QLAB-Courses/summer_python_econ
Jan 2025 – Jan 2025
1 Month active
Languages Used
CSSCSVHTMLJavaScriptJupyter NotebookPython
Technical Skills
AlgorithmsArraysBasic Arithmetic OperationsBrowser CompatibilityConditional StatementsControl Flow
Amanda Gomez
PROFILE
Amanda Gomez
During January 2025, this developer contributed to the QLAB-Courses/summer_python_econ repository by delivering five new features focused on both frontend and data engineering. They enhanced the user experience with frontend utilities, including internationalization support using Jed and modern HTML5 UI components, while ensuring browser compatibility through JavaScript polyfills. On the data side, they created and organized Jupyter notebooks for Python fundamentals, expanded economic datasets with new indicators, and developed Pandas-based workflows for data loading, cleaning, and merging. Their work demonstrated strong skills in Python, JavaScript, and data manipulation, resulting in well-structured, reproducible course materials and improved analysis capabilities.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
16Total
Bugs
0
Commits
16
Features
5
Lines of code
447,355
Activity Months1
Your Network
65 people
Work History
January 2025
16 Commits • 5 Features
Jan 1, 2025Month: 2025-01 | QLAB-Courses/summer_python_econ Key features delivered: - Frontend utilities and i18n enhancements: Add comprehensive frontend utilities including internationalization support (i18n) via Jed, modal dialog management, and HTML5 UI components such as tooltips, popovers, and scrollspy, plus polyfills for modern JS features. Representative commits: 38e36471e8c6e7c3230db85678a5f9b9f322ae3c. - Assignment 1: Python fundamentals notebooks: Create and organize Jupyter notebooks covering Python basics, data types, containers, and basic operations for Assignment 1. Commits: 60bee8e5fa8a5a4e25429e590d3bb2a456fb929c; 5e6d7975d6d72fc2f2a08ae1da05ab19b260870d. - Economic dataset expansion for analysis and modeling: Expand the dataset with economic indicators and poverty-related features to support analysis and modeling efforts. Commits: 6f60b56a221c76cf62fbdd1e9afe3f2b4a273b8a; 9088ea813db6c9c26a3a014197e8382a14bb1d33. - Assignment 2: Notebook and data processing with Pandas: Develop Assignment 2 notebooks focusing on data manipulation with Pandas, including loading, cleaning, merging datasets, and basic control flow. Commits: dd0f98d881ac29ac8cee61545285951b7b0d420f; 39a7945675fee62cdf4b54773bfad1bf0a13df08; 025123f1e5314c59fc0abac6e285291801288ea5; 812f3afb060c9e38c9c35dd304a0ffb925b59e4a. - Assignment 3 Group 4 materials and organization: Organize and manage Assignment 3 Group 4 materials: create notebooks, rename files for group identification, and manage PDFs. Commits: 696f49afa94c7e73f35e26c7b8449821af170364; b9ddb6a9a237a711807bec008dd18894ba2c85ad; b6f11b637e1084ed2b75b96eab8c37a82b3be2a4; 6869b98db12bd00436fe4a9997c7b0563014a53a; d90619671c0230a1133d1e9c38dce9b4f1b92812; 73661b70239c41acd9b0bcf8b845ec14a99454cf; 785fbecebb915c28944ac9af61628cf6848ed3e1. Major bugs fixed: - No major bugs reported in this period; focus on feature delivery and cleanup. Overall impact and accomplishments: - Strengthened course delivery with enriched frontend UX, expanded data resources for analysis, and improved reproducibility and scalability of notebooks and materials. Technologies/skills demonstrated: - Frontend: i18n with Jed, modal/dialog management, HTML5 UI components, polyfills - Python data stack: Jupyter, Pandas, data loading/cleaning/merging - Data engineering: dataset expansion with indicators - Collaboration: notebook organization and commit discipline
16 Commits • 5 Features
Jan 1, 2025Month: 2025-01 | QLAB-Courses/summer_python_econ Key features delivered: - Frontend utilities and i18n enhancements: Add comprehensive frontend utilities including internationalization support (i18n) via Jed, modal dialog management, and HTML5 UI components such as tooltips, popovers, and scrollspy, plus polyfills for modern JS features. Representative commits: 38e36471e8c6e7c3230db85678a5f9b9f322ae3c. - Assignment 1: Python fundamentals notebooks: Create and organize Jupyter notebooks covering Python basics, data types, containers, and basic operations for Assignment 1. Commits: 60bee8e5fa8a5a4e25429e590d3bb2a456fb929c; 5e6d7975d6d72fc2f2a08ae1da05ab19b260870d. - Economic dataset expansion for analysis and modeling: Expand the dataset with economic indicators and poverty-related features to support analysis and modeling efforts. Commits: 6f60b56a221c76cf62fbdd1e9afe3f2b4a273b8a; 9088ea813db6c9c26a3a014197e8382a14bb1d33. - Assignment 2: Notebook and data processing with Pandas: Develop Assignment 2 notebooks focusing on data manipulation with Pandas, including loading, cleaning, merging datasets, and basic control flow. Commits: dd0f98d881ac29ac8cee61545285951b7b0d420f; 39a7945675fee62cdf4b54773bfad1bf0a13df08; 025123f1e5314c59fc0abac6e285291801288ea5; 812f3afb060c9e38c9c35dd304a0ffb925b59e4a. - Assignment 3 Group 4 materials and organization: Organize and manage Assignment 3 Group 4 materials: create notebooks, rename files for group identification, and manage PDFs. Commits: 696f49afa94c7e73f35e26c7b8449821af170364; b9ddb6a9a237a711807bec008dd18894ba2c85ad; b6f11b637e1084ed2b75b96eab8c37a82b3be2a4; 6869b98db12bd00436fe4a9997c7b0563014a53a; d90619671c0230a1133d1e9c38dce9b4f1b92812; 73661b70239c41acd9b0bcf8b845ec14a99454cf; 785fbecebb915c28944ac9af61628cf6848ed3e1. Major bugs fixed: - No major bugs reported in this period; focus on feature delivery and cleanup. Overall impact and accomplishments: - Strengthened course delivery with enriched frontend UX, expanded data resources for analysis, and improved reproducibility and scalability of notebooks and materials. Technologies/skills demonstrated: - Frontend: i18n with Jed, modal/dialog management, HTML5 UI components, polyfills - Python data stack: Jupyter, Pandas, data loading/cleaning/merging - Data engineering: dataset expansion with indicators - Collaboration: notebook organization and commit discipline
January 2025
Activity
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Quality Metrics
Correctness93.2%
Maintainability92.6%
Architecture92.6%
Performance91.2%
AI Usage22.6%
Skills & Technologies
Programming Languages
CSSCSVHTMLJavaScriptJupyter NotebookPython
Technical Skills
AlgorithmsArraysBasic Arithmetic OperationsBrowser CompatibilityConditional StatementsControl FlowDOM ManipulationData AnalysisData CleaningData EngineeringData ManipulationData MergingData StructuresData TypesDictionaries
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