Spot the Difference — It’s NumPy! | Discovering Python & R

My first brush with NumPy happened over writing a block of code to make a plot using pylab. ⇣

pylab is part of matplotlib (in matplotlib.pylab) and tries to give you a MatLab like environment. matplotlib has a number of dependencies, among them numpy which it imports under the common alias np. scipy is not a dependency of matplotlib.

I had a tuple (of lows and highs of temperature) of lengh 2 with 31 entries in each (the number of days in the month of July), parsed from this text file:

	Boston July Temperatures
	-------------------------

	Day High Low
	------------

	1 91 70
	2 84 69
	3 86 68
	4 84 68
	5 83 70
	6 80 68
	7 86 73
	8 89 71
	9 84 67
	10 83 65
	11 80 66
	12 86 63
	13 90 69
	14 91 72
	15 91 72
	16 88 72
	17 97 76
	18 89 70
	19 74 66
	20 71 64
	21 74 61
	22 84 61
	23 86 66
	24 91 68
	25 83 65
	26 84 66
	27 79 64
	28 72 63
	29 73 64
	30 81 63
	31 73 63

view raw julyTemps.txt hosted with ❤ by GitHub

Given below, are 2 sets of code that do the same thing; one without NumPy and the other with NumPy. They output the following graph using PyLab:

Code without NumPy

	import pylab

	def loadfile():
	inFile = open('julyTemps.txt', 'r')
	high =[]; low = []
	for line in inFile:
	fields = line.split()
	if len(fields) < 3 or not fields[0].isdigit():
	pass
	else:
	high.append(int(fields[1]))
	low.append(int(fields[2]))
	return low, high

	def producePlot(lowTemps, highTemps):
	diffTemps = [highTemps[i] - lowTemps[i] for i in range(len(lowTemps))]
	pylab.title('Day by Day Ranges in Temperature in Boston in July 2012')
	pylab.xlabel('Days')
	pylab.ylabel('Temperature Ranges')
	return pylab.plot(range(1,32),diffTemps)

	producePlot(loadfile()[1], loadfile()[0])

view raw withoutNumPy.py hosted with ❤ by GitHub

Code with NumPy

	import pylab
	import numpy as np

	def loadFile():
	inFile = open('julyTemps.txt')
	high = [];vlow = []
	for line in inFile:
	fields = line.split()
	if len(fields) != 3 or 'Boston' == fields[0] or 'Day' == fields[0]:
	continue
	else:
	high.append(int(fields[1]))
	low.append(int(fields[2]))
	return (low, high)

	def producePlot(lowTemps, highTemps):
	diffTemps = list(np.array(highTemps) - np.array(lowTemps))
	pylab.plot(range(1,32), diffTemps)
	pylab.title('Day by Day Ranges in Temperature in Boston in July 2012')
	pylab.xlabel('Days')
	pylab.ylabel('Temperature Ranges')
	pylab.show()


	(low, high) = loadFile()
	producePlot(low, high)

view raw withNumPy.py hosted with ❤ by GitHub

The difference in code lies in how the variable diffTemps is calculated.

diffTemps = list(np.array(highTemps) - np.array(lowTemps))

seems more readable than

diffTemps = [highTemps[i] - lowTemps[i] for i in range(len(lowTemps))]

Notice how straight forward it is with NumPy. At the core of the NumPy package, is the ndarray object. This encapsulates n-dimensional arrays of homogeneous data types, with many operations being performed in compiled code for performance. element-by-element operations are the “default mode” when an ndarray is involved, but the element-by-element operation is speedily executed by pre-compiled C code.