Python Data Analysis with Text Data
Budget: ₹1,500 – ₹12,500 INR
I'm looking for a skilled Python programmer to assist in creating a metrics system for my data analysis project. Below are the key aspects of the project:
- Data Type: The project will primarily involve the processing and analysis of text data. Prior experience with this data type is a definite advantage.
- Goal: The main objective of this project is to perform in-depth data analysis. I need the freelancer to assist in developing metrics and calculations for the data.
- Deliverables: The final output should be presented in a report format.
Ideal skills for this project would include expertise in Python, data analysis, experience with text data, and the ability to create comprehensive reports.
I want to write a python code for the following calculation of Code Synchronization adoption rate:
I've 4 excel files, 2 Jira extracts and 2 solman extracts.
1 for Jira Support state(support user stories & bugs) and 1 for Solman Support state(Support CRs).
Other would be same Jira & SolMan extract but for Project State.
Project might have more items that Support which is normal.
The idea of code retrofit is every change that Support team introduces i.e N item should have a corresponding N+1 item in Solman and Jira
If not the adoption rate metrics should be taxed
So for this calcualtion we need to perform the calucation in following steps using 4 extracts
1. Convert 4 extracts to 4 data frames
2. Join Support Jira & Solman data frames on the description field and such that none of the items should be ignored from both the files, it huld be the superset. - this will be join1 and DF5
3. Perform the similar join on Project Jira & Solman data frames - this will be join2 and DF6
4. Compare the DF5 and DF6 such that all the items in DF5 should have a corresponding item in DF6 if not that row should be taxed
and the final output should be following Metrics:
1. Adoption rate - if any of the items is missing from the 4 files then that row will be marked as X i.e. miss.
example : Bug1 - had a Support CR and Jira and also Project Jira but Project CR was missing then it'll be X
similarly the row will be considered completed only when all the respectively objects are successfully creates
2. Control Adoption Accuracy: Each Jira and Solman extract should have few mandatory fields that must be filled and should be completed
e.g Bug1 has 5 Jira mandatory fields & 4 Solman mandatory fields and if any of these fields are missing then the missing column would be marked as X. and similarly if all the fields are filled but one field is wrong input compared to what's expected then it should mark the Complete column as X
- Data Type: The project will primarily involve the processing and analysis of text data. Prior experience with this data type is a definite advantage.
- Goal: The main objective of this project is to perform in-depth data analysis. I need the freelancer to assist in developing metrics and calculations for the data.
- Deliverables: The final output should be presented in a report format.
Ideal skills for this project would include expertise in Python, data analysis, experience with text data, and the ability to create comprehensive reports.
I want to write a python code for the following calculation of Code Synchronization adoption rate:
I've 4 excel files, 2 Jira extracts and 2 solman extracts.
1 for Jira Support state(support user stories & bugs) and 1 for Solman Support state(Support CRs).
Other would be same Jira & SolMan extract but for Project State.
Project might have more items that Support which is normal.
The idea of code retrofit is every change that Support team introduces i.e N item should have a corresponding N+1 item in Solman and Jira
If not the adoption rate metrics should be taxed
So for this calcualtion we need to perform the calucation in following steps using 4 extracts
1. Convert 4 extracts to 4 data frames
2. Join Support Jira & Solman data frames on the description field and such that none of the items should be ignored from both the files, it huld be the superset. - this will be join1 and DF5
3. Perform the similar join on Project Jira & Solman data frames - this will be join2 and DF6
4. Compare the DF5 and DF6 such that all the items in DF5 should have a corresponding item in DF6 if not that row should be taxed
and the final output should be following Metrics:
1. Adoption rate - if any of the items is missing from the 4 files then that row will be marked as X i.e. miss.
example : Bug1 - had a Support CR and Jira and also Project Jira but Project CR was missing then it'll be X
similarly the row will be considered completed only when all the respectively objects are successfully creates
2. Control Adoption Accuracy: Each Jira and Solman extract should have few mandatory fields that must be filled and should be completed
e.g Bug1 has 5 Jira mandatory fields & 4 Solman mandatory fields and if any of these fields are missing then the missing column would be marked as X. and similarly if all the fields are filled but one field is wrong input compared to what's expected then it should mark the Complete column as X