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Excel automation using Python

Last updated on 30 days ago
C
caaSuper Admin
Posted 30 days ago
Python offers powerful libraries for automating Excel, allowing you to create, modify, and analyze spreadsheets programmatically. Here’s how to use Python to automate Excel tasks with a focus on common libraries like **openpyxl** (for .xlsx files), **pandas** (for data manipulation), and **xlwings** (for automating Excel with VBA-like functionality).

### 1. **Setting Up Your Environment**
- Install the libraries:
bash
 pip install openpyxl pandas xlwings
 

### 2. **Basic Excel Operations with openpyxl**
- **openpyxl** allows you to read and write .xlsx files.

#### Example: Reading and Writing Excel Files
python
import openpyxl

# Load workbook and select sheet
workbook = openpyxl.load_workbook("example.xlsx")
sheet = workbook.active

# Reading a cell value
print(sheet["A1"].value)

# Writing a value to a cell
sheet["B2"] = "Automated Entry"
workbook.save("example_modified.xlsx")

#### Example: Creating a New Excel File
python
from openpyxl import Workbook

# Create a new workbook and sheet
wb = Workbook()
sheet = wb.active
sheet.title = "Invoice Data"
# Add data
sheet["A1"] = "Invoice Number"
sheet["B1"] = "Customer Name"
sheet["A2"] = 1001
sheet["B2"] = "John Doe"

# Save workbook
wb.save("new_invoice.xlsx")
C
caaSuper Admin
Posted 30 days ago
Example: Creating a New Excel File

python

from openpyxl import Workbook

# Create a new workbook and sheet
wb = Workbook()
sheet = wb.active
sheet.title = "Invoice Data"

# Add data
sheet["A1"] = "Invoice Number"
sheet["B1"] = "Customer Name"
sheet["A2"] = 1001
sheet["B2"] = "John Doe"

# Save workbook
wb.save("new_invoice.xlsx")


### 3. **Data Analysis and Manipulation with Pandas**
- **pandas** is great for reading, manipulating, and writing Excel files, especially for data-heavy tasks.

#### Example: Reading an Excel File into a DataFrame
python
import pandas as pd

# Load the Excel file
df = pd.read_excel("example.xlsx", sheet_name="Sheet1")

# Display data
print(df.head())
C
caaSuper Admin
Posted 30 days ago
#### Example: Manipulating Data and Saving Back to Excel
python
# Perform data operations
df["Total"] = df["Quantity"] * df["Unit Price"]

# Save to a new Excel file
df.to_excel("updated_invoice_data.xlsx", index=False)

---
### 4. **Automating Excel Actions with xlwings**
- **xlwings** allows Python to control Excel just like VBA, which is great for more interactive automation.

#### Example: Running VBA Code from Python
`python
import xlwings as xw

# Open Excel file and access sheet
wb = xw.Book("example.xlsx")
sheet = wb.sheets["Sheet1"]

# Write to Excel using xlwings
sheet.range("A1").value = "Automated Data Entry"

# Execute a VBA macro (if one exists in the workbook)
wb.macro("MyMacroName")()
C
caaSuper Admin
Posted 30 days ago
# Save and close the workbook
wb.save()
wb.close()


#### Example: Creating an Automated Invoice System
python
def generate_invoice(customer_name, items):
wb = xw.Book("invoice_template.xlsx")
sheet = wb.sheets["Invoice"]

# Set customer name and date
sheet.range("B2").value = customer_name
sheet.range("B3").value = pd.Timestamp.now().strftime("%Y-%m-%d")

# Write item details to the invoice
for i, item in enumerate(items, start=6): # Starting from row 6
sheet.range(f"A{i}").value = item['description']
sheet.range(f"B{i}").value = item['quantity']
sheet.range(f"C{i}").value = item['unit_price']
sheet.range(f"D{i}").value = item['quantity'] * item['unit_price']

# Save and print the file
wb.save(f"{customer_name}_invoice.xlsx")
wb.app.quit()

# Sample items list
items = [
{"description": "Item 1", "quantity": 2, "unit_price": 50},
{"description": "Item 2", "quantity": 1, "unit_price": 100}
]
generate_invoice("John Doe", items)
`
C
caaSuper Admin
Posted 30 days ago
---

### 5. **Combining Everything for Full Automation**
- **Automated Reporting**: Combine pandas and xlwings to create monthly or weekly reports.
- **Bulk Processing**: Process multiple Excel files automatically, updating or generating new data based on a template.
- **Data Analysis Pipelines**: Use pandas to clean and prepare data, then save it back to Excel for reporting.

By combining these Python libraries, you can fully automate data processing, reporting, and file generation in Excel.
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