Create data_processing/analytics_data_processing.py

data_processing/analytics_data_processing.py

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+#analytics_data_processing.py
+import pandas as pd
+from datetime import datetime, timedelta, time
+import logging
+import numpy as np
+
+# 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:
+        # Ensure the date column is pandas datetime objects
+        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')
+
+        # Drop rows where date conversion might have failed (NaT) or was originally NaT
+        df_copy.dropna(subset=[date_column], inplace=True)
+        if df_copy.empty:
+            logging.info(f"Filter by date: DataFrame empty after to_datetime and dropna for column '{date_column}'.")
+            return pd.DataFrame()
+
+        # Normalize to midnight. This preserves timezone information if present.
+        df_copy[date_column] = df_copy[date_column].dt.normalize()
+
+        # If the column is timezone-aware, convert its values to naive UTC equivalent.
+        # This allows comparison with naive filter dates.
+        if hasattr(df_copy[date_column].dt, 'tz') and df_copy[date_column].dt.tz is not None:
+            logging.info(f"Column '{date_column}' is timezone-aware ({df_copy[date_column].dt.tz}). Converting to naive (from UTC) for comparison.")
+            df_copy[date_column] = df_copy[date_column].dt.tz_convert('UTC').dt.tz_localize(None)
+
+    except Exception as e:
+        logging.error(f"Error processing date column '{date_column}': {e}", exc_info=True)
+        return pd.DataFrame() 
+
+    # Convert start_date and end_date (which are naive Python datetime or naive Pandas Timestamp)
+    # to naive pandas Timestamps and normalize them.
+    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
+
+    # Perform the filtering
+    # df_filtered is already df_copy which has NaNs dropped and dates processed
+    if start_dt_obj and end_dt_obj:
+        df_filtered_final = df_copy[(df_copy[date_column] >= start_dt_obj) & (df_copy[date_column] <= end_dt_obj)]
+    elif start_dt_obj:
+        df_filtered_final = df_copy[df_copy[date_column] >= start_dt_obj]
+    elif end_dt_obj:
+        df_filtered_final = df_copy[df_copy[date_column] <= end_dt_obj]
+    else:
+        df_filtered_final = df_copy # No date filtering if neither start_date nor end_date is provided
+    
+    if df_filtered_final.empty:
+        logging.info(f"Filter by date: DataFrame became empty after applying date range to column '{date_column}'.")
+    
+    return df_filtered_final
+
+
+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, mentions, and follower time-series data.
+    Merges posts with post stats.
+    Returns:
+        - filtered_merged_posts_df: Posts merged with stats, filtered by date.
+        - filtered_mentions_df: Mentions filtered by date.
+        - date_filtered_follower_stats_df: Follower stats filtered by date (for time-series plots).
+        - raw_follower_stats_df: Unfiltered follower stats (for demographic plots).
+        - start_dt_filter: Determined start date for filtering.
+        - end_dt_filter: Determined end date for filtering.
+    """
+    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()).copy()
+    mentions_df = token_state_value.get("bubble_mentions_df", pd.DataFrame()).copy()
+    follower_stats_df = token_state_value.get("bubble_follower_stats_df", pd.DataFrame()).copy()
+    post_stats_df = token_state_value.get("bubble_post_stats_df", pd.DataFrame()).copy() # Fetch post_stats_df
+
+    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")
+    # Assuming follower_stats_df has a 'date' column for time-series data
+    date_column_followers = token_state_value.get("config_date_col_followers", "date") 
+
+    # Determine date range for filtering
+    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":
+        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')
+        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
+    
+    logging.info(f"Date range for filtering: Start: {start_dt_filter}, End: {end_dt_filter}")
+
+    # Merge posts_df and post_stats_df
+    merged_posts_df = pd.DataFrame()
+    if not posts_df.empty and not post_stats_df.empty:
+        # Assuming posts_df has 'id' and post_stats_df has 'post_id' for merging
+        if 'id' in posts_df.columns and 'post_id' in post_stats_df.columns:
+            merged_posts_df = pd.merge(posts_df, post_stats_df, left_on='id', right_on='post_id', how='left')
+            logging.info(f"Merged posts_df ({len(posts_df)} rows) and post_stats_df ({len(post_stats_df)} rows) into merged_posts_df ({len(merged_posts_df)} rows).")
