WaterFrame.corr(parameter1, parameter2, method=‘pearson’, min_periods=1)

Reference

Compute pairwise correlation of data columns of parameter1 and parameter2, excluding NA/null values.

Parameters

• parameter1: Column name of WaterFrame.data to correlate. (str)
• parameter2: Column name of WaterFrame.data to correlate. (str)
• method: ‘pearson’, ‘kendall’ or ‘spearman’. (str)
  • pearson: standard correlation coefficient
  • kendall: Kendall Tau correlation coefficient
  • spearman: Spearman rank correlation
• min_periods: Minimum number of observations required per pair of columns to have a valid result. Currently only available for Pearson and Spearman correlation. (int)

Returns

• correlation_number: float

Example

To reproduce the example, download the NetCDF file MO_TS_MO_OBSEA_201401.nc and save it in the same python script folder.

import mooda as md

path = "MO_TS_MO_OBSEA_201401.nc" # Path to the NetCDF file

wf = md.read_nc_emodnet(path)

print(f'Available parameters: {list(wf.parameters)}')

parameter1 = 'TEMP'  # Temperature
parameter2 = 'CNDC'  # Conductivity

print('Correlation factor between ',
      f"{wf.vocabulary.get(parameter1).get('long_name')} and ",
      f"{wf.vocabulary.get(parameter2).get('long_name')}: ",
      f"{wf.corr(parameter1, parameter2)}")

Output:

Available parameters: ['DEPH', 'ATMS', 'CNDC', 'DRYT', 'PRES', 'PSAL', 'SVEL', 'TEMP', 'WDIR', 'WSPD']
Correlation factor between  Sea temperature and  Electrical conductivity:  0.9745679306570474

Return to mooda.WaterFrame.