An Incomplete Data Tools Landscape for Hackers in 2015
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Wes McKinney gives an overview of the current data analysis tools landscape in Python and R. He discusses essential Python packages like NumPy, pandas, and scikit-learn. For R, he covers packages in the "Hadley stack" like dplyr and ggplot2. IPython/Jupyter notebooks are also mentioned as a platform for interactive data analysis across languages. The talk aims to highlight trends, opportunities, and challenges in the open source data science tool ecosystem.
![1
©
Cloudera,
Inc.
All
rights
reserved.
A
[Incomplete]
Data
Tools
Landscape
[for
Hackers]
in
2015
Wes
McKinney
@wesmckinn
Data^3
MeeMng
—
Minneapolis,
MN](https://image.slidesharecdn.com/slides-150307115108-conversion-gate01/85/An-Incomplete-Data-Tools-Landscape-for-Hackers-in-2015-1-320.jpg)
![2
©
Cloudera,
Inc.
All
rights
reserved.
This
talk
• A
parMal
look
at
different
languages
and
tools
• LimiMng
scope
to
either:
• Permissively
licensed
open
source
soSware,
e.g.
Apache-‐licensed
(OSS)
• Non-‐dual-‐licensed
copyleS
OSS
(e.g.
GPL)
• i.e.
“do
you
[the
community]
have
any
incenMve
to
create
patches?”
• Some
trends
(that
I
see,
anyway)
• Challenges
and
opportuniMes](https://image.slidesharecdn.com/slides-150307115108-conversion-gate01/85/An-Incomplete-Data-Tools-Landscape-for-Hackers-in-2015-2-320.jpg)
An Incomplete Data Tools Landscape for Hackers in 2015
- 1. 1 © Cloudera, Inc. All rights reserved. A [Incomplete] Data Tools Landscape [for Hackers] in 2015 Wes McKinney @wesmckinn Data^3 MeeMng — Minneapolis, MN
- 2. 2 © Cloudera, Inc. All rights reserved. This talk • A parMal look at different languages and tools • LimiMng scope to either: • Permissively licensed open source soSware, e.g. Apache-‐licensed (OSS) • Non-‐dual-‐licensed copyleS OSS (e.g. GPL) • i.e. “do you [the community] have any incenMve to create patches?” • Some trends (that I see, anyway) • Challenges and opportuniMes
- 3. 3 © Cloudera, Inc. All rights reserved. Who am I? • Python data firestarter • Financial analyMcs in R / Python starMng 2007 • pandas project born of frustraMon in 2008 • 2010-‐2012 • Hiatus from gainful employment • Make pandas ready for primeMme • Write "Python for Data Analysis"
- 4. 4 © Cloudera, Inc. All rights reserved. Who am I? (cont’d) • 2013-‐2014: Co-‐founder/CEO of DataPad (analyMcs startup, with early pandas collaborator Chang She) • Late 2014: DataPad team joins Cloudera • Now: backend systems and all-‐things-‐Python @ Cloudera
- 5. 5 © Cloudera, Inc. All rights reserved. SQL: SMll a lingua franca • “SQL: the Fortran of AnalyMcs” • OSen a concise, declaraMve way to express data transforms, analyMcs, etc. • RelaMvely easy to parse, analyze • SQL recently has seen resurgence with focus on interacMve-‐speed SQL engines, especially on top of HDFS/Hadoop • Relevant and impaclul features (e.g. JSON support) sMll arriving in established RDBMS like PostgreSQL
- 6. 6 © Cloudera, Inc. All rights reserved. Historical Python Context • ScienMfic / HPC compuMng focus in 1990s, 2000s • Python web community developed in parallel, matured faster! • NumPy became community standard in 2005, born from Numeric + Numarray • Pyrex, later Cython, easier C / C++ wrapping • f2py: easy Fortran wrapping • Anaconda distribuMon • Finally solving Python deployment for all
- 7. 7 © Cloudera, Inc. All rights reserved. EssenMal Python stack • NumPy: low-‐level array processing • SciPy: essenMal computaMonal algos • pandas: data wrangling • scikit-‐learn: machine learning • matplotlib (+ add-‐ons, like seaborn): visualizaMon • numba: numeric hotspot LLVM compiler • Domain-‐specific toolkits: nltk, scikit-‐image, statsmodels, Theano, PyCUDA/ PyOpenCL and many others
