report.py

import os
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):
 plt.figure(figsize=(12, 8))
 sns.heatmap(df.isnull(), cbar=False, cmap='viridis')
 plt.title(title)
 plt.tight_layout()
 save_plot(plt.gcf(), f'{title.lower().replace(" ", "_")}.png')


def plot_valid_data_percentage(original_df, cleaned_df):
 original_valid = (original_df.notna().sum() / len(original_df)) * 100
 cleaned_valid = (cleaned_df.notna().sum() / len(cleaned_df)) * 100
 
 # Combine the data and fill missing values with 0
 combined_data = pd.concat([original_valid, cleaned_valid], axis=1, keys=['Original', 'Cleaned']).fillna(0)
 
 plt.figure(figsize=(15, 8))
 
 x = range(len(combined_data))
 width = 0.35
 
 plt.bar(x, combined_data['Original'], width, label='Before Cleaning', alpha=0.8)
 plt.bar([i + width for i in x], combined_data['Cleaned'], width, label='After Cleaning', alpha=0.8)
 
 plt.xlabel('Columns')
 plt.ylabel('Percentage of Valid Data')
 plt.title('Percentage of Valid Data Before and After Cleaning')
 plt.xticks([i + width/2 for i in x], combined_data.index, rotation=90)
 plt.legend()
 
 # Add percentage labels on the bars with smaller font size
 for i, v in enumerate(combined_data['Original']):
 plt.text(i, v, f'{v:.1f}%', ha='center', va='bottom', fontsize=6)
 for i, v in enumerate(combined_data['Cleaned']):
 plt.text(i + width, v, f'{v:.1f}%', ha='center', va='bottom', fontsize=6)
 
 plt.tight_layout()
 plt.savefig(os.path.join(REPORT_DIR, 'valid_data_percentage.png'))
 plt.close()

def plot_column_schemas(df):
 schemas = df.dtypes.astype(str).value_counts()
 fig, ax = plt.subplots(figsize=(10, 6))
 sns.barplot(x=schemas.index, y=schemas.values, ax=ax)
 ax.set_title('Column Data Types')
 ax.set_xlabel('Data Type')
 ax.set_ylabel('Count')
 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))
 sns.barplot(x=list(nonconforming_cells.keys()), y=list(nonconforming_cells.values()), ax=ax)
 ax.set_title('Nonconforming Cells by Column')
 ax.set_xlabel('Columns')
 ax.set_ylabel('Number of Nonconforming Cells')
 plt.xticks(rotation=90)
 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(original_df, cleaned_df):
 numeric_columns = original_df.select_dtypes(include=[np.number]).columns
 num_columns = len(numeric_columns)

 if num_columns == 0:
 print("No numeric columns found for distribution plots.")
 return

 # Create subplots for distributions
 fig, axes = plt.subplots(nrows=(num_columns + 2) // 3, ncols=3, figsize=(18, 5 * ((num_columns + 2) // 3)))
 axes = axes.flatten() if num_columns > 1 else [axes]

 for i, column in enumerate(numeric_columns):
 if column in cleaned_df.columns:
 sns.histplot(original_df[column].dropna(), ax=axes[i], kde=True, color='blue', label='Before Cleaning', alpha=0.5)
 sns.histplot(cleaned_df[column].dropna(), ax=axes[i], kde=True, color='orange', label='After Cleaning', alpha=0.5)
 axes[i].set_title(f'{column} - Distribution Before & 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_before_after_cleaning.png')


def plot_boxplot_with_outliers(df):
 print("Plotting boxplots with outliers...")
 numeric_columns = df.select_dtypes(include=[np.number]).columns
 num_columns = len(numeric_columns)

 if num_columns == 0:
 print("No numeric columns found for boxplot.")
 return

 # Create subplots based on the number of numeric columns
 fig, axes = plt.subplots(nrows=(num_columns + 2) // 3, ncols=3, figsize=(15, 5 * ((num_columns + 2) // 3)))
 axes = axes.flatten() if num_columns > 1 else [axes]

 for i, column in enumerate(numeric_columns):
 sns.boxplot(x=df[column], ax=axes[i])
 axes[i].set_title(f'Boxplot of {column} with Outliers')

 # Remove any unused subplots
 for j in range(i + 1, len(axes)):
 fig.delaxes(axes[j])

 plt.tight_layout()
 save_plot(fig, 'boxplots_with_outliers.png')


def plot_correlation_heatmap(df):
 # Select only numeric, float, and integer columns
 numeric_df = df.select_dtypes(include=[np.number])

 # Compute the correlation matrix
 correlation_matrix = numeric_df.corr()

 # Plot the heatmap
 fig, ax = plt.subplots(figsize=(15, 10))
 sns.heatmap(correlation_matrix, annot=True, fmt=".2f", cmap='coolwarm', ax=ax, cbar_kws={'label': 'Correlation'})
 ax.set_title('Correlation Heatmap')
 save_plot(fig, '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
 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:.2f}', ha='center', va='bottom')

 # Add total time to the plot
 total_time = sum(process_times_minutes.values())
 fig.suptitle(f'Process Times (Total: {total_time:.2f} 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):
 os.makedirs(REPORT_DIR, exist_ok=True)

 sns.set_style("whitegrid")
 plt.rcParams['figure.dpi'] = 400

 print("Plotting valid data percentages...")
 plot_valid_data_percentage(original_df, cleaned_df)

 print("Plotting column schemas...")
 plot_column_schemas(cleaned_df)

 print("Plotting nonconforming cells before cleaning...")
 plot_nonconforming_cells(nonconforming_cells_before)

 print("Plotting column distributions...")
 plot_column_distributions(original_df, cleaned_df)

 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)

 print(f"All visualization reports saved in directory: {REPORT_DIR}")