How to group by and plot sales data after grouping by month and year, and sales amount for each part in python?

Question

Below is my current code. I can get everything to run, except for the plot. Any suggestions would be greatly appreciated. The data being used is sales data over the coarse of 5 years. Column headings are:

Column Dtype

---

0 internal_fl object 1 invoicedate object 2 refkey3 object 3 refkey_desc object 4 ordernbr int64
5 orderlinenbr int64
6 ecwebordnbr object 7 ordertype object 8 order_source_group object 9 order_source_desc object 10 claim_no float64 11 resp object 12 cl_type object 13 credit_type object 14 acmodel object 15 acserial object 16 customer int64
17 cust_type_desc object 18 cust_type_group object 19 qty int64
20 salesamount_usd float64 21 item object 22 non_item float64 23 listprice float64 24 custprice float64 25 addl_disc_fl object 26 unitprice float64 27 disccode object 28 pricelist object 29 pct float64 30 alphacode object 31 purchmajcl object 32 policycode object

import pandas as pd
import csv
import os

df = pd.read_csv (r'/Users/me/Desktop/Part Sales.csv')
df_2 = pd.read_csv (r'/Users/me/Desktop/Part Sales2.csv')

concatenated = pd.concat([df, df_2]).drop_duplicates().reset_index(drop=True)
concatenated.head(10)

import matplotlib.pyplot as plt 
import seaborn as sns

concatenated.shape
concatenated.columns 
concatenated.info()
concatenated.isnull().sum()
concatenated.describe()

concatenated['invoicedate'].min()
concatenated['invoicedate'].max()

concatenated['month_year'] = concatenated['invoicedate'].apply(lambda x: '/'.join(x.split('/')[0:2]))
concatenated['month_year']

concatenated_trend = concatenated.groupby(['month_year'])['salesamount_usd'].sum() #reset_index()
concatenated_trend

plt.figure(figsize=(15,6))
plt.plot(concatenated_trend['month_year'], concatenated_trend['salesamount_usd'])
plt.xticks(rotation='vertical', size=8)
plt.show()

This is the error message displayed: Error message

enter image description here