- 8. 8 © Cloudera, Inc. All rights reserved. pandas • A Pythonic take on the classic R “data frame” data structure • CriMcal piece to make the Python stack useful in everyday work • Added axis metadata / labeling for represenMng mulMdimensional data • Focus on easy data wrangling, IO, ploung, and basic analyMcs
- 9. 9 © Cloudera, Inc. All rights reserved. Jeff Reback’s “pandas as PyData middleware” diagram
- 10. 10 © Cloudera, Inc. All rights reserved. Newer / Up-‐and-‐coming Python projects • Bokeh: interacMve / reacMve visualizaMon for the web • Blaze: uniform data expression API • Odo: easy data migraMon
- 11. 11 © Cloudera, Inc. All rights reserved. R Project • Trusted base of staMsMcs libraries • Latest and greatest stats research oSen hits R first • RStudio • The "Hadley stack” • VisualizaMon: ggplot2 (staMc) and ggvis (interacMve) • Data Wrangling: dplyr • legacy: plyr / reshape2
- 12. 12 © Cloudera, Inc. All rights reserved. dplyr • Started late 2012 by Hadley Wickham, supported by RStudio • Composable / chainable analyMcs and data wrangling expressions • In-‐memory and SQL backends • Has avracted folks back to R from Python in a lot of cases
- 13. 13 © Cloudera, Inc. All rights reserved. Some other great R stuff • shiny: interacMve web apps in R • Rcpp • data.table • xts
- 14. 14 © Cloudera, Inc. All rights reserved. IPython • IPython started out as a bever interacMve Python • Grew to include web-‐based computaMonal notebook, GUI console, and other components • (Google even integrated into Google Drive!) • IPython Notebook architecture enabled “kernel” processes to be wriven in nearly any language (even bash!) • How to build community beyond Python?
- 15. 15 © Cloudera, Inc. All rights reserved. Enter Jupyter • hvp://jupyter.org • Breaking out notebook machinery into a standalone non-‐Python-‐specific project • Enable project components to evolve at own pace, without large monolithic releases • JupyterHub: upcoming mulM-‐user notebook server
- 16. 16 © Cloudera, Inc. All rights reserved. A few words about Hadoop + Big Data
- 17. 17 © Cloudera, Inc. All rights reserved. Apache Spark • Originated from Berkeley AMPLab • General purpose distributed memory-‐centric data processing framework • Official APIs: Scala, Java, Python Source: databricks.com
- 18. 18 © Cloudera, Inc. All rights reserved. Spark 1.3: DataFrames! • R/pandas-‐inspired API for tabular data manipulaMon in Scala, Python, etc. • Logical operaMon graphs rewriven internally in more efficient form • Good interop with Spark SQL • Some interoperability with pandas • Will help close the semanMc gap between Spark and R/Python
- 19. 19 © Cloudera, Inc. All rights reserved. Some problems in need of solving • A Shiny-‐like quick-‐and-‐dirty data app development framework for Python • IPython/Jupyter notebook collaboraMon • A community-‐standard, Apache-‐licensed C/C++ data frame library with best-‐in-‐ class performance • Ubiquitous support for emerging analyMcal on-‐disk storage standards like Parquet
- 20. 20 © Cloudera, Inc. All rights reserved. Other interesMng stuff to look at • Torch7 / LuaJIT: high performance ML / deep learning on GPUs • Facebook AI group open sourced several ML modules • Apache Flink • Up-‐and-‐coming Scala-‐based data processing framework • Some overlap with Spark use cases
- 21. 21 © Cloudera, Inc. All rights reserved. Some other interesMng industry trends • MicrosoS • Acquired RevoluMon AnalyMcs, leading commercial R vendor • Launched Azure ML: R, Python, and more on Azure cloud • Dato (ya GraphLab) • faster, more scalable machine learning, with Python interface (Paid commercial product, free for non-‐commercial/academic use) • Largest-‐ever VC investment in a data tools company beung big on Python • Databricks • Offering cloud Spark-‐notebook-‐as-‐a-‐service
- 22. 22 © Cloudera, Inc. All rights reserved. Thank you @wesmckinn