Data Transformation Issue on End-to-End ML Project - convert_to_minutes() Takes 1 Positional Argument But 2 Were Given

Question

I am following the process shown on Wine Quality Prediction End-to-End ML Project on Krish Naik's YouTube channel to do a Flight Fare Prediction Project.

I run this cell of data transformation pipeline on 03_data_transformation.ipynb:

try:
    config = ConfigurationManager()
    data_transformation_config = config.get_data_transformation_config()
    data_transformation = DataTransformation(config=data_transformation_config)
    # data_transformation.train_test_spliting()
    # New Line
    data_transformation.initiate_data_transformation()
except Exception as e:
    raise e

I get this error:

TypeError: convert_to_minutes() takes 1 positional argument but 2 were given

Here is the traceback:

TypeError                                 Traceback (most recent call last)
g:\Machine_Learning_Projects\iNeuron internship\Flight-Fare-Prediction-End-to-End-ML-Project\research\03_data_transformation.ipynb Cell 10 line 9
      <a href='vscode-notebook-cell:/g%3A/Machine_Learning_Projects/iNeuron%20internship/Flight-Fare-Prediction-End-to-End-ML-Project/research/03_data_transformation.ipynb#X12sZmlsZQ%3D%3D?line=6'>7</a>     data_transformation.initiate_data_transformation()
      <a href='vscode-notebook-cell:/g%3A/Machine_Learning_Projects/iNeuron%20internship/Flight-Fare-Prediction-End-to-End-ML-Project/research/03_data_transformation.ipynb#X12sZmlsZQ%3D%3D?line=7'>8</a> except Exception as e:
----> <a href='vscode-notebook-cell:/g%3A/Machine_Learning_Projects/iNeuron%20internship/Flight-Fare-Prediction-End-to-End-ML-Project/research/03_data_transformation.ipynb#X12sZmlsZQ%3D%3D?line=8'>9</a>     raise e

g:\Machine_Learning_Projects\iNeuron internship\Flight-Fare-Prediction-End-to-End-ML-Project\research\03_data_transformation.ipynb Cell 10 line 7
      <a href='vscode-notebook-cell:/g%3A/Machine_Learning_Projects/iNeuron%20internship/Flight-Fare-Prediction-End-to-End-ML-Project/research/03_data_transformation.ipynb#X12sZmlsZQ%3D%3D?line=3'>4</a>     data_transformation = DataTransformation(config=data_transformation_config)
      <a href='vscode-notebook-cell:/g%3A/Machine_Learning_Projects/iNeuron%20internship/Flight-Fare-Prediction-End-to-End-ML-Project/research/03_data_transformation.ipynb#X12sZmlsZQ%3D%3D?line=4'>5</a>     # data_transformation.train_test_spliting()
      <a href='vscode-notebook-cell:/g%3A/Machine_Learning_Projects/iNeuron%20internship/Flight-Fare-Prediction-End-to-End-ML-Project/research/03_data_transformation.ipynb#X12sZmlsZQ%3D%3D?line=5'>6</a>     # New Line
----> <a href='vscode-notebook-cell:/g%3A/Machine_Learning_Projects/iNeuron%20internship/Flight-Fare-Prediction-End-to-End-ML-Project/research/03_data_transformation.ipynb#X12sZmlsZQ%3D%3D?line=6'>7</a>     data_transformation.initiate_data_transformation()
      <a href='vscode-notebook-cell:/g%3A/Machine_Learning_Projects/iNeuron%20internship/Flight-Fare-Prediction-End-to-End-ML-Project/research/03_data_transformation.ipynb#X12sZmlsZQ%3D%3D?line=7'>8</a> except Exception as e:
      <a href='vscode-notebook-cell:/g%3A/Machine_Learning_Projects/iNeuron%20internship/Flight-Fare-Prediction-End-to-End-ML-Project/research/03_data_transformation.ipynb#X12sZmlsZQ%3D%3D?line=8'>9</a>     raise e

