使用python将特定数据从一个Excel文件传输到另一个

Question

I just started learning Python and I need help with a script my internship asked me to write.

I have a csv file (sheet1.csv) and I need to extract data from only two of the columns which have the headers referenceID and PartNumber that correspond to each other. I need to update a separate csv file called sheet2.csv which also contains the two columns referenceID and PartNumber however many of the PartNumber cells are empty.

Basically I need to fill in the “PartNumber” field with the values from sheet1. From the research I've done I've decided using dictionaries are a solid approach to writing this script (I think). So far I have been able to read the files and create two dictionaries with the referenceIDs as the keys and the PartNumber as values… Here is what I have showing an example of what the dictionaries look like.

import csv 
a = open('sheet1.csv', 'rU')
b = open('sheet2.csv', 'rU')
csvReadera = csv.DictReader(a)
csvReaderb = csv.DictReader(b)
a_dict = {}
b_dict = {}

for line in csvReadera:
    a_dict[line["ReferenceID"]] = line["PartNumber"]
print(a_dict)

for line in csvReaderb:
    b_dict[line["ReferenceID"]] = line["PartNumber"]
print(b_dict)

a_dict = {'R150': 'PN000123', 'R331': 'PN000873', 'C774': 'PN000064', 'L7896': 'PN000447', 'R0640': 'PN000878', 'R454': 'PN000333'}
b_dict = {'C774': '', 'R331': '', 'R454': '', 'L7896': 'PN000000', 'R0640': '', 'R150': 'PN000333'}

How can I compare the two dictionaries and fill in/overwrite the missing values for b-dict and then write to sheet2? Certainly, there must be more efficient methods than what I have come up with, but I have never used Python before so please forgive my pitiful attempt!

Answer 1

have a look at the pandas library.

import padas as pd

#this is how you read
dfa = pd.read_csv("sheet1.csv")
dfb = pd.read_csv("sheet2.csv")

let s jus take the dicts you defined as testdata

a_dict = {'R150': 'PN000123', 'R331': 'PN000873', 'C774': 'PN000064', 'L7896': 'PN000447', 'R0640': 'PN000878', 'R454': 'PN000333'}
b_dict = {'C774': '', 'R331': '', 'R454': '', 'L7896': 'PN000000', 'R0640': '', 'R150': 'PN000333'}
dfar = pd.DataFrame(a_dict.items(), columns = ['ReferenceID', 'PartNumber'])
dfbr = pd.DataFrame(b_dict.items(), columns = ['ReferenceID', 'PartNumber'])
dfa = dfar[['ReferenceID', 'PartNumber']]
dfa.columns = ['ReferenceIDA', 'PartNumberA']
dfb = dfbr[['ReferenceID', 'PartNumber']]
dfb.columns = ['ReferenceIDB', 'PartNumberB']

you get this

  In [97]: dfa
Out[97]: 
  ReferenceIDA PartNumberA
0         R331    PN000873
1         R454    PN000333
2        L7896    PN000447
3         R150    PN000123
4         C774    PN000064
5        R0640    PN000878

In [98]: dfb
Out[98]: 
  ReferenceIDB PartNumberB
0         R331            
1         R454            
2        R0640            
3         R150    PN000333
4         C774            
5        L7896    PN000000

now

    In [67]: cd = pd.concat([dfa,dfb], axis=1)

    In [68]: cd
    Out[68]: 
  ReferenceIDA PartNumberA ReferenceIDB PartNumberB
0         R331    PN000873         R331            
1         R454    PN000333         R454            
2        L7896    PN000447        R0640            
3         R150    PN000123         R150    PN000333
4         C774    PN000064         C774            
5        R0640    PN000878        L7896    PN000000




cd["res"] = cd.apply(lambda x : x["PartNumberB"] if x["PartNumberB"] else x["PartNumberA"], axis=1)

 cd
Out[106]: 
  ReferenceIDA PartNumberA ReferenceIDB PartNumberB       res
0         R331    PN000873         R331              PN000873
1         R454    PN000333         R454              PN000333
2        L7896    PN000447        R0640              PN000447
3         R150    PN000123         R150    PN000333  PN000333
4         C774    PN000064         C774              PN000064
5        R0640    PN000878        L7896    PN000000  PN000000

this is what you wanted

just set

dfbr['PartNumber'] = cd['res']

and dump to csv

dfbr.to_csv('sheet2.csv')