Re-write custom feature encoding function python

Question

I have a question regarding code quality and best practices. My task - to write a feature encoding function that will encode categorical labels, boolean labels as one-hot encoding, timestamps for further usage for ml training.

Input - dictionary of several dataframes, each dataframe consist of several columns of different types.

The function should return a dictionary of correctly encoded dataframes and a dictionary of label encoders for categorical columns.

Here is what I did:

# Encode bool values to one hot encoding, change NaN numerical values to single const value, make timestamp be time columns, add categorical encoding
def df_encoder(data_dict) :
    #encode all NA values of continuos data as a constant
    NA_values = 0.001
    # dictionary to save dictionary of label encodings from LabelEncoder
    labels_codes = dict()

    for names_df in data_dict:
        # list, where to save label encodings from LabelEncoder from one dataframe
        labels_codes[names_df] = list()

        #take iteratively dataframe from the dictionary of dataframes
        df_additional = data_dict[names_df]

        for col in df_additional:

            if is_bool_dtype(df_additional[col]):
                loc_col = df_additional.columns.get_loc(col)
                df_additional_one_hot = pd.get_dummies(df_additional[col], prefix=col, dummy_na=True)
                df_additional = pd.concat([df_additional.iloc[:, :loc_col], df_additional_one_hot, df_additional.iloc[:, loc_col:]], axis=1).drop(col, axis=1)

            elif is_numeric_dtype(df_additional[col]):
                df_additional[col].fillna(NA_values)

            elif is_datetime64_any_dtype(df_additional[col]):
                loc_col = df_additional.columns.get_loc(col)
                date_df = pd.DataFrame()
                date_df[col+'_year'] = df_additional[col].dt.year.fillna(0)
                date_df[col+'_month'] = df_additional[col].dt.month.fillna(0)
                date_df[col+'_day'] = df_additional[col].dt.day.fillna(0)
                date_df[col+'_hour'] = df_additional[col].dt.hour.fillna(25)
                date_df[col+'_minute'] = df_additional[col].dt.minute.fillna(60)
                date_df[col+'_seconds'] = df_additional[col].dt.second.fillna(60)
                df_additional = pd.concat([df_additional.iloc[:, :loc_col], date_df, df_additional.iloc[:, loc_col:]], axis=1).drop(col, axis=1)

            elif is_categorical_dtype(df_additional[col]) and df_additional[col].nunique()== 2:
                loc_col = df_additional.columns.get_loc(col)
                df_additional_two_val_categ = pd.get_dummies(df_additional[col], prefix=col, dummy_na=True)
                df_additional = pd.concat([df_additional.iloc[:, :loc_col], df_additional_two_val_categ, df_additional.iloc[:, loc_col:]], axis=1).drop(col, axis=1)

            elif is_categorical_dtype(df_additional[col]) and df_additional[col].nunique()>2:
                #keep only alphanumeric and space, and ignore non-ASCII
                df_additional[col].replace(regex=True,inplace=True,to_replace=r'[^A-Za-z0-9 ]+',value=r'')
                label_enc = LabelEncoder()
                df_additional[col] = label_enc.fit_transform(df_additional[col].astype(str))
                labels_codes[names_df].append({col: label_enc})

            data_dict[names_df] = df_additional
    return data_dict, labels_codes

The functions work well, but I'm not happy with its quality. I need some useful advice or examples of how to make this function more efficient, and more "best-coding practises" alike. Will appreciate any insights and critique.