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楼主，求某个图表的演示代码{:1_101:}，之前有这想法，不知道咋开始
感谢分享
黄牛大人 发表于 2026-6-24 15:04
楼主，求某个图表的演示代码，之前有这想法，不知道咋开始
1. 安装Python以及IDE，如Anaconda、PyCharm或Jupyter Noteboook，网上教程很多，选择一个
2. 安装必要的库，网络教程或者AI教程都有
3. 打开你的IDE，开始写代码
第一步：导包（导入常用的库）
# import libraries
import pandas as pd
pd.set_option('display.unicode.ambiguous_as_wide', True)
pd.set_option('display.unicode.east_asian_width', True)
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
import statsmodels.api as sm
from statsmodels.formula.api import ols
import pylab
import seaborn as sns
import openpyxl
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False第二步：准备数据
我是将基本的数据放到了EXCEL文件中
PartAppraiserTrialMeasurement
1A10.29
1A20.41
1A30.64
2A1-0.56
2A2-0.68
2A3-0.58
3A11.34
3A21.17
3A31.27
4A10.47
4A20.50
4A30.64
5A1-0.80
5A2-0.92
5A3-0.84
6A10.02
6A2-0.11
6A3-0.21
7A10.59
7A20.75
7A30.68
8A1-0.31
8A2-0.20
8A3-0.17
9A12.26
9A21.99
9A32.01
10A1-1.36
10A2-1.25
10A3-1.31
1B10.08
1B20.25
1B30.07
2B1-0.47
2B2-1.22
2B3-0.68
3B11.19
3B20.94
3B31.34
4B10.01
4B21.03
4B30.20
5B1-0.56
5B2-1.20
5B3-1.28
6B1-0.20
6B20.22
6B30.06
7B10.47
7B20.55
7B30.83
8B1-0.63
8B2-0.08
8B3-0.34
9B11.80
9B22.12
9B32.19
10B1-1.68
10B2-1.82
10B3-1.50
1C10.04
1C2-0.11
1C3-0.15
2C1-1.38
2C2-1.13
2C3-0.96
3C10.88
3C21.09
3C30.67
4C10.14
4C20.20
4C30.11
5C1-1.46
5C2-1.07
5C3-1.45
6C1-0.29
6C2-0.67
6C3-0.49
7C10.02
7C20.01
7C30.21
8C1-0.46
8C2-0.66
8C3-0.49
9C11.77
9C21.45
9C31.87
10C1-1.49
10C2-1.77
10C3-2.16
第三步：写代码
# 均值极差法
# 重复性和再现性，不能分析交互作用
# 3人10件各3次测量
# 样件能代表过程实际变差或预期范围
study_name = 'Repeatability and Reproducibility Study(Xbar-R)' # 研究名称
df_GRR = pd.read_excel("MSA1_GRR.xlsx", sheet_name = "GRR2") # 读取存储的EXCEL数据
# print(df_GRR)
Xbarbar = df_GRR["Measurement"].mean() # 计算测量值的平均值
Xmax = df_GRR["Measurement"].max() # 计算测量值的最大值
Xmin = df_GRR["Measurement"].min() # 计算测量值的最小值
Xbar_ij = df_GRR.groupby(["Appraiser","Part"]).agg(Xbar_ij=("Measurement","mean")).reset_index() # 按照检验员、零件计算测量值的平均值
# print(Xbar_ij)
R_ij = df_GRR.groupby(["Appraiser","Part"]).agg(R_ij=("Measurement", lambda x: x.max()-x.min())).reset_index() # 按照检验员、零件计算测量值的极差
Rmax = R_ij["R_ij"].max() # 计算极差的最大值
# print(R_ij)
fig, ax = plt.subplots(nrows=2, ncols=2, figsize= , layout = "tight") # 创建画布，为绘图做准备
ax = ax.ravel()
Xbar_Aj = Xbar_ij.query("Appraiser=='A'")["Xbar_ij"] # 从Xbar_ij中提取检验员A的平均值
Xbar_Bj = Xbar_ij.query("Appraiser=='B'")["Xbar_ij"] # 从Xbar_ij中提取检验员B的平均值
Xbar_Cj = Xbar_ij.query("Appraiser=='C'")["Xbar_ij"] # 从Xbar_ij中提取检验员C的平均值
std_X = 0.05 # 计算标准差，为了控制图上控制线计算做准备，我这里直接假设标准差为0.05
UCL_Xbar = Xbarbar + 3 * std_X # 上控制线，这里直接采用的3倍标准差，实际按照控制图去计算
LCL_Xbar = Xbarbar - 3 * std_X # 下控制线，这里直接采用的3倍标准差，实际按照控制图去计算
R_Aj = R_ij.query("Appraiser=='A'")["R_ij"] # 从R_ij中提取检验员A的极差
R_Bj = R_ij.query("Appraiser=='B'")["R_ij"] # 从R_ij中提取检验员B的极差
R_Cj = R_ij.query("Appraiser=='C'")["R_ij"] # 从R_ij中提取检验员C的极差
R_Abar = R_ij.query("Appraiser=='A'")["R_ij"].mean() # 计算检验员A的极差平均值
R_Bbar = R_ij.query("Appraiser=='B'")["R_ij"].mean() # 计算检验员B的极差平均值
R_Cbar = R_ij.query("Appraiser=='C'")["R_ij"].mean() # 计算检验员C的极差平均值
Rbarbar = (R_Abar + R_Bbar + R_Cbar) / 3 # 计算整体极差的平均值
D4 = 2.58 #
UCL_R = Rbarbar * D4 # 计算极差控制图的控制上线
LCL_R = 0
# 下面是绘图代码
# 均值图（重叠）
ax.plot(range(1,11), Xbar_Aj, marker = "o", linestyle = "--", label ="Appraiser A") # 绘制检验员A的均值图
