如何使用熊猫或普通python覆盖csv文件的特定列？

Question

I am new to python. I have a .csv file which has 13 columns. I want to round off the floating values of the 2nd column which I was able to achieve successfully. I did this and stored it in a list. Now I am unable to figure out how to overwrite the rounded off values into the same csv file and into the same column ie column 2? I am using python3. Any help will be much appreciated.

My code is as follows:

Import statements for module import:

import csv

Creating an empty list:

list_string = []

Reading a csv file

with open('/home/user/Desktop/wine.csv', 'r') as csvDataFile:

    csvReader = csv.reader(csvDataFile, delimiter = ',')
    next(csvReader, None)                              
    for row in csvReader:
        floatParse = float(row[1])               
        closestInteger = int(round(floatParse))
        stringConvert = str(closestInteger)
        list_string.append(stringConvert)
print(list_string)

Writing into the same csv file for the second column (Overwrites the entire Excel file)

with open('/home/user/Desktop/wine.csv', 'w') as csvDataFile:

    writer = csv.writer(csvDataFile)
    next(csvDataFile)
    row[1] = list_string
    writer.writerows(row[1])

PS: The writing into the csv overwrites the entire csv and removes all the other columns which I don't want. I just want to overwrite the 2nd column with rounded off values and keep the rest of the data same.

Answer 1

this might be what you're looking for.

import pandas as pd
import numpy as np
#Some sample data
data = {"Document_ID": [102994,51861,51879,38242,60880,76139,76139],
    "SecondColumnName":    [7.256,1.222,3.16547,4.145658,4.154656,6.12,17.1568],
   }
wine = pd.DataFrame(data)

#This is how you'd read in your data
#wine = pd.read_csv('/home/user/Desktop/wine.csv')

#Replace the SecondColumnName with the real name
wine["SecondColumnName"] = wine["SecondColumnName"].map('{:,.2f}'.format)

#This will overwrite the sheet, but it will have all the data as before
wine.to_csv(/home/user/Desktop/wine.csv')

Pandas is way easier than read csv...I'd recommended checking it out.

Answer 2

I think this better answers the specific question. The key to this is to define an input_file and an output_file during the with part.

The StringIO part is just there for sample data in this example. newline='' is for Python 3. Without it, blank lines between each row appears in the output. More info .

import csv
from io import StringIO

s = '''A,B,C,D,E,F,G,H,I,J,K,L
1,4.4343,3,4,5,6,7,8,9,10,11
1,8.6775433,3,4,5,6,7,8,9,10,11
1,16.83389832,3,4,5,6,7,8,9,10,11
1,32.2711122,3,4,5,6,7,8,9,10,11
1,128.949483,3,4,5,6,7,8,9,10,11'''

list_string = []

with StringIO(s) as input_file, open('output_file.csv', 'w', newline='') as output_file:
  reader = csv.reader(input_file)
  next(reader, None)
  writer = csv.writer(output_file)
  for row in reader:
    floatParse = float(row[1]) + 1
    closestInteger = int(round(floatParse))
    stringConvert = str(closestInteger)
    row[1] = stringConvert
    writer.writerow(row)