Python: Add data to first CSV column

Question

Using Python v2.7.4:

I have the following CSV file:

Item Number,Item Description,List Price,QTY Available
2000-000-000-380,AC - CF/M Series Green For White Hood,299.99,3
2000-000-000-400,AC - CF/M Series Orange For Black Hood,299.99,3
2000-000-000-480,AC - CF/M Series Orange For White Hood,299.99,3

I have been trying to change the file to:

Fulfillment,SKU,Qty
US,2000-000-300,3
US,2000-000-380,3
US,2000-000-400,3

So far I have the following code:

import csv
import os

inputFileName = "temp_modified.csv"
outputFileName = os.path.splitext(inputFileName)[0] + "_pro.csv"

with open(inputFileName, "rb") as inFile, open(outputFileName, "wb") as outfile:
    r = csv.reader(inFile)    
    w = csv.writer(outfile)

    r.next()    
    w.writerow(['Fulfillment', 'SKU', 'Qty'])

    for row in r:
        w.writerow((row[0], row[3]))

With this code I get the following output:

Fulfillment,SKU,Qty
2000-000-000-380,3
2000-000-000-400,3
2000-000-000-480,3

How do I insert US to the beginning column? (Just for reference there is more than just 3 rows in these csv files but for space I left out the rest.)

Martijn Pieters · Accepted Answer · 2013-05-01 15:20:59Z

5

Just add a literal string to your row:

for row in r:
    w.writerow(('US', row[0], row[3]))

answered May 1, 2013 at 15:20

Martijn Pieters

1.1m326 gold badges4.2k silver badges3.4k bronze badges

Sign up to request clarification or add additional context in comments.

Comments

DSM · Accepted Answer · 2013-05-01 15:52:07Z

If you're going to be doing a lot of csv manipulation, I strongly recommend looking at the pandas library. It makes a lot of things much simpler. Your code would become something like

import pandas as pd

df = pd.read_csv("temp_modified.csv")
df["Fulfillment"] = "US"
df = df.rename_axis({"Item Number": "SKU", "QTY Available": "QTY"})
df = df[["Fulfillment", "SKU", "QTY"]]
df.to_csv("temp_modified_pro.csv", index=False)

Some explanation follows. First, read in the csv file into an object called a DataFrame:

>>> import pandas as pd
>>> df = pd.read_csv("temp_modified.csv")
>>> df
        Item Number                        Item Description  List Price  \
0  2000-000-000-380   AC - CF/M Series Green For White Hood      299.99   
1  2000-000-000-400  AC - CF/M Series Orange For Black Hood      299.99   
2  2000-000-000-480  AC - CF/M Series Orange For White Hood      299.99   

   QTY Available  
0              3  
1              3  
2              3

Then add a column called "Fulfillment":

>>> df["Fulfillment"] = "US"
>>> df
        Item Number                        Item Description  List Price  \
0  2000-000-000-380   AC - CF/M Series Green For White Hood      299.99   
1  2000-000-000-400  AC - CF/M Series Orange For Black Hood      299.99   
2  2000-000-000-480  AC - CF/M Series Orange For White Hood      299.99   

   QTY Available Fulfillment  
0              3          US  
1              3          US  
2              3          US

Then rename the axes:

>>> df = df.rename_axis({"Item Number": "SKU", "QTY Available": "QTY"})
>>> df
                SKU                        Item Description  List Price  QTY  \
0  2000-000-000-380   AC - CF/M Series Green For White Hood      299.99    3   
1  2000-000-000-400  AC - CF/M Series Orange For Black Hood      299.99    3   
2  2000-000-000-480  AC - CF/M Series Orange For White Hood      299.99    3   

  Fulfillment  
0          US  
1          US  
2          US

Select the columns you want:

>>> df = df[["Fulfillment", "SKU", "QTY"]]
>>> df
  Fulfillment               SKU  QTY
0          US  2000-000-000-380    3
1          US  2000-000-000-400    3
2          US  2000-000-000-480    3

And finally write it out to a csv, without including an extra index column (the numbers on the left, the row labels):

>>> df.to_csv("temp_modified_pro.csv", index=False)
>>> !cat temp_modified_pro.csv
Fulfillment,SKU,QTY
US,2000-000-000-380,3
US,2000-000-000-400,3
US,2000-000-000-480,3

Collectives™ on Stack Overflow

Python: Add data to first CSV column

2 Answers 2

Comments

1 Comment

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

2 Answers 2

Comments

1 Comment

Your Answer

Sign up or log in

Post as a guest

Related