concat with join='inner' instead of special column order

Author: SabineHaas

windpowerlib/wind_farm.py

@@ -362,13 +362,13 @@ def assign_power_curve(self, wake_losses_model='wind_farm_efficiency',
                     power_curve = pd.concat(
                         [pd.DataFrame(data={
                             'wind_speed': [0.0], 'value': [0.0]}),
-                            power_curve])
+                            power_curve], join='inner')
                 if power_curve.iloc[-1]['value'] != 0.0:
                     power_curve = pd.concat(
                         [power_curve, pd.DataFrame(data={
                             'wind_speed': [power_curve['wind_speed'].loc[
                                                power_curve.index[-1]] + 0.5],
-                            'value': [0.0]})])
+                            'value': [0.0]})], join='inner')
             # Add power curves of all turbine types to data frame
             # (multiplied by turbine amount)
             df = pd.concat(

windpowerlib/wind_turbine.py

@@ -179,15 +179,11 @@ def __init__(self, hub_height, nominal_power=None, path='oedb',
             if isinstance(self.power_coefficient_curve, dict):
                 self.power_coefficient_curve = pd.DataFrame(
                     self.power_coefficient_curve)
-            # sort power (coefficient) curve by wind speed and bring columns in
-            # order: 1) 'wind_speed' 2) 'value'
+            # sort power (coefficient) curve by wind speed
             if isinstance(self.power_curve, pd.DataFrame):
                 self.power_curve.sort_values(by='wind_speed')
-                self.power_curve = self.power_curve[['wind_speed', 'value']]
             if isinstance(self.power_coefficient_curve, pd.DataFrame):
                 self.power_coefficient_curve.sort_values(by='wind_speed')
-                self.power_coefficient_curve = self.power_coefficient_curve[[
-                    'wind_speed', 'value']]
 
     def __repr__(self):
         info = []