Data are becoming the new raw material of business
The Economist


SQLite vs Pandas: Performance Benchmarks

This technical article was written for The Data Incubator by Paul Paczuski, a Fellow of our 2016 Spring cohort in New York City who landed a job with our hiring partner, Genentech as a Clinical Data Scientist.

As a data scientist, we all know that unglamorous data manipulation is 90% of the work. Two of the most common data manipulation tools are SQL and pandas. In this blog, we’ll compare the performance of pandas and SQLite, a simple form of SQL favored by Data Scientists.

Let’s find out the tasks at which each of these excel. Below, we compare Python’s pandas to sqlite for some common data analysis operations: sort, select, load, join, filter, and group by.

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Python Multi-Threading vs Multi-Processing

There is a library called threading in Python and it uses threads (rather than just processes) to implement parallelism. This may be surprising news if you know about the Python’s Global Interpreter Lock, or GIL, but it actually works well for certain instances without violating the GIL. And this is all done without any overhead — simply define functions that make I/O requests and the system will handle the rest.

 

Global Interpreter Lock

The Global Interpreter Lock reduces the usefulness of threads in Python (more precisely CPython) by allowing only one native thread to execute at a time. This made implementing Python easier to implement in the (usually thread-unsafe) C libraries and can increase the execution speed of single-threaded programs. However, it remains controvertial because it prevents true lightweight parallelism. You can achieve parallelism, but it requires using multi-processing, which is implemented by the eponymous library multiprocessing. Instead of spinning up threads, this library uses processes, which bypasses the GIL.

It may appear that the GIL would kill Python multithreading but not quite. In general, there are two main use cases for multithreading:

  1. To take advantage of multiple cores on a single machine
  2. To take advantage of I/O latency to process other threads

In general, we cannot benefit from (1) with threading but we can benefit from (2).

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Tensorflow with Keras – Empowering Neural Networks for Deep Learning

Building deep neural networks just got easier. TensorFlow has announced that they are incorporating the popular deep learning API, Keras, as part of the core code that ships with TensorFlow 1.2. In the words of Keras’ author François Chollet, “Theano and TensorFlow are closer to NumPy, while Keras is closer to scikit-learn,” which is to say that Keras is at a higher level compared to pure TensorFlow and makes building deep learning models much more manageable.

TensorFlow is one of the fastest, most flexible, and most scalable machine-learning libraries available. It was developed internally by Google Brain and released as an open-source library in November 2015. Almost immediately upon its release, TensorFlow became one of the most popular machine learning libraries. But, as is the case with many libraries that emphasize speed and flexibility, TensorFlow tends to be a bit low-level.

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Ranking Popular Deep Learning Libraries for Data Science

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At The Data Incubator, we pride ourselves on having the most up to date data science curriculum available. Much of our curriculum is based on feedback from corporate and government partners about the technologies they are using and learning. In addition to their feedback we wanted to develop a data-driven approach for determining what we should be teaching in our data science corporate training and our free fellowship for masters and PhDs looking to enter data science careers in industry. Here are the results.

The Rankings

Below is a ranking of 23 open-source deep learning libraries that are useful for Data Science, based on Github and Stack Overflow activity, as well as Google search results. The table shows standardized scores, where a value of 1 means one standard deviation above average (average = score of 0). For example, Caffe is one standard deviation above average in Github activity, while deeplearning4j is close to average. See below for methods.


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