mkt

In [ ]:
mkt(vdf, 
    column: str, 
    ts: str, 
    alpha: float = 0.05)

Mann-Kendall test (time series trend).

⚠ Warning: This test is computationally expensive because it uses a CROSS JOIN, the complexity of which is O(n * k), where n is the total count of the vDataFrame and k the number of rows with which to perform the test.

Parameters

Name Type Optional Description
vdf
vDataFrame
❌
 Input vDataFrame.
column
str
❌
 Input vcolumn to test.
ts
str
❌
 vcolumn used as timeline. It will be to use to order the data. It can be a numerical or type date like (date, datetime, timestamp...) vcolumn.
alpha
float
✓
 Significance Level. Probability to accept H0.

Returns

tablesample : An object containing the result. For more information, see utilities.tablesample.

Example

In [107]:
from verticapy import *
texas = vDataFrame("Texas")
texas["cases"].plot(ts = "date")
In [108]:
from verticapy.stats import mkt
mkt(texas,
    column = "cases",
    ts = "date")
Out[108]:
value
Mann Kendall Test Statistic344.9739241353378
S3780.0
STDS10.9544511501033
p_value0.0
Monotonic Trend
✅
Rows: 1-5 | Columns: 2