Loop for merging dictionaries with the same key

I have two excel files with two sheets each, which I have stored as dictionaries:

my_first_file = pd.read_excel(my_path, sheet_name=None, skiprows=2)
my_second_file = pd.read_excel(my_path, sheet_name=None, skiprows=2)

Ideally, I would like to write a loop that allows me to apply a left merge to the sheets with the same name.
So then I could filter the NaN values (just like a vlookup would do in Excel).

• my_first_file:

{'Sheet_1':     ID     Name  Surname  Grade
 0  104  Eleanor    Rigby      6
 1  168  Barbara      Ann      8
 2  450    Polly  Cracker      7
 3   90  Little       Joe     10,
 'Sheet_2':     ID       Name   Surname  Grade
 0  106       Lucy       Sky      8
 1  128    Delilah  Gonzalez      5
 2  100  Christina   Rodwell      3
 3   40      Ziggy  Stardust      7,
 'Sheet_3':     ID   Name   Surname  Grade
 0   22   Lucy  Diamonds      9
 1   50  Grace     Kelly      7
 2  105    Uma   Thurman      7
 3   29   Lola      King      3}

• my_second_file:

{'Sheet_1':     ID     Name  Surname  Grade favourite color    favourite sport
 0  104  Eleanor    Rigby      6            blue  American football
 1  168  Barbara      Ann      8            pink             Hockey
 2  450    Polly  Cracker      7           black      Skateboarding
 3   90  Little      Josy     10          orange            Cycling,
 'Sheet_2':     ID       Name   Surname  Grade favourite color favourite sport
 0  106       Lucy       Sky      8          yellow          Tennis
 1  128    Delilah     Perez      5     light green      Basketball
 2  100  Christina   Rodwell      3           black       Badminton
 3   40      Ziggy  Stardust      7             red          Squash,
 'Sheet_3':     ID   Name   Surname  Grade favourite color favourite sport
 0   22   Lucy  Diamonds      9           brown            Judo
 1   50  Grace     Kelly      7           white       Taekwondo
 2  105    Uma   Thurman      7          purple      videogames
 3   29   Lola   McQueen      3             red            Surf}

I am aware that pd.df.merge(right, how='left', on='Concatenation') is only appliable to DataFrames and not dictionaries like in this scenario but I have no clue on how to make it.
My expected output after merging the two dict keys for Sheet_1 would be:

{'Sheet_1':     ID      Name  Surname  Contatenation  Grade favourite color  \
 0  104  Eleanor     Rigby  Eleanor Rigby      6            blue   
 1  168  Barbara       Ann    Barbara Ann      8            pink   
 2  450    Polly   Cracker  Polly Cracker      7           black   
 3   90   Little       Joe     Little Joe     10             NaN   
 
      favourite sport  
 0  American football  
 1             Hockey  
 2      Skateboarding  
 3                NaN  ,

I have proceded with this code so far:

# Importing modules
import openpyxl as op
import pandas as pd
import numpy as np
import xlsxwriter
from openpyxl import Workbook, load_workbook

# Defining the two file paths
path_first_file = r'C:\Users\machukovich\Desktop\stack.xlsx'
path_second_file = r'C:\Users\machukovich\Desktop\stack_2.xlsx'

# Loading the files into a dictionary of Dataframes
dfs_first_file = pd.read_excel(path_first_file, sheet_name=None, skiprows=2)
dfs_second_file = pd.read_excel(path_second_file, sheet_name=None, skiprows=2)

# Creating a new column in each sheet to merge later respectively
for sheet_name, df in dfs_first_file.items():
    df.insert(3, 'Concatenation', df['Name'].map(str) + ' ' + df['Surname'].map(str))

for sheet_name, df in dfs_second_file.items():
    df.insert(3, 'Concatenation', df['Name'].map(str) + ' ' + df['Surname'].map(str))

Thanks in advance for any tip and or help.

IIUC, you can use :

sheets = dfs_first_file.keys() & dfs_second_file.keys() #common keys/sheets

dfs_output_file = {
    sh: pd.merge(dfs_first_file[sh],
                 dfs_second_file[sh],
        on=["Name", "Surname"], suffixes=("", "_"), how="left")
                .drop(columns=["ID_", "Grade_"]) for sh in sheets
}

Explanation :

Here we use merge inside a dictcomp to overwrite the values (which are DataFrames) of the two dictionnaries (dfs_first_file and dfs_second_file). We overwrite them with the result of the left merge between the corresponding dataframes of the same sheet. For example, in the first iteration, sh equals "Sheet1", so in this case we merge dfs_first_file[sh] with dfs_second_file[sh] (while sh==Sheet1).

Output :

print(dfs_output_file["Sheet_1"])

    ID     Name  Surname  Grade favourite color    favourite sport
0  104  Eleanor    Rigby      6            blue  American football
1  168  Barbara      Ann      8            pink             Hockey
2  450    Polly  Cracker      7           black      Skateboarding
3   90   Little      Joe     10             NaN                NaN

print(dfs_output_file["Sheet_2"])


    ID       Name   Surname  Grade favourite color favourite sport
0  106       Lucy       Sky      8          yellow          Tennis
1  128    Delilah  Gonzalez      5             NaN             NaN
2  100  Christina   Rodwell      3           black       Badminton
3   40      Ziggy  Stardust      7             red          Squash

print(dfs_output_file["Sheet_3"])

    ID   Name   Surname  Grade favourite color favourite sport
0   22   Lucy  Diamonds      9           brown            Judo
1   50  Grace     Kelly      7           white       Taekwondo
2  105    Uma   Thurman      7          purple      videogames
3   29   Lola      King      3             NaN             NaN

You can try

out = {}
for k in dfs_first_file.keys() & dfs_second_file.keys():
    out[k] =  pd.merge(dct1[k], dct2[k], on=['ID', 'Name', 'Surname', 'Grade'])
    out[k]['Concatenation'] = out[k]['Name'] + ' ' + out[k]['Surname']

print(out)

Prints:

{'Sheet_3':     ID   Name   Surname  Grade favourite color favourite sport  Concatenation
0   22   Lucy  Diamonds      9           brown            Judo  Lucy Diamonds
1   50  Grace     Kelly      7           white       Taekwondo    Grace Kelly
2  105    Uma   Thurman      7          purple      videogames    Uma Thurman, 'Sheet_1':     ID     Name  Surname  Grade favourite color    favourite sport  Concatenation
0  104  Eleanor    Rigby      6            blue  American football  Eleanor Rigby
1  168  Barbara      Ann      8            pink             Hockey    Barbara Ann
2  450    Polly  Cracker      7           black      Skateboarding  Polly Cracker, 'Sheet_2':     ID       Name   Surname  Grade favourite color favourite sport      Concatenation
0  106       Lucy       Sky      8          yellow          Tennis           Lucy Sky
1  100  Christina   Rodwell      3           black       Badminton  Christina Rodwell
2   40      Ziggy  Stardust      7             red          Squash     Ziggy Stardust}