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Found 103 Skills
Best practices for Pandas data manipulation, analysis, and DataFrame operations in Python
Use when working with pandas DataFrames, data cleaning, aggregation, merging, or time series analysis. Invoke for data manipulation, missing value handling, groupby operations, or performance optimization.
Expert data analysis and manipulation for customer support operations using pandas
Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.
Pandas for time series analysis, OrcaFlex results processing, and marine engineering data workflows
Pandas data manipulation with DataFrames. Use for data analysis.
This skill should be used when the user asks to "use pandas", "analyze data with pandas", "work with DataFrames", "clean data with pandas", or needs guidance on pandas best practices, data manipulation, performance optimization, or common pandas patterns.
Use when "GeoPandas", "geospatial", "GIS", "shapefile", "GeoJSON", or asking about "spatial analysis", "coordinate transformation", "spatial join", "choropleth map", "buffer analysis", "geographic data", "map visualization"
Reading and writing data with Pandas from/to cloud storage (S3, GCS, Azure) using fsspec and PyArrow filesystems.
Python data processing with pandas, openpyxl, and lxml. Covers DataFrame operations, Excel I/O, XML parsing, bulk data transformation, and large-file handling. Use when processing tabular data, spreadsheets, or XML in Python. USE WHEN: user mentions "pandas", "DataFrame", "openpyxl", "read_excel", "lxml", "XPath", "CSV processing", "Excel parsing", "bulk data", "large file", "data transformation", "UTF-16", "codecs" DO NOT USE FOR: SQL databases (use sql-expert), NumPy-only math, ML/training
Technical analysis with 130+ indicators using pandas-ta for crypto market data
Refactor Pandas code to improve maintainability, readability, and performance. Identifies and fixes loops/.iterrows() that should be vectorized, overuse of .apply() where vectorized alternatives exist, chained indexing patterns, inplace=True usage, inefficient dtypes, missing method chaining opportunities, complex filters, merge operations without validation, and SettingWithCopyWarning patterns. Applies Pandas 2.0+ features including PyArrow backend, Copy-on-Write, vectorized operations, method chaining, .query()/.eval(), optimized dtypes, and pipeline patterns.