Stack Explorer

Pandas

data-science

High-performance data analysis and manipulation library

20M/week → Stable

Features

dataframeanalysisetltabular

Pros and Cons

Ventajas

  • + Powerful data structures (DataFrame, Series)
  • + Excellent for tabular data manipulation
  • + Integration with NumPy and Matplotlib
  • + Support for multiple formats (CSV, Excel, SQL)
  • + Fast vectorized operations

Desventajas

  • - High memory consumption with large datasets
  • - API can be inconsistent
  • - Not ideal for big data (use Polars/Dask)
  • - Moderate learning curve

Use Cases

  • Data cleaning and preparation
  • Exploratory data analysis (EDA)
  • ETL pipelines
  • Reports and aggregations
  • Feature engineering for ML

Tecnologías Relacionadas