Reading column names alone in a csv file

Solution 1:

Though you already have an accepted answer, I figured I'd add this for anyone else interested in a different solution-

• Python's DictReader object in the CSV module (as of Python 2.6 and above) has a public attribute called fieldnames. https://docs.python.org/3.4/library/csv.html#csv.csvreader.fieldnames

An implementation could be as follows:

import csv

with open('C:/mypath/to/csvfile.csv', 'r') as f:
    d_reader = csv.DictReader(f)

    #get fieldnames from DictReader object and store in list
    headers = d_reader.fieldnames

    for line in d_reader:
        #print value in MyCol1 for each row
        print(line['MyCol1'])

In the above, d_reader.fieldnames returns a list of your headers (assuming the headers are in the top row). Which allows...

>>> print(headers)
['MyCol1', 'MyCol2', 'MyCol3']

If your headers are in, say the 2nd row (with the very top row being row 1), you could do as follows:

import csv

with open('C:/mypath/to/csvfile.csv', 'r') as f:
    #you can eat the first line before creating DictReader.
    #if no "fieldnames" param is passed into
    #DictReader object upon creation, DictReader
    #will read the upper-most line as the headers
    f.readline()

    d_reader = csv.DictReader(f)
    headers = d_reader.fieldnames

    for line in d_reader:
        #print value in MyCol1 for each row
        print(line['MyCol1'])

Solution 2:

You can read the header by using the next() function which return the next row of the reader’s iterable object as a list. then you can add the content of the file to a list.

import csv
with open("C:/path/to/.filecsv", "rb") as f:
    reader = csv.reader(f)
    i = reader.next()
    rest = list(reader)

Now i has the column's names as a list.

print i
>>>['id', 'name', 'age', 'sex']

Also note that reader.next() does not work in python 3. Instead use the the inbuilt next() to get the first line of the csv immediately after reading like so:

import csv
with open("C:/path/to/.filecsv", "rb") as f:
    reader = csv.reader(f)
    i = next(reader)

    print(i)
    >>>['id', 'name', 'age', 'sex']

Solution 3:

The csv.DictReader object exposes an attribute called fieldnames, and that is what you'd use. Here's example code, followed by input and corresponding output:

import csv
file = "/path/to/file.csv"
with open(file, mode='r', encoding='utf-8') as f:
    reader = csv.DictReader(f, delimiter=',')
    for row in reader:
        print([col + '=' + row[col] for col in reader.fieldnames])

Input file contents:

col0,col1,col2,col3,col4,col5,col6,col7,col8,col9
00,01,02,03,04,05,06,07,08,09
10,11,12,13,14,15,16,17,18,19
20,21,22,23,24,25,26,27,28,29
30,31,32,33,34,35,36,37,38,39
40,41,42,43,44,45,46,47,48,49
50,51,52,53,54,55,56,57,58,59
60,61,62,63,64,65,66,67,68,69
70,71,72,73,74,75,76,77,78,79
80,81,82,83,84,85,86,87,88,89
90,91,92,93,94,95,96,97,98,99

Output of print statements:

['col0=00', 'col1=01', 'col2=02', 'col3=03', 'col4=04', 'col5=05', 'col6=06', 'col7=07', 'col8=08', 'col9=09']
['col0=10', 'col1=11', 'col2=12', 'col3=13', 'col4=14', 'col5=15', 'col6=16', 'col7=17', 'col8=18', 'col9=19']
['col0=20', 'col1=21', 'col2=22', 'col3=23', 'col4=24', 'col5=25', 'col6=26', 'col7=27', 'col8=28', 'col9=29']
['col0=30', 'col1=31', 'col2=32', 'col3=33', 'col4=34', 'col5=35', 'col6=36', 'col7=37', 'col8=38', 'col9=39']
['col0=40', 'col1=41', 'col2=42', 'col3=43', 'col4=44', 'col5=45', 'col6=46', 'col7=47', 'col8=48', 'col9=49']
['col0=50', 'col1=51', 'col2=52', 'col3=53', 'col4=54', 'col5=55', 'col6=56', 'col7=57', 'col8=58', 'col9=59']
['col0=60', 'col1=61', 'col2=62', 'col3=63', 'col4=64', 'col5=65', 'col6=66', 'col7=67', 'col8=68', 'col9=69']
['col0=70', 'col1=71', 'col2=72', 'col3=73', 'col4=74', 'col5=75', 'col6=76', 'col7=77', 'col8=78', 'col9=79']
['col0=80', 'col1=81', 'col2=82', 'col3=83', 'col4=84', 'col5=85', 'col6=86', 'col7=87', 'col8=88', 'col9=89']
['col0=90', 'col1=91', 'col2=92', 'col3=93', 'col4=94', 'col5=95', 'col6=96', 'col7=97', 'col8=98', 'col9=99']

Solution 4:

How about

with open(csv_input_path + file, 'r') as ft:
    header = ft.readline() # read only first line; returns string
    header_list = header.split(',') # returns list

I am assuming your input file is CSV format. If using pandas, it takes more time if the file is big size because it loads the entire data as the dataset.