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value_counts.plot(kind='pie', + autopct='%1.1f%%', + startangle=90, + colors=colors, + wedgeprops={'edgecolor': 'black', 'linewidth': 1.5}, + textprops={'fontsize': 12, 'fontweight': 'bold'}, + figsize=(8, 8)) + + # Set axis to be equal (so pie chart is circular) + plt.axis('equal') + + # Add a title + plt.title(title, fontsize=16, fontweight='bold') + + # Remove the legend (optional, since we have labels on the pie chart) + plt.legend().set_visible(False) + + # Display the chart + plt.show() + + +def calculate_statistics(series: pd.Series) -> dict: + """Calculate mean, median, and standard deviation for a given series.""" + return { + 'mean': series.mean(), + 'median': series.median(), + 'std': series.std() + } + +def plot_histogram(series: pd.Series, stats: dict, title: str, xlabel: str, color: str) -> None: + """Plot histogram with KDE and statistical lines.""" + sns.histplot(series, kde=True, color=color, bins=10) + plt.title(title, fontsize=16) + plt.xlabel(xlabel, fontsize=12) + plt.ylabel("Frequency", fontsize=12) + plt.axvline(stats['mean'], color='red', linestyle='--', label=f'Mean: {stats["mean"]:.2f}') + plt.axvline(stats['median'], color='green', linestyle='-', label=f'Median: {stats["median"]:.2f}') + plt.axvline(stats['mean'] + stats['std'], color='orange', linestyle=':', label=f'+1 Std Dev: {stats["mean"] + stats["std"]:.2f}') + plt.axvline(stats['mean'] - stats['std'], color='orange', linestyle=':', label=f'-1 Std Dev: {stats["mean"] - stats["std"]:.2f}') + plt.legend() + +def plot_distribution_text_length(col: pd.Series, title: str) -> None: + """Plot distribution of text lengths in terms of characters and words.""" + col_name = col.name + col = col.astype(str) + text_lengths_chars = col.apply(len) # Length in characters + text_lengths_words = col.apply(lambda x: len(x.split())) # Length in words + + # Calculate statistics + stats_chars = calculate_statistics(text_lengths_chars) + stats_words = calculate_statistics(text_lengths_words) + + # Set up the plot + plt.figure(figsize=(14, 7)) + + # Plot histogram for text lengths in characters + plt.subplot(1, 2, 1) + plot_histogram(text_lengths_chars, stats_chars, f"Distribution of {col_name} Lengths (Characters)", f"{col_name} Length (Characters)", "skyblue") + + # Plot histogram for text lengths in words + plt.subplot(1, 2, 2) + plot_histogram(text_lengths_words, stats_words, f"Distribution of {col_name} Lengths (Words)", f"{col_name} Length (Words)", "lightgreen") + + # Adjust layout and show the plot + plt.tight_layout() + plt.show() + + # Print the statistics for reference + print(f"Character-based stats: Mean = {stats_chars['mean']:.2f}, Median = {stats_chars['median']:.2f}, Std Dev = {stats_chars['std']:.2f}") + print(f"Word-based stats: Mean = {stats_words['mean']:.2f}, Median = {stats_words['median']:.2f}, Std Dev = {stats_words['std']:.2f}") + +if __name__ == '__main__': + pass \ No newline at end of file