feat: make df cols more readable

app.py

@@ -49,6 +49,16 @@ def _fix_dtypes(df: pd.DataFrame, is_tournament: bool) -> pd.DataFrame:
 
     return df
 
+def make_df_columns_readable(df: Optional[pd.DataFrame], is_tournament: bool) -> Optional[pd.DataFrame]:
+    """Make a data frame's columns human-readable."""
+    if df is None:
+        return None
+
+    if not is_tournament:
+        df = df.rename(columns={"league_name": "league"})
+
+    df = df.rename(columns=lambda c: " ".join(c.capitalize().split("_")))
+    return df
 
 def _check_match_type(match_type: str) -> str:
     allowed_match_types = {"tournament", "league"}
@@ -198,12 +208,12 @@ def usatt_rating_analyzer(file_obj):
     matches_per_competition_fig = get_matches_per_competition_fig(df, is_tournament)
     opponent_name_word_cloud_fig = get_opponent_name_word_cloud_fig(df)
     competition_name_word_cloud_fig = get_competition_name_word_cloud_fig(df, is_tournament)
-    most_frequent_opponents = get_most_frequent_opponents(df)
-    best_wins = get_best_wins(df)
-    biggest_upsets = get_biggest_upsets(df)
-    highest_rated_opponent = get_highest_rated_opponent(df)
+    most_frequent_opponents = make_df_columns_readable(get_most_frequent_opponents(df), is_tournament)
+    best_wins = make_df_columns_readable(get_best_wins(df), is_tournament)
+    biggest_upsets = make_df_columns_readable(get_biggest_upsets(df), is_tournament)
+    highest_rated_opponent = make_df_columns_readable(get_highest_rated_opponent(df), is_tournament)
     rating_over_time_fig = get_rating_over_time_fig(df, is_tournament)
-    match_with_longest_game = get_match_with_longest_game(df, is_tournament)
+    match_with_longest_game = make_df_columns_readable(get_match_with_longest_game(df, is_tournament), is_tournament)
     opponent_rating_distr_fig = get_opponent_rating_distr_fig(df)
     opponent_rating_dist_over_time_fig = get_opponent_rating_dist_over_time_fig(df, is_tournament)
 
@@ -227,12 +237,10 @@ with gr.Blocks() as demo:
     analyze_btn_title = "Analyze"
     gr.Markdown(f"""# USATT rating analyzer
     Analyze [USA table tennis](https://www.teamusa.org/usa-table-tennis) tournament and league results. 
-
     ## Downloading match results
     1. Make sure you are [logged in](https://usatt.simplycompete.com/login/auth) to your USATT account.
     2. Find the *active* player you wish to analyze (e.g.,  [Kanak Jha](https://usatt.simplycompete.com/userAccount/up/3431)).
     3. Under 'Tournaments' or 'Leagues', click *Download Tournament/League Match History*.
-
     ## Usage
     1. Simply add your tournament/league match history CSV file and click the "{analyze_btn_title}" button.
     """)
@@ -247,13 +255,13 @@ with gr.Blocks() as demo:
                 num_comps_box = gr.Textbox(lines=1, label="Number of competitions (tournaments/leagues) played")
             with gr.Column():
                 num_matches_box = gr.Textbox(lines=1, label="Number of matches played")
-        
+
         with gr.Row():
             with gr.Column():
                 rating_over_time_plot = gr.Plot(show_label=False)
             with gr.Column():
                 matches_per_comp_plot = gr.Plot(show_label=False)
-                
+
         with gr.Row():
             with gr.Column():
                 opponent_names_plot = gr.Plot(label="Opponent names")
@@ -263,7 +271,7 @@ with gr.Blocks() as demo:
         with gr.Row():
             with gr.Column():
                 most_frequent_opponents_gdf = gr.Dataframe(label="Most frequent opponents", max_rows=5)
-                best_wins_gdf = gr.Dataframe(label="Best wins (matches won sorted by opponent post-competition rating)", 
+                best_wins_gdf = gr.Dataframe(label="Best wins (matches won sorted by opponent post-competition rating)",
                                              max_rows=5)
                 biggest_upsets_gdf = gr.Dataframe(label="Biggest upsets (matches won sorted by rating - opponent post-competition rating)",
                                                   max_rows=5)
@@ -294,7 +302,6 @@ with gr.Blocks() as demo:
         opponent_rating_dist_over_time_plot,
     ]
 
-
     btn.click(usatt_rating_analyzer, inputs=inputs, outputs=outputs)
 
 if __name__ == "__main__":