import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from pathlib import Path def summarize_csv(file_path): """ Comprehensively analyzes a CSV file and generates multiple visualizations. Args: file_path (str): Path to the CSV file Returns: str: Formatted comprehensive analysis of the dataset """ df = pd.read_csv(file_path) summary = [] charts_created = [] # Basic info summary.append("=" * 60) summary.append("šŸ“Š DATA OVERVIEW") summary.append("=" * 60) summary.append(f"Rows: {df.shape[0]:,} | Columns: {df.shape[1]}") summary.append(f"\nColumns: {', '.join(df.columns.tolist())}") # Data types summary.append(f"\nšŸ“‹ DATA TYPES:") for col, dtype in df.dtypes.items(): summary.append(f" ā€¢ {col}: {dtype}") # Missing data analysis missing = df.isnull().sum().sum() missing_pct = (missing / (df.shape[0] * df.shape[1])) * 100 summary.append(f"\nšŸ” DATA QUALITY:") if missing: summary.append(f"Missing values: {missing:,} ({missing_pct:.2f}% of total data)") summary.append("Missing by column:") for col in df.columns: col_missing = df[col].isnull().sum() if col_missing > 0: col_pct = (col_missing / len(df)) * 100 summary.append(f" ā€¢ {col}: {col_missing:,} ({col_pct:.1f}%)") else: summary.append("āœ“ No missing values - dataset is complete!") # Numeric analysis numeric_cols = df.select_dtypes(include='number').columns.tolist() if numeric_cols: summary.append(f"\nšŸ“ˆ NUMERICAL ANALYSIS:") summary.append(str(df[numeric_cols].describe())) # Correlations if multiple numeric columns if len(numeric_cols) > 1: summary.append(f"\nšŸ”— CORRELATIONS:") corr_matrix = df[numeric_cols].corr() summary.append(str(corr_matrix)) # Create correlation heatmap plt.figure(figsize=(10, 8)) sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', center=0, square=True, linewidths=1) plt.title('Correlation Heatmap') plt.tight_layout() plt.savefig('correlation_heatmap.png', dpi=150) plt.close() charts_created.append('correlation_heatmap.png') # Categorical analysis categorical_cols = df.select_dtypes(include=['object']).columns.tolist() categorical_cols = [c for c in categorical_cols if 'id' not in c.lower()] if categorical_cols: summary.append(f"\nšŸ“Š CATEGORICAL ANALYSIS:") for col in categorical_cols[:5]: # Limit to first 5 value_counts = df[col].value_counts() summary.append(f"\n{col}:") for val, count in value_counts.head(10).items(): pct = (count / len(df)) * 100 summary.append(f" ā€¢ {val}: {count:,} ({pct:.1f}%)") # Time series analysis date_cols = [c for c in df.columns if 'date' in c.lower() or 'time' in c.lower()] if date_cols: summary.append(f"\nšŸ“… TIME SERIES ANALYSIS:") date_col = date_cols[0] df[date_col] = pd.to_datetime(df[date_col], errors='coerce') date_range = df[date_col].max() - df[date_col].min() summary.append(f"Date range: {df[date_col].min()} to {df[date_col].max()}") summary.append(f"Span: {date_range.days} days") # Create time-series plots for numeric columns if numeric_cols: fig, axes = plt.subplots(min(3, len(numeric_cols)), 1, figsize=(12, 4 * min(3, len(numeric_cols)))) if len(numeric_cols) == 1: axes = [axes] for idx, num_col in enumerate(numeric_cols[:3]): ax = axes[idx] if len(numeric_cols) > 1 else axes[0] daily_data = df.groupby(date_col)[num_col].agg(['mean', 'sum', 'count']) daily_data['mean'].plot(ax=ax, label='Average', linewidth=2) ax.set_title(f'{num_col} Over Time') ax.set_xlabel('Date') ax.set_ylabel(num_col) ax.legend() ax.grid(True, alpha=0.3) plt.tight_layout() plt.savefig('time_series_analysis.png', dpi=150) plt.close() charts_created.append('time_series_analysis.png') # Distribution plots for numeric columns if numeric_cols: n_cols = min(4, len(numeric_cols)) fig, axes = plt.subplots(2, 2, figsize=(12, 10)) axes = axes.flatten() for idx, col in enumerate(numeric_cols[:4]): axes[idx].hist(df[col].dropna(), bins=30, edgecolor='black', alpha=0.7) axes[idx].set_title(f'Distribution of {col}') axes[idx].set_xlabel(col) axes[idx].set_ylabel('Frequency') axes[idx].grid(True, alpha=0.3) # Hide unused subplots for idx in range(len(numeric_cols[:4]), 4): axes[idx].set_visible(False) plt.tight_layout() plt.savefig('distributions.png', dpi=150) plt.close() charts_created.append('distributions.png') # Categorical distributions if categorical_cols: fig, axes = plt.subplots(2, 2, figsize=(14, 10)) axes = axes.flatten() for idx, col in enumerate(categorical_cols[:4]): value_counts = df[col].value_counts().head(10) axes[idx].barh(range(len(value_counts)), value_counts.values) axes[idx].set_yticks(range(len(value_counts))) axes[idx].set_yticklabels(value_counts.index) axes[idx].set_title(f'Top Values in {col}') axes[idx].set_xlabel('Count') axes[idx].grid(True, alpha=0.3, axis='x') # Hide unused subplots for idx in range(len(categorical_cols[:4]), 4): axes[idx].set_visible(False) plt.tight_layout() plt.savefig('categorical_distributions.png', dpi=150) plt.close() charts_created.append('categorical_distributions.png') # Summary of visualizations if charts_created: summary.append(f"\nšŸ“Š VISUALIZATIONS CREATED:") for chart in charts_created: summary.append(f" āœ“ {chart}") summary.append("\n" + "=" * 60) summary.append("āœ… COMPREHENSIVE ANALYSIS COMPLETE") summary.append("=" * 60) return "\n".join(summary) if __name__ == "__main__": # Test with sample data import sys if len(sys.argv) > 1: file_path = sys.argv[1] else: file_path = "resources/sample.csv" print(summarize_csv(file_path))