ax.plot(range(1,11), Xbar_Bj, marker = "s", linestyle = "-.", label ="Appraiser B") # 绘制检验员B的均值图
ax.plot(range(1,11), Xbar_Cj, marker = "d", linestyle = ":", label ="Appraiser C") # 绘制检验员C的均值图
ax.axhline(Xbarbar, color="g", linestyle = "-") # 添加平均值的参考线
ax.axhline(UCL_Xbar, color="r", linestyle = "-.") # 添上控制限的参考线
ax.axhline(LCL_Xbar, color="r", linestyle = "-.") # 添加下控制限的参考线
ax.text(10.5, UCL_Xbar, "UCL", va="bottom") # 添加控制限文字
ax.text(10.5, LCL_Xbar, "LCL", va="top") # 添加控制限文字
ax.legend(loc="best") # 显示图例
ax.set_xticks(range(1,11)) # 设置X轴刻度
ax.set_xlabel("Part") # 设置X轴标签
ax.set_ylabel("Average") # 设置Y轴标签
ax.set_title("Average Chart") # 设置图表名称
# 均值图（展开）
ax.plot(range(1,11), Xbar_Aj, marker = "o", linestyle = "--", label ="Appraiser A")
ax.plot(range(11,21), Xbar_Bj, marker = "s", linestyle = "--", label ="Appraiser B")
ax.plot(range(21,31), Xbar_Cj, marker = "d", linestyle = "--", label ="Appraiser C")
ax.text(1, Xmax,"Appraiser A", va="bottom")
ax.text(11, Xmax,"Appraiser B", va="bottom")
ax.text(21, Xmax,"Appraiser C", va="bottom")
ax.axhline(Xbarbar, color="g", linestyle = "-")
ax.axhline(UCL_Xbar, color="r", linestyle = "-.")
ax.axhline(LCL_Xbar, color="r", linestyle = "-.")
ax.axvline(10.5, color = "b", linestyle = "--")
ax.axvline(20.5, color = "b", linestyle = "--")
ax.text(31.5, UCL_Xbar, "UCL", va="bottom")
ax.text(31.5, LCL_Xbar, "LCL", va="top")
ax.set_xticks(ticks=np.arange(1,31),labels=)
ax.set_xlabel("Appraiser - Part")
ax.set_ylabel("Average")
ax.set_title("Average Chart")
# 极差图（重叠）
ax.plot(range(1,11), R_Aj, marker = "o", linestyle = "--", label ="Appraiser A")
ax.plot(range(1,11), R_Bj, marker = "s", linestyle = "-.", label ="Appraiser B")
ax.plot(range(1,11), R_Cj, marker = "d", linestyle = ":", label ="Appraiser C")
ax.axhline(Rbarbar, color="g", linestyle = "-")
ax.axhline(UCL_R, color="r", linestyle = "-.")
ax.axhline(LCL_R, color="r", linestyle = "-.")
ax.text(10.5, UCL_R, "UCL", va="bottom")
ax.text(10.5, LCL_R, "LCL", va="top")
ax.legend(loc="best")
ax.set_xticks(range(1,11))
ax.set_xlabel("Part")
ax.set_ylabel("Range")
ax.set_title("Range Chart")
# 极差图（展开）
ax.plot(range(1,11), R_Aj, marker = "o", linestyle = "--", label ="Appraiser A")
ax.plot(range(11,21), R_Bj, marker = "s", linestyle = "--", label ="Appraiser B")
ax.plot(range(21,31), R_Cj, marker = "d", linestyle = "--", label ="Appraiser C")
ax.text(1, Rmax,"Appraiser A", va="bottom")
ax.text(11, Rmax,"Appraiser B", va="bottom")
ax.text(21, Rmax,"Appraiser C", va="bottom")
ax.axhline(Rbarbar, color="g", linestyle = "-")
ax.axhline(UCL_R, color="r", linestyle = "-.")
ax.axhline(LCL_R, color="r", linestyle = "-.")
ax.axvline(10.5, color = "b", linestyle = "--")
ax.axvline(20.5, color = "b", linestyle = "--")
ax.text(31.5, UCL_R, "UCL", va="bottom")
ax.text(31.5, LCL_R, "LCL", va="top")
ax.set_xticks(ticks=np.arange(1,31),labels=)
ax.set_xlabel("Appraiser - Part")
ax.set_ylabel("Range")
ax.set_title("Range Chart")
plt.show()我这里写的代码是最简单的入门代码，没有使用循环去绘图，使用循环绘图代码会简短一些，等后面再去优化更新。
其实现在Python的教程资料很多，AI也能帮忙写代码。
希望上面的代码可以帮到你，有问题再联系。
看起来很复杂的样子
人才{:1_89:}
martinxiao 发表于 2026-6-24 21:43
1. 安装Python以及IDE，如Anaconda、PyCharm或Jupyter Noteboook，网上教程很多，选择一个
2. 安装必要的 ...
感谢大佬{:1_101:}