# Workshop 4: Nested Loops¶

In [1]:
#in row i we have data for setting i
#in column j we have data for subject j
hr = [ [ 72,   75,  71,  73, 0],   # resting
[ 91,   90,  94,  93, 0],   # walking slowly
[ 130, 135, 139, 142, 0],   # running on treadmill
[ 120, 118, 110, 105, 0]]   # after minute recovery


Suppose we want to get the average for every subject. (That is, we want the average for each column.) First, let's write a function that computes the average for column col_ind.

In [2]:
def get_col_avg(data, col_ind):
'''Return the average of column col_ind in a the
nested list data'''
s = 0
for setting in data:
s += setting[col_ind]
return s/len(data)


Now, we can get the average for each subject/column using a for-loop:

In [3]:
col_avgs = []
for col_ind in range(len(hr[0])):
col_avgs.append(get_col_avg(hr, col_ind))


Here's a different approach: make a nested loop -- a loop within a loop

In [5]:
subj_avgs = []
for subject_ind in range(len(hr[0])):
s = 0
for setting_ind in range(len(hr)):
s += hr[setting_ind][subject_ind]

subj_avgs.append(s/len(hr))

In [6]:
from IPython.display import YouTubeVideo

Out[6]:

Here is something that is more directly analguous to the approach we took when we called get_call_avg:

In [7]:
subj_avgs = []
for subject_ind in range(len(hr[0])):
s = 0
for setting in hr:
s += setting[subject_ind]
subj_avgs.append(s/len(hr))


As you can see, nested loops -- loops within loops -- can be avoided by instead calling functions (which contain loops) from within loops.