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https://github.com/santosjorge/cufflinks

Productivity Tools for Plotly + Pandas
https://github.com/santosjorge/cufflinks

Last synced: 5 days ago
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Productivity Tools for Plotly + Pandas

Host: GitHub
URL: https://github.com/santosjorge/cufflinks
Owner: santosjorge
License: mit
Created: 2014-11-19T20:59:33.000Z (about 10 years ago)
Default Branch: master
Last Pushed: 2024-07-03T14:15:42.000Z (7 months ago)
Last Synced: 2025-01-07T00:28:05.276Z (12 days ago)
Language: Jupyter Notebook
Size: 17 MB
Stars: 3,041
Watchers: 108
Forks: 675
Open Issues: 103
Metadata Files:
- Readme: README.md
- License: license.txt

Awesome Lists containing this project

awesome-python-machine-learning-resources - GitHub - 41% open · ⏱️ 25.02.2021): (数据可视化)
awesome-python-machine-learning - Cufflinks - This library binds the power of plotly with the flexibility of pandas for easy plotting. (Uncategorized / Uncategorized)

README

        
## Cufflinks

This library binds the power of [plotly](http://www.plot.ly) with the flexibility of [pandas](http://pandas.pydata.org/) for easy plotting.

This library is available on [https://github.com/santosjorge/cufflinks](https://github.com/santosjorge/cufflinks)

This tutorial assumes that the plotly user credentials have already been configured as stated on the [getting started](https://plot.ly/python/getting-started/) guide.

### Tutorials:

* [Chart Gallery](http://nbviewer.ipython.org/gist/santosjorge/b278ce0ae2448f47c31d)

* [Pandas Like Visualization](http://nbviewer.ipython.org/gist/santosjorge/aba934a0d20023a136c2)

* [The Basics](http://nbviewer.ipython.org/gist/santosjorge/f3b07b2be8094deea8c6)

* [Color Management](http://nbviewer.ipython.org/gist/santosjorge/00ca17b121fa2463e18b)

* [Offline Mode](http://nbviewer.ipython.org/gist/santosjorge/5fdbe947496faf7af5e6)

![3D Charts](img/ukswaps.gif)

### Release Notes

### v0.17.0

Support for Plotly 4.x  

Cufflinks is no longer compatible with Plotly 3.x

### v0.14.0

Support for Plotly 3.0

### v0.13.0

New `iplot` helper. 

To see a comprehensive list of parameters 

**cf.help()**

```python

# 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:

```python

df=cf.datagen.ohlc()

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

qf.add_bollinger_bands()

qf.iplot()

```

![QuantFigure](img/qf1.png)

* **Technical Analysis Studies** can be added on demand. 

```python

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()

```

![Technical Analysis](img/qf2.png)

### 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](http://nbviewer.ipython.org/gist/santosjorge/72665839a6f05a0567e0?flush_cache=true)

* 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](https://github.com/jackparmer/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'])`