Productivity Tools for Plotly + Pandas

Overview

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:

3D Charts

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()

# 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()

QuantFigure

  • 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()

Technical Analysis

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'])
Owner
Jorge Santos
Jorge Santos
A concise grammar of interactive graphics, built on Vega.

Vega-Lite Vega-Lite provides a higher-level grammar for visual analysis that generates complete Vega specifications. You can find more details, docume

Vega 4k Jan 08, 2023
A high performance implementation of HDBSCAN clustering. http://hdbscan.readthedocs.io/en/latest/

HDBSCAN Now a part of scikit-learn-contrib HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over va

Leland McInnes 91 Dec 29, 2022
The Python ensemble sampling toolkit for affine-invariant MCMC

emcee The Python ensemble sampling toolkit for affine-invariant MCMC emcee is a stable, well tested Python implementation of the affine-invariant ense

Dan Foreman-Mackey 1.3k Jan 04, 2023
An open-source tool for visual and modular block programing in python

PyFlow PyFlow is an open-source tool for modular visual programing in python ! Although for now the tool is in Beta and features are coming in bit by

1.1k Jan 06, 2023
An interactive dashboard for visualisation, integration and classification of data using Active Learning.

AstronomicAL An interactive dashboard for visualisation, integration and classification of data using Active Learning. AstronomicAL is a human-in-the-

45 Nov 28, 2022
Fast scatter density plots for Matplotlib

About Plotting millions of points can be slow. Real slow... 😴 So why not use density maps? ⚡ The mpl-scatter-density mini-package provides functional

Thomas Robitaille 473 Dec 12, 2022
Using SQLite within Python to create database and analyze Starcraft 2 units data (Pandas also used)

SQLite python Starcraft 2 English This project shows the usage of SQLite with python. To create, modify and communicate with the SQLite database from

1 Dec 30, 2021
Color maps for POV-Ray v3.7 from the Plasma, Inferno, Magma and Viridis color maps in Python's Matplotlib

POV-Ray-color-maps Color maps for POV-Ray v3.7 from the Plasma, Inferno, Magma and Viridis color maps in Python's Matplotlib. The include file Color_M

Tor Olav Kristensen 1 Apr 05, 2022
An application that allows you to design and test your own stock trading algorithms in an attempt to beat the market.

StockBot is a Python application for designing and testing your own daily stock trading algorithms. Installation Use the

Ryan Cullen 280 Dec 19, 2022
High-level geospatial data visualization library for Python.

geoplot: geospatial data visualization geoplot is a high-level Python geospatial plotting library. It's an extension to cartopy and matplotlib which m

Aleksey Bilogur 1k Jan 01, 2023
This is a super simple visualization toolbox (script) for transformer attention visualization ✌

Trans_attention_vis This is a super simple visualization toolbox (script) for transformer attention visualization ✌ 1. How to prepare your attention m

Mingyu Wang 3 Jul 09, 2022
Simple implementation of Self Organizing Maps (SOMs) with rectangular and hexagonal grid topologies

py-self-organizing-map Simple implementation of Self Organizing Maps (SOMs) with rectangular and hexagonal grid topologies. A SOM is a simple unsuperv

Jonas Grebe 1 Feb 10, 2022
Rick and Morty Data Visualization with python

Rick and Morty Data Visualization For this project I looked at data for the TV show Rick and Morty Number of Episodes at a Certain Location Here is th

7 Aug 29, 2022
Drug design and development team HackBio internship is a virtual bioinformatics program that introduces students and professional to advanced practical bioinformatics and its applications globally.

-Nyokong. Drug design and development team HackBio internship is a virtual bioinformatics program that introduces students and professional to advance

4 Aug 04, 2022
A package for plotting maps in R with ggplot2

Attention! Google has recently changed its API requirements, and ggmap users are now required to register with Google. From a user’s perspective, ther

David Kahle 719 Jan 04, 2023
Decision Border Visualizer for Classification Algorithms

dbv Decision Border Visualizer for Classification Algorithms Project description A python package for Machine Learning Engineers who want to visualize

Sven Eschlbeck 1 Nov 01, 2021
Simple and lightweight Spotify Overlay written in Python.

Simple Spotify Overlay This is a simple yet powerful Spotify Overlay. About I have been looking for something like this ever since I got Spotify. I th

27 Sep 03, 2022
Political elections, appointment, analysis and visualization in Python

Political elections, appointment, analysis and visualization in Python poli-sci-kit is a Python package for political science appointment and election

Andrew Tavis McAllister 9 Dec 01, 2022
An open-source plotting library for statistical data.

Lets-Plot Lets-Plot is an open-source plotting library for statistical data. It is implemented using the Kotlin programming language. The design of Le

JetBrains 820 Jan 06, 2023
Generate a 3D Skyline in STL format and a OpenSCAD file from Gitlab contributions

Your Gitlab's contributions in a 3D Skyline gitlab-skyline is a Python command to generate a skyline figure from Gitlab contributions as Github did at

Félix Gómez 70 Dec 22, 2022