Is there an efficient way to store each column of a tab-delimited file in a separate dictionary using python?
A sample input file: (Real input file contains thousands of lines and hundreds of columns. Number of columns is not fixed, it changes frequently.)
A B C
1 4 7
2 5 8
3 6 9
I need to print values in column A:
for cell in mydict["A"]:
print cell
and to print values in the same row:
for i in range(1, numrows):
for key in keysOfMydict:
print mydict[key][i]
The simplest way is to use DictReader from the csv module:
with open('somefile.txt', 'r') as f:
reader = csv.DictReader(f, delimiter='\t')
rows = list(reader) # If your file is not large, you can
# consume it entirely
# If your file is large, you might want to
# step over each row:
#for row in reader:
# print(row['A'])
for row in rows:
print(row['A'])
@Marius made a good point - that you might be looking to collect all columns separately by their header.
If that's the case, you'll have to adjust your reading logic a bit:
from collections import defaultdict
by_column = defaultdict(list)
for row in rows:
for k,v in row.iteritems():
by_column[k].append(v)
Another option is pandas:
>>> import pandas as pd
>>> i = pd.read_csv('foo.csv', sep=' ')
>>> i
A B C
0 1 4 7
1 2 5 8
2 3 6 9
>>> i['A']
0 1
1 2
2 3
Name: A, dtype: int64
{'A': [all vals in column A], 'B': [all vals in column B]}, not individual dicts for each row like DictReader provides.Not sure this is relevant, but you can do this using rpy2.
from rpy2 import robjects
dframe = robjects.DataFrame.from_csvfile('/your/csv/file.csv', sep=' ')
d = dict([(k, list(v)) for k, v in dframe.items()])
output:
{'A': [1, 2, 3], 'C': [7, 8, 9], 'B': [4, 5, 6]}