+        else:
+            logging.warning("Cannot merge posts_df and post_stats_df due to missing 'id' or 'post_id' columns.")
+            # Fallback to using posts_df if merge fails but provide an empty df for stats-dependent plots
+            merged_posts_df = posts_df # Or handle as an error / empty DF for those plots
+    elif not posts_df.empty:
+        logging.warning("post_stats_df is empty. Proceeding with posts_df only for plots that don't require stats.")
+        merged_posts_df = posts_df # Create necessary columns with NaN if they are expected by plots
+        # For columns expected from post_stats_df, add them with NaNs if not present
+        expected_stat_cols = ['engagement', 'impressionCount', 'clickCount', 'likeCount', 'commentCount', 'shareCount']
+        for col in expected_stat_cols:
+            if col not in merged_posts_df.columns:
+                merged_posts_df[col] = pd.NA
+
+
+    # Filter DataFrames by date
+    filtered_merged_posts_data = pd.DataFrame()
+    if not merged_posts_df.empty and date_column_posts in merged_posts_df.columns:
+        filtered_merged_posts_data = filter_dataframe_by_date(merged_posts_df, date_column_posts, start_dt_filter, end_dt_filter)
+    elif not merged_posts_df.empty:
+        logging.warning(f"Date column '{date_column_posts}' not found in merged_posts_df. Returning unfiltered merged posts data.")
+        filtered_merged_posts_data = merged_posts_df # Or apply other logic
+    
+    filtered_mentions_data = pd.DataFrame()
+    if not mentions_df.empty and date_column_mentions in mentions_df.columns:
+        filtered_mentions_data = filter_dataframe_by_date(mentions_df, date_column_mentions, start_dt_filter, end_dt_filter)
+    elif not mentions_df.empty:
+        logging.warning(f"Date column '{date_column_mentions}' not found in mentions_df. Returning unfiltered mentions data.")
+        filtered_mentions_data = mentions_df
+
+    date_filtered_follower_stats_df = pd.DataFrame()
+    raw_follower_stats_df = follower_stats_df.copy() # For demographic plots, use raw (or latest snapshot logic)
+
+    if not follower_stats_df.empty and date_column_followers in follower_stats_df.columns:
+        date_filtered_follower_stats_df = filter_dataframe_by_date(follower_stats_df, date_column_followers, start_dt_filter, end_dt_filter)
+    elif not follower_stats_df.empty:
+        logging.warning(f"Date column '{date_column_followers}' not found in follower_stats_df. Time-series follower plots might be empty or use unfiltered data.")
+        # Decide if date_filtered_follower_stats_df should be raw_follower_stats_df or empty
+        date_filtered_follower_stats_df = follower_stats_df # Or pd.DataFrame() if strict filtering is required
+
+    logging.info(f"Processed - Filtered Merged Posts: {len(filtered_merged_posts_data)} rows, Filtered Mentions: {len(filtered_mentions_data)} rows, Date-Filtered Follower Stats: {len(date_filtered_follower_stats_df)} rows.")
+    
+    return filtered_merged_posts_data, filtered_mentions_data, date_filtered_follower_stats_df, raw_follower_stats_df, start_dt_filter, end_dt_filter
+
+# --- Helper function to generate textual data summaries for chatbot ---
+def generate_chatbot_data_summaries(
+    plot_configs_list,
+    filtered_merged_posts_df,
+    filtered_mentions_df,
+    date_filtered_follower_stats_df, # Expected to contain 'follower_gains_monthly'
+    raw_follower_stats_df,          # Expected to contain other demographics like 'follower_geo', 'follower_industry'
+    token_state_value
+    ):
+    """
+    Generates textual summaries for each plot ID to be used by the chatbot,
+    based on the corrected understanding of DataFrame structures and follower count columns.
