I've just started to learn python. I'm curious about what are the efficient ways to count the occurrence of a specific word in a CSV file, other than simply use for loop to go through line by line and read.
To be more specific, let's say I have a CSV file contain two columns, "Name" and "Grade", with millions of records.
How would one count the occurrence of "A" under "Grade"?
Python code samples would be greatly appreciated!
Basic example, with using csv and collections.Counter (Python 2.7+) from standard Python libraly:
import csv
import collections
grades = collections.Counter()
with open('file.csv') as input_file:
for row in csv.reader(input_file, delimiter=';'):
grades[row[1]] += 1
print 'Number of A grades: %s' % grades['A']
print grades.most_common()
Output (for small dataset):
Number of A grades: 2055
[('A', 2055), ('B', 2034), ('D', 1995), ('E', 1977), ('C', 1939)]
Counter on a generator expression for the first element of the lines in the file
Counter is a dict subclass, but it's little slower. If you really need speedup collections.defaultdict(int) or if ... count += 1 probably will be faster. But you always can benchmark it by yourself with timeit, see Johnsyweb's answerYou should of course read all the grades, which in this case also means reading the entire file. You can use the csv module to easily read comma separated value files:
import csv
my_reader = csv.reader(open('my_file.csv'))
ctr = 0
for record in my_reader:
if record[1] == 'A':
ctr += 1
print(ctr)
This is pretty fast, and I couldn't do better with the Counter method:
from collections import Counter
grades = [rec[1] for rec in my_reader] # generator expression was actually slower
result = Counter(grades)
print(result)
Last but not least, lists have a count method:
from collections import Counter
grades = [rec[1] for rec in my_reader]
result = grades.count('A')
print(result)
import csv; count = sum(1 for row in csv.dictreader(open(filename)) if row['Grade'] == 'A')