#!/usr/bin/env python
# coding: utf-8

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# #### Imports
# The tutorial below imports [Numpy](http://www.numpy.org/), [Pandas](https://plotly.com/pandas/intro-to-pandas-tutorial/), and [SciPy](https://www.scipy.org/).

# 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')


# #### 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')


# #### 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')


# #### 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')


# #### 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')


# In[1]:


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">'))

get_ipython().system(' pip install git+https://github.com/plotly/publisher.git --upgrade')
import publisher
publisher.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')


# In[ ]: