Plotly and Cufflinks

Plotly is a library that allows you to create interactive plots that you can use in dashboards or websites (you can save them as html files or static images).

Installation

In order for this all to work, you’ll need to install plotly and cufflinks to call plots directly off of a pandas dataframe. These libraries are not currently available through conda but are available through pip. Install the libraries at your command line/terminal using:

pip install plotly
pip install cufflinks

** NOTE: Make sure you only have one installation of Python on your computer when you do this, otherwise the installation may not work. **

Imports and Set-up

import pandas as pd
import numpy as np
#%matplotlib inline
from plotly import __version__
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot

print(__version__) # requires version >= 1.9.0
4.3.0
import cufflinks as cf
# For Notebooks
init_notebook_mode(connected=True)
# For offline use
cf.go_offline()

Fake Data

df = pd.DataFrame(np.random.randn(100,4),columns='A B C D'.split())
df.head()

ABCD
01.4430941.049955-2.1719060.493646
10.215467-0.428265-1.846725-0.239502
2-0.540795-1.273190-0.3269490.787827
3-0.7908610.408180-1.3246401.621891
4-0.8656030.9112661.1807610.146927
df2 = pd.DataFrame({'Category':['A','B','C'],'Values':[32,43,50]})
df2.head()

CategoryValues
0A32
1B43
2C50
  • scatter
  • bar
  • box
  • spread
  • ratio
  • heatmap
  • surface
  • histogram
  • bubble

Scatter

df.iplot(kind='scatter',x='A',y='B',mode='markers',size=10)
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Bar Plots

df2.iplot(kind='bar',x='Category',y='Values')
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df.count().iplot(kind='bar')
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Boxplots

df.iplot(kind='box')
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3d Surface

df3 = pd.DataFrame({'x':[1,2,3,4,5],'y':[10,20,30,20,10],'z':[5,4,3,2,1]})
df3.iplot(kind='surface',colorscale='rdylbu')
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Spread

df[['A','B']].iplot(kind='spread')
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histogram

df['A'].iplot(kind='hist',bins=25)
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scatter_matrix()

Similar to sns.pairplot()

df.scatter_matrix()
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Greydon Gilmore
Greydon Gilmore
Ph.D. Candidate in Biomedical Engineering

My research interests include deep brain stimulation, machine learning and signal processing.

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