Number of cells, using CSV module

[tunacubes]

I'm using the csv module to get information from a csv file. I have items listed in Column A. I want to know how many items are listed in Column A.

import csv
with open('test.csv', 'r') as f:
reader = csv.reader(f)
for column in reader:
column = (column[0])
print(column)

We are given

a
b
c
d
e
f

How would I go about getting the amount of numbers that are printed? If I try printing len(column), I get

 

[Skip Montanaro]

Perhaps you want len(reader) instead? Or a counter which increments for
every row read which has an item in column A?

Skip

 

[tunacubes]

Perhaps you want len(reader) instead?  Or a counter which increments for every row read which has an item in column A?



Skip

len(reader) gives me an error.

I tried a counter, but unfortunately due to the simplicity of CSV files, any formulas are not saved.

 

[tunacubes]

I guess another way to accomplish this would be, is there any way that I can turn the returned value for (column) into 1 list?

So rather than

a
b
c
d
e
f

I would get [a, b, c, d, e, f]

 

[Skip Montanaro]

len(reader) gives me an error.

Apologies. len(list(reader)) should work. Of course, you'll wind up
loading the entire CSV file into memory. You might want to just count
row-by-row:

n = 0
for row in reader:
n += 1

Skip

 

[Skip Montanaro]

So rather than

a
b
c
d
e
f

I would get [a, b, c, d, e, f]

all_items = []
for row in reader:
all_items.append(row[0])

Skip

 

[Tim Chase]

Apologies. len(list(reader)) should work. Of course, you'll wind
up loading the entire CSV file into memory. You might want to just
count row-by-row:

n = 0
for row in reader:
n += 1

which can nicely be rewritten as

n = sum(1 for row in reader)



-tkc

 

[Tim Chase]

So rather than

a
b
c
d
e
f

I would get [a, b, c, d, e, f]

all_items = []
for row in reader:
all_items.append(row[0])

And following up here, this could be tidily rewritten as

all_items = [row[0] for row in reader]

-tkc

 

[tunacubes]

Thank you Skip, worked great. And thank you Tim for Tidying things up!