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Analysis

SmellscaPy provides two functions for statistical data analysis of the pleasantness and presence score of a provided DataFrame.

# Descriptive statistics
df = descriptive_statistics(df)

# Linear Mixed Model (LMM)
df = lmm_pleasantness(df)
df = lmm_presence(df)

Descriptive statistics

The descriptive_statistics() computes and summarises the descriptive statistics of the smellscape dataset, focusing on the numerical variables pleasantness_score and presence_score.

This function provides a concise statistical overview of the dataset, allowing users to interpret how perceived pleasantness and presence values are distributed—either overall or within subgroups defined by a categorical variable.

The analysis includes three main statistical dimensions:

  • Central tendency : mean and median.
  • Dispersion : variance, standard deviation, minimum, maximum, interquartile range (IQR), and coefficient of variation.
  • Distribution shape: skewness and kurtosis, indicating whether perceptions are symmetrically distributed or exhibit heavy/light tails.

If a group_by_col parameter is provided, the statistics are computed for each subgroup. Otherwise, the function returns an aggregated summary for the entire dataset.

#Descriptive statistics
s = descriptive_statistics(df)

#Descriptive statistics, grouped
s = descriptive_statistics (df, group_by_col="Smell source")
Type Pleasantness Presence Subgroup
count 40.0 40.0 Body odours
mean -0.0455 -0.1461 Body odours
std 0.1966 0.2419 Body odours
min -0.3536 -0.6464 Body odours
25% -0.2071 -0.3308 Body odours
50% -0.0732 -0.0581 Body odours
75% 0.1036 0.0000 Body odours
max 0.5000 0.5000 Body odours
variance 0.0386 0.0585 Body odours
skewness 0.5230 0.0322 Body odours
kurtosis -0.1412 -0.0707 Body odours
count 15.0 15.0 Cleaning products
mean 0.2715 0.0775 Cleaning products
std 0.1415 0.1431 Cleaning products
min 0.0732 -0.1893 Cleaning products
25% 0.1679 0.0089 Cleaning products
50% 0.2803 0.0607 Cleaning products
75% 0.3384 0.1919 Cleaning products
max 0.5732 0.2803 Cleaning products
variance 0.0200 0.0205 Cleaning products
skewness 0.3948 -0.2815 Cleaning products
kurtosis -0.0680 -0.6162 Cleaning products
... ... ... ...