Say I have a CSV file. It contains:
a,b,c
1,2,3
4,5,6
7,8,9
How can I turn the column labeled 'a' into a list, without hard-coding a row to it?
Originally, I was doing a readline for the first line a,b,c and then hard-coding a row to each variable. But then I thought: what if I wanted to do it with the possibility of the CSV file not being arranged the same way - say:
b,a,c
2,1,3
5,4,6
8,7,9
What's the best way to go about this?
You can do this using the pandas library:
import pandas
data = pandas.read_csv("test.csv", header=0)
col_a = list(data.a)
col_b = list(data.b)
col_c = list(data.c)
The values in col_a, col_b and col_c are as follows:
[1, 4, 7]
[2, 5, 8]
[3, 6, 9]
csv.DictReader turns each row of your CSV file into a dictionary with the column headers as keys. For your case (assuming the two examples in your question are named abc.csv and bac.csv respectively), you could use it like this:
from csv import DictReader
with open("abc.csv") as f:
a1 = [row["a"] for row in DictReader(f)]
with open("bac.csv") as f:
a2 = [row["a"] for row in DictReader(f)]
# a1 == a2 == ['1', '4', '7']
you can use collections.defaultdict:
import collections
my_dict = collections.defaultdict(list)
with open('your_file') as f:
header = next(f).strip().split(',')
for x in f:
x = x.strip().split(',')
my_dict[header[0]].append(x[0])
my_dict[header[1]].append(x[1])
my_dict[header[2]].append(x[2])
