Python Automation Script for Data Transformation and Variant Creation for Mystore ONDC Bulk Upload
Budget: ₹600 – ₹1,500 INR
Greetings,
We are seeking a talented and experienced Python developer to automate the process of transforming product data into a format compatible with Mystore ONDC (Open Network for Digital Commerce) bulk upload requirements. This project involves reading data from a source CSV file, mapping the fields to the target structure, creating variants for products, and generating the final output CSV in the ONDC-required format.
Below is the comprehensive guide for the freelancer to understand the requirements and develop the script accurately.
Project Overview:
Source File: CS_210_171124_1.csv (contains product data in our current format).
Target File: sample-csv-fashion.csv (ONDC-required bulk upload format).
Reference File: fashion csv - Sheet1.csv (example file showing how to create product variants).
The task involves transforming and mapping the data from CS_210_171124_1.csv to match the format of sample-csv-fashion.csv, while ensuring the correct creation of product variants as illustrated in fashion csv - Sheet1.csv.
Details of the Transformation:
1. Column Mapping:
Map the columns in the source file to the required columns in the ONDC format:
Title -> name
Body (HTML) -> description
Variant Price -> price
Variant Compare At Price -> compare_price:
If Variant Compare At Price exists, map it directly.
If not, set the default value of 1050.
Variant SKU -> sku
Product Category or Type -> category:
If Product Category is missing, use Type.
Image Src -> image1, image2, image3:
Map based on the Image Position in the source file.
2. Variant Creation:
Create product variants based on multiple attributes (e.g., size, color). Refer to fashion csv - Sheet1.csv for the correct structure. Ensure:
Each product variant has unique values in option1_value (e.g., size) and option2_value (e.g., color).
All variants for the same product share the same name, description, and image columns.
3. Default Values:
Handle missing fields by assigning default values:
ondc.category_id -> "fashion"
manufacturer_or_packer_name -> "Beast Of Cloud"
manufacturer_or_packer_address -> "Beast Of Cloud,Erode-638002"
country_of_origin -> "IN"
month_year_of_manufacturing_or_packing -> "24-Jul"
unit_count -> 1
unit_count_type -> "pair"
ondc.time_to_ship -> 2880
4. Image Handling:
Map images to the appropriate columns (image1, image2, image3, etc.) based on Image Position in the source file. Ensure:
All variants share the same set of images.
Fill missing values for images using forward fill.
5. Output File Requirements:
Save the transformed data in a CSV file following the structure of sample-csv-fashion.csv.
Ensure the format is compatible with ONDC bulk upload requirements.
6. Error Handling:
Implement robust error handling to log issues (e.g., missing columns, data inconsistencies) during the transformation.
Validate the output file before submission.
Required Deliverables:
Python Script:
The complete Python script with detailed comments and clear documentation.
Use Python libraries such as pandas for data manipulation.
Output File:
A sample transformed output file based on CS_210_171124_1.csv.
Documentation:
Provide clear and comprehensive documentation explaining:
How the script works.
Instructions to run the script.
Details of the data mapping process.
Handling of variants and default values.
Files Provided:
The following files will be provided for reference:
CS_210_171124_1.csv - The source file with product data in our current format.
sample-csv-fashion.csv - The required ONDC format for bulk uploads.
fashion csv - Sheet1.csv - An example file showing how to create variants for products.
Skills Required:
Strong proficiency in Python (data manipulation using pandas, file I/O, etc.).
Experience with data transformation and ETL processes.
Familiarity with e-commerce platforms and bulk upload formats is a plus.
Expected Workflow:
Analyze the provided files and understand the required mappings.
Develop the Python script to perform the transformations.
Validate the output against the sample-csv-fashion.csv format.
Submit the script, sample output file, and documentation.
Deadline:
2 days from project award.
How to Apply:
Interested freelancers are requested to submit the following:
A brief overview of your approach to the project.
Examples of similar projects you have completed.
Your estimated timeline for completion.
Additional Notes:
Accuracy and attention to detail are critical for this project.
