Manipulate and analyze tabular data with Pandas operations.
npx skills add https://github.com/hung-phan/ml-skills --skill pandas-hung-phan
Please help me install this Agent Skill. Skill: pandas Source: https://github.com/hung-phan/ml-skills/tree/main/skills/ml-review/references/ml-libraries/pandas Command: npx skills add https://github.com/hung-phan/ml-skills --skill pandas-hung-phan
This Skill addresses the challenges of working with tabular data, offering efficient data manipulation, aggregation, and analysis capabilities.
Use the pandas skill to create a DataFrame from the provided CSV file 'sales_data.csv'.
High-intent search queries and answers about installing and using this skill.
To manipulate tabular data for exploratory data analysis, you can perform operations like groupby, merge/join, and pivot. This allows efficient data preparation and aggregation to calculate insights like total revenue per category.
The best way to aggregate large sales datasets by category is using groupby operations on a DataFrame. This approach quickly performs necessary aggregations and transformations to provide desired revenue insights.
Pandas works directly with parquet and csv files for data preparation through read_csv and read_parquet functions. You can easily load structured data into a DataFrame to begin your analysis pipeline.
You can use polars alongside pandas for performance optimization during data manipulation. Integrating polars is supported as an optional library to achieve faster processing on large datasets.
You need numpy installed as a required dependency to perform data manipulation and analysis. Numpy provides the foundational numerical support required for pandas operations like indexing and grouping.
To merge and join DataFrames for machine learning pipelines, use built-in merge and join operations. This enables efficient indexing and combining of structured datasets prior to model training.
omicverseClean, reshape, normalize and engineer features from CSV/TSV/Parquet inputs using pandas and numpy locally.
biomaps-infraProcess and analyze large tabular datasets exceeding RAM using Vaex.
ovachieverPerform lazy, expression-based DataFrame operations with Polars for CSV and Parquet pipelines.
leesk212Performs GPU-accelerated groupby, descriptive statistics, and anomaly detection on tabular data with cuDF.
