pd.merge “类型错误：字符串索引必须是整数”

Question

I have 3 files and my code is basically a series of merges that populates data from files "lookup" and "NonPO" into the file "supplier" and create a new df called "final2" . The code runs perfectly fine and produces output I am expecting until the very last merge.

The issue occurs when the very last merge is done based on the new column on "supplier" (vendor number + vendor site code) called "Unique" with a column of the same name in the file "NonPO" . The only thing different with this merge is that it is based on a column that was created by concatenation (previous merges used columns that were already in the files). The concatenation joins columns that may contain letters and/or numbers, eg "260549" + "EXPENSE" = "260549EXPENSE" .

The error I am getting is:

    runfile('//eu.ad.hertz.com/userdocs/irac920/Desktop/My Files/Python/Supplier cat testing/file.py', wdir='//eu.ad.hertz.com/userdocs/irac920/Desktop/My Files/Python/Supplier cat testing')
Traceback (most recent call last):

  File "\\eu.ad.hertz.com\userdocs\irac920\Desktop\My Files\Python\Supplier cat testing\file.py", line 33, in <module>
    final2 = pd.merge(final2, NonPO[['Unique','Category']], on='Unique', how='left')

TypeError: string indices must be integers

My files:

1. "supplier" - ( File link )
2. "lookup" - ( File link )
3. "NonPO" - ( File link )

Any help with resolving this will be greatly appreciated. Thank you!

My code:

import pandas as pd
import numpy as np
pd.set_option('display.expand_frame_repr', False)


supplier = r'//eu.ad.hertz.com/userdocs/irac920/Desktop/My Files/Python/Supplier cat testing/Suppliers.xlsx'
lookup = r'//eu.ad.hertz.com/userdocs/irac920/Desktop/My Files/Python/Supplier cat testing/Lookup.xlsx'
NonPO = r'//eu.ad.hertz.com/userdocs/irac920/Desktop/My Files/Python/Supplier cat testing/Non-PO Suppliers.xlsx'

sr = pd.read_excel(supplier)
lp_type = pd.read_excel(lookup, sheet_name=0)
lp_paygroup = pd.read_excel(lookup, sheet_name=1)
NonPO_Suppliers = pd.read_excel(NonPO)

results_type = pd.merge(sr, lp_type[['Type','L1']], on='Type', how='left')
results_type.sort_values(by='Supplier', inplace=True)

results_paygroup = pd.merge(results_type, lp_paygroup[['Paygroup','L2']], on='Paygroup', how='left')
results_paygroup.sort_values(by='Supplier', inplace=True)

type_from_paygroup = results_paygroup.copy()
type_from_paygroup['L1'] = results_paygroup.merge(lp_paygroup, on='Paygroup', how='left').apply(lambda r: r.L1_x if (r.L1_y is np.nan or r.L2_y == 'Vendor Level') else r.L1_y, axis=1)
type_from_paygroup.sort_values(by='Supplier', inplace=True)

paygroup_from_type = type_from_paygroup.copy()
paygroup_from_type['L2'] = type_from_paygroup.merge(lp_type, on='Type', how='left').apply(lambda r: r.L2_x if (r.L2_y is np.nan or r.L2_y == 'Vendor Level') else r.L2_y, axis=1)
paygroup_from_type.sort_values(by='Supplier', inplace=True)
final = paygroup_from_type.replace(np.nan,'Missing')


final['Unique']=final['Vendor Number'].astype(str) + final['Vendor Site Code'].astype(str)
final2 = final.copy()
final2 = pd.merge(final2, NonPO[['Unique','Category']], on='Unique', how='left')
print(final2)

Answer 1

You are trying to access NonPO as your data frame, but in fact this is the variable that contains that filename, which is a string. Here it's clear

NonPO_Suppliers = pd.read_excel(NonPO)

Just change NonPO to NonPO_Suppliers and you should be fine.

final2 = pd.merge(final2, NonPO_Suppliers[['Unique','Category']], on='Unique', how='left')

Answer 2

Consider this:

NonPO = r'//eu.ad.hertz.com/userdocs/irac920/Desktop/My Files/Python/Supplier cat testing/Non-PO Suppliers.xlsx'
NonPO_Suppliers = pd.read_excel(NonPO) # this is the name of the DataFrame, not NonPO.

Consequently, you need to change your code to this:

final2 = pd.merge(final2, NonPO[['Unique','Category']], on='Unique', how='left')
final2 = pd.merge(final2, NonPO_Suppliers[['Unique','Category']], on='Unique', how='left')

Hopefully this will work.