+    """
+    data_summaries = {}
+    
+    # --- Date and Config Columns from token_state ---
+    # For Posts
+    date_col_posts = token_state_value.get("config_date_col_posts", "published_at")
+    media_type_col_name = token_state_value.get("config_media_type_col", "media_type")
+    eb_labels_col_name = token_state_value.get("config_eb_labels_col", "li_eb_label")
+    # For Mentions
+    date_col_mentions = token_state_value.get("config_date_col_mentions", "date")
+    mentions_sentiment_col = "sentiment_label" # As per user's mention df structure
+    
+    # For Follower Stats - Actual column names provided by user
+    follower_count_organic_col = "follower_count_organic"
+    follower_count_paid_col = "follower_count_paid"
+
+    # For Follower Stats (Demographics from raw_follower_stats_df)
+    follower_demographics_type_col = "follower_count_type" # Column indicating 'follower_geo', 'follower_industry'
+    follower_demographics_category_col = "category_name"   # Column indicating 'USA', 'Technology'
+
+    # For Follower Gains/Growth (from date_filtered_follower_stats_df)
+    follower_gains_type_col = "follower_count_type" # Should be 'follower_gains_monthly'
+    follower_gains_date_col = "category_name"       # This is 'YYYY-MM-DD'
+
+    # --- Helper: Safely convert to datetime ---
+    def safe_to_datetime(series, errors='coerce'):
+        return pd.to_datetime(series, errors=errors)
+
+    # --- Prepare DataFrames (copy and convert dates) ---
+    if filtered_merged_posts_df is not None and not filtered_merged_posts_df.empty:
+        posts_df = filtered_merged_posts_df.copy()
+        if date_col_posts in posts_df.columns:
+            posts_df[date_col_posts] = safe_to_datetime(posts_df[date_col_posts])
+        else:
+            logging.warning(f"Date column '{date_col_posts}' not found in posts_df for chatbot summary.")
+    else:
+        posts_df = pd.DataFrame()
+
+    if filtered_mentions_df is not None and not filtered_mentions_df.empty:
+        mentions_df = filtered_mentions_df.copy()
+        if date_col_mentions in mentions_df.columns:
+            mentions_df[date_col_mentions] = safe_to_datetime(mentions_df[date_col_mentions])
+        else:
+            logging.warning(f"Date column '{date_col_mentions}' not found in mentions_df for chatbot summary.")
+    else:
+        mentions_df = pd.DataFrame()
+
+    # For date_filtered_follower_stats_df (monthly gains)
+    if date_filtered_follower_stats_df is not None and not date_filtered_follower_stats_df.empty:
+        follower_monthly_df = date_filtered_follower_stats_df.copy()
+        if follower_gains_type_col in follower_monthly_df.columns:
+             follower_monthly_df = follower_monthly_df[follower_monthly_df[follower_gains_type_col] == 'follower_gains_monthly'].copy()
+        
+        if follower_gains_date_col in follower_monthly_df.columns:
+            follower_monthly_df['datetime_obj'] = safe_to_datetime(follower_monthly_df[follower_gains_date_col])
+            follower_monthly_df = follower_monthly_df.dropna(subset=['datetime_obj'])
+            
+            # Calculate total gains
+            if follower_count_organic_col in follower_monthly_df.columns and follower_count_paid_col in follower_monthly_df.columns:
+                follower_monthly_df[follower_count_organic_col] = pd.to_numeric(follower_monthly_df[follower_count_organic_col], errors='coerce').fillna(0)
+                follower_monthly_df[follower_count_paid_col] = pd.to_numeric(follower_monthly_df[follower_count_paid_col], errors='coerce').fillna(0)
+                follower_monthly_df['total_monthly_gains'] = follower_monthly_df[follower_count_organic_col] + follower_monthly_df[follower_count_paid_col]
+            elif follower_count_organic_col in follower_monthly_df.columns: # Only organic exists
+                 follower_monthly_df[follower_count_organic_col] = pd.to_numeric(follower_monthly_df[follower_count_organic_col], errors='coerce').fillna(0)
+                 follower_monthly_df['total_monthly_gains'] = follower_monthly_df[follower_count_organic_col]
+            elif follower_count_paid_col in follower_monthly_df.columns: # Only paid exists
+                 follower_monthly_df[follower_count_paid_col] = pd.to_numeric(follower_monthly_df[follower_count_paid_col], errors='coerce').fillna(0)
+                 follower_monthly_df['total_monthly_gains'] = follower_monthly_df[follower_count_paid_col]
+            else:
+                logging.warning(f"Neither '{follower_count_organic_col}' nor '{follower_count_paid_col}' found in follower_monthly_df for total gains calculation.")
