Productivity Tools for Plotly + Pandas
Jupyter Notebook Python Shell
Latest commit a4b2e31 Jul 6, 2016 Jorge Santos Merge branch 'jmportilla-master'
Pandas rolling updates


This library binds the power of plotly with the flexibility of pandas for easy plotting.

This library is available on

This tutorial assumes that the plotly user credentials have already been configured as stated on the getting started guide.


3D Charts

Release Notes


  • 'cf.datagen.choropleth()' to for sample choropleth data.
  • 'cf.datagen.scattergeo()' to for sample scattergeo data.
  • Support for choropleth and scattergeo figures in iplot
  • 'cf.get_colorscale' for maps and plotly objects that support colorscales


  • xrange, yrange and zrange can be specified in iplot and getLayout
    • cf.datagen.lines(1).iplot(yrange=[5,15])
  • layout_update can be set in iplot and getLayout to explicitly update any Layout value


  • Support for Python 3


See the IPython Notebook

  • Support for pie charts
    • cf.datagen.pie().iplot(kind='pie',labels='labels',values='values')
  • Generate Open, High, Low, Close data
    • datagen.ohlc()
  • Candle Charts support
    • ohlc=cf.datagen.ohlc()
  • OHLC (Bar) Charts support
    • ohlc=cf.datagen.ohlc()
  • Support for logarithmic charts ( logx | logy )
    • df=pd.DataFrame([x**2] for x in range(100))
  • Support for MulitIndex DataFrames
  • Support for Error Bars ( error_x | error_y )
    • cf.datagen.lines(1,5).iplot(kind='bar',error_y=[1,2,3.5,2,2])
    • cf.datagen.lines(1,5).iplot(kind='bar',error_y=20, error_type='percent')
  • Support for continuous error bars
    • cf.datagen.lines(1).iplot(kind='lines',error_y=20,error_type='continuous_percent')
    • cf.datagen.lines(1).iplot(kind='lines',error_y=10,error_type='continuous',color='blue')
  • Technical Analysis Studies for Timeseries (beta)
    • Simple Moving Averages (SMA)
      • cf.datagen.lines(1,500).ta_plot(study='sma',periods=[13,21,55])
    • Relative Strength Indicator (RSI)
      • cf.datagen.lines(1,200).ta_plot(study='boll',periods=14)
    • Bollinger Bands (BOLL)
      • cf.datagen.lines(1,200).ta_plot(study='rsi',periods=14)
    • Moving Average Convergence Divergence (MACD)
      • cf.datagen.lines(1,200).ta_plot(study='macd',fast_period=12,slow_period=26, signal_period=9)


  • Support of offline charts
    • cf.go_offline()
    • cf.go_online()
    • cf.iplot(figure,online=True) (To force online whilst on offline mode)
  • Support for secondary axis
    • fig=cf.datagen.lines(3,columns=['a','b','c']).figure()


  • Support for global theme setting
    • cufflinks.set_config_file(theme='pearl')
  • New theme ggplot
    • cufflinks.datagen.lines(5).iplot(theme='ggplot')
  • Support for horizontal bar charts barh
    • cufflinks.datagen.lines(2).iplot(kind='barh',barmode='stack',bargap=.1)
  • Support for histogram orientation and normalization
    • cufflinks.datagen.histogram().iplot(kind='histogram',orientation='h',norm='probability')
  • Support for area plots
    • cufflinks.datagen.lines(4).iplot(kind='area',fill=True,opacity=1)
  • Support for subplots
    • cufflinks.datagen.histogram(4).iplot(kind='histogram',subplots=True,bins=50)
    • cufflinks.datagen.lines(4).iplot(subplots=True,shape=(4,1),shared_xaxes=True,vertical_spacing=.02,fill=True)
  • Support for scatter matrix to display the distribution amongst every series in the DataFrame
    • cufflinks.datagen.lines(4,1000).scatter_matrix()
  • Support for vline and hline for horizontal and vertical lines
    • cufflinks.datagen.lines(3).iplot(hline=[2,3])
    • cufflinks.datagen.lines(3).iplot(hline=dict(y=2,color='blue',width=3))
  • Support for vspan and hspan for horizontal and vertical areas
    • cufflinks.datagen.lines(3).iplot(hspan=(-1,2))
    • cufflinks.datagen.lines(3).iplot(hspan=dict(y0=-1,y1=2,color='orange',fill=True,opacity=.4))


  • Global setting for public charts
    • cufflinks.set_config_file(world_readable=True)


  • Enhanced Spread charts
    • cufflinks.datagen.lines(2).iplot(kind='spread')
  • Support for Heatmap charts
    • cufflinks.datagen.heatmap().iplot(kind='heatmap')
  • Support for Bubble charts
    • cufflinks.datagen.bubble(4).iplot(kind='bubble',x='x',y='y',text='text',size='size',categories='categories')
  • Support for Bubble3d charts
    • cufflinks.datagen.bubble3d(4).iplot(kind='bubble3d',x='x',y='y',z='z',text='text',size='size',categories='categories')
  • Support for Box charts
  • Support for Surface charts
    • cufflinks.datagen.surface().iplot(kind='surface')
  • Support for Scatter3d charts
    • cufflinks.datagen.scatter3d().iplot(kind='scatter3d',x='x',y='y',z='z',text='text',categories='categories')
  • Support for Histograms
    • cufflinks.datagen.histogram(2).iplot(kind='histogram')
  • Data generation for most common plot types
    • cufflinks.datagen
  • Data extraction: Extract data from any Plotly chart. Data is delivered in DataFrame
    • cufflinks.to_df(Figure)
  • Integration with colorlover
    • Support for scales iplot(colorscale='accent') to plot a chart using an accent color scale
    • cufflinks.scales() to see all available scales
  • Support for named colors * iplot(colors=['pink','red','yellow'])