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6.6. Data Structures#
SUEWS uses pandas DataFrames as the primary data structure for all inputs and outputs, enabling powerful data analysis and manipulation.
6.6.1. Core Data Objects#
6.6.1.1. State DataFrame#
The state DataFrame contains model state variables and parameters for each grid cell.
df_state variables - Complete reference
6.6.1.2. Forcing DataFrame#
The forcing DataFrame contains meteorological forcing data (temperature, radiation, wind, etc.).
df_forcing variables - Complete reference
6.6.1.3. Output DataFrame#
The output DataFrame contains simulation results and diagnostics.
df_output variables - Complete reference
6.6.2. Integration with pandas#
All SUEWS data uses pandas DataFrames with proper indexing, allowing powerful data analysis:
from supy import SUEWSSimulation
# Load sample data and run simulation
sim = SUEWSSimulation.from_sample_data()
sim.run()
# Analyse results using pandas
monthly_temp = sim.results['T2'].resample('M').mean()
energy_balance = sim.results[['QE', 'QH', 'QS', 'QF']].describe()
6.6.3. Benefits of DataFrame Structure#
Familiar interface: Leverage pandas’ powerful data manipulation capabilities
Time series analysis: Built-in resampling, rolling windows, and time-based operations
Easy visualisation: Direct integration with matplotlib and seaborn
Data export: Simple conversion to CSV, Excel, HDF5, and other formats
Memory efficient: Sparse data structures and chunked processing support