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

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]:

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">'))

! 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')

Collecting git+https://github.com/plotly/publisher.git
  Cloning https://github.com/plotly/publisher.git to /var/folders/ld/6cl3s_l50wd40tdjq2b03jxh0000gp/T/pip-ctBFME-build
Installing collected packages: publisher
  Found existing installation: publisher 0.10
    Uninstalling publisher-0.10:
      Successfully uninstalled publisher-0.10
  Running setup.py install for publisher ... - \ done
Successfully installed publisher-0.10

/Users/brandendunbar/Desktop/test/venv/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from nbconvert instead.
  "You should import from nbconvert instead.", ShimWarning)
/Users/brandendunbar/Desktop/test/venv/lib/python2.7/site-packages/publisher/publisher.py:53: UserWarning: Did you "Save" this notebook before running this command? Remember to save, always save.
  warnings.warn('Did you "Save" this notebook before running this command? '

In [ ]: