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# Discrete Frequency in Python

Learn how to perform discrete frequency analysis using Python.

#### New to Plotly?¶

Plotly's Python library is free and open source! Get started by dowloading the client and reading the primer.
You can set up Plotly to work in online or offline mode, or in jupyter notebooks.
We also have a quick-reference cheatsheet (new!) to help you get started!

#### 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


#### 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.

In [2]:
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')

Out[2]:

#### Probability Distribution¶

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

In [3]:
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')

Out[3]:

#### Frequency Counts¶

In [4]:
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')

Out[4]:

#### Percentage¶

In [5]:
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')

Out[5]:

#### Cumulative Density Function¶

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

In [6]:
cumsum = np.cumsum(x)

trace = go.Scatter(x=[i for i in range(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')

Out[6]:
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