To read the data, we're going to use the csv module. First, we need to import it:
In [1]:
import csv
Then, we need to go through all the rows in the file, and for each add the RecombinantFraction to the right genetic Line and InfectionStatus. To do so, we need to choose a data structure. Here we use a dictionary, where the keys are given by Line, and each value of the dictionary is another dictionary where the keys W and I index lists of RecombinantFraction.
In [2]:
my_data = {}
In [3]:
with open('../data/Singh2015_data.csv') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
my_line = row['Line']
my_status = row['InfectionStatus']
my_recomb = float(row['RecombinantFraction'])
# Test by printing the values
print(my_line, my_status, my_recomb)
# just print the first row
break
Now we need to perform operations for each row. First, we're going to check whether my_data already contains the Line for that row. If not, we'll create the key-value in the dictionary. Then, we're going to add the value to the list.
In [4]:
with open('../data/Singh2015_data.csv') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
my_line = row['Line']
my_status = row['InfectionStatus']
my_recomb = float(row['RecombinantFraction'])
# if my_line is not present in the dictionary:
if my_line not in my_data:
# create and initialize with a dictionary containing
# two empty lists
my_data[my_line] = {'W': [], 'I': []}
# Now insert the value in the right list
my_data[my_line][my_status].append(my_recomb)
Now we should have the data organized in a nice structure:
In [5]:
my_data
Out[5]:
Time to calculate the means and print the results:
In [6]:
for line in my_data:
print('Line', line, 'Average Recombination Rate:')
# extract the relevant data
my_subset = my_data[line]
for status in ['W', 'I']:
print(status, ':', end = '') # to prevent new line
my_mean = sum(my_subset[status])
my_num_elements = len(my_subset[status])
my_mean = my_mean / my_num_elements
print(' ', round(my_mean, 3))
print('') # to separate the lines