In this blog post, we'll explore how to efficiently traverse a specified folder containing multiple Excel files. Each file may have several sheets, and our goal is to extract specific data starting from the 16th row until the last row with data. We will focus on extracting information from columns D and E, and then we'll save this extracted data into new Excel files.
Overview of the Process
Step 1: Set Up the Environment
First, ensure that you have the necessary Python libraries installed, specifically pandas for data handling and openpyxl for reading and writing Excel files. These libraries provide powerful functionalities for manipulating Excel data.
Step 2: Define the Folder Structure
Identify the folder containing the Excel files. Each file can have multiple sheets, and we will be extracting specific data from each of them.
Step 3: Read Each Excel File
Using a loop, navigate through each Excel file in the specified folder. For each file:
Open the Excel File: Load each Excel file and retrieve its sheet names.
Iterate Through Sheets: For each sheet, read the data starting from the 16th row.
Step 4: Extract Data from Columns D and E
For each sheet, focus on the following:
Column D: Extract the substring before the first space. This will be labeled "Size Position".
Column E: Perform several extractions:
Extract the substring between "from" and "to", labeling it "Measurement Start Position".
Extract the substring between "to" and the first comma, labeling it "Measurement End Position".
Extract the substring after the first comma and before the first period, labeling it "Measurement Method".
Step 5: Create a New Excel File
For each processed Excel file:
Create a new Excel file to store the extracted data.
For each sheet processed, create a corresponding new sheet in the output file and save the extracted values.
Step 6: Save the Output
After processing all sheets in a file, save the new Excel file. Ensure that each output file is clearly named to indicate it contains the processed data, such as prefixing the original filename with "processed_".
Conclusion
This process provides a systematic approach to extracting and reorganizing data from multiple Excel files. By following these steps, you can handle large datasets efficiently and ensure that the information is easily accessible for analysis or reporting. This method can be customized further based on specific requirements, making it a versatile solution for data extraction tasks.
