Vfrdtyky

To skip the first line just call:

next(inf)

Files in Python are iterators over lines.

You would normally use next(incsv) which advances the iterator one row, so you skip the header. The other (say you wanted to skip 30 rows) would be:

from itertools import islice

for row in islice(incsv, 30, None):

    # process

In a similar use case I had to skip annoying lines before the line with my actual column names. This solution worked nicely. Read the file first, then pass the list to csv.DictReader.

with open('all16.csv') as tmp:

    # Skip first line (if any)

    next(tmp, None)



    # {line_num: row}

    data = dict(enumerate(csv.DictReader(tmp)))

Borrowed from python cookbook,

A more concise template code might look like this:

import csv

with open('stocks.csv') as f:

    f_csv = csv.reader(f) 

    headers = next(f_csv) 

    for row in f_csv:

        # Process row ...

use csv.DictReader instead of csv.Reader.
If the fieldnames parameter is omitted, the values in the first row of the csvfile will be used as field names. you would then be able to access field values using row["1"] etc

The new 'pandas' package might be more relevant than 'csv'. The code below will read a CSV file, by default interpreting the first line as the column header and find the minimum across columns.

import pandas as pd



data = pd.read_csv('all16.csv')

data.min()

Well, my mini wrapper library would do the job as well.

>>> import pyexcel as pe

>>> data = pe.load('all16.csv', name_columns_by_row=0)

>>> min(data.column[1])

Meanwhile, if you know what header column index one is, for example "Column 1", you can do this instead:

>>> min(data.column["Column 1"])

For me the easiest way to go is to use range.

import csv



with open('files/filename.csv') as I:

    reader = csv.reader(I)

    fulllist = list(reader)



# Starting with data skipping header

for item in range(1, len(fulllist)): 

    # Print each row using "item" as the index value

    print (fulllist[item])  

The documentation for the Python 3 CSV module provides this example:

with open('example.csv', newline='') as csvfile:

    dialect = csv.Sniffer().sniff(csvfile.read(1024))

    csvfile.seek(0)

    reader = csv.reader(csvfile, dialect)

    # ... process CSV file contents here ...

The Sniffer will try to auto-detect many things about the CSV file. You need to explicitly call its has_header() method to determine whether the file has a header line. If it does, then skip the first row when iterating the CSV rows. You can do it like this:

if sniffer.has_header():

    for header_row in reader:

        break

for data_row in reader:

    # do something with the row

I would use tail to get rid of the unwanted first line:

tail -n +2 $INFIL | whatever_script.py 

just add [1:]

example below:

data = pd.read_csv("/Users/xyz/Desktop/xyxData/xyz.csv", sep=',', header=None)**[1:]**

that works for me in iPython

Python 3.X

Handles UTF8 BOM + HEADER

It was quite frustrating that the csv module could not easily get the header, there is also a bug with the UTF-8 BOM (first char in file).
This works for me using only the csv module:

import csv



def read_csv(self, csv_path, delimiter):

    with open(csv_path, newline='', encoding='utf-8') as f:

        # https://bugs.python.org/issue7185

        # Remove UTF8 BOM.

        txt = f.read()[1:]



    # Remove header line.

    header = txt.splitlines()[:1]

    lines = txt.splitlines()[1:]



    # Convert to list.

    csv_rows = list(csv.reader(lines, delimiter=delimiter))



    for row in csv_rows:

        value = row[INDEX_HERE]

Because this is related to something I was doing, I'll share here.

What if we're not sure if there's a header and you also don't feel like importing sniffer and other things?

If your task is basic, such as printing or appending to a list or array, you could just use an if statement:

# Let's say there's 4 columns

with open('file.csv') as csvfile:

     csvreader = csv.reader(csvfile)

# read first line

     first_line = next(csvreader)

# My headers were just text. You can use any suitable conditional here

     if len(first_line) == 4:

          array.append(first_line)

# Now we'll just iterate over everything else as usual:

     for row in csvreader:

          array.append(row)