seaborn实践

本文章基于豆包整理了使用seaborn生成若干典型图表的示例代码可供学习seaborn使用。一、图表结果预览二、测试数据生成生成了两组数据一组是银行贷款存款数据一组是学生成绩数据生成数据代码如下-- 1. 创建数据库 CREATE DATABASE IF NOT EXISTS seaborn DEFAULT CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci; USE seaborn; -- 2. 分行表 CREATE TABLE bank_branches ( branch_id INT PRIMARY KEY AUTO_INCREMENT, branch_name VARCHAR(50) NOT NULL COMMENT 分行名称 ); -- 3. 行业分类表贷款专用 CREATE TABLE loan_industries ( industry_id INT PRIMARY KEY AUTO_INCREMENT, industry_name VARCHAR(50) NOT NULL COMMENT 行业名称 ); -- 4. 月度存款表适合环比、同比 CREATE TABLE monthly_deposit ( id INT PRIMARY KEY AUTO_INCREMENT, branch_id INT NOT NULL, stat_year INT NOT NULL COMMENT 年份, stat_month INT NOT NULL COMMENT 月份, deposit_amount DECIMAL(18,2) NOT NULL COMMENT 存款金额万元, FOREIGN KEY (branch_id) REFERENCES bank_branches(branch_id) ); -- 5. 月度贷款表带行业适合分组对比 CREATE TABLE monthly_loan ( id INT PRIMARY KEY AUTO_INCREMENT, branch_id INT NOT NULL, industry_id INT NOT NULL, stat_year INT NOT NULL, stat_month INT NOT NULL, loan_amount DECIMAL(18,2) NOT NULL COMMENT 贷款金额万元, FOREIGN KEY (branch_id) REFERENCES bank_branches(branch_id), FOREIGN KEY (industry_id) REFERENCES loan_industries(industry_id) ); -- -- 下面开始造数据2023年1月 ~ 2025年3月共27个月 -- 包含趋势、季节性波动完美画同比/环比 -- INSERT INTO bank_branches (branch_name) VALUES (北京分行),(上海分行),(广州分行),(深圳分行),(杭州分行); INSERT INTO loan_industries (industry_name) VALUES (制造业),(批发零售),(房地产),(信息技术),(建筑业),(服务业); -- 插入月度存款带增长趋势 INSERT INTO monthly_deposit (branch_id, stat_year, stat_month, deposit_amount) SELECT b.branch_id, y.year, m.month, ROUND( (5000 b.branch_id * 1000) * (1 (y.year - 2023) * 0.12) * (0.85 RAND() * 0.3) * CASE WHEN m.month IN (1,12) THEN 1.2 ELSE 1 END, 2 ) AS deposit FROM (SELECT 2023 AS year UNION SELECT 2024 UNION SELECT 2025) y CROSS JOIN (SELECT 1 AS month UNION SELECT 2 UNION SELECT 3 UNION SELECT 4 UNION SELECT 5 UNION SELECT 6 UNION SELECT 7 UNION SELECT 8 UNION SELECT 9 UNION SELECT 10 UNION SELECT 11 UNION SELECT 12) m CROSS JOIN bank_branches b WHERE NOT (y.year 2025 AND m.month 3); -- 插入月度贷款带行业、分行、趋势 INSERT INTO monthly_loan (branch_id, industry_id, stat_year, stat_month, loan_amount) SELECT b.branch_id, i.industry_id, y.year, m.month, ROUND( (3000 b.branch_id * 600 i.industry_id * 300) * (1 (y.year - 2023) * 0.15) * (0.8 RAND() * 0.4) * CASE WHEN i.industry_id 3 THEN 1.4 ELSE 1 END, 2 ) AS loan FROM (SELECT 2023 AS year UNION SELECT 2024 UNION SELECT 2025) y CROSS