Clearly structuring pandas transformations

tweak_twitter.py

def tweak_twitter(df: pd.DataFrame) -> pd.DataFrame:
    df = df.copy(deep=False)
    normalised_columns = {col: col.replace(' ', '_') for col in df.columns}
    df = df.rename(columns=normalised_columns)
    # filter uninteresting columns
    excluded_columns = [
        'permalink_clicks', 'app_opens', 'app_installs', 'email_tweet', 'dial_phone',
    ]
    excluded_columns += [col for col in df.columns if 'promoted' in col]
    df = df.drop(columns=excluded_columns)
    # fix types
    type_map = dict(
        impressions=np.uint32,
        engagements=np.uint16,
        replies=np.uint8,
        hashtag_clicks=np.uint8,
        follows=np.uint8,
        retweets=np.uint16,
        likes=np.uint16,
        user_profile_clicks=np.uint16,
        url_clicks=np.uint16,
        detail_expands=np.uint16,
        media_views=np.uint16,
        media_engagements=np.uint16,
        Tweet_text='category',
    )
    df = df.astype(type_map)
    df['time'] = df['time'].dt.tz_convert('America/Denver')
    # assign feature columns
    feature_columns = dict(
        Tweet_permalink='https://twitter.com/__mharrison__/status/',
        is_reply=df.Tweet_text.str.startswith('@'),
        length=df.Tweet_text.str.len(),
        num_words=df.Tweet_text.str.split().apply(len),
        is_unicode=(
            df.Tweet_text.str.encode('ascii', errors='replace').str.decode('ascii')
            != df.Tweet_text
        ),
        hour=df.time.dt.hour,
        dom=df.time.dt.day,
        dow=df.time.dt.dayofweek,
        at_tweet=df.Tweet_text.str.contains('@'),
        has_newlines=df.Tweet_text.str.contains('\n'),
        num_lines=df.Tweet_text.str.count('\n'),
        num_mentions=df.Tweet_text.str.count('@'),
        has_hashtag=df.Tweet_text.str.count('#'),
    )
    df = df.assign(**feature_columns)
    return df.reset_index()

From a discussion with __mharrison__:
https://twitter.com/dave_hirschfeld/status/1485499794982596612