STATISTICS
mode
Return an array of the modal (most common) value in the passed array.
Example of usage
Python API reference included; executable data demonstration pending review.
Usage / limitations
+Use when: Summarise measurements and uncertainty.
−Limitations: Check input requirements, mathematical assumptions and version-specific behaviour before using.
Mathematical formula
See method documentation: implementation and assumptions vary.
Sample dataShow
{
"variable": "patient_wait_times",
"domain": "Hospital outpatient waiting-time analysis",
"feature_names": [
"wait_minutes"
],
"records": [
{
"wait_minutes": 12
},
{
"wait_minutes": 16
},
{
"wait_minutes": 18
},
{
"wait_minutes": 20
},
{
"wait_minutes": 24
},
{
"wait_minutes": 30
},
{
"wait_minutes": 34
},
{
"wait_minutes": 40
}
]
}
Code example (Python)
patient_wait_times = {'variable': 'patient_wait_times', 'domain': 'Hospital outpatient waiting-time analysis', 'feature_names': ['wait_minutes'], 'records': [{'wait_minutes': 12}, {'wait_minutes': 16}, {'wait_minutes': 18}, {'wait_minutes': 20}, {'wait_minutes': 24}, {'wait_minutes': 30}, {'wait_minutes': 34}, {'wait_minutes': 40}]}
# DataLens.ai example — mode
# Domain: hospital outpatient waiting-time analysis.
# Sample data are included by Copy input + code + test.
import numpy as np
from scipy.stats import mode
# Extract named waiting-time observations into a numeric array.
values = np.array([row["wait_minutes"] for row in patient_wait_times["records"]], dtype=float)
# Apply this statistical method to the observations.
values_with_repeats = np.concatenate([values,[20,20]]); result = mode(values_with_repeats)
# Verify the calculated statistic against a mathematical property.
assert result.mode == 20 and result.count == 3
print("Expected output verified")
# DataLens.ai example — mode
# Domain: hospital outpatient waiting-time analysis.
# Sample data are included by Copy input + code + test.
import numpy as np
from scipy.stats import mode
# Extract named waiting-time observations into a numeric array.
values = np.array([row["wait_minutes"] for row in patient_wait_times["records"]], dtype=float)
# Apply this statistical method to the observations.
values_with_repeats = np.concatenate([values,[20,20]]); result = mode(values_with_repeats)
# Verify the calculated statistic against a mathematical property.
assert result.mode == 20 and result.count == 3
print("Expected output verified")