How to run the same code with input different files and output different files? Same as SAS Macro

Question

Hi I am looking for a step to iterate the following:

1. Data_Dec2019.xlsx
2. Data_Mar2020.xlsx
3. Data_Jun2020.xlsx
4. ...
5. ...
6. Base_Data.xlsx

The code is as below:

#Importing package
import pandas as pd

df_base = pd.read_excel(r'...\Base_Data.xlsx')

df_data = pd.read_excel(r'...\Data_Jun2020.xlsx')

#Data Cleaning for Base
df_base = df_base.sort_values(by='CustomerNumber')

df_base = df_base.drop_duplicates('CustomerNumber')


#Data Cleaning for Data
df_data = df_data.groupby (['CustomerNumber'])['Amount'].sum().reset_index()

#Map both data
df_mapped = pd.merge (df_base, df_data, on = "CustomerNumber", how = "inner")

#Export Data
df_mapped.to_excel(r'...\Merged_June2020.xlsx')

I have more than 10 data with different snapshots, would like to iterate the process. Tried to look through the community post but mostly was a complex issues. Note that the data processing code is longer and more complex, but for illustration purpose, I shared the simplified version just to understand if there is a easy way to do so.

Appreciate the help.

Answer 1

Create a function to process file and iterate over filenames with pathlib. Adapt the code below:

import pathlib

data_dir = 'file/path/to/data'

# load base data
df_base = pd.read_excel(r'...\Base_Data.xlsx') \
            .sort_values(by='CustomerNumber') \
            .drop_duplicates('CustomerNumber')


# load, process and export a file
def process_data(file):
    df_data = pd.read_excel(file) \
                .groupby (['CustomerNumber'])['Amount'] \
                .sum().reset_index()

    df_base.merge(df_data, on="CustomerNumber") \
           .to_excel(file.parent / f'Merged_{file.name}')


for file in data_dir.glob('Data_*.xlsx'):
   process_data(file)