Python 数据可视化 boxplot


import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns# 读取 TSV 文件
df = pd.read_csv('result.tsv', sep='\t')normal_df = df[df["sample_name"].str.contains("normal")]
tumor_df = df[df["sample_name"].str.contains("tumor")]# sns.boxplot(x='down_level', y='loci_median_depth', data=tumor_df)
# sns.boxplot(x='down_level', y='loci_average_depth', data=tumor_df)def box_plot_1(df):# 提取数据sample_data = {}# 遍历 DataFrame，根据样本和级别组织数据for index, row in df.iterrows():sample = row['sample_name']level = row['down_level']data = row['loci_median_depth']  # 这里假设你想绘制 loci_median_depth 列的箱线图# 如果样本不在字典中，则将其添加为新的键，并将数据存储为列表if sample not in sample_data:sample_data[sample] = {}if level not in sample_data[sample]:sample_data[sample][level] = []sample_data[sample][level].append(data)# 绘制箱线图plt.figure(figsize=(100, 60))# 遍历样本和级别，绘制箱线图position = 1for sample, levels in sample_data.items():for level, data in levels.items():label = f"{sample} - {level}"plt.boxplot(data, positions=[position], labels=[label])position += 1plt.ylabel('Depth')plt.title('Box Plot of Depth Data by Sample and Level')plt.grid(True)plt.xticks(rotation=45)# 保存箱线图为文件plt.savefig('boxplot.png')plt.show()def box_plot_2(df, target_header_list):lvl_list = ["ori", '40', '36', '32', '28', '24', '20']for level_to_plot in lvl_list:filtered_df = df[df['down_level'] == level_to_plot]plt.figure(figsize=(20, 15))# plt.boxplot(filtered_df[' loci_median_depth'])plt.boxplot([filtered_df[i] for i in target_header_list], labels=target_header_list)plt.ylabel('Depth')# plt.xlabel(level_to_plot)plt.title(f'Box Plot of Depth Data for {level_to_plot} Level')plt.grid(True)plt.xticks(rotation=45)# 在箱线图上绘制每个数据点for i, col in enumerate(target_header_list):x = [i + 1] * len(filtered_df[col])plt.plot(x, filtered_df[col], 'ro', alpha=0.5)# 保存箱线图为文件plt.savefig(f'boxplot_{level_to_plot}.png')# 显示箱线图plt.show()def box_plot_3(df, target_header_list):# 选择要包含在 y 轴中的列y_columns = target_header_list# 将这些列数据整合到一个单独的 DataFrame 中y_data = df[y_columns]# 使用 pd.melt() 函数将其转换为适合绘制箱线图的格式melted_df = pd.melt(df, id_vars=['down_level'], value_vars=y_columns, var_name='Depth_Type', value_name='Depth')# 使用 seaborn 绘制箱线图plt.figure(figsize=(12, 8))sns.boxplot(x='down_level', y='Depth', hue='Depth_Type', data=melted_df, dodge=True)plt.xlabel('Down Level (G)')plt.ylabel('Depth')plt.title('Box Plot of Depth Data by Down Level')plt.legend(title='Depth Type', loc='upper right')plt.grid(True)plt.savefig(f'boxplot.png')print()lvl_list = ["ori", '40', '36', '32', '28', '24', '20']target_header_list = ["loci_median_depth", "loci_average_depth", "dedup_loci_median_depth", "dedup_loci_average_depth", "average_depth", "median_depth", "dedup_average_depth", "dedup_median_depth"]# box_plot(tumor_df, target_header_list)
# box_plot_3(normal_df, target_header_list)
# box_plot_2(normal_df, target_header_list)
box_plot_3(normal_df, target_header_list)
# box_plot_1(normal_df)