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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!
The tutorial below imports NumPy, Pandas, and SciPy.
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 scipyTo properly visualize our data and normalization, let us import a dataset of Apple Stock prices in 2014:
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 a dataset by dividing each data point by a constant, such as the standard deviation of the data.
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 a dataset by dividing each data point by the norm of the dataset.
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')Normalize a dataset to an interval [a, b] where a, b are real numbers.
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')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')