+                follower_monthly_df['total_monthly_gains'] = 0 # Avoid KeyError later
+        else:
+            logging.warning(f"Date column '{follower_gains_date_col}' (from category_name) not found in follower_monthly_df for chatbot summary.")
+            if 'datetime_obj' not in follower_monthly_df.columns:
+                 follower_monthly_df['datetime_obj'] = pd.NaT 
+            if 'total_monthly_gains' not in follower_monthly_df.columns:
+                 follower_monthly_df['total_monthly_gains'] = 0
+    else:
+        follower_monthly_df = pd.DataFrame(columns=[follower_gains_date_col, 'total_monthly_gains', 'datetime_obj'])
+
+
+    if raw_follower_stats_df is not None and not raw_follower_stats_df.empty:
+        follower_demographics_df = raw_follower_stats_df.copy()
+        # Calculate total followers for demographics
+        if follower_count_organic_col in follower_demographics_df.columns and follower_count_paid_col in follower_demographics_df.columns:
+            follower_demographics_df[follower_count_organic_col] = pd.to_numeric(follower_demographics_df[follower_count_organic_col], errors='coerce').fillna(0)
+            follower_demographics_df[follower_count_paid_col] = pd.to_numeric(follower_demographics_df[follower_count_paid_col], errors='coerce').fillna(0)
+            follower_demographics_df['total_follower_count'] = follower_demographics_df[follower_count_organic_col] + follower_demographics_df[follower_count_paid_col]
+        elif follower_count_organic_col in follower_demographics_df.columns:
+            follower_demographics_df[follower_count_organic_col] = pd.to_numeric(follower_demographics_df[follower_count_organic_col], errors='coerce').fillna(0)
+            follower_demographics_df['total_follower_count'] = follower_demographics_df[follower_count_organic_col]
+        elif follower_count_paid_col in follower_demographics_df.columns:
+            follower_demographics_df[follower_count_paid_col] = pd.to_numeric(follower_demographics_df[follower_count_paid_col], errors='coerce').fillna(0)
+            follower_demographics_df['total_follower_count'] = follower_demographics_df[follower_count_paid_col]
+        else:
+            logging.warning(f"Neither '{follower_count_organic_col}' nor '{follower_count_paid_col}' found in follower_demographics_df for total count calculation.")
+            if 'total_follower_count' not in follower_demographics_df.columns:
+                 follower_demographics_df['total_follower_count'] = 0
+    else:
+        follower_demographics_df = pd.DataFrame()
+
+
+    for plot_cfg in plot_configs_list:
+        plot_id = plot_cfg["id"]
+        plot_label = plot_cfg["label"]
+        summary_text = f"No specific data summary available for '{plot_label}' for the selected period."
+
+        try:
+            # --- FOLLOWER STATS ---
+            if plot_id == "followers_count": # Uses follower_monthly_df
+                if not follower_monthly_df.empty and 'total_monthly_gains' in follower_monthly_df.columns and 'datetime_obj' in follower_monthly_df.columns and not follower_monthly_df['datetime_obj'].isnull().all():
+                    df_summary = follower_monthly_df[['datetime_obj', 'total_monthly_gains']].copy()
+                    df_summary['datetime_obj'] = df_summary['datetime_obj'].dt.strftime('%Y-%m-%d')
+                    df_summary.rename(columns={'datetime_obj': 'Date', 'total_monthly_gains': 'Total Monthly Gains'}, inplace=True)
+                    summary_text = f"Follower Count (Total Monthly Gains):\n{df_summary.sort_values(by='Date').tail(5).to_string(index=False)}"
+                else:
+                    summary_text = f"Follower count data (total monthly gains) is unavailable or incomplete for '{plot_label}'."
+            
+            elif plot_id == "followers_growth_rate": # Uses follower_monthly_df
+                if not follower_monthly_df.empty and 'total_monthly_gains' in follower_monthly_df.columns and 'datetime_obj' in follower_monthly_df.columns and not follower_monthly_df['datetime_obj'].isnull().all():
+                    df_calc = follower_monthly_df.sort_values(by='datetime_obj').copy()
+                    # Growth rate is calculated on the total monthly gains (which are changes, not cumulative counts)
+                    # To calculate growth rate of followers, we'd need cumulative follower count.
+                    # The plot logic also uses pct_change on the gains themselves.
+                    # If 'total_monthly_gains' represents the *change* in followers, then pct_change on this is rate of change of gains.
+                    # If it represents the *cumulative* followers at that point, then pct_change is follower growth rate.
+                    # Assuming 'total_monthly_gains' is the *change* for the month, like the plot logic.
+                    df_calc['total_monthly_gains'] = pd.to_numeric(df_calc['total_monthly_gains'], errors='coerce')
+                    if len(df_calc) >= 2:
+                        # Calculate cumulative sum to get follower count if 'total_monthly_gains' are indeed just gains
+                        # If your 'total_monthly_gains' already IS the total follower count at end of month, remove next line
+                        # For now, assuming it's GAINS, so we need cumulative for growth rate of total followers.
+                        # However, the original plot logic applies pct_change directly to 'follower_gains_monthly'.
+                        # Let's stick to pct_change on the gains/count column for consistency with plot.
+                        
+                        # If 'total_monthly_gains' is the actual follower count for that month:
+                        df_calc['growth_rate_monthly'] = df_calc['total_monthly_gains'].pct_change() * 100
+                        df_calc['growth_rate_monthly'] = df_calc['growth_rate_monthly'].round(2)
+                        df_calc.replace([np.inf, -np.inf], np.nan, inplace=True) # Handle division by zero if a gain was 0
+                        
+                        df_summary = df_calc[['datetime_obj', 'growth_rate_monthly']].dropna().copy()
+                        df_summary['datetime_obj'] = df_summary['datetime_obj'].dt.strftime('%Y-%m-%d')
+                        df_summary.rename(columns={'datetime_obj': 'Date', 'growth_rate_monthly': 'Growth Rate (%)'}, inplace=True)
+                        if not df_summary.empty:
+                            summary_text = f"Follower Growth Rate (Monthly % based on Total Follower Count/Gains):\n{df_summary.sort_values(by='Date').tail(5).to_string(index=False)}"
+                        else:
+                            summary_text = f"Not enough data points or valid transitions to calculate follower growth rate for '{plot_label}'."
+                    else:
+                        summary_text = f"Not enough data points (need at least 2) to calculate follower growth rate for '{plot_label}'."
+                else:
+                    summary_text = f"Follower growth rate data (total monthly gains) is unavailable or incomplete for '{plot_label}'."
+
+            elif plot_id in ["followers_by_location", "followers_by_role", "followers_by_industry", "followers_by_seniority"]:
+                demographic_type_map = {
+                    "followers_by_location": "follower_geo",
+                    "followers_by_role": "follower_function",
+                    "followers_by_industry": "follower_industry",
+                    "followers_by_seniority": "follower_seniority"
+                }
+                current_demographic_type = demographic_type_map.get(plot_id)
+                if not follower_demographics_df.empty and \
+                   follower_demographics_type_col in follower_demographics_df.columns and \
+                   follower_demographics_category_col in follower_demographics_df.columns and \
+                   'total_follower_count' in follower_demographics_df.columns: # Check for the calculated total
+                    
+                    df_filtered_demographics = follower_demographics_df[
+                        follower_demographics_df[follower_demographics_type_col] == current_demographic_type
+                    ].copy()
+
+                    if not df_filtered_demographics.empty:
+                        df_summary = df_filtered_demographics.groupby(follower_demographics_category_col)['total_follower_count'].sum().reset_index()
+                        df_summary.rename(columns={follower_demographics_category_col: 'Category', 'total_follower_count': 'Total Follower Count'}, inplace=True)
+                        top_5 = df_summary.nlargest(5, 'Total Follower Count')
+                        summary_text = f"Top 5 {plot_label} (Total Followers):\n{top_5.to_string(index=False)}"
+                    else:
+                        summary_text = f"No data available for demographic type '{current_demographic_type}' in '{plot_label}'."
+                else:
+                    summary_text = f"Follower demographic data columns (including total_follower_count) are missing or incomplete for '{plot_label}'."
+
+            # --- POSTS STATS ---
+            elif plot_id == "engagement_rate":
+                if not posts_df.empty and 'engagement' in posts_df.columns and date_col_posts in posts_df.columns and not posts_df[date_col_posts].isnull().all():
+                    df_resampled = posts_df.set_index(date_col_posts)['engagement'].resample('W').mean().reset_index()
+                    df_resampled['engagement'] = pd.to_numeric(df_resampled['engagement'], errors='coerce').round(2)
+                    df_summary = df_resampled[[date_col_posts, 'engagement']].dropna().copy()
+                    df_summary[date_col_posts] = df_summary[date_col_posts].dt.strftime('%Y-%m-%d')
+                    summary_text = f"Engagement Rate Over Time (Weekly Avg %):\n{df_summary.sort_values(by=date_col_posts).tail(5).to_string(index=False)}"
+                else:
+                    summary_text = f"Engagement rate data is unavailable for '{plot_label}'."
+            
+            elif plot_id == "reach_over_time": 
+                if not posts_df.empty and 'reach' in posts_df.columns and date_col_posts in posts_df.columns and not posts_df[date_col_posts].isnull().all():
+                    df_resampled = posts_df.set_index(date_col_posts)['reach'].resample('W').sum().reset_index()
+                    df_resampled['reach'] = pd.to_numeric(df_resampled['reach'], errors='coerce')
+                    df_summary = df_resampled[[date_col_posts, 'reach']].dropna().copy()
+                    df_summary[date_col_posts] = df_summary[date_col_posts].dt.strftime('%Y-%m-%d')
+                    summary_text = f"Reach Over Time (Weekly Sum):\n{df_summary.sort_values(by=date_col_posts).tail(5).to_string(index=False)}"
+                else:
+                    summary_text = f"Reach data is unavailable for '{plot_label}'."
+
+            elif plot_id == "impressions_over_time": 
+                if not posts_df.empty and 'impressionCount' in posts_df.columns and date_col_posts in posts_df.columns and not posts_df[date_col_posts].isnull().all():
+                    df_resampled = posts_df.set_index(date_col_posts)['impressionCount'].resample('W').sum().reset_index()
+                    df_resampled['impressionCount'] = pd.to_numeric(df_resampled['impressionCount'], errors='coerce')
+                    df_summary = df_resampled[[date_col_posts, 'impressionCount']].dropna().copy()
+                    df_summary[date_col_posts] = df_summary[date_col_posts].dt.strftime('%Y-%m-%d')
+                    df_summary.rename(columns={'impressionCount': 'Impressions'}, inplace=True)
+                    summary_text = f"Impressions Over Time (Weekly Sum):\n{df_summary.sort_values(by=date_col_posts).tail(5).to_string(index=False)}"
+                else:
+                    summary_text = f"Impressions data is unavailable for '{plot_label}'."
+
+            elif plot_id == "likes_over_time": 
+                if not posts_df.empty and 'likeCount' in posts_df.columns and date_col_posts in posts_df.columns and not posts_df[date_col_posts].isnull().all():
+                    df_resampled = posts_df.set_index(date_col_posts)['likeCount'].resample('W').sum().reset_index()
+                    df_resampled['likeCount'] = pd.to_numeric(df_resampled['likeCount'], errors='coerce')
+                    df_summary = df_resampled[[date_col_posts, 'likeCount']].dropna().copy()
+                    df_summary[date_col_posts] = df_summary[date_col_posts].dt.strftime('%Y-%m-%d')
+                    df_summary.rename(columns={'likeCount': 'Likes'}, inplace=True)
+                    summary_text = f"Likes Over Time (Weekly Sum):\n{df_summary.sort_values(by=date_col_posts).tail(5).to_string(index=False)}"
+                else:
+                    summary_text = f"Likes data is unavailable for '{plot_label}'."
+
+            elif plot_id == "clicks_over_time": 
+                if not posts_df.empty and 'clickCount' in posts_df.columns and date_col_posts in posts_df.columns and not posts_df[date_col_posts].isnull().all():
+                    df_resampled = posts_df.set_index(date_col_posts)['clickCount'].resample('W').sum().reset_index()
+                    df_resampled['clickCount'] = pd.to_numeric(df_resampled['clickCount'], errors='coerce')
+                    df_summary = df_resampled[[date_col_posts, 'clickCount']].dropna().copy()
+                    df_summary[date_col_posts] = df_summary[date_col_posts].dt.strftime('%Y-%m-%d')
+                    df_summary.rename(columns={'clickCount': 'Clicks'}, inplace=True)
+                    summary_text = f"Clicks Over Time (Weekly Sum):\n{df_summary.sort_values(by=date_col_posts).tail(5).to_string(index=False)}"
+                else:
+                    summary_text = f"Clicks data is unavailable for '{plot_label}'."
+
+            elif plot_id == "shares_over_time": 
+                if not posts_df.empty and 'shareCount' in posts_df.columns and date_col_posts in posts_df.columns and not posts_df[date_col_posts].isnull().all():
+                    df_resampled = posts_df.set_index(date_col_posts)['shareCount'].resample('W').sum().reset_index()
+                    df_resampled['shareCount'] = pd.to_numeric(df_resampled['shareCount'], errors='coerce')
+                    df_summary = df_resampled[[date_col_posts, 'shareCount']].dropna().copy()
+                    df_summary[date_col_posts] = df_summary[date_col_posts].dt.strftime('%Y-%m-%d')
+                    df_summary.rename(columns={'shareCount': 'Shares'}, inplace=True)
+                    summary_text = f"Shares Over Time (Weekly Sum):\n{df_summary.sort_values(by=date_col_posts).tail(5).to_string(index=False)}"
+                elif 'shareCount' not in posts_df.columns and not posts_df.empty : # Check if posts_df is not empty before assuming column is the only issue
+                     summary_text = f"Shares data column ('shareCount') not found for '{plot_label}'."
+                else:
+                    summary_text = f"Shares data is unavailable for '{plot_label}'."
+            
+            elif plot_id == "comments_over_time": 
+                if not posts_df.empty and 'commentCount' in posts_df.columns and date_col_posts in posts_df.columns and not posts_df[date_col_posts].isnull().all():
+                    df_resampled = posts_df.set_index(date_col_posts)['commentCount'].resample('W').sum().reset_index()
+                    df_resampled['commentCount'] = pd.to_numeric(df_resampled['commentCount'], errors='coerce')
+                    df_summary = df_resampled[[date_col_posts, 'commentCount']].dropna().copy()
+                    df_summary[date_col_posts] = df_summary[date_col_posts].dt.strftime('%Y-%m-%d')
+                    df_summary.rename(columns={'commentCount': 'Comments'}, inplace=True)
+                    summary_text = f"Comments Over Time (Weekly Sum):\n{df_summary.sort_values(by=date_col_posts).tail(5).to_string(index=False)}"
+                else:
+                    summary_text = f"Comments data is unavailable for '{plot_label}'."
+
+            elif plot_id == "comments_sentiment": 
+                comment_sentiment_col_posts = "sentiment" 
+                if not posts_df.empty and comment_sentiment_col_posts in posts_df.columns:
+                    sentiment_counts = posts_df[comment_sentiment_col_posts].value_counts().reset_index()
+                    sentiment_counts.columns = ['Sentiment', 'Count']
+                    summary_text = f"Comments Sentiment Breakdown (Posts Data):\n{sentiment_counts.to_string(index=False)}"
+                else:
+                    summary_text = f"Comment sentiment data ('{comment_sentiment_col_posts}') is unavailable for '{plot_label}'."
+            
+            elif plot_id == "post_frequency_cs":
+                if not posts_df.empty and date_col_posts in posts_df.columns and not posts_df[date_col_posts].isnull().all():
+                    post_counts_weekly = posts_df.set_index(date_col_posts).resample('W').size().reset_index(name='post_count')
+                    post_counts_weekly.rename(columns={date_col_posts: 'Week', 'post_count': 'Posts'}, inplace=True)
+                    post_counts_weekly['Week'] = post_counts_weekly['Week'].dt.strftime('%Y-%m-%d (Week of)')
+                    summary_text = f"Post Frequency (Weekly):\n{post_counts_weekly.sort_values(by='Week').tail(5).to_string(index=False)}"
+                else:
+                    summary_text = f"Post frequency data is unavailable for '{plot_label}'."
+
+            elif plot_id == "content_format_breakdown_cs":
+                if not posts_df.empty and media_type_col_name in posts_df.columns:
+                    format_counts = posts_df[media_type_col_name].value_counts().reset_index()
+                    format_counts.columns = ['Format', 'Count']
+                    summary_text = f"Content Format Breakdown:\n{format_counts.nlargest(5, 'Count').to_string(index=False)}"
+                else:
+                    summary_text = f"Content format data ('{media_type_col_name}') is unavailable for '{plot_label}'."
+
+            elif plot_id == "content_topic_breakdown_cs":
+                if not posts_df.empty and eb_labels_col_name in posts_df.columns:
+                    try:
+                        # Ensure the column is not all NaN before trying to check for lists or explode
+                        if posts_df[eb_labels_col_name].notna().any():
+                            if posts_df[eb_labels_col_name].apply(lambda x: isinstance(x, list)).any():
+                                topic_counts = posts_df.explode(eb_labels_col_name)[eb_labels_col_name].value_counts().reset_index()
+                            else: 
+                                topic_counts = posts_df[eb_labels_col_name].value_counts().reset_index()
+                            topic_counts.columns = ['Topic', 'Count']
+                            summary_text = f"Content Topic Breakdown (Top 5):\n{topic_counts.nlargest(5, 'Count').to_string(index=False)}"
+                        else:
+                            summary_text = f"Content topic data ('{eb_labels_col_name}') contains no valid topics for '{plot_label}'."
+                    except Exception as e_topic:
+                        logging.warning(f"Could not process topic breakdown for '{eb_labels_col_name}': {e_topic}")
+                        summary_text = f"Content topic data ('{eb_labels_col_name}') could not be processed for '{plot_label}'."
+                else:
+                    summary_text = f"Content topic data ('{eb_labels_col_name}') is unavailable for '{plot_label}'."
+
+            # --- MENTIONS STATS ---
+            elif plot_id == "mention_analysis_volume": 
+                if not mentions_df.empty and date_col_mentions in mentions_df.columns and not mentions_df[date_col_mentions].isnull().all():
+                    mentions_over_time = mentions_df.set_index(date_col_mentions).resample('W').size().reset_index(name='mention_count')
+                    mentions_over_time.rename(columns={date_col_mentions: 'Week', 'mention_count': 'Mentions'}, inplace=True)
+                    mentions_over_time['Week'] = mentions_over_time['Week'].dt.strftime('%Y-%m-%d (Week of)')
+                    if not mentions_over_time.empty:
+                        summary_text = f"Mentions Volume (Weekly):\n{mentions_over_time.sort_values(by='Week').tail(5).to_string(index=False)}"
+                    else:
+                        summary_text = f"No mention activity found for '{plot_label}' in the selected period."
+                else:
+                    summary_text = f"Mentions volume data is unavailable for '{plot_label}'."
+            
+            elif plot_id == "mention_analysis_sentiment": 
+                if not mentions_df.empty and mentions_sentiment_col in mentions_df.columns:
+                    sentiment_counts = mentions_df[mentions_sentiment_col].value_counts().reset_index()
+                    sentiment_counts.columns = ['Sentiment', 'Count']
+                    summary_text = f"Mentions Sentiment Breakdown:\n{sentiment_counts.to_string(index=False)}"
+                else:
+                    summary_text = f"Mention sentiment data ('{mentions_sentiment_col}') is unavailable for '{plot_label}'."
+
+            data_summaries[plot_id] = summary_text
+        except KeyError as e:
+            logging.warning(f"KeyError generating summary for {plot_id} ('{plot_label}'): {e}. Using default summary.")
+            data_summaries[plot_id] = f"Data summary generation error for '{plot_label}' (missing column: {e})."
+        except Exception as e:
+            logging.error(f"Error generating summary for {plot_id} ('{plot_label}'): {e}", exc_info=True)
+            data_summaries[plot_id] = f"Error generating data summary for '{plot_label}'."
+            
+    return data_summaries