Is there an alternative to apply and lambda in python?

I am a newbie and trying to figure our if I can run the following code by not using apply and lambda functions. Any help is greatly appreciated.

The code is:

df1['Category'] = df[key_column].apply(lambda x: process_df1(x, 'category'))  

where df1 is a dataframe, key_column is a specific column identified to be operated upon process_df1 is a function defined to run on df1.

The problem is I am trying to avoid the error: "A value is trying to be set on a copy of a slice from a DataFrame. Try using

.loc[row_indexer,col_indexer] = value instead"  

I don't want to ignore / suppress the warnings or set pd.options.mode.chained_assignment = None.

Is there an alternative besides these 2?

I have tried using

df.loc[df1['Category'] = df[key_column].apply(lambda x: process_df1(x, 'category'))]   

but it still produces the same error.

Apologies if it is a confusing question.

Update:

Here is what process_df1 is doing:

def process_df1(string, item_type):          d = {'category': 0, 'classification': 1, 'subclassification': 2, 'line_item':3}                    if not pd.isnull(string):              if string not in exception_list:                  string = get_cleaned_word(string)                    for tup in x1:              if tup[2] == string:                  return tup[0][d[item_type]]            return np.nan    And key_column is find_key_column(df).        def find_key_column(df):          columns = df.columns          max_count = 0                    most_likely_column = None            for col in columns:              count = 0              for string in df[col].values:                  if (type(string) == str):                      for item in flat_list:                          if (fuzz.token_set_ratio(string, item) > 95):                              count += 1                        else:                      continue                                    if count > max_count:                  most_likely_column = col                  max_count = count          return most_likely_column  

https://stackoverflow.com/questions/65622765/is-there-an-alternative-to-apply-and-lambda-in-python January 08, 2021 at 10:48AM