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科研绘图-python/R语言批量绘制小提琴箱线图

  • 2026-02-26 00:52:50
科研绘图-python/R语言批量绘制小提琴箱线图

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代码基于 Python 数据处理与可视化库,先配置中文字体解决显示问题,读取或生成水文站点多要素观测数据后,按站点绘制小提琴图与箱线图叠加的可视化图表:小提琴图反映各要素观测值的分布密度,箱线图展示中位数、四分位数等统计特征;同时对指定要素组合进行独立样本 t 检验,依据 p 值标注显著性符号(***p<0.001,**p<0.01,*p<0.05,ns 无显著性),最终调整图表样式并按站点保存图片,实现要素数据分布可视化与统计差异分析。

Python:

import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport seaborn as snsfrom scipy import statsimport osfrom matplotlib.font_manager import FontPropertiesfont_path = 'C:/Windows/Fonts/simhei.ttf'if os.path.exists(font_path):    font = FontProperties(fname=font_path, size=22, weight='bold')else:    font = FontProperties(family=['SimHei''Microsoft YaHei''SimSun''Arial Unicode MS'], size=22, weight='bold')plt.rcParams['axes.unicode_minus'] = Falseplt.rcParams['xtick.bottom'] = Trueplt.rcParams['ytick.left'] = Truesns.set_style("white", {'xtick.bottom'True'ytick.left'True})file_path = 'data(小提琴箱线图).xlsx'if os.path.exists(file_path):    df = pd.read_excel(file_path)else:    sites = ['水文站A''水文站B''水文站C']    factors = ['水位''雨量''蒸发''流量']    np.random.seed(42)    data = []    for site in sites:        for factor in factors:            n = np.random.randint(2050)            if factor == '水位':                vals = np.random.normal(252, n)            elif factor == '雨量':                vals = np.random.normal(5010, n)            elif factor == '蒸发':                vals = np.random.normal(102, n)            else:                vals = np.random.normal(305, n)            data.extend([{                '站点': site,                '水文要素': factor,                '观测值': v            } for v in vals])    df = pd.DataFrame(data)    df.to_excel(file_path, index=False)    print(f"已生成并保存随机数据至: {file_path}")site_order = ['水文站A''水文站B''水文站C']site_color_map = {    "水文站A""#1f77b4",    "水文站B""#ff7f0e",    "水文站C""#2ca02c"}group_order = ["水位""雨量""蒸发""流量"]site_pairs = [    ("水文站A""水文站B"),    ("水文站B""水文站C")]for factor in group_order:    subset = df[df['水文要素'] == factor]    plt.figure(figsize=(129))    ax = plt.gca()    sns.violinplot(        data=subset,        x='站点',        y='观测值',        hue='站点',        order=site_order,        palette=site_color_map,        alpha=0.5,        inner=None,        linewidth=0,        legend=False,        bw_method='scott',        gridsize=1024,        cut=0,        ax=ax    )    sns.boxplot(        data=subset,        x='站点',        y='观测值',        hue='站点',        order=site_order,        palette=site_color_map,        width=0.1,        showcaps=True,        boxprops={'zorder'3'facecolor''none''edgecolor''black''linewidth'2},        medianprops={'color''black''linewidth'4'zorder'4},        whiskerprops={'linewidth'2'color''black'},        capprops={'linewidth'2'color''black'},        showfliers=True,        legend=False,        ax=ax    )    y_min, y_max = subset['观测值'].min(), subset['观测值'].max()    y_range = y_max - y_min    current_y = y_max + 0.05 * y_range    for (s1, s2) in site_pairs:        d1 = subset[subset['站点'] == s1]['观测值']        d2 = subset[subset['站点'] == s2]['观测值']        t_stat, p_val = stats.ttest_ind(d1, d2, nan_policy='omit')        if p_val < 0.001:            sig = '***'        elif p_val < 0.01:            sig = '**'        elif p_val < 0.05:            sig = '*'        else:            sig = 'ns'        x1 = site_order.index(s1)        x2 = site_order.index(s2)        tip_len = 0.01 * y_range        plt.plot([x1, x1, x2, x2],                 [current_y, current_y + tip_len, current_y + tip_len, current_y],                 color='black', linewidth=2.5)        plt.text((x1 + x2) / 2, current_y + tip_len, sig,                 ha='center', va='bottom', fontsize=18, fontweight='bold')        current_y += 0.02 * y_range + tip_len    plt.ylim(y_min - 0.1 * y_range, current_y + 0.05 * y_range)    plt.title(factor, fontsize=24, fontweight='bold', ha='center', fontproperties=font)    plt.xlabel('站点', fontsize=18, fontweight='bold', fontproperties=font)    plt.ylabel('观测值', fontsize=18, fontweight='bold', fontproperties=font)    for label in ax.get_xticklabels():        label.set_fontproperties(font)        label.set_fontsize(16)        label.set_fontweight('bold')        label.set_rotation(60)        label.set_ha('center')        label.set_va('top')    for label in ax.get_yticklabels():        label.set_fontsize(16)        label.set_fontweight('bold')    ax.tick_params(axis='x', which='major', length=12, width=2.5, direction='out', color='black', top=False, right=False)    ax.tick_params(axis='y', which='major', length=12, width=2.5, direction='out', color='black', top=False, right=False)    for spine in ax.spines.values():        spine.set_linewidth(2.5)    plt.legend([], [], frameon=False)    sns.despine(top=True, right=True, left=False, bottom=False)    plt.tight_layout()    output_name = f"{factor}.png"    plt.savefig(output_name, dpi=300, bbox_inches='tight')    plt.close()    print(f"已保存图片: {output_name}")print("所有图片处理完成。")

R语言:

if (requireNamespace("rstudioapi", quietly = TRUE&& rstudioapi::isAvailable()) {  script_path <- rstudioapi::getActiveDocumentContext()$path  setwd(dirname(script_path))}library(readxl)library(ggplot2)library(dplyr)if (Sys.info()['sysname'] == "Windows") {  windowsFonts(SimHei = windowsFont("SimHei"))else {  warning("当前系统非 Windows,中文字体可能显示异常,请手动设置。")}file_path <- "data(小提琴箱线图).xlsx"if (!file.exists(file_path)) {  stop("文件不存在:", file_path)}df <- read_excel(file_path)df$站点 <- factor(df$站点, levels = unique(df$站点))site_names <- unique(df$站点)colors <- hcl.colors(length(site_names), palette = "Set2")color_map <- setNames(colors, site_names)group_order <- c("水位""雨量""蒸发""流量")for (element in unique(df$水文要素)) {  subset <- df[df$水文要素 == element, ]  y_min <- min(subset$观测值, na.rm = TRUE)  y_max <- max(subset$观测值, na.rm = TRUE)  y_range <- y_max - y_min  current_y <- y_max + 0.05 * y_range  site_levels <- levels(subset$站点)  if (length(site_levels) < 2) {    comparisons <- list()  } else {    pairs <- combn(site_levels, 2, simplify = FALSE)    comparisons <- pairs  }  sig_layers <- list()  for (comp in comparisons) {    g1 <- comp[1]    g2 <- comp[2]    d1 <- subset$观测值[subset$站点 == g1]    d2 <- subset$观测值[subset$站点 == g2]    t_test <- t.test(d1, d2)    p_val <- t_test$p.value    sig <- ifelse(p_val < 0.001"***",                  ifelse(p_val < 0.01"**",                         ifelse(p_val < 0.05"*""ns")))    x1 <- match(g1, site_levels)    x2 <- match(g2, site_levels)    tip_len <- 0.01 * y_range    start_y <- current_y    left_seg  <- annotate("segment", x = x1, xend = x1,                          y = start_y, yend = start_y + tip_len,                          color = "black", linewidth = 1)    horiz_seg <- annotate("segment", x = x1, xend = x2,                          y = start_y + tip_len, yend = start_y + tip_len,                          color = "black", linewidth = 1)    right_seg <- annotate("segment", x = x2, xend = x2,                          y = start_y, yend = start_y + tip_len,                          color = "black", linewidth = 1)    text_ann <- annotate("text", x = (x1 + x2) / 2, y = start_y + tip_len,                         label = sig, hjust = 0.5, vjust = 0,                         size = 7.5, family = "SimHei")    sig_layers <- append(sig_layers, list(left_seg, horiz_seg, right_seg, text_ann))    current_y <- current_y + 0.02 * y_range + tip_len  }  ylim_upper <- current_y + 0.05 * y_range  p <- ggplot(subset, aes(x = 站点, y = 观测值, fill = 站点)) +    geom_violin(alpha = 0.5, color = NA,                trim = TRUE                scale = "area",                bw = "nrd"                adjust = 2                kernel = "gaussian"+    geom_boxplot(width = 0.1, fill = NA, color = "black",                 outlier.color = "black", outlier.size = 1,                 median.color = "black", median.linewidth = 1.5,                 linewidth = 0.5+    scale_fill_manual(values = color_map) +    guides(fill = guide_legend(title = "站点")) +    labs(title = element, x = "站点", y = "观测值"+    coord_cartesian(ylim = c(y_min - 0.1 * y_range, ylim_upper), expand = FALSE+    theme_minimal() +    theme(      text = element_text(family = "SimHei", size = 12),      plot.title = element_text(hjust = 0.5, size = 18, face = "bold"),      axis.title.x = element_text(size = 18, face = "bold"),      axis.title.y = element_text(size = 18, face = "bold"),      axis.text.x = element_text(size = 15, angle = 60, hjust = 1, vjust = 1,                                 face = "bold", color = "black"),      axis.text.y = element_text(size = 12, face = "bold"),      axis.ticks = element_line(color = "black", linewidth = 1),      axis.ticks.length = unit(0.15"cm"),      panel.grid = element_blank(),      panel.border = element_blank(),      axis.line = element_line(color = "black", linewidth = 1)    )  for (layer in sig_layers) {    p <- p + layer  }  output_file <- paste0(element, ".png")  ggsave(output_file, plot = p, dpi = 300, width = 12, height = 9, units = "in")  cat("已保存图片:", output_file, "\n")}cat("所有图片处理完成。\n")

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  1. CONNECT:[ UseTime:0.000644s ] mysql:host=127.0.0.1;port=3306;dbname=f_mffb;charset=utf8mb4
  2. SHOW FULL COLUMNS FROM `fenlei` [ RunTime:0.000716s ]
  3. SELECT * FROM `fenlei` WHERE `fid` = 0 [ RunTime:0.001367s ]
  4. SELECT * FROM `fenlei` WHERE `fid` = 63 [ RunTime:0.000267s ]
  5. SHOW FULL COLUMNS FROM `set` [ RunTime:0.000641s ]
  6. SELECT * FROM `set` [ RunTime:0.000227s ]
  7. SHOW FULL COLUMNS FROM `article` [ RunTime:0.000757s ]
  8. SELECT * FROM `article` WHERE `id` = 477101 LIMIT 1 [ RunTime:0.000638s ]
  9. UPDATE `article` SET `lasttime` = 1772247190 WHERE `id` = 477101 [ RunTime:0.027735s ]
  10. SELECT * FROM `fenlei` WHERE `id` = 66 LIMIT 1 [ RunTime:0.000288s ]
  11. SELECT * FROM `article` WHERE `id` < 477101 ORDER BY `id` DESC LIMIT 1 [ RunTime:0.000716s ]
  12. SELECT * FROM `article` WHERE `id` > 477101 ORDER BY `id` ASC LIMIT 1 [ RunTime:0.004647s ]
  13. SELECT * FROM `article` WHERE `id` < 477101 ORDER BY `id` DESC LIMIT 10 [ RunTime:0.000919s ]
  14. SELECT * FROM `article` WHERE `id` < 477101 ORDER BY `id` DESC LIMIT 10,10 [ RunTime:0.002919s ]
  15. SELECT * FROM `article` WHERE `id` < 477101 ORDER BY `id` DESC LIMIT 20,10 [ RunTime:0.000926s ]
0.187015s