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| import pandas as pd | |
| from datetime import datetime, timedelta, time # Added time for min.time | |
| import logging | |
| # Configure logging for this module | |
| logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(module)s - %(message)s') | |
| def filter_dataframe_by_date(df, date_column, start_date, end_date): | |
| """Filters a DataFrame by a date column within a given date range.""" | |
| if df is None or df.empty or not date_column: | |
| logging.warning(f"Filter by date: DataFrame is None, empty, or no date_column provided. DF: {df is not None}, empty: {df.empty if df is not None else 'N/A'}, date_column: {date_column}") | |
| return pd.DataFrame() | |
| if date_column not in df.columns: | |
| logging.warning(f"Filter by date: Date column '{date_column}' not found in DataFrame columns: {df.columns.tolist()}.") | |
| return pd.DataFrame() | |
| df_copy = df.copy() # Work on a copy to avoid SettingWithCopyWarning | |
| try: | |
| # Convert the DataFrame's date column to pandas datetime objects first | |
| if not pd.api.types.is_datetime64_any_dtype(df_copy[date_column]): | |
| df_copy[date_column] = pd.to_datetime(df_copy[date_column], errors='coerce') | |
| # Normalize the DataFrame's date column to midnight (date part only) | |
| df_copy[date_column] = df_copy[date_column].dt.normalize() | |
| except Exception as e: | |
| logging.error(f"Error converting or normalizing date column '{date_column}' to datetime: {e}") | |
| return pd.DataFrame() # Return empty if conversion fails | |
| df_filtered = df_copy.dropna(subset=[date_column]) | |
| if df_filtered.empty: | |
| logging.info(f"Filter by date: DataFrame became empty after dropping NaNs in date column '{date_column}'.") | |
| return pd.DataFrame() | |
| # Convert start_date and end_date (which are expected to be datetime.datetime or None) | |
| # to pandas Timestamps and normalize them for comparison | |
| start_dt_obj = pd.to_datetime(start_date, errors='coerce').normalize() if start_date else None | |
| end_dt_obj = pd.to_datetime(end_date, errors='coerce').normalize() if end_date else None | |
| if start_dt_obj and end_dt_obj: | |
| return df_filtered[(df_filtered[date_column] >= start_dt_obj) & (df_filtered[date_column] <= end_dt_obj)] | |
| elif start_dt_obj: | |
| return df_filtered[df_filtered[date_column] >= start_dt_obj] | |
| elif end_dt_obj: | |
| return df_filtered[df_filtered[date_column] <= end_dt_obj] | |
| return df_filtered # No date filtering if neither start_date nor end_date is provided | |
| def prepare_filtered_analytics_data(token_state_value, date_filter_option, custom_start_date, custom_end_date): | |
| """ | |
| Retrieves data from token_state, determines date range, filters posts and mentions. | |
| Returns filtered_posts_df, filtered_mentions_df, follower_stats_df (unfiltered), | |
| and the determined start_dt, end_dt for messaging. | |
| """ | |
| logging.info(f"Preparing filtered analytics data. Filter: {date_filter_option}, Custom Start: {custom_start_date}, Custom End: {custom_end_date}") | |
| posts_df = token_state_value.get("bubble_posts_df", pd.DataFrame()) | |
| mentions_df = token_state_value.get("bubble_mentions_df", pd.DataFrame()) | |
| follower_stats_df = token_state_value.get("bubble_follower_stats_df", pd.DataFrame()) | |
| date_column_posts = token_state_value.get("config_date_col_posts", "published_at") | |
| date_column_mentions = token_state_value.get("config_date_col_mentions", "date") | |
| # Determine date range for filtering posts and mentions | |
| # Normalize current time to midnight using datetime.replace | |
| current_datetime_obj = datetime.now() | |
| current_time_normalized = current_datetime_obj.replace(hour=0, minute=0, second=0, microsecond=0) | |
| end_dt_filter = current_time_normalized | |
| start_dt_filter = None | |
| if date_filter_option == "Last 7 Days": | |
| start_dt_filter = current_time_normalized - timedelta(days=6) | |
| elif date_filter_option == "Last 30 Days": | |
| start_dt_filter = current_time_normalized - timedelta(days=29) | |
| elif date_filter_option == "Custom Range": | |
| # custom_start_date and custom_end_date are strings from gr.DateTime(type="string") | |
| # Convert to datetime objects and then normalize | |
| start_dt_filter_temp = pd.to_datetime(custom_start_date, errors='coerce') | |
| start_dt_filter = start_dt_filter_temp.replace(hour=0, minute=0, second=0, microsecond=0) if pd.notna(start_dt_filter_temp) else None | |
| end_dt_filter_temp = pd.to_datetime(custom_end_date, errors='coerce') | |
| # If custom_end_date is not provided or invalid, use current_time_normalized | |
| end_dt_filter = end_dt_filter_temp.replace(hour=0, minute=0, second=0, microsecond=0) if pd.notna(end_dt_filter_temp) else current_time_normalized | |
| # "All Time" means start_dt_filter remains None, end_dt_filter effectively means up to now. | |
| logging.info(f"Date range for filtering: Start: {start_dt_filter}, End: {end_dt_filter}") | |
| # Filter DataFrames | |
| filtered_posts_data = pd.DataFrame() | |
| if not posts_df.empty: | |
| filtered_posts_data = filter_dataframe_by_date(posts_df, date_column_posts, start_dt_filter, end_dt_filter) | |
| filtered_mentions_data = pd.DataFrame() | |
| if not mentions_df.empty: | |
| filtered_mentions_data = filter_dataframe_by_date(mentions_df, date_column_mentions, start_dt_filter, end_dt_filter) | |
| logging.info(f"Processed - Filtered posts: {len(filtered_posts_data)} rows, Filtered Mentions: {len(filtered_mentions_data)} rows.") | |
| return filtered_posts_data, filtered_mentions_data, follower_stats_df, start_dt_filter, end_dt_filter | |