How to remove duplicates from a csv file

You can use the following script:

pre-condition:

1. 1.csv is the file that consists the duplicates
2. 2.csv is the output file that will be devoid of the duplicates once this script is executed.

code

inFile = open('1.csv','r')

outFile = open('2.csv','w')

listLines = []

for line in inFile:

    if line in listLines:
        continue

    else:
        outFile.write(line)
        listLines.append(line)

outFile.close()

inFile.close()

Algorithm Explanation

Here, what I am doing is:

1. opening a file in the read mode. This is the file that has the duplicates.
2. Then in a loop that runs till the file is over, we check if the line
   has already encountered.
3. If it has been encountered than we don't write it to the output file.
4. If not we will write it to the output file and add it to the list of records that have been encountered already

A more efficient version of @jamylak's solution: (with one less instruction)

with open('1.csv','r') as in_file, open('2.csv','w') as out_file:
    seen = set() # set for fast O(1) amortized lookup
    for line in in_file:
        if line not in seen: 
            seen.add(line)
            out_file.write(line)

To edit the same file in-place you could use this

import fileinput
seen = set() # set for fast O(1) amortized lookup
for line in fileinput.FileInput('1.csv', inplace=1):
    if line not in seen:
        seen.add(line)
        print line, # standard output is now redirected to the file   

You can efficiently remove duplicates using Pandas, which can be installed with pip, or comes installed with the Anaconda distribution of python.

See pandas.DataFrame.drop_duplicates

pip install pandas

The code

import pandas as pd
file_name = "my_file_with_dupes.csv"
file_name_output = "my_file_without_dupes.csv"

df = pd.read_csv(file_name, sep="t or ,")

# Notes:
# - the `subset=None` means that every column is used 
#    to determine if two rows are different; to change that specify
#    the columns as an array
# - the `inplace=True` means that the data structure is changed and
#   the duplicate rows are gone  
df.drop_duplicates(subset=None, inplace=True)

# Write the results to a different file
df.to_csv(file_name_output, index=False)

For encoding issues, set encoding=... with the appropriate type from python Standard Encodings.

See Import CSV file as a pandas DataFrame for more details about pd.read_csv

I know this is long settled, but I have had a closely related problem whereby I was to remove duplicates based on one column. The input csv file was quite large to be opened on my pc by MS Excel/Libre Office Calc/Google Sheets; 147MB with about 2.5 million records. Since I did not want to install a whole external library for such a simple thing, I wrote the python script below to do the job in less than 5 minutes. I didn't focus on optimization, but I believe it can be optimized to run faster and more efficient for even bigger files. The algorithm is similar to @IcyFlame above, except that I am removing duplicates based on a column ('CCC') instead of whole row/line.

import csv

with open('results.csv', 'r') as infile, open('unique_ccc.csv', 'a') as outfile:
    # this list will hold unique ccc numbers,
    ccc_numbers = []
    # read input file into a dictionary, there were some null bytes in the infile
    results = csv.DictReader(infile)
    writer = csv.writer(outfile)

    # write column headers to output file
    writer.writerow(
        ['ID', 'CCC', 'MFLCode', 'DateCollected', 'DateTested', 'Result', 'Justification']
    )
    for result in results:
        ccc_number = result.get('CCC')
        # if value already exists in the list, skip writing it whole row to output file
        if ccc_number in ccc_numbers:
            continue
        writer.writerow([
            result.get('ID'),
            ccc_number,
            result.get('MFLCode'),
            result.get('datecollected'),
            result.get('DateTested'),
            result.get('Result'),
            result.get('Justification')
        ])

        # add the value to the list to so as to be skipped subsequently
        ccc_numbers.append(ccc_number)