JOIN (SELECT 1 AS month UNION SELECT 2 UNION SELECT 3 UNION SELECT 4 UNION SELECT 5 UNION SELECT 6 UNION SELECT 7 UNION SELECT 8 UNION SELECT 9 UNION SELECT 10 UNION SELECT 11 UNION SELECT 12) m CROSS JOIN bank_branches b CROSS JOIN loan_industries i WHERE NOT (y.year 2025 AND m.month 3); -- 创建学生表如果已存在可忽略 CREATE TABLE IF NOT EXISTS student ( class VARCHAR(20) COMMENT 班级, name VARCHAR(20) COMMENT 姓名, age INT COMMENT 年龄, height DECIMAL(5,2) COMMENT 身高(cm), score INT COMMENT 成绩(0-100) ) COMMENT 学生信息表; -- 清空表可选避免重复插入 TRUNCATE TABLE student; INSERT INTO student (class, name, age, height, score) VALUES -- 高一1班 40人 (高一1班,陈佳宁,16,155.2,76), (高一1班,林雨桐,16,163.5,82), (高一1班,王子轩,17,174.1,69), (高一1班,刘思远,16,168.7,88), (高一1班,赵一诺,17,161.3,73), (高一1班,周欣怡,16,152.8,79), (高一1班,吴俊豪,17,176.4,65), (高一1班,郑雅琪,16,158.6,81), (高一1班,黄浩然,17,180.2,92), (高一1班,孙梦瑶,16,162.4,70), (高一1班,朱梓晨,17,170.5,78), (高一1班,高诗涵,16,156.9,85), (高一1班,马泽宇,17,173.8,63), (高一1班,胡雨萱,16,160.1,74), (高一1班,何俊熙,17,169.3,80), (高一1班,罗艺菲,16,151.5,59), (高一1班,梁博文,17,178.6,90), (高一1班,宋欣悦,16,164.2,72), (高一1班,唐子昂,17,172.0,83), (高一1班,韩雨桐,16,157.4,68), (高一1班,邓嘉豪,17,175.3,77), (高一1班,曹雨彤,16,161.9,86), (高一1班,许泽轩,17,167.2,61), (高一1班,彭思琪,16,159.0,75), (高一1班,蒋俊彦,17,171.4,89), (高一1班,蔡欣妍,16,153.7,66), (高一1班,潘俊辰,17,179.1,94), (高一1班,袁雨薇,16,160.8,71), (高一1班,戴泽阳,17,168.5,84), (高一1班,杜欣冉,16,154.6,64), (高一1班,沈浩宇,17,170.1,79), (高一1班,姚思怡,16,162.7,87), (高一1班,钟俊凯,17,177.2,62), (高一1班,姜雨汐,16,158.1,73), (高一1班,方梓豪,17,173.3,81), (高一1班,石欣悦,16,150.9,57), (高一1班,熊俊熙,17,176.8,91), (高一1班,金雨萱,16,163.0,70), (高一1班,陆泽宇,17,169.8,76), (高一1班,白思琪,16,161.4,82), -- 高一2班 40人 (高一2班,温佳琪,16,154.8,75), (高一2班,季子轩,17,172.9,83), (高一2班,江雨桐,16,156.3,68), (高一2班,雷俊豪,17,179.5,90), (高一2班,方一诺,16,160.5,72), (高一2班,柳欣怡,17,167.7,78), (高一2班,贺思远,16,163.2,85), (高一2班,顾雅琪,17,174.6,64), (高一2班,毛浩然,16,159.4,71), (高一2班,邵梦瑶,17,168.2,80), (高一2班,严梓晨,16,164.7,93), (高一2班,程诗涵,17,171.0,66), (高一2班,秦泽宇,16,152.1,74), (高一2班,薛雨萱,17,175.8,88), (高一2班,白俊熙,16,157.9,61), (高一2班,尹艺菲,17,169.6,77), (高一2班,江博文,16,162.2,81), (高一2班,段欣悦,17,173.4,69), (高一2班,钱子昂,16,155.5,84), (高一2班,易雨桐,17,177.9,58), (高一2班,汤嘉豪,16,161.1,73), (高一2班,汪雨彤,17,168.9,79), (高一2班,盛泽轩,16,163.8,86), (高一2班,庞思琪,17,167.0,63), (高一2班,樊俊彦,16,158.4,70), (高一2班,葛欣妍,17,176.3,91), (高一2班,邢俊辰,16,153.2,67), (高一2班,安雨薇,17,172.2,82), (高一2班,齐泽阳,16,160.0,76), (高一2班,伍欣冉,17,169.1,89), (高一2班,焦浩宇,16,151.2,60), (高一2班,葛思怡,17,174.9,74), (高一2班,俞俊凯,16,162.9,80), (高一2班,舒雨汐,17,173.7,94), (高一2班,狄梓豪,16,156.7,65), (高一2班,成欣悦,17,178.4,78), (高一2班,麻俊熙,16,161.8,85), (高一2班,屈雨萱,17,167.4,62), (高一2班,封泽宇,16,159.9,71), (高一2班,阮思琪,17,171.7,83), -- 