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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
import plotly.tools as tools
from plotly.tools import FigureFactory as FF

import numpy as np
import pandas as pd
import scipy

Import Data¶

To properly visualize our data and normalization, let us import a dataset of Apple Stock prices in 2014:

In [2]:

apple_data = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_apple_stock.csv')
df = apple_data[0:10]

table = FF.create_table(df)
py.iplot(table, filename='apple-data-sample')

Out[2]:

Normalize by a Constant¶

Normalize a dataset by dividing each data point by a constant, such as the standard deviation of the data.

In [4]:

data = apple_data['AAPL_y']

data_norm_by_std = [number/scipy.std(data) for number in data]

trace1 = go.Histogram(
    x=data,
    opacity=0.75,
    name='data'
)

trace2 = go.Histogram(
    x=data_norm_by_std,
    opacity=0.75,
    name='normalized by std = ' + str(scipy.std(data)),
)

fig = tools.make_subplots(rows=2, cols=1)

fig.append_trace(trace1, 1, 1)
fig.append_trace(trace2, 2, 1)

fig['layout'].update(height=600, width=800, title='Normalize by a Constant')
py.iplot(fig, filename='apple-data-normalize-constant')

This is the format of your plot grid:
[ (1,1) x1,y1 ]
[ (2,1) x2,y2 ]

Out[4]:

Normalize to [0, 1]¶

Normalize a dataset by dividing each data point by the norm of the dataset.

In [5]:

data_norm_to_0_1 = [number/scipy.linalg.norm(data) for number in data]

trace1 = go.Histogram(
    x=data,
    opacity=0.75,
    name='data',
)

trace2 = go.Histogram(
    x=data_norm_to_0_1,
    opacity=0.75,
    name='normalized to [0,1]',
)

fig = tools.make_subplots(rows=2, cols=1)

fig.append_trace(trace1, 1, 1)
fig.append_trace(trace2, 2, 1)

fig['layout'].update(height=600, width=800, title='Normalize to [0,1]')
py.iplot(fig, filename='apple-data-normalize-0-1')

This is the format of your plot grid:
[ (1,1) x1,y1 ]
[ (2,1) x2,y2 ]

Out[5]:

Normalizing to any Interval¶

Normalize a dataset to an interval [a, b] where a, b are real numbers.

In [6]:

a = 10
b = 50
data_norm_to_a_b = [(number - a)/(b - a) for number in data]

trace1 = go.Histogram(
    x=data,
    opacity=0.75,
    name='data',
)

trace2 = go.Histogram(
    x=data_norm_to_a_b,
    opacity=0.75,
    name='normalized to [10,50]',
)

fig = tools.make_subplots(rows=2, cols=1)

fig.append_trace(trace1, 1, 1)
fig.append_trace(trace2, 2, 1)

fig['layout'].update(height=600, width=800, title='Normalize to [10,50]')
py.iplot(fig, filename='apple-data-normalize-a-b')

This is the format of your plot grid:
[ (1,1) x1,y1 ]
[ (2,1) x2,y2 ]

Out[6]:

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_Normalization.ipynb', 'python/normalization/', 'Normalization | plotly',
    'Learn how to normalize data by fitting to intervals on the real line and dividing by a constant',
    title='Normalization in Python. | plotly',
    name='Normalization',
    language='python',
    page_type='example_index', has_thumbnail='false', display_as='mathematics', order=2,
    ipynb= '~notebook_demo/103')

Collecting git+https://github.com/plotly/publisher.git
  Cloning https://github.com/plotly/publisher.git to /var/folders/ld/6cl3s_l50wd40tdjq2b03jxh0000gp/T/pip-cIVPBZ-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 [ ]: