Skip to main content

Big Data, Small Data and Pandas

Welp, I finally got this through my thick head, thanks to a hint by Jeff who answered my cry for help on stack overflow, and pointed me to this thread on the pandas issues list.

So here's my use case again: I have small data and big data. Small data is relatively lightweight heterogeneous table-type data. Big data is potentially gigabytes in size, homogenous data. Conditionals on the small data table are used to select out rows which then indicate to us the subset of the big data needed for further processing.

Here's one way to do things: (Things to note: saving in frame_table format, common indexing, use of 'where' to select the big data)
import pandas as pd, numpy

df = pd.DataFrame(data=numpy.random.randint(10,size=(8,4)),columns=['a','b','c','d'])
df1 = pd.DataFrame(data=numpy.random.randint(10,size=(8,20)),index=df.index)
df2 = pd.Panel(data=numpy.random.randint(10,size=(8,20,5)),items=df.index)
df3 = pd.Panel4D(data=numpy.random.randint(10,size=(8,20,5,5)),labels=df.index)

store = pd.HDFStore('data.h5')
print store

row ='small',where=['a>2','b<5'],columns=['a','b'])
print 'Small data:'
print row

da ='big',pd.Term(['index',row.index]))
print 'Big data:' 
print da

da ='big_panel',pd.Term(['items',row.index]))
print 'Big data (Panel):'
print da.items

da ='big_panel4',pd.Term(['labels',row.index]))
print 'Big data (Panel4d):'
print da.labels
With a sample output of:
<class ''>
File path: data.h5
/big                   frame_table  (typ->appendable,nrows->8,ncols->20,indexers->[index])                       
/big_panel             wide_table   (typ->appendable,nrows->100,ncols->8,indexers->[major_axis,minor_axis])      
/big_panel4            wide_table   (typ->appendable,nrows->500,ncols->8,indexers->[items,major_axis,minor_axis])
/small                 frame_table  (typ->appendable,nrows->8,ncols->4,indexers->[index],dc->[a,b])              
Small data:
   a  b
3  6  2
5  9  4
Big data:
   0   1   2   3   4   5   6   7   8   9   10  11  12  13  14  15  16  17  18  19
3   9   1   1   4   0   3   2   8   3   4   2   9   9   7   0   4   5   2   5   0
5   0   5   3   5   4   3   4   5   5   9   9   8   6   3   8   0   5   8   8   4
Big data (Panel):
Int64Index([3, 5], dtype=int64)
Big data (Panel4d):
Int64Index([3, 5], dtype=int64)

In the Pandas issue thread there is a very interesting monkey patch that can return arbitrary data based on our small data selection, but I generally tend to shy away from monkey patches as being hard to debug after a few months have passed since the code was written.


Popular posts from this blog

Python: Multiprocessing: passing multiple arguments to a function

Write a wrapper function to unpack the arguments before calling the real function. Lambda won't work, for some strange un-Pythonic reason.

import multiprocessing as mp def myfun(a,b): print a + b def mf_wrap(args): return myfun(*args) p = mp.Pool(4) fl = [(a,b) for a in range(3) for b in range(2)] #mf_wrap = lambda args: myfun(*args) -> this sucker, though more pythonic and compact, won't work, fl)

Flowing text in inkscape (Poster making)

You can flow text into arbitrary shapes in inkscape. (From a hint here).

You simply create a text box, type your text into it, create a frame with some drawing tool, select both the text box and the frame (click and shift) and then go to text->flow into frame.


The omnipresent anonymous asked:
Trying to enter sentence so that text forms the number three...any ideas?
The solution:
Type '3' using the text toolConvert to path using object->pathSize as necessaryRemove fillUngroupType in actual text in new text boxSelect the text and the '3' pathFlow the text

Drawing circles using matplotlib

Use the pylab.Circle command

import pylab #Imports matplotlib and a host of other useful modules cir1 = pylab.Circle((0,0), radius=0.75, fc='y') #Creates a patch that looks like a circle (fc= face color) cir2 = pylab.Circle((.5,.5), radius=0.25, alpha =.2, fc='b') #Repeat (alpha=.2 means make it very translucent) ax = pylab.axes(aspect=1) #Creates empty axes (aspect=1 means scale things so that circles look like circles) ax.add_patch(cir1) #Grab the current axes, add the patch to it ax.add_patch(cir2) #Repeat