Boxplots comparing two different populations
A common scenario in research is trying to determine whether two groups systematically differ on some characteristic. We can simulate this situation by generating normally distributed random variates for imaginary groups 1 and 2. In fact there is a difference: group 2 has a higher mean of 105 compared to 95. Parallel boxplots are a good way of showing the distribution of scores across the two groups.
import pylab import random popSize = 100 category1 =  category2 =  ## Start by generating scores for category 1 individuals ## Their mean score is 95 for i in range(popSize): category1.append(random.normalvariate(95,10)) ## Now generate scores for category 2 individuals ## They have a higher mean of 105 for i in range(popSize): category2.append(random.normalvariate(105,10)) scores = [category1,category2] pylab.boxplot(scores) pylab.savefig('boxplots.png') pylab.show()
To show how to plot a line graph in Python, we can generate a fictional time series. Imagine a 100-day time span, and that the price of some commodity tends to increase by about 0.2 units per day.
```python import pylab import random
duration = 100 meanIncrease = 0.2 stDevIncrease = 1.2
Here we generate a fictional time series, for a
variable that generally increases over time but
has significant noise.
x = range(duration) y =  yNow = 0
for i in x: nextDelta = random.normalvariate(meanIncrease,stDevIncrease) yNow += nextDelta y.append(yNow)
pylab.plot(x,y) pylab.xlabel(“Time”) pylab.ylabel(“Value”) pylab.savefig(“lineGraph.png”) pylab.show()
 Ken Lambert, Fundamentals of Python: First Programs, 2012.