Cleanup processing code

process.py

@@ -51,11 +51,6 @@ def process_dataset(dataset):
     source = SOURCE.format(dataset)
     if os.path.exists(xml_file):
         df = xml_to_df(xml_file, source)
-        # df = filter_only_questions_with_accepted_answers(df)
-        # df = filter_scores_above(df, QUESTION_SCORE_TRESHOLD, ANSWER_SCORE_TRESHOLD)
-        # df = clean_tags(df)
-        # df = convert_html_to_markdown(df)
-        # df = group_qa(df)
         oa = convert_to_oa(df)
         save_parquet(oa, dataset)
         # upload_hf(dataset)
@@ -148,43 +143,6 @@ def xml_to_df(path: str, source: str):
         return df
 
 
-def filter_only_questions_with_accepted_answers(df):
-    """
-    Filter only to Questions with Accepted Answers
-
-    Filter dataframe by questions that have accepted answers, should also include
-    all rows of answers for those questions, even if not accepted.
-
-    Parameters:
-    df (DataFrame): containing a "AcceptedAnswerId", "Id", and "ParentId" columns
-
-    Returns:
-    df (DataFrame): current dataframe with filtered results
-    """
-    accepted_ids = df[df["AcceptedAnswerId"] != 0]["Id"].tolist()
-    return df[(df["AcceptedAnswerId"] != 0) | (df["ParentId"].isin(accepted_ids))]
-
-
-def filter_scores_above(
-    df, question_score_threshold: int = 20, answer_score_threshold: int = 20
-):
-    """
-    Filter Dataframe by minimum scores
-
-    Filter Question and Answer columns by score thresholds to trim lower scoring results
-
-    Parameters:
-    df (DataFrame): containing a "Score" column
-
-    Returns:
-    df (DataFrame): current dataframe with filtered results
-    """
-    return df[
-        ((df["Score"] >= question_score_threshold) & (df.PostTypeId == 1))
-        | ((df["Score"] >= answer_score_threshold) & (df.PostTypeId == 2))
-    ]
-
-
 remove_markdown_links_pattern = r"\[([^\]]+)\]\(([^\)]+)\)"
 remove_remaining_links = r"https?:\/\/[^\s]+"
 
@@ -200,120 +158,5 @@ def to_markdown(text):
     return text
 
 
-def convert_html_to_markdown(df, column: str = "Body"):
-    """
-    Convert HTML tags to markdown
-
-    Feeds HTML text body into markdown. Remove final newline from <p> tags
-
-    Parameters:
-    df (DataFrame): containing a "Body" column with HTML
-
-    Returns:
-    df (DataFrame): current dataframe with parsed column
-    """
-    df.dropna(subset=[column], inplace=True)
-    df[f"{column}Clean"] = df[column].apply(to_markdown)
-    return df
-
-
-def clean_tags(df):
-    """
-    Convert Tags into Comma separated
-
-    Converts Tag slugs into commas separated tags
-
-    Parameters:
-    df (DataFrame): containing a "Tags" column with slugs
-
-    Returns:
-    df (DataFrame): current dataframe with parsed column
-    """
-    df["TagsClean"] = (
-        df["Tags"]
-        .str.replace("-", " ")
-        .str.replace("><", ", ")
-        .str.replace("<", "")
-        .str.replace(">", "")
-    )
-    return df
-
-
-def group_qa(df):
-    """
-    Group Questions and Answers
-    """
-    questions = df[df.PostTypeId == 1]
-    answers = df[df.PostTypeId == 2]
-
-    df = pd.merge(
-        questions,
-        answers[
-            [
-                "Id",
-                "CreationDate",
-                "Score",
-                "ViewCount",
-                "CommentCount",
-                "ContentLicense",
-                "TagsClean",
-                "BodyClean",
-                "ParentId",
-            ]
-        ],
-        left_on="Id",
-        right_on="ParentId",
-        suffixes=("_q", "_a"),
-        how="left",
-    )
-
-    df["AcceptedAnswerFlag"] = df.apply(
-        lambda row: row["Id_a"] == row["AcceptedAnswerId"], axis=1
-    )
-
-    df = df.rename(
-        columns={
-            "BodyClean_q": "Question",
-            "Score_q": "QuestionScore",
-            "TagsClean_q": "QuestionTags",
-            "BodyClean_a": "Answer",
-            "Score_a": "AnswerScore",
-            "ContentLicense_q": "QuestionContentLicense",
-            "ContentLicense_a": "AnswerContentLicense",
-            "CreationDate_q": "CreationDate",
-        }
-    )
-
-    df = (
-        df.sort_values(
-            by=["AcceptedAnswerFlag", "AnswerScore"], ascending=[False, False]
-        )
-        .groupby("Question")
-        .head(MAX_ANSWERS)
-        .reset_index(drop=True)
-    )
-    df = (
-        df.groupby(
-            [
-                "Title",
-                "Question",
-                "QuestionScore",
-                "QuestionTags",
-                "QuestionContentLicense",
-                "DataSource",
-                "CreationDate",
-            ]
-        )
-        .apply(
-            lambda x: x[["Answer", "AnswerScore", "AcceptedAnswerFlag"]].to_dict(
-                "records"
-            )
-        )
-        .reset_index()
-        .rename(columns={0: "Answers"})
-    )
-    return df
-
-
 if __name__ == "__main__":
     main()