Normalization - Plotly Py

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Imports

The tutorial below imports NumPy, Pandas, and SciPy.

python

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:

python

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')

Normalize by a Constant

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

python

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')

Normalize to [0, 1]

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

python

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')

Normalizing to any Interval

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

python

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')

python

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')

python