高一3班 40人 (高一3班,纪佳宁,16,157.1,78), (高一3班,盛雨桐,17,173.9,84), (高一3班,温子轩,16,154.3,67), (高一3班,卢思远,17,169.4,73), (高一3班,欧一诺,16,162.6,81), (高一3班,米欣怡,17,166.8,90), (高一3班,莫俊豪,16,158.8,64), (高一3班,郁雅琪,17,177.6,76), (高一3班,冷浩然,16,152.4,82), (高一3班,应梦瑶,17,170.8,88), (高一3班,管梓晨,16,160.3,61), (高一3班,房诗涵,17,172.5,70), (高一3班,经泽宇,16,155.9,79), (高一3班,边雨萱,17,168.5,85), (高一3班,卫俊熙,16,163.4,92), (高一3班,虞艺菲,17,174.3,66), (高一3班,仇博文,16,151.8,74), (高一3班,狄欣悦,17,171.4,80), (高一3班,糜子昂,16,159.2,63), (高一3班,盛雨桐,17,169.9,86), (高一3班,储嘉豪,16,154.9,71), (高一3班,隗雨彤,17,175.4,77), (高一3班,仲泽轩,16,161.5,83), (高一3班,栾思琪,17,166.3,59), (高一3班,厉俊彦,16,157.5,69), (高一3班,终欣妍,17,177.9,94), (高一3班,闻俊辰,16,153.0,75), (高一3班,骆雨薇,17,171.9,81), (高一3班,艾泽阳,16,156.4,68), (高一3班,沙欣冉,17,168.0,89), (高一3班,柴浩宇,16,160.9,72), (高一3班,慕思怡,17,172.8,78), (高一3班,桑俊凯,16,155.1,84), (高一3班,商雨汐,17,176.6,60), (高一3班,屠梓豪,16,158.2,70), (高一3班,阚欣悦,17,169.4,82), (高一3班,盖俊熙,16,150.7,65), (高一3班,晏雨萱,17,174.8,91), (高一3班,饶泽宇,16,161.2,73), (高一3班,戚思琪,17,172.4,87), -- 高一4班 40人 (高一4班,费佳宁,16,156.0,72), (高一4班,卫雨桐,17,171.6,80), (高一4班,舒子轩,16,160.4,66), (高一4班,葛思远,17,167.1,77), (高一4班,邢一诺,16,162.8,83), (高一4班,纪欣怡,17,173.3,89), (高一4班,阮俊豪,16,158.0,63), (高一4班,封雅琪,17,168.4,74), (高一4班,屈浩然,16,157.3,81), (高一4班,庞梦瑶,17,175.5,92), (高一4班,盛梓晨,16,161.4,69), (高一4班,汤诗涵,17,170.5,78), (高一4班,汪泽宇,16,162.3,85), (高一4班,姜雨萱,17,177.2,61), (高一4班,钟俊熙,16,151.0,70), (高一4班,姚艺菲,17,174.0,86), (高一4班,杜博文,16,159.7,75), (高一4班,沈欣悦,17,167.8,82), (高一4班,戴子昂,16,163.1,94), (高一4班,蔡雨桐,17,171.2,64), (高一4班,潘嘉豪,16,153.4,71), (高一4班,蒋雨彤,17,172.9,79), (高一4班,彭泽轩,16,160.1,88), (高一4班,罗思琪,17,169.0,67), (高一4班,何俊彦,16,161.7,76), (高一4班,胡欣妍,17,177.5,84), (高一4班,马俊辰,16,157.0,90), (高一4班,高雨薇,17,175.1,60), (高一4班,宋泽阳,16,162.0,73), (高一4班,唐欣冉,17,170.9,81), (高一4班,韩浩宇,16,150.6,68), (高一4班,邓思怡,17,174.4,87), (高一4班,曹俊凯,16,155.8,72), (高一4班,许雨汐,17,171.5,78), (高一4班,陆梓豪,16,160.6,91), (高一4班,白金悦,17,166.9,62), (高一4班,熊俊熙,16,154.1,70), (高一4班,石雨萱,17,172.6,83), (高一4班,方泽宇,16,158.5,65), (高一4班,柳思琪,17,173.8,80), -- 高一5班 40人 (高一5班,雷佳宁,16,156.8,74), (高一5班,贺雨桐,17,169.7,81), (高一5班,顾子轩,16,152.2,68), (高一5班,邵思远,17,171.8,77), (高一5班,严一诺,16,161.6,85), (高一5班,程欣怡,17,175.9,90), (高一5班,秦俊豪,16,159.0,64), (高一5班,薛雅琪,17,170.2,72), (高一5班,白浩然,16,160.0,79), (高一5班,尹梦瑶,17,166.5,86), (高一5班,江梓晨,16,154.4,61), (高一5班,段诗涵,17,172.4,70), (高一5班,钱泽宇,16,161.0,83), (高一5班,易雨萱,17,169.2,88), (高一5班,汤俊熙,16,150.3,93), (高一5班,汪艺菲,17,177.0,66), (高一5班,盛博文,16,157.7,75), (高一5班,庞欣悦,17,174.2,82), (高一5班,樊子昂,16,162.4,69), (高一5班,葛雨桐,17,171.1,78), (高一5班,邢嘉豪,16,153.6,84), (高一5班,安雨彤,17,173.5,63), (高一5班,齐泽轩,16,159.5,71), (高一5班,伍思琪,17,167.5,80), (高一5班,焦俊彦,16,163.0,91), (高一5班,葛欣妍,17,176.8,60), (高一5班,俞俊辰,16,155.3,73), (高一5班,舒雨薇,17,172.1,87), (高一5班,狄泽阳,16,160.7,76), (高一5班,成欣冉,17,168.7,81), (高一5班,麻浩宇,16,156.2,67), (高一5班,屈思怡,17,174.7,79), (高一5班,封俊凯,16,162.1,89), (高一5班,阮雨汐,17,170.6,62), (高一5班,戚梓豪,16,152.9,70), (高一5班,饶欣悦,17,171.4,85), (高一5班,阚俊熙,16,154.0,65), (高一5班,屠雨萱,17,168.2,94), (高一5班,商泽宇,16,160.2,74), (高一5班,桑思琪,17,175.3,82); ALTER TABLE student ADD COLUMN gender VARCHAR(10); UPDATE student SET gender IF(RAND() 0.5, 男, 女);三、表格生成代码完整代码使用了mysql数据库修改数据库名称密码即可直接运行import seaborn as sns import matplotlib.pyplot as plt import pandas as pd from sqlalchemy import create_engine import warnings import os import numpy as np from matplotlib.ticker import ScalarFormatter # 自动创建images文件夹 if not os.path.exists(images): os.mkdir(images) warnings.filterwarnings(ignore) plt.rcParams[font.sans-serif] [Microsoft YaHei] plt.rcParams[axes.unicode_minus] False sns.set_theme(styledarkgrid, palettemuted) # sns.set_theme(stylewhitegrid) # 汉语字体 plt.rcParams.update({ font.sans-serif: [SimHei, Microsoft YaHei], axes.unicode_minus: False, font.size: 12 }) # 数据库配置你只改这里 user root pwd password host 127.0.0.1 port 3306 db_name seaborn # engine create_engine(fmysqlpymysql://{user}:{pwd}{host}:{port}/{db_name}?charsetutf8mb4) # 1. 存款数据 df_deposit pd.read_sql( SELECT b.branch_name,d.stat_year,d.stat_month, CONCAT(d.stat_year,-,LPAD(d.stat_month,2,0)) dt, d.deposit_amount FROM monthly_deposit d LEFT JOIN bank_branches b ON d.branch_idb.branch_id ORDER BY d.stat_year, d.stat_month; , engine) # 2. 行业贷款 df_loan_ind pd.read_sql( SELECT i.industry_name,l.stat_year,l.stat_month, CONCAT(l.stat_year,-,LPAD(l.stat_month,2,0)) dt, SUM(l.loan_amount) loan_total FROM monthly_loan l LEFT JOIN loan_industries i ON l.industry_idi.industry_id GROUP BY 1,2,3 ORDER BY 2,3; , engine) # 3. 存贷对比 df_balance pd.read_sql( SELECT d.stat_year,d.stat_month, CONCAT(d.stat_year,-,LPAD(d.stat_month,2,0)) dt, SUM(d.deposit_amount) total_deposit, SUM(l.loan_amount) total_loan FROM monthly_deposit d LEFT JOIN monthly_loan l ON d.stat_yearl.stat_year AND d.stat_monthl.stat_month GROUP BY 1,2 ORDER BY 1,2; , engine) # 4. 年度存款 df_year_dep df_deposit.groupby(stat_year)[deposit_amount].sum().reset_index() # 5.年度贷款 loan_year df_loan_ind.groupby(stat_year)[loan_total].sum().reset_index() # 7 2024年月度贷款 dep_month_2024 pd.read_sql( select stat_month, sum(loan_amount) as loan_total from monthly_loan where stat_year2024 group by stat_month , engine) # 8 学生成绩 stu_score pd.read_sql( select age, height, class, score, gender from student , engine) # # 开始画图 → 保存到 images/ 文件夹 # # --图1-- plt.figure(figsize(14,6)) ax1 sns.lineplot(datadf_deposit, xdt, ydeposit_amount, huebranch_name, markero) ax1.legend(title分 行) plt.xlabel(年-月) plt.ylabel(存款总额) plt.title(各分行存款月度趋势, fontsize14) plt.xticks(rotation45) plt.tight_layout() plt.savefig(images/1.png, dpi100) plt.close() # --图2-- plt.figure(figsize(14,6)) ax2 sns.lineplot(datadf_loan_ind, xdt, yloan_total, hueindustry_name, markero) ax2.legend(title行 业) plt.xlabel(年-月) plt.ylabel(贷款总额) plt.title(各行业贷款趋势, fontsize14) plt.xticks(rotation45) plt.tight_layout() plt.savefig(images/2.png, dpi100) plt.close() # --图3-- plt.figure(figsize(14,6)) sns.lineplot(datadf_balance, xdt, ytotal_deposit, label存款, color#007bff) sns.lineplot(datadf_balance, xdt, ytotal_loan, label贷款, color#e63946) plt.title(全行存贷对比, fontsize14) plt.xticks(rotation45) plt.xlabel(年-月) plt.ylabel(金额) plt.legend(title类型) plt.tight_layout() plt.savefig(images/3.png, dpi100) plt.close() # --图4-- 换一种方式画图3 plt.figure(figsize(14,6)) df_balance_melted df_balance.melt( id_vars[dt, stat_year, stat_month], value_vars[total_deposit, total_loan], var_nametype, value_nameamount ) ax4sns.lineplot(datadf_balance_melted, xdt, yamount, huetype, markero) plt.title(全行存贷对比2, fontsize14) chinese_labels [存款, 贷款] # 修改图例标签 handles, labels ax4.get_legend_handles_labels() ax4.legend(handles, chinese_labels, title类型) plt.xlabel(年-月) plt.ylabel(金额) plt.xticks(rotation45) plt.tight_layout() plt.savefig(images/4.png, dpi100) plt.close() # --图5-- 年度存款总额对比柱状图 plt.figure(figsize(10,5)) ax5sns.barplot(datadf_year_dep, xstat_year, ydeposit_amount, palettepastel) plt.title(年度存款总额对比, fontsize14) plt.xlabel(年度) plt.ylabel(存款金额) for container in ax5.containers: ax5.bar_label(container, fmt%.0f, fontsize10, padding3) plt.tight_layout() plt.savefig(images/5.png, dpi100) plt.close() # --图6-- 年度贷款总额对比柱状图 plt.figure(figsize(10,5)) ax6sns.barplot(dataloan_year, xstat_year, yloan_total, palettepastel) plt.title(年度贷款总额对比, fontsize14) plt.xlabel(年度) plt.ylabel(贷款金额) for container in ax6.containers: ax6.bar_label(container, fmt%.0f, fontsize10, padding3) # 关闭科学计数法 ax6.yaxis.set_major_formatter(ScalarFormatter(useOffsetFalse)) ax6.ticklabel_format(styleplain, axisy) plt.tight_layout() plt.savefig(images/6.png, dpi100) plt.close() # --图7-- 2024年贷款金额散点图 # 绘制散点图 plt.figure(figsize(10,3)) ax7sns.scatterplot(xstat_month, yloan_total, datadep_month_2024) # 为每个点添加数值标签 for i, row in dep_month_2024.iterrows(): ax7.text(row[stat_month], row[loan_total], f{row[loan_total]:.0f}, hacenter, vabottom, fontsize9) plt.title(2024年月度贷款散点图, fontsize14) plt.xlabel(月度) plt.ylabel(贷款金额) plt.tight_layout() plt.savefig(images/7.png, dpi100) plt.close() # 图8 分类散点图 plt.figure(figsize(10,3)) sns.stripplot(xclass,yscore,huegender,datastu_score) plt.title(各班成绩性别散点图, fontsize14) plt.legend( title性别, fontsize8, # 图例文字变小 title_fontsize9, # 图例标题变小 markerscale0.7 # 图例里的小图标变小 ) plt.xlabel(班级) plt.ylabel(成绩) plt.tight_layout() plt.savefig(images/8.png, dpi300) plt.close() # --图9-- 箱线图 plt.figure(figsize(10,5)) class_order [高一1班, 高一2班, 高一3班, 高一4班, 高一5班] sns.boxplot(xclass, yscore, datastu_score, orderclass_order) for i, cls in enumerate(class_order): data stu_score[stu_score[class] cls][score] q1 np.percentile(data, 25) med np.percentile(data, 50) q3 np.percentile(data, 75) min_val data.min() max_val data.max() plt.text(i, min_val-1.5, f{min_val:.0f}, hacenter, fontsize9) plt.text(i, q1-2, f{q1:.0f}, hacenter, fontsize9) plt.text(i, med2, f{med:.0f}, hacenter, fontsize9, colorred) plt.text(i, q32, f{q3:.0f}, hacenter, fontsize9) plt.text(i, max_val1, f{max_val:.0f}, hacenter, fontsize9) plt.title(学生成绩箱线图, fontsize14) plt.xlabel(班级) plt.ylabel(成绩) plt.tight_layout() plt.savefig(images/9.png, dpi100) plt.close() # 图10 热力图 plt.figure(figsize(3,2)) stu_num stu_score[[age, height, score]] # 计算相关系数矩阵 correlation_matrix stu_num.corr() # 生成一个掩码遮住上三角重复部分 mask np.triu(np.ones_like(correlation_matrix, dtypebool)) plt.figure(figsize(8, 6)) sns.heatmap( correlation_matrix, annotTrue, # 显示相关系数数值 cmapcoolwarm, # 蓝-白-红配色 fmt.2f, # 保留2位小数 linewidths0.5, # 格子间加细线更清晰 maskmask # 开启掩码只显示一半 ) plt.title(年龄-身高-成绩相关性热力图, fontsize14) plt.tight_layout() plt.savefig(images/10.png, dpi300) # --11-- 小提琴图 plt.figure(figsize(10,5)) df pd.DataFrame(data) # 绘制小提琴图 sns.violinplot(xclass, yscore, datastu_score) plt.title(成绩小提琴图, fontsize14) plt.tight_layout() plt.savefig(images/11.png, dpi300) # --图12-- 直方图 plt.figure(figsize(8, 5)) # bins12 柱子数量可改 ax12sns.histplot(datastu_score, xscore, bins12, color#69b3a2) # 叠加平滑核密度曲线 # sns.kdeplot(datastu_score, xscore, axax12, statcount, colordarkred, linewidth2) ax12_twin ax12.twinx() sns.kdeplot(datastu_score, xscore, axax12_twin, colordarkred, linewidth2) ax12_twin.set_yticks([]) # 隐藏密度轴只显示曲线 for bar in ax12.patches: height bar.get_height() if height 0: ax12.text(bar.get_x() bar.get_width()/2, height/2, f{int(height)}, hacenter, fontsize9) # 在顶部表区间范围 bin_edges np.histogram_bin_edges(stu_score[score], bins12) bin_centers (bin_edges[:-1] bin_edges[1:]) / 2 bin_counts np.histogram(stu_score[score], bins12)[0] # histogram会返回( 每段人数, 区间边界 ) for i in range(len(bin_edges)-1): left int(bin_edges[i]) right int(bin_edges[i1]) total_h bin_counts[i] ax12.text( bin_centers[i], total_h 0.3, f{left}~{right}分, hacenter, fontsize9, colorblack ) plt.title(11.学生成绩分布直方图, fontsize14) plt.xlabel(成绩) ax12.set_ylabel(人数) ax12_twin.set_ylabel(人数, fontsize11) plt.tight_layout() plt.savefig(images/12.png, dpi300) # --图13-- 复合直方图 plt.figure(figsize(8, 5)) ax13 sns.histplot( datastu_score, xscore, hueclass, bins10, alpha0.7, # multipledodge # 柱子并排不重叠 multiplestack # 柱子并排不重叠 ) # 获取分数区间 bin_edges np.histogram_bin_edges(stu_score[score], bins10) bin_centers (bin_edges[:-1] bin_edges[1:]) / 2 # 先获取每个区间堆叠后的总高度 bin_counts np.histogram(stu_score[score], bins10)[0] # 标注每个区间范围 → 放在对应柱子的顶部上方一点点 for i in range(len(bin_edges)-1): left int(bin_edges[i]) right int(bin_edges[i1]) total_h bin_counts[i] ax13.text( bin_centers[i], total_h 0.3, # 放在当前柱子顶部一点偏移 f{left}~{right}分, hacenter, fontsize9, colorblack ) # 每层堆叠内部标注人数 for bar in ax13.patches: height bar.get_height() if height 0.1: ax13.text( bar.get_x() bar.get_width() / 2, bar.get_y() height / 2, f{int(height)}, hacenter, vacenter, fontsize10, colorwhite ) # 缩小图例核心代码 leg ax13.legend_ leg.set_title(班级, prop{size:6}) # 图例标题大小 for text in leg.get_texts(): text.set_fontsize(5) # 图例文字大小 leg.get_frame().set_linewidth(0.3) # 边框粗细可选 plt.title(班级混合成绩直方图, fontsize14) plt.xlabel(成绩) plt.ylabel(人数) plt.tight_layout() plt.savefig(images/13.png, dpi300) # --图14-- 九宫格直方图 plt.figure(figsize(8, 5)) # 1. 获取所有班级 classes stu_score[class].unique() n len(classes) # 2. 创建 3×3 九宫格子图 fig, axes plt.subplots(3, 3, figsize(15, 12), sharexTrue, shareyTrue) axes axes.flatten() # 展平成一维数组方便循环 # 3. 