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Loading Smellscape Data

Loading example data

Two example datasets are included with SmellscaPy to allow users to explore the package’s functionalities immediately. DataExample_Eurac.csv contains data collected by Eurac Research from 17 participants through a repeated questionnaire campaign conducted in an office building in Bolzano (Italy) between May and September 2025, and can be loaded using the load_example_data_Eurac() function. DataExample_Measure2_Unitn.csv refers to a controlled laboratory olfactory experiment involving 35 participants, carried out within the MEASURE 2.0 project at the University of Trento (Italy), and can be loaded using the load_example_data_Measure2_Unitn() function. 

# Load example dataset
df = smpy.load_example_data_Eurac()
#or
df = smpy.load_example_Measure2_Unitn()

Both datasets contain both mandatory and optional variables. The mandatory columns are:

  • pleasant, present, light, engaging, unpleasant, absent, overpowering, detached: perceptual quality attributes
# Display basic information about the example dataset
print(f"Dataset shape: {df}")
print(f"Columns ({len(df.columns)}): {list(df.columns)}")

Loading your data

If you have your own smellscape survey data, you can load it using pandas and then process it with SmellscaPy.

import pandas as pd

# Load data from a CSV file
my_data = pd.read_csv('path/to/your/data.csv')

To use smellscapy, your input dataset must contain the following mandatory columns:

  • pleasant, present, light, engaging, unpleasant, absent, overpowering, detached

Make sure that the column names match exactly as listed above. If your dataset uses different names, you will need to rename the columns before using SmellscaPy.

Additional requirements:

  • No missing values are allowed in these columns.
  • The values for the eight perceptual attribute columns must be numeric, ranging from 1 (corresponding to "Strongly disagree") to 5 (corresponding to "Strongly agree).

Besides these required fields, you are free to include any other additional columns in your dataset as needed.

Please, refer to DataExample.csv for a reference dataset.