cheatsheet

Cheatsheet for Plotly

Plotly command

Posted on March 2, 2021

Cheatsheet for Plotly basic

Plotly is an interactive plotting tool.

Initialize Plotly in this way:

import pandas as pd
import numpy as np

# import plotly

from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)

import cufflinks as cf
cf.go_offline()

Possible plotting methods using Plotly:

  • scatter
  • bar
  • box
  • spread
  • ratio
  • heatmap
  • surface
  • histogram
  • bubble
  • candle (must specify ‘Open’,’High’,’Low’,’Close’ of the data)

Here are some examples:

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

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

One more plotting method, scatter_matrix(), is similar to sns.pairplot():

df.scatter_matrix()

Other miscellaneous plotting methods:

df.ta_plot(study='sma')

Plotly provides various plotting methods for geographical plotting

Initialization:

import chart_studio.plotly as py
import plotly.graph_objs as go 
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)

We need to begin to build our data dictionary. Easiest way to do this is to use the dict() function of the general form:

  • type = ‘choropleth’,
  • locations = list of states
  • locationmode = ‘USA-states’
  • colorscale=

Either a predefined string:

'pairs' | 'Greys' | 'Greens' | 'Bluered' | 'Hot' | 'Picnic' | 'Portland' | 'Jet' | 'RdBu' | 'Blackbody' | 'Earth' | 'Electric' | 'YIOrRd' | 'YIGnBu'

or create a custom colorscale

  • text= list or array of text to display per point
  • z= array of values on z axis (color of state)
  • colorbar = {‘title’:’Colorbar Title’})

Here are two short examples:

data = dict(type = 'choropleth',
            locations = ['AZ','CA','NY'],
            locationmode = 'USA-states',
            colorscale= 'Portland',
            text= ['text1','text2','text3'],
            z=[1.0,2.0,3.0],
            colorbar = {'title':'Colorbar Title'})
            
layout = dict(title = '2011 US Agriculture Exports by State',
              geo = dict(scope='usa',
                         showlakes = True,
                         lakecolor = 'rgb(160,90,240)')
             )
choromap = go.Figure(data = [data],layout = layout)
iplot(choromap)

or

data = dict(type = 'choropleth',
            locations = df['CODE'],
            z = df['GDP (BILLIONS)'],
            text = df['COUNTRY'],
            colorbar = {'title' : 'Here is colorbar title'})
layout = dict(title = '2014 Global GDP',
              geo = dict(showframe = True,
                         projection = {'type':'natural earth'}) # or mercator
           )

For more details, please refer to Plotly choropleth.