Please ensure all mappings and transformations are handled as specified.
Communication and regular updates are expected throughout the project.
We are seeking a talented and experienced Python developer to automate the process of transforming product data into a format compatible with Mystore ONDC (Open Network for Digital Commerce) bulk upload requirements. This project involves reading data from a source CSV file, mapping the fields to the target structure, creating variants for products, and generating the final output CSV in the ONDC-required format.
Below is the comprehensive guide for the freelancer to understand the requirements and develop the script accurately.
Project Overview:
Source File: CS_210_171124_1.csv (contains product data in our current format).
Target File: sample-csv-fashion.csv (ONDC-required bulk upload format).
Reference File: fashion csv - Sheet1.csv (example file showing how to create product variants).
The task involves transforming and mapping the data from CS_210_171124_1.csv to match the format of sample-csv-fashion.csv, while ensuring the correct creation of product variants as illustrated in fashion csv - Sheet1.csv.
Details of the Transformation:
1. Column Mapping:
Map the columns in the source file to the required columns in the ONDC format:
Title -> name
Body (HTML) -> description
Variant Price -> price
Variant Compare At Price -> compare_price:
If Variant Compare At Price exists, map it directly.
If not, set the default value of 1050.
Variant SKU -> sku
Product Category or Type -> category:
If Product Category is missing, use Type.
Image Src -> image1, image2, image3:
Map based on the Image Position in the source file.
2. Variant Creation:
Create product variants based on multiple attributes (e.g., size, color). Refer to fashion csv - Sheet1.csv for the correct structure. Ensure:
Each product variant has unique values in option1_value (e.g., size) and option2_value (e.g., color).
All variants for the same product share the same name, description, and image columns.
3. Default Values:
Handle missing fields by assigning default values:
ondc.category_id -> "fashion"
manufacturer_or_packer_name -> "Beast Of Cloud"
manufacturer_or_packer_address -> "Beast Of Cloud,Erode-638002"
country_of_origin -> "IN"
month_year_of_manufacturing_or_packing -> "24-Jul"
unit_count -> 1
unit_count_type -> "pair"
ondc.time_to_ship -> 2880
4. Image Handling:
Map images to the appropriate columns (image1, image2, image3, etc.) based on Image Position in the source file. Ensure:
All variants share the same set of images.
Fill missing values for images using forward fill.
5. Output File Requirements:
Save the transformed data in a CSV file following the structure of sample-csv-fashion.csv.
Ensure the format is compatible with ONDC bulk upload requirements.
6. Error Handling:
Implement robust error handling to log issues (e.g., missing columns, data inconsistencies) during the transformation.
Validate the output file before submission.
Required Deliverables:
Python Script:
The complete Python script with detailed comments and clear documentation.
Use Python libraries such as pandas for data manipulation.
Output File:
A sample transformed output file based on CS_210_171124_1.csv.
Documentation:
Provide clear and comprehensive documentation explaining:
How the script works.
Instructions to run the script.
Details of the data mapping process.
Handling of variants and default values.
Files Provided:
The following files will be provided for reference:
CS_210_171124_1.csv - The source file with product data in our current format.
sample-csv-fashion.csv - The required ONDC format for bulk uploads.
fashion csv - Sheet1.csv - An example file showing how to create variants for products.
Skills Required:
Strong proficiency in Python (data manipulation using pandas, file I/O, etc.).
Experience with data transformation and ETL processes.
Familiarity with e-commerce platforms and bulk upload formats is a plus.
Expected Workflow:
Analyze the provided files and understand the required mappings.
Develop the Python script to perform the transformations.
Validate the output against the sample-csv-fashion.csv format.
Submit the script, sample output file, and documentation.
Deadline:
2 days from project award.
How to Apply:
Interested freelancers are requested to submit the following:
A brief overview of your approach to the project.
Examples of similar projects you have completed.
Your estimated timeline for completion.
Additional Notes:
Accuracy and attention to detail are critical for this project.
Please ensure all mappings and transformations are handled as specified.
Communication and regular updates are expected throughout the project.