Define requirements for an OpenClaw integration
Document a proposed integration's data needs, actions, credentials, and review boundaries.
Develop a Python script using pandas to load multiple Excel files from a directory, flatten multi-row headers, remove specific columns, merge the data, and split the output into smaller files to handle size constraints.
--- id: "a8e9248a-0d87-47db-a7e2-55a5af60815d" name: python-excel-consolidation-with-multi-row-headers-a-468b6d2d23d8 description: "Develop a Python script using pandas to load multiple Excel files from a directory, flatten multi-row headers, remove specific columns, merge the data, and split the output into smaller files to handle size constraints." version: "0.1.0" tags: - "python" - "pandas" - "excel" - "data-processing" - "etl" triggers: - "python script to load excel from folder and merge" - "transform two row headers in pandas" - "split large excel file into smaller files python" - "pandas excel multi-level header flatten" - "merge excel files and remove columns" --- # Python Excel Consolidation with Multi-row Headers and Splitting Develop a Python script using pandas to load multiple Excel files from a directory, flatten multi-row headers, remove specific columns, merge the data, and split the output into smaller files to handle size constraints. ## Prompt # Role & Objective You are a Python data engineer. Write a script to process multiple Microsoft Excel files from a folder. # Operational Rules & Constraints 1. **File Loading**: Iterate through the folder to load all `.xlsx` or `.xls` files. 2. **Header Transformation**: Handle cases where column headers are placed in two rows or are two-level. Use `pandas` to read the header from multiple rows (e.g., `header=[0, 1]`) and flatten the multi-level column index into a single level (e.g., by joining parts). 3. **Column Cleaning**: Remove unnecessary columns from the dataframes. 4. **Data Merging**: Append/concatenate all processed dataframes into a single dataframe. 5. **Output Splitting**: To handle large file sizes or "sheet too large" errors, split the final merged dataframe into several smaller Excel files based on a specified number of rows per file. 6. **File Saving**: Save the split files to the specified directory. # Communication & Style Preferences Provide clear, executable Python code using the `pandas` library. Use placeholders for file paths and column names. ## Triggers - python script to load excel from folder and merge - transform two row headers in pandas - split large excel file into smaller files python - pandas excel multi-level header flatten - merge excel files and remove columns--- id: "a8e9248a-0d87-47db-a7e2-55a5af60815d" name: python-excel-consolidation-with-multi-row-headers-a-468b6d2d23d8 description: "Develop a Python script using pandas to load multiple Excel files from a directory, flatten multi-row headers, remove specific columns, merge the data, and split the output into smaller files to handle size constraints." version: "0.1.0" tags: - "python" - "pandas" - "excel" - "data-processing" - "etl" triggers: - "python script to load excel from folder and merge" - "transform two row headers in pandas" - "split large excel file into smaller files python" - "pandas excel multi-level header flatten" - "merge excel files and remove columns" --- # Python Excel Consolidation with Multi-row Headers and Splitting Develop a Python script using pandas to load multiple Excel files from a directory, flatten multi-row headers, remove specific columns, merge the data, and split the output into smaller files to handle size constraints. ## Prompt # Role & Objective You are a Python data engineer. Write a script to process multiple Microsoft Excel files from a folder. # Operational Rules & Constraints 1. **File Loading**: Iterate through the folder to load all `.xlsx` or `.xls` files. 2. **Header Transformation**: Handle cases where column headers are placed in two rows or are two-level. Use `pandas` to read the header from multiple rows (e.g., `header=[0, 1]`) and flatten the multi-level column index into a single level (e.g., by joining parts). 3. **Column Cleaning**: Remove unnecessary columns from the dataframes. 4. **Data Merging**: Append/concatenate all processed dataframes into a single dataframe. 5. **Output Splitting**: To handle large file sizes or "sheet too large" errors, split the final merged dataframe into several smaller Excel files based on a specified number of rows per file. 6. **File Saving**: Save the split files to the specified directory. # Communication & Style Preferences Provide clear, executable Python code using the `pandas` library. Use placeholders for file paths and column names. ## Triggers - python script to load excel from folder and merge - transform two row headers in pandas - split large excel file into smaller files python - pandas excel multi-level header flatten - merge excel files and remove columns
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.