What is the best way to take a data file that contains a header row and read this row into a named tuple so that the data rows can be accessed by header name?
I was attempting something like this:
import csv
from collections import namedtuple
with open('data_file.txt', mode="r") as infile:
reader = csv.reader(infile)
Data = namedtuple("Data", ", ".join(i for i in reader[0]))
next(reader)
for row in reader:
data = Data(*row)
The reader object is not subscriptable, so the above code throws a TypeError
. What is the pythonic way to reader a file header into a namedtuple?
Use:
Data = namedtuple("Data", next(reader))
and omit the line:
next(reader)
Combining this with an iterative version based on martineau's comment below, the example becomes for Python 2
import csv
from collections import namedtuple
from itertools import imap
with open("data_file.txt", mode="rb") as infile:
reader = csv.reader(infile)
Data = namedtuple("Data", next(reader)) # get names from column headers
for data in imap(Data._make, reader):
print data.foo
# ...further processing of a line...
and for Python 3
import csv
from collections import namedtuple
with open("data_file.txt", newline="") as infile:
reader = csv.reader(infile)
Data = namedtuple("Data", next(reader)) # get names from column headers
for data in map(Data._make, reader):
print(data.foo)
# ...further processing of a line...
Please have a look at csv.DictReader
. Basically, it provides the ability to get the column names from the first row as you're looking for and, after that, lets you access to each column in a row by name using a dictionary.
If for some reason you still need to access the rows as a collections.namedtuple
, it should be easy to transform the dictionaries to named tuples as follows:
with open('data_file.txt') as infile:
reader = csv.DictReader(infile)
Data = collections.namedtuple('Data', reader.fieldnames)
tuples = [Data(**row) for row in reader]