[Fixed]-pandasnotimplementederror: the method `pd.series.__iter__()` is not implemented. if you want to collect your data as an numpy array, use 'to_numpy()' instead.

To resolve the pandas NotImplementedError with the message "the method `pd.series.__iter__()` is not implemented," you can use the 'to_numpy()' method instead of iterating directly over the Pandas Series.

Here's how you can do it:

import pandas as pd

# Create a Pandas Series
s = pd.Series([1, 2, 3, 4, 5])

# Instead of iterating directly over the Series like this:
# for value in s:
#     print(value)

# Use the 'to_numpy()' method to convert the Series to a NumPy array:
arr = s.to_numpy()

# Now you can iterate over the NumPy array if needed:
for value in arr:
    print(value)
  

A detailed explanation of the function is written line by line.

    def my_func(df):
      
      # be sure to create a column with unique identifiers
      df = df.reset_index(drop=True).reset_index()
      
      # create dataframe to be removed
      # the additional dummy column is needed to correctly filter out rows later on
      to_remove_df = df.drop_duplicates(['team_code', 'TeamName'])[['index']]
      to_remove_df = to_remove_df.assign(check = lambda x: 'remove_me')
      
      # merge the two dataframes and remove rows
      merged_df = df.merge(to_remove_df, on='index', how='outer')
      result = merged_df.loc[merged_df['check'].isna()]
      
      # drop unnecessary columns
      result = result.drop(columns=['index', 'check'])
      
      return result
  

Example 1

    # your data
    data = {'team_code':['A1', 'S1'],
            'TeamName':['JohnTeam', 'SusanTeam']}
    input_df = ks.DataFrame(data)
    
    
    df = my_func(input_df)
    print(df)
    # Empty DataFrame
    # Columns: [team_code, TeamName]
    # Index: []
  

Example 2

    # other sample data
    data = {'team_code':['A1', 'S1', 'A1', 'S1'],
            'TeamName':['JohnTeam', 'SusanTeam', 'RickTeam', 'SusanTeam']}
    input_df = ks.DataFrame(data)
    
    
    df = my_func(input_df)
    print(df)
    #   team_code   TeamName
    # 3        S1  SusanTeam
  

Importing the necessary library and creating sample data:

    import pyspark.pandas as ps
    
    data = {"col_1": [1,2,3], "col_2": [4,5,6]}
    df = ps.DataFrame(data)
  

Calculating the median for each row and adding it as a new column:

    median_series = df[["col_1","col_2"]].apply(lambda x: x.median(), axis=1)
    median_series.name = "median"
    
    df = ps.merge(df, median_series, left_index=True, right_index=True, how='left')