Pandas
data-processing library
Data analysis library for Python
Supported languages
Concepts
DataFrameSeriesgroupbymergepivot
Pros and Cons
Ventajas
- + Intuitive API for tabular data
- + Excellent for EDA
- + Integration with numpy/scikit-learn
- + Many I/O formats
- + 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
- - Slow for big data
- - Consumes a lot of memory
- - Inconsistent API in parts
- - High memory consumption with large datasets
- - API can be inconsistent
- - Not ideal for big data (use Polars/Dask)
- - Moderate learning curve
Casos de Uso
- Exploratory analysis
- Data cleaning
- Feature engineering
- Reports
- Data cleaning and preparation
- Exploratory data analysis (EDA)
- ETL pipelines
- Reports and aggregations
- Feature engineering for ML