Python: skip comment lines marked with # in csv.DictReader

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Processing CSV files with csv.DictReader is great – but I have CSV files with comment lines (indicated by a hash at the start of a line), for example:

# step size=1.61853
val0,val1,val2,hybridisation,temp,smattr
0.206895,0.797923,0.202077,0.631199,0.368801,0.311052,0.688948,0.597237,0.402763
-169.32,1,1.61853,2.04069e-92,1,0.000906546,0.999093,0.241356,0.758644,0.202382
# adaptation finished

The csv module doesn’t include any way to skip such lines.

I could easily do something hacky, but I imagine there’s a nice way to wrap a csv.DictReader around some other iterator object, which preprocesses to discard the lines.

Actually this works nicely with filter:

import csv
fp = open('samples.csv')
rdr = csv.DictReader(filter(lambda row: row[0]!='#', fp))
for row in rdr:
    print(row)
fp.close()

Good question. Python’s CSV library lacks basic support for comments (not uncommon at the top of CSV files). While Dan Stowell’s solution works for the specific case of the OP, it is limited in that # must appear as the first symbol. A more generic solution would be:

def decomment(csvfile):
    for row in csvfile:
        raw = row.split('#')[0].strip()
        if raw: yield raw

with open('dummy.csv') as csvfile:
    reader = csv.reader(decomment(csvfile))
    for row in reader:
        print(row)

As an example, the following dummy.csv file:

# comment
 # comment
a,b,c # comment
1,2,3
10,20,30
# comment

returns

['a', 'b', 'c']
['1', '2', '3']
['10', '20', '30']

Of course, this works just as well with csv.DictReader().

Another way to read a CSV file is using pandas

Here’s a sample code:

df = pd.read_csv('test.csv',
                 sep=',',     # field separator
                 comment="#", # comment
                 index_col=0, # number or label of index column
                 skipinitialspace=True,
                 skip_blank_lines=True,
                 error_bad_lines=False,
                 warn_bad_lines=True
                 ).sort_index()
print(df)
df.fillna('no value', inplace=True) # replace NaN with 'no value'
print(df)

For this csv file:

a,b,c,d,e
1,,16,,55#,,65##77
8,77,77,,16#86,18#
#This is a comment
13,19,25,28,82

we will get this output:

       b   c     d   e
a                     
1    NaN  16   NaN  55
8   77.0  77   NaN  16
13  19.0  25  28.0  82
           b   c         d   e
a                             
1   no value  16  no value  55
8         77  77  no value  16
13        19  25        28  82

Just posting the bugfix from @sigvaldm’s solution.

def decomment(csvfile):
for row in csvfile:
    raw = row.split('#')[0].strip()
    if raw: yield row

with open('dummy.csv') as csvfile:
    reader = csv.reader(decomment(csvfile))
    for row in reader:
        print(row)

A CSV line can contain “#” characters in quoted strings and is perfectly valid. The previous solution was cutting off strings containing ‘#’ characters.


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