Tips For Processing Large Data Sets With Python

Kevin Feasel

2018-03-30

Python

Julien Heiduk has a few tips for people looking to process large data sets within Python:

In order to aggregate our data, we have to use chunksize. This option of read_csvallows you to load massive file as small chunks in Pandas. We decide to take 10% of the total length for the chunksize which corresponds to 40 Million rows.
Be careful it is not necessarily interesting to take a small value. The time between each iteration can be too long with a small chaunksize. In order to find the best trade-off “Memory usage – Time” you can try different chunksize and select the best which will consume the lesser memory and which will be the faster.

Click through for more tips.

Related Posts

P-Hacking and Multiple Comparison Bias

Patrick David has a great article on hypothesis testing, p-hacking, and multiple comparison bias: The most important part of hypothesis testing is being clear what question we are trying to answer. In our case we are asking:“Could the most extreme value happen by chance?”The most extreme value we define as the greatest absolute AMVR deviation from […]

Read More

An Explanation Of Convolutional Neural Networks

Shirin Glander explains some of the mechanics behind Convolutional Neural Networks: Convolutional Neural Nets are usually abbreviated either CNNs or ConvNets. They are a specific type of neural network that has very particular differences compared to MLPs. Basically, you can think of CNNs as working similarly to the receptive fields of photoreceptors in the human eye. Receptive fields in our […]

Read More

Categories

March 2018
MTWTFSS
« Feb Apr »
 1234
567891011
12131415161718
19202122232425
262728293031