), size=0.25, end=np.max(x)), marker=dict(color='rgb(50, 50, 125)')) layout=go.Layout( title="Histogram with Frequency Count" ) fig=go.Figure(data=go.Data([trace]), layout=layout) py.iplot(fig, filename='histogram-percentage')

Cumulative Density Function

We can also take the cumulative sum of our dataset and then plot the cumulative density function, or CDF, as a scatter plot

cumsum=np.cumsum(x) trace=go.Scatter(x=[iforiinrange(len(cumsum))], y=10*cumsum/np.linalg.norm(cumsum), marker=dict(color='rgb(150, 25, 120)')) layout=go.Layout( title="Cumulative Distribution Function" ) fig=go.Figure(data=go.Data([trace]), layout=layout) py.iplot(fig, filename='cdf-dataset')

fromIPython.displayimportdisplay, HTMLdisplay(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">')) ! pipinstallgit+https://github.com/plotly/publisher.git--upgradeimportpublisherpublisher.publish( 'python-Discrete-Frequency.ipynb', 'python/discrete-frequency/', 'Discrete Frequency | plotly', 'Learn how to perform discrete frequency analysis using Python.', title='Discrete Frequency in Python. | plotly', name='Discrete Frequency', language='python', page_type='example_index', has_thumbnail='false', display_as='statistics', order=3, ipynb='~notebook_demo/110')