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In [1]:
import pandas as pd import numpy as np
In [2]:
with pd.ExcelFile("/home/bobmaster/Downloads/数学建模/附件1：化学成分及力学性能.xlsx") as origin_data: pd_chemicals_raw = pd.read_excel(origin_data, "化学成分", usecols=[0,2,3,4,5,6,7]) pd_physics_raw = pd.read_excel(origin_data, "力学性能")
In [3]:
pd_chemical = pd_chemicals_raw.iloc[1:,:] pd_physics = pd_physics_raw.dropna(how = "any") # pd_chemical = pd_chemical.reindex(index = pd_chemical.index[::-1]) pd_physics_ronglianhao = pd_physics.iloc[:,0].astype("int64") pd_physics_qufu = pd_physics.iloc[:,2] pd_physics_kangla = pd_physics.iloc[:,3] pd_physics_yanshen = pd_physics.iloc[:,4]
In [4]:
# 提取相同熔炼号的数据 comp_table = pd_physics.iloc[:,0].duplicated(keep = "last") #比较表 # phy_num = pd_physics.count() # 力学表数据量 11213 phy_num = 11213 #phy_ronglianhao = [] phy_dict = {} phy_qufu = [] phy_kangla = [] phy_yanshen = [] temp = 0 for i in range(phy_num): phy_qufu.append(pd_physics_qufu[i]) phy_kangla.append(pd_physics_kangla[i]) phy_yanshen.append(pd_physics_yanshen[i]) if (comp_table[i] == False): #phy_ronglianhao[temp] = pd_physics_ronglianhao[i] phy_dict[pd_physics_ronglianhao[i]] = [phy_qufu, phy_kangla, phy_yanshen] temp += 1 phy_qufu = [] phy_kangla = [] phy_yanshen = []
In [5]:
# 数据规约 - 力学性能数据均值和标准差 phy_dict_qufu_mean = {} phy_dict_qufu_std = {} phy_dict_kangla_mean = {} phy_dict_kangla_std = {} phy_dict_yanshen_mean = {} phy_dict_yanshen_std = {} phy_dict_qufu_mean_list = [] phy_dict_qufu_std_list = [] phy_dict_kangla_mean_list = [] phy_dict_kangla_std_list = [] phy_dict_yanshen_mean_list = [] phy_dict_yanshen_std_list = [] for key in phy_dict: np_physics_array_qufu = np.array(phy_dict[key][0]) np_physics_array_kangla = np.array(phy_dict[key][1]) np_physics_array_yanshen = np.array(phy_dict[key][2]) phy_dict_qufu_mean[key] = np_physics_array_qufu.mean() phy_dict_qufu_std[key] = np_physics_array_qufu.std() phy_dict_kangla_mean[key] = np_physics_array_kangla.mean() phy_dict_kangla_std[key] = np_physics_array_kangla.std() phy_dict_yanshen_mean[key] = np_physics_array_yanshen.mean() phy_dict_yanshen_std[key] = np_physics_array_yanshen.std()
In [6]:
# 清洗化学成分 # 重建索引保证在同一熔炼号的情况下与力学指标数据匹配 pd_chem_ronglianhao = pd_chemical.iloc[:,0].astype("int64") pd_chem_ronglianhao = pd_chem_ronglianhao.drop_duplicates().reset_index().iloc[:,1] pd_chem_E1_data = pd_chemical.iloc[:,1].reset_index().iloc[:,1] pd_chem_E2_data = pd_chemical.iloc[:,2].reset_index().iloc[:,1] pd_chem_E3_data = pd_chemical.iloc[:,3].reset_index().iloc[:,1] pd_chem_E4_data = pd_chemical.iloc[:,4].reset_index().iloc[:,1] pd_chem_E5_data = pd_chemical.iloc[:,5].reset_index().iloc[:,1] pd_chem_E6_data = pd_chemical.iloc[:,6].reset_index().iloc[:,1] pd_chem_E1 = {} pd_chem_E2 = {} pd_chem_E3 = {} pd_chem_E4 = {} pd_chem_E5 = {} pd_chem_E6 = {} temp = 0 # 数据规约 - 化学成分 # 0-701 清洗后得到的范围 for i in range(702): if (i%2 != 0 and temp != 321): pd_chem_E1[pd_chem_ronglianhao[temp]] = (pd_chem_E1_data[i-1] + pd_chem_E1_data[i])/2 pd_chem_E2[pd_chem_ronglianhao[temp]] = (pd_chem_E2_data[i-1] + pd_chem_E2_data[i])/2 pd_chem_E3[pd_chem_ronglianhao[temp]] = (pd_chem_E3_data[i-1] + pd_chem_E3_data[i])/2 pd_chem_E4[pd_chem_ronglianhao[temp]] = (pd_chem_E4_data[i-1] + pd_chem_E4_data[i])/2 pd_chem_E5[pd_chem_ronglianhao[temp]] = (pd_chem_E5_data[i-1] + pd_chem_E5_data[i])/2 pd_chem_E6[pd_chem_ronglianhao[temp]] = (pd_chem_E6_data[i-1] + pd_chem_E6_data[i])/2 temp += 1 E1_list = [] E2_list = [] E3_list = [] E4_list = [] E5_list = [] E6_list = [] # 整理出最终所需数据并保证化学成分与力学性能数据一致性 for key in pd_chem_E1: if key in phy_dict: E1_list.append(pd_chem_E1[key]) E2_list.append(pd_chem_E2[key]) E3_list.append(pd_chem_E3[key]) E4_list.append(pd_chem_E4[key]) E5_list.append(pd_chem_E5[key]) E6_list.append(pd_chem_E6[key]) phy_dict_qufu_mean_list.append(phy_dict_qufu_mean[key]) phy_dict_qufu_std_list.append(phy_dict_qufu_std[key]) phy_dict_kangla_mean_list.append(phy_dict_kangla_mean[key]) phy_dict_kangla_std_list.append(phy_dict_kangla_std[key]) phy_dict_yanshen_mean_list.append(phy_dict_yanshen_mean[key]) phy_dict_yanshen_std_list.append(phy_dict_yanshen_std[key]) np_E1 = np.array(E1_list) np_E2 = np.array(E2_list) np_E3 = np.array(E3_list) np_E4 = np.array(E4_list) np_E5 = np.array(E5_list) np_E6 = np.array(E6_list)
In [ ]:
In [7]:
import matplotlib.pyplot as plt import numpy as np from matplotlib import colors from matplotlib.ticker import PercentFormatter import matplotlib.font_manager as fm myfont = fm.FontProperties(fname='/usr/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/wqy-zenhei.ttc') fig, ax = plt.subplots(figsize=(5, 5), tight_layout=True) #dist1 材料 dist1_E1 = np_E1 dist1_E2 = np_E2 dist1_E3 = np_E3 dist1_E4 = np_E4 dist1_E5 = np_E5 dist1_E6 = np_E6 #dist2 力学性能均值 dist2_qufu = np.array(phy_dict_qufu_mean_list) dist2_kangla = np.array(phy_dict_kangla_mean_list) dist2_yanshen = np.array(phy_dict_yanshen_mean_list) #dist3 力学性能标准差 dist3_qufu = np.array(phy_dict_qufu_std_list) dist3_kangla = np.array(phy_dict_kangla_std_list) dist3_yanshen = np.array(phy_dict_yanshen_std_list) ax.set_title("化学成分E1与屈服特性的关系", fontproperties=myfont) ax.set_xlabel("E1 %", fontproperties=myfont) ax.set_ylabel("屈服特性均值", fontproperties=myfont) hist_qufu_E1 = ax.hist2d(dist1_E1, dist2_qufu, bins=20)
In [8]:
fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True, tight_layout=True) ax.set_ylabel("屈服特性均值", fontproperties=myfont) ax.set_title("化学成分E2与屈服特性的关系", fontproperties=myfont) ax.set_xlabel("E2 %", fontproperties=myfont) hist_qufu_E2 = ax.hist2d(dist1_E2, dist2_qufu, bins=20)
In [9]:
fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True, tight_layout=True) ax.set_ylabel("屈服特性均值", fontproperties=myfont) ax.set_title("化学成分E3与屈服特性的关系", fontproperties=myfont) ax.set_xlabel("E3 %", fontproperties=myfont) hist_qufu_E3 = ax.hist2d(dist1_E3, dist2_qufu, bins=20)
In [10]:
fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True, tight_layout=True) ax.set_ylabel("屈服特性均值", fontproperties=myfont) ax.set_title("化学成分E4与屈服特性的关系", fontproperties=myfont) ax.set_xlabel("E4 %", fontproperties=myfont) hist_qufu_E4 = ax.hist2d(dist1_E4, dist2_qufu, bins=20)
In [11]:
fig, ax = plt.subplots(figsize=(10, 10), sharex=True, sharey=True, tight_layout=True) ax.set_ylabel("屈服特性均值", fontproperties=myfont) ax.set_title("化学成分E5与屈服特性的关系", fontproperties=myfont) ax.set_xlabel("E5 %", fontproperties=myfont) hist_qufu_E5 = ax.hist2d(dist1_E5, dist2_qufu, bins=20)
In [12]:
fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True, tight_layout=True) ax.set_ylabel("屈服特性均值", fontproperties=myfont) ax.set_title("化学成分E6与屈服特性的关系", fontproperties=myfont) ax.set_xlabel("E6 %", fontproperties=myfont) hist_qufu_E6 = ax.hist2d(dist1_E6, dist2_qufu, bins=20)
In [13]:
# 抗拉 fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True, tight_layout=True) ax.set_ylabel("抗拉特性均值", fontproperties=myfont) ax.set_title("化学成分E1与抗拉特性的关系", fontproperties=myfont) ax.set_xlabel("E1 %", fontproperties=myfont) hist_kangla_E1 = ax.hist2d(dist1_E1, dist2_kangla, bins=20)
In [14]:
fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True, tight_layout=True) ax.set_ylabel("抗拉特性均值", fontproperties=myfont) ax.set_title("化学成分E2与抗拉特性的关系", fontproperties=myfont) ax.set_xlabel("E2 %", fontproperties=myfont) hist_kangla_E2 = ax.hist2d(dist1_E2, dist2_kangla, bins=20)
In [15]:
fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True, tight_layout=True) ax.set_ylabel("抗拉特性均值", fontproperties=myfont) ax.set_title("化学成分E3与抗拉特性的关系", fontproperties=myfont) ax.set_xlabel("E3 %", fontproperties=myfont) hist_kangla_E3 = ax.hist2d(dist1_E3, dist2_kangla, bins=20)
In [16]:
fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True, tight_layout=True) ax.set_ylabel("抗拉特性均值", fontproperties=myfont) ax.set_title("化学成分E4与抗拉特性的关系", fontproperties=myfont) ax.set_xlabel("E4 %", fontproperties=myfont) hist_kangla_E4 = ax.hist2d(dist1_E4, dist2_kangla, bins=20)
In [17]:
fig, ax = plt.subplots(figsize=(10, 10), sharex=True, sharey=True, tight_layout=True) ax.set_ylabel("抗拉特性均值", fontproperties=myfont) ax.set_title("化学成分E5与抗拉特性的关系", fontproperties=myfont) ax.set_xlabel("E5 %", fontproperties=myfont) hist_kangla_E5 = ax.hist2d(dist1_E5, dist2_kangla, bins=20)
In [18]:
fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True, tight_layout=True) ax.set_ylabel("抗拉特性均值", fontproperties=myfont) ax.set_title("化学成分E6与抗拉特性的关系", fontproperties=myfont) ax.set_xlabel("E6 %", fontproperties=myfont) hist_kangla_E6 = ