import os from collections import Counter import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt from datetime import datetime REPORT_DIR = f"cleaning_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}" os.makedirs(REPORT_DIR, exist_ok=True) def save_plot(fig, filename): fig.savefig(os.path.join(REPORT_DIR, filename), dpi=400, bbox_inches='tight') plt.close(fig) def plot_heatmap(df, title): # Calculate the percentage of null values for each column null_percentages = df.isnull().mean() * 100 plt.figure(figsize=(12, 8)) sns.heatmap(null_percentages.to_frame().T, cbar=True, cmap='Reds', annot=True, fmt='.1f') plt.title(title) plt.ylabel('Percentage of Missing Values') plt.tight_layout() save_plot(plt.gcf(), f'{title.lower().replace(" ", "_")}.png') def plot_column_schemas(df): # Get the data types of all columns data_types = df.dtypes.astype(str).tolist() data_types = [dtype.capitalize() for dtype in data_types] # Count the occurrences of each data type type_counts = Counter(data_types) fig, ax = plt.subplots(figsize=(10, 6)) # Generate a color palette with as many colors as there are bars colors = plt.cm.tab20(np.linspace(0, 1, len(type_counts))) # Plot the bars bars = ax.bar(type_counts.keys(), type_counts.values(), color=colors) ax.set_title('Column Data Types') ax.set_xlabel('Data Type') ax.set_ylabel('Count') # Add value labels on top of each bar for bar in bars: height = bar.get_height() ax.text(bar.get_x() + bar.get_width() / 2., height, f'{int(height)}', ha='center', va='bottom') plt.xticks(rotation=45) plt.tight_layout() save_plot(fig, 'column_schemas.png') def plot_nonconforming_cells(nonconforming_cells): # Ensure that nonconforming_cells is a dictionary if isinstance(nonconforming_cells, dict): # Proceed with plotting if it's a dictionary fig, ax = plt.subplots(figsize=(12, 6)) # Generate a color palette with as many colors as there are bars colors = plt.cm.rainbow(np.linspace(0, 1, len(nonconforming_cells))) # Plot the bars bars = ax.bar(list(nonconforming_cells.keys()), list(nonconforming_cells.values()), color=colors) ax.set_title('Nonconforming Cells by Column') ax.set_xlabel('Columns') ax.set_ylabel('Number of Nonconforming Cells') plt.xticks(rotation=90) # Add value labels on top of each bar for bar in bars: height = bar.get_height() ax.text(bar.get_x() + bar.get_width() / 2., height, f'{height:,}', ha='center', va='bottom') save_plot(fig, 'nonconforming_cells.png') else: print(f"Expected nonconforming_cells to be a dictionary, but got {type(nonconforming_cells)}.") def plot_column_distributions(cleaned_df, primary_key_column): print("Plotting distribution charts for numeric columns in the cleaned DataFrame...") numeric_columns = cleaned_df.select_dtypes(include=[np.number]).columns.tolist() numeric_columns = [col for col in numeric_columns if col != primary_key_column] num_columns = len(numeric_columns) if num_columns == 0: print("No numeric columns found in the cleaned DataFrame for distribution plots.") return # Create subplots for distributions ncols = 3 nrows = (num_columns + ncols - 1) // ncols # Ceiling division fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(18, 5 * nrows)) axes = axes.flatten() if num_columns > 1 else [axes] for i, column in enumerate(numeric_columns): cleaned_data = cleaned_df[column].dropna() sns.histplot(cleaned_data, ax=axes[i], kde=True, color='orange', label='After Cleaning', alpha=0.7) axes[i].set_title(f'{column} - Distribution After Cleaning') axes[i].legend() # Remove any unused subplots for j in range(i + 1, len(axes)): fig.delaxes(axes[j]) plt.tight_layout() save_plot(fig, 'distributions_after_cleaning.png') def plot_boxplot_with_outliers(original_df, primary_key_column): print("Plotting boxplots for numeric columns in the original DataFrame...") numeric_columns = original_df.select_dtypes(include=[np.number]).columns.tolist() numeric_columns = [col for col in numeric_columns if col != primary_key_column] num_columns = len(numeric_columns) if num_columns == 0: print("No numeric columns found in the original DataFrame for boxplots.") return # Create subplots based on the number of numeric columns ncols = 3 nrows = (num_columns + ncols - 1) // ncols # Ceiling division fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(18, 5 * nrows)) axes = axes.flatten() if num_columns > 1 else [axes] for i, column in enumerate(numeric_columns): data = original_df[column].dropna() sns.boxplot(x=data, ax=axes[i], color='blue', orient='h') axes[i].set_title(f'Boxplot of {column} (Before Cleaning)') # Remove any unused subplots for j in range(i + 1, len(axes)): fig.delaxes(axes[j]) plt.tight_layout() save_plot(fig, 'boxplots_before_cleaning.png') def plot_correlation_heatmap(df, primary_key_column): numeric_columns = df.select_dtypes(include=[np.number]).columns.tolist() numeric_columns = [col for col in numeric_columns if col != primary_key_column] if not numeric_columns: print("No numeric columns found for correlation heatmap.") return corr_matrix = df[numeric_columns].corr() plt.figure(figsize=(15, 10)) sns.heatmap(corr_matrix, annot=True, fmt=".2f", cmap='coolwarm', cbar_kws={'label': 'Correlation'}) plt.title('Correlation Heatmap') plt.tight_layout() save_plot(plt.gcf(), 'correlation_heatmap.png') def plot_process_times(process_times): # Convert seconds to minutes process_times_minutes = {k: v / 60 for k, v in process_times.items()} # Separate main processes and column cleaning processes main_processes = {k: v for k, v in process_times_minutes.items() if not k.startswith("Clean column:")} column_processes = {k: v for k, v in process_times_minutes.items() if k.startswith("Clean column:")} # Create the plot fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 10)) # Plot main processes bars1 = ax1.bar(main_processes.keys(), main_processes.values()) ax1.set_title('Main Process Times') ax1.set_ylabel('Time (minutes)') ax1.tick_params(axis='x', rotation=45) # Plot column cleaning processes bars2 = ax2.bar(column_processes.keys(), column_processes.values()) ax2.set_title('Column Cleaning Times') ax2.set_ylabel('Time (minutes)') ax2.tick_params(axis='x', rotation=90) # Add value labels on top of each bar with 3 decimal places for ax, bars in zip([ax1, ax2], [bars1, bars2]): for bar in bars: height = bar.get_height() ax.text(bar.get_x() + bar.get_width() / 2., height, f'{height:.4f}', ha='center', va='bottom') # Add total time to the plot with 3 decimal places total_time = sum(process_times_minutes.values()) fig.suptitle(f'Process Times (Total: {total_time:.3f} minutes)', fontsize=16) plt.tight_layout() save_plot(fig, 'process_times.png') def create_full_report(original_df, cleaned_df, nonconforming_cells_before, process_times, removed_columns, removed_rows, primary_key_column): os.makedirs(REPORT_DIR, exist_ok=True) sns.set_style("whitegrid") plt.rcParams['figure.dpi'] = 400 print("Plotting nonconforming cells before cleaning...") plot_nonconforming_cells(nonconforming_cells_before) print("Plotting column distributions...") plot_column_distributions(cleaned_df, primary_key_column) print("Plotting boxplots for original data...") plot_boxplot_with_outliers(original_df, primary_key_column) print("Plotting process times...") plot_process_times(process_times) print("Plotting heatmaps...") plot_heatmap(original_df, "Missing Values Before Cleaning") print("Plotting correlation heatmap...") plot_correlation_heatmap(cleaned_df, primary_key_column) print("Plotting column schemas...") plot_column_schemas(cleaned_df) print(f"All visualization reports saved in directory: {REPORT_DIR}")