遍历每个班级画图 for idx, cls in enumerate(classes): ax axes[idx] data_cls stu_score[stu_score[class] cls] # 画单个班级直方图 sns.histplot( datadata_cls, xscore, bins10, alpha0.8, axax ) # 分数区间 bin_edges np.histogram_bin_edges(data_cls[score], bins10) bin_centers (bin_edges[:-1] bin_edges[1:]) / 2 bin_counts np.histogram(data_cls[score], bins10)[0] # 标注区间柱子顶部 for i in range(len(bin_edges)-1): left int(bin_edges[i]) right int(bin_edges[i1]) h bin_counts[i] if h 0: ax.text(bin_centers[i], h 0.2, f{left}~{right}, hacenter, fontsize7) # 标注每个柱子人数 for bar in ax.patches: h bar.get_height() if h 0: ax.text(bar.get_x()bar.get_width()/2, h/2, f{int(h)}, hacenter, vacenter, fontsize8, colorwhite) ax.set_title(cls, fontsize10) ax.set_xlabel(成绩) ax.set_ylabel(人数) # 4. 隐藏多余的空格子 for idx in range(len(classes), 9): axes[idx].axis(off) plt.suptitle(各班成绩直方图九宫格展示, fontsize16) plt.tight_layout() plt.savefig(images/14.png, dpi300) # --图15-- 九宫格折线图各银行存款折线图 fig, axes plt.subplots(3, 3, figsize(15, 12), sharexFalse, shareyTrue) axes axes.flatten() # 展平成一维数组方便循环 branchs df_deposit[branch_name].unique() # 3. 遍历每个分行画图 for idx, bran in enumerate(branchs): ax13 axes[idx] data_cls df_deposit[df_deposit[branch_name] bran] # 画单个分行折线图 sns.lineplot(datadata_cls, xdt, ydeposit_amount, axax13, markero) values data_cls[deposit_amount].values # 标注每个折点金额 last_va_pos for i in range(len(data_cls)): x_val data_cls[dt].iloc[i] y_val values[i] # 判断位置逻辑 if i 0: if(values[i] values[i1]): va_pos bottom # 第一个点标上面 last_va_pos bottom offset 300 else: va_pos top # 第一个点标下面 offset -300 elif i len(values)-1: if(values[i] values[i-1]): va_pos bottom # 第一个点标上面 offset 300 else: va_pos top # 第一个点标下面 offset -300 else: prev values[i-1] next_v values[i1] if y_val prev: # 上升 → 标上面 va_pos bottom if last_va_posbottom: offset 600 # 避免连续上升的时候重叠 else: offset 300 last_va_pos bottom else: # 下降/平稳 → 标下面 va_pos top if last_va_postop: offset -600 # 避免连续下降的时候重叠 else: offset -300 last_va_pos top ax13.text(x_val, y_valoffset,f{y_val:.0f}, hacenter, vava_pos, fontsize8, colorblack ) ax13.set_title(bran, fontsize10) ax13.set_xlabel(年月) ax13.set_ylabel(金额) # 让横坐标文字显示出来旋转45度不重叠 ax13.tick_params(axisx, rotation45, labelsize7) ax13.set_xticks(data_cls[dt]) # 强制显示所有月份 # 4. 隐藏多余的空格子 for idx in range(len(branchs), 9): axes[idx].axis(off) plt.suptitle(各分行存款折线图九宫格展示, fontsize16) plt.tight_layout() plt.savefig(images/15.png, dpi300) plt.close() print(✅ 15张图表已保存到 images 文件夹)