Stop warning about NaNs. They are perfectly legitimate datapoints, when using derived KPIs
@@ -113,15 +113,6 @@ def delta(x, y, x_denominators=1, y_denominators=1, assume_normal=True, alpha=0.
del x_denominators
del y_denominators
- x_nan = np.isnan(_x_ratio).sum()
- y_nan = np.isnan(_y_ratio).sum()
- if x_nan > 0:
- warnings.warn('Discarding ' + str(x_nan) + ' NaN(s) in the x array!')
- logger.warning('Discarding ' + str(x_nan) + ' NaN(s) in the x array!')
- if y_nan > 0:
- warnings.warn('Discarding ' + str(y_nan) + ' NaN(s) in the y array!')
- logger.warning('Discarding ' + str(x_nan) + ' NaN(s) in the x array!')
-
ss_x = sample_size(_x_ratio)
ss_y = sample_size(_y_ratio)
