Cufflinks

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

This library is available on https://github.com/santosjorge/cufflinks

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

Tutorials:

• Chart Gallery
• Pandas Like Visualization
• The Basics
• Color Management
• Offline Mode

Release Notes

v0.14.0

Support for Plotly 3.0

v0.13.0

New iplot helper. To see a comprehensive list of parameters cf.help()

# For a list of supported figures
cf.help()
# Or to see the parameters supported that apply to a given figure try
cf.help('scatter')
cf.help('candle') #etc

v0.12.0

Removed dependecies on ta-lib. This library is no longer required. All studies have be rewritten in Python.

v0.11.0

• QuantFigure is a new class that will generate a graph object with persistence. Parameters can be added/modified at any given point.

This can be as easy as:

df=cf.datagen.ohlc()
qf=cf.QuantFig(df,title='First Quant Figure',legend='top',name='GS')
qf.add_bollinger_bands()
qf.iplot()

• Technical Analysis Studies can be added on demand.

qf.add_sma([10,20],width=2,color=['green','lightgreen'],legendgroup=True)
qf.add_rsi(periods=20,color='java')
qf.add_bollinger_bands(periods=20,boll_std=2,colors=['magenta','grey'],fill=True)
qf.add_volume()
qf.add_macd()
qf.iplot()

v0.10.0

• rangeslider to display a date range slider at the bottom
  • cf.datagen.ohlc().iplot(kind='candle',rangeslider=True)
• rangeselector to display buttons to change the date range displayed
  • cf.datagen.ohlc(500).iplot(kind='candle', rangeselector={ 'steps':['1y','2 months','5 weeks','ytd','2mtd','reset'], 'bgcolor' : ('grey',.3), 'x': 0.3 , 'y' : 0.95})
• Customise annotions, with fontsize,fontcolor,textangle
  • Label mode
    • cf.datagen.lines(1,mode='stocks').iplot(kind='line', annotations={'2015-02-02':'Market Crash', '2015-03-01':'Recovery'}, textangle=-70,fontsize=13,fontcolor='grey')
  • Explicit mode
    • cf.datagen.lines(1,mode='stocks').iplot(kind='line', annotations=[{'text':'exactly here','x':'0.2', 'xref':'paper','arrowhead':2, 'textangle':-10,'ay':150,'arrowcolor':'red'}])

v0.9.0

• Figure.iplot() to plot figures
• New high performing candle and ohlc plots
  • cf.datagen.ohlc().iplot(kind='candle')

v0.8.0

• '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

v0.7.1

• 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

v0.7

• Support for Python 3

v0.6

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.iplot(kind='candle',up_color='blue',down_color='red')
• OHLC (Bar) Charts support
  • ohlc=cf.datagen.ohlc()
ohlc.iplot(kind='ohlc',up_color='blue',down_color='red')
• Support for logarithmic charts ( logx | logy )
  • df=pd.DataFrame([x**2] for x in range(100))
df.iplot(kind='lines',logy=True)
• 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)

v0.5

• 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()
fig=fig.set_axis('b',side='right')
cf.iplot(fig)

v0.4

• 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))

v0.3.2

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

v0.3

• 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
  • cufflinks.datagen.box().iplot(kind='box')
• 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'])