Introduction
In this blog post, we will explore how to loop through Excel rows using the Python library Openpyxl and update cells based on a transaction ID. This can be a useful technique when working with large datasets or when you need to perform specific operations on Excel data programmatically. We will walk you through the process step-by-step, providing clear instructions and code examples. When working with Excel files in Python, Openpyxl is a powerful library that allows us to read and modify Excel data. In this tutorial, we will focus on updating cells based on a transaction ID. We will load the Excel file, iterate through the rows, and update the desired cells accordingly.
Prerequisites
Before we get started, make sure you have the following prerequisites:
- Python 3 installed on your system
- Openpyxl library installed (you can install it using pip:
pip install openpyxl
)
Loading the Excel File
To begin, we need to load the Excel file using Openpyxl. We can accomplish this by using the load_workbook()
function and specifying the filename of the Excel file we want to work with. For example, if our Excel file is named “Testing.xlsx”, we can use the following code:
from openpyxl import load_workbook
wb = load_workbook(filename=’Testing.xlsx’)
ws = wb[‘Test’]
In the code above, we import the load_workbook()
function from the Openpyxl library. We then use this function to load the Excel file and assign it to the variable wb
. Next, we access a specific worksheet within the Excel file by specifying its name (in this case, “Test”) and assign it to the variable ws
.
Looping Through the Rows
Now that we have loaded the Excel file, we can start looping through the rows and updating the desired cells based on the transaction ID. We will use a for
loop to iterate over the rows, starting from the second row (row 1 contains the header).
for r in range(2, ws.max_row + 1):
column_c = ws.cell(row=r, column=3).value
column_k = ws.cell(row=r, column=11)
# Update cells based on transaction ID
# Add your logic here
In the code above, we use the range()
function to iterate over the rows from the second row (2
) to the last row (ws.max_row + 1
). Inside the loop, we retrieve the values of columns C and K using the ws.cell()
function and assign them to the variables column_c
and column_k
, respectively.
Updating Cells
Now comes the important part – updating the cells based on the transaction ID. You can add your custom logic here, depending on your specific requirements. In the example below, we will update column K with a value from column J if the transaction ID in column C matches the previous row’s transaction ID.
previous_transaction_id = None
for r in range(2, ws.max_row + 1):
current_transaction_id = ws.cell(row=r, column=3).value
current_column_j = ws.cell(row=r, column=10).value
column_k = ws.cell(row=r, column=11)
if current_transaction_id == previous_transaction_id:
column_k.value = current_column_j
previous_transaction_id = current_transaction_id
In the code above, we introduce a new variable previous_transaction_id
to keep track of the previous row’s transaction ID. Inside the loop, we compare the current transaction ID (current_transaction_id
) with the previous one. If they match, we update the value of column K (column_k
) with the value from column J (current_column_j
).
Saving the Updated Excel File
Once we have finished updating the cells, we need to save the changes to the Excel file. We can use the save()
function to accomplish this.
wb.save(‘Testing_processed.xlsx’)
In the code above, we call the save()
function on the workbook (wb
) and provide the filename as an argument. The updated Excel file will be saved with the specified filename (in this case, “Testing_processed.xlsx”).
Refining the Loop and Error Handling
To ensure the loop executes smoothly and handle any potential errors, we can add some refinements to the code. This will help improve the overall robustness of the script.
previous_transaction_id = None
for r in range(2, ws.max_row + 1):
try:
current_transaction_id = ws.cell(row=r, column=3).value
current_column_j = ws.cell(row=r, column=10).value
column_k = ws.cell(row=r, column=11)
if current_transaction_id == previous_transaction_id:
column_k.value = current_column_j
previous_transaction_id = current_transaction_id
except Exception as e:
print(f"An error occurred in row {r}: {str(e)}")
In the refined code above, we have wrapped the main logic inside a try-except
block. This helps catch any potential exceptions that might occur during the execution of the loop. If an error occurs, the code inside the except
block will be executed, printing an error message along with the row number and the specific exception message.
By implementing error handling, you can easily identify and debug any issues that might arise during the execution of the script.
Finalizing the Code
Now that we have refined our code and added error handling, we are ready to finalize the script. Below is the complete code for looping through Excel rows using Openpyxl and updating cells based on a transaction ID:
from openpyxl import load_workbook # Step 1: Loading the Excel File wb = load_workbook(filename=’Testing.xlsx’) ws = wb[‘Test’] # Step 2: Looping Through the Rows and Updating Cells previous_transaction_id = None for r in range(2, ws.max_row + 1): try: current_transaction_id = ws.cell(row=r, column=3).value current_column_j = ws.cell(row=r, column=10).value column_k = ws.cell(row=r, column=11) if current_transaction_id == previous_transaction_id: column_k.value = current_column_j previous_transaction_id = current_transaction_id except Exception as e: print(f”An error occurred in row {r}: {str(e)}”) # Step 3: Saving the Updated Excel File wb.save(‘Testing_processed.xlsx’)
Make sure to replace 'Testing.xlsx'
with the actual filename of your Excel file. Also, feel free to modify the column numbers and variable names according to your specific Excel file structure.
Conclusion
In this tutorial, we have learned how to loop through Excel rows using Openpyxl in Python and update cells based on a transaction ID. We covered the steps of loading the Excel file, looping through the rows, updating the cells, and saving the changes. This technique can be helpful when working with large datasets or performing specific operations on Excel data programmatically.