g:\Machine_Learning_Projects\iNeuron internship\Flight-Fare-Prediction-End-to-End-ML-Project\research\03_data_transformation.ipynb Cell 10 line 6
     <a href='vscode-notebook-cell:/g%3A/Machine_Learning_Projects/iNeuron%20internship/Flight-Fare-Prediction-End-to-End-ML-Project/research/03_data_transformation.ipynb#X12sZmlsZQ%3D%3D?line=55'>56</a> df = pd.concat([df, df_airline, df_source, df_dest], axis = 1)
     <a href='vscode-notebook-cell:/g%3A/Machine_Learning_Projects/iNeuron%20internship/Flight-Fare-Prediction-End-to-End-ML-Project/research/03_data_transformation.ipynb#X12sZmlsZQ%3D%3D?line=57'>58</a> ## handling duration column
     <a href='vscode-notebook-cell:/g%3A/Machine_Learning_Projects/iNeuron%20internship/Flight-Fare-Prediction-End-to-End-ML-Project/research/03_data_transformation.ipynb#X12sZmlsZQ%3D%3D?line=58'>59</a> # df['duration'] = df['Duration'].apply(convert_to_minutes)
     <a href='vscode-notebook-cell:/g%3A/Machine_Learning_Projects/iNeuron%20internship/Flight-Fare-Prediction-End-to-End-ML-Project/research/03_data_transformation.ipynb#X12sZmlsZQ%3D%3D?line=59'>60</a> # New Line Added
---> <a href='vscode-notebook-cell:/g%3A/Machine_Learning_Projects/iNeuron%20internship/Flight-Fare-Prediction-End-to-End-ML-Project/research/03_data_transformation.ipynb#X12sZmlsZQ%3D%3D?line=60'>61</a> df['duration'] = df['Duration'].apply(self.convert_to_minutes)
     <a href='vscode-notebook-cell:/g%3A/Machine_Learning_Projects/iNeuron%20internship/Flight-Fare-Prediction-End-to-End-ML-Project/research/03_data_transformation.ipynb#X12sZmlsZQ%3D%3D?line=61'>62</a> upper_time_limit = df.duration.mean() + 1.5 * df.duration.std()
     <a href='vscode-notebook-cell:/g%3A/Machine_Learning_Projects/iNeuron%20internship/Flight-Fare-Prediction-End-to-End-ML-Project/research/03_data_transformation.ipynb#X12sZmlsZQ%3D%3D?line=62'>63</a> df['duration'] = df['duration'].clip(upper = upper_time_limit)

File c:\Users\2021\.conda\envs\flightfareprediction\lib\site-packages\pandas\core\series.py:4630, in Series.apply(self, func, convert_dtype, args, **kwargs)
   4520 def apply(
   4521     self,
   4522     func: AggFuncType,
   (...)
   4525     **kwargs,
   4526 ) -> DataFrame | Series:
   4527     """
   4528     Invoke function on values of Series.
   4529 
   (...)
   4628     dtype: float64
   4629     """
-> 4630     return SeriesApply(self, func, convert_dtype, args, kwargs).apply()

File c:\Users\2021\.conda\envs\flightfareprediction\lib\site-packages\pandas\core\apply.py:1025, in SeriesApply.apply(self)
   1022     return self.apply_str()
   1024 # self.f is Callable
-> 1025 return self.apply_standard()

File c:\Users\2021\.conda\envs\flightfareprediction\lib\site-packages\pandas\core\apply.py:1076, in SeriesApply.apply_standard(self)
   1074     else:
   1075         values = obj.astype(object)._values
-> 1076         mapped = lib.map_infer(
   1077             values,
   1078             f,
   1079             convert=self.convert_dtype,
   1080         )
   1082 if len(mapped) and isinstance(mapped[0], ABCSeries):
   1083     # GH#43986 Need to do list(mapped) in order to get treated as nested
   1084     #  See also GH#25959 regarding EA support
   1085     return obj._constructor_expanddim(list(mapped), index=obj.index)

File c:\Users\2021\.conda\envs\flightfareprediction\lib\site-packages\pandas\_libs\lib.pyx:2834, in pandas._libs.lib.map_infer()

TypeError: convert_to_minutes() takes 1 positional argument but 2 were given

Here is the code of data transformation cell, which contains convert_to_minutes() function.

class DataTransformation:

    # New Function Added
    # https://github.com/yash1314/Flight-Price-Prediction/blob/main/src/utils.py
    def convert_to_minutes(duration):
        try:
            hours, minute = 0, 0
            for i in duration.split():
                if 'h' in i:
                    hours = int(i[:-1])
                elif 'm' in i:
                    minute = int(i[:-1])
            return hours * 60 + minute
        except :
            return None 

    def __init__(self, config: DataTransformationConfig):
        self.config = config

    
    ## Note: You can add different data transformation techniques such as Scaler, PCA and all
    #You can perform all kinds of EDA in ML cycle here before passing this data to the model

    # I am only adding train_test_spliting cz this data is already cleaned up

    # New Code Added Start
    def initiate_data_transformation(self):
        ## reading the data
        # df = pd.read_csv(self.config.data_path)
        # New Line
        df = pd.read_excel(self.config.data_path)

        logger.info('Read data completed')
        logger.info(f'df dataframe head: \n{df.head().to_string()}')

        ## dropping null values
        df.dropna(inplace = True)

        ## Date of journey column transformation
        df['journey_date'] = pd.to_datetime(df['Date_of_Journey'], format ="%d/%m/%Y").dt.day
        df['journey_month'] = pd.to_datetime(df['Date_of_Journey'], format ="%d/%m/%Y").dt.month

        ## encoding total stops.
        df.replace({'Total_Stops': {'non-stop' : 0, '1 stop': 1, '2 stops': 2, '3 stops': 3, '4 stops': 4}}, inplace = True)

        ## ecoding airline, source, and destination
        df_airline = pd.get_dummies(df['Airline'], dtype=int)
        df_source = pd.get_dummies(df['Source'],  dtype=int)
        df_dest = pd.get_dummies(df['Destination'], dtype=int)

        ## dropping first columns of each categorical variables.
        df_airline.drop('Trujet', axis = 1, inplace = True)
        df_source.drop('Banglore', axis = 1, inplace = True)
        df_dest.drop('Banglore', axis = 1, inplace = True)

        df = pd.concat([df, df_airline, df_source, df_dest], axis = 1)
       
        ## handling duration column
        # df['duration'] = df['Duration'].apply(convert_to_minutes)
        # New Line Added
        df['duration'] = df['Duration'].apply(self.convert_to_minutes)
        upper_time_limit = df.duration.mean() + 1.5 * df.duration.std()
        df['duration'] = df['duration'].clip(upper = upper_time_limit)

        ## encodign duration column
        bins = [0, 120, 360, 1440]  # custom bin intervals for 'Short,' 'Medium,' and 'Long'
        labels = ['Short', 'Medium', 'Long'] # creating labels for encoding

        df['duration'] = pd.cut(df['duration'], bins=bins, labels=labels)
        df.replace({'duration': {'Short':1, 'Medium':2, 'Long': 3}}, inplace = True)
        
        ## dropping the columns
        cols_to_drop = cols_to_drop = ['Airline', 'Date_of_Journey', 'Source', 'Destination', 'Route', 'Dep_Time', 'Arrival_Time', 'Duration', 'Additional_Info', 'Delhi', 'Kolkata']

        df.drop(cols_to_drop, axis = 1, inplace = True)

        logger.info('df data transformation completed')
        logger.info(f' transformed df data head: \n{df.head().to_string()}')

        # df.to_csv(self.data_transformation_config.transformed_data_file_path, index = False, header= True)
        # New Line
        df.to_excel(self.data_transformation_config.transformed_data_file_path, index = False, header= True)
        logger.info("transformed data is stored")
        df.head(1)
        ## splitting the data into training and target data
        X = df.drop('Price', axis = 1)
        y = df['Price']
        
        ## accessing the feature importance.
        select = ExtraTreesRegressor()
        select.fit(X, y)

        # plt.figure(figsize=(12, 8))
        # fig_importances = pd.Series(select.feature_importances_, index=X.columns)
        # fig_importances.nlargest(20).plot(kind='barh')
    
        # ## specify the path to the "visuals" folder using os.path.join
        # visuals_folder = 'visuals'
        # if not os.path.exists(visuals_folder):
        #     os.makedirs(visuals_folder)

        # ## save the plot in the visuals folder
        # plt.savefig(os.path.join(visuals_folder, 'feature_importance_plot.png'))
        # logger.info('feature imp figure saving is successful')

        ## further Splitting the data.
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42, shuffle = True) 
        logger.info('final splitting the data is successful')
        

        ## returning splitted data and data_path.
        return (
            X_train, 
            X_test, 
            y_train, 
            y_test,
            self.data_transformation_config.transformed_data_file_path
        )    

Here is my file in GitHub.

My file encoding is UTF-8

Would you please help me to fix this issue?

Answer 1

You have convert_to_minutes located inside the class, but you don't have a self parameter. It doesn't look like you NEED to be part of the class, but that's how you have it, and as such the quickest fix is just to make it:

    def convert_to_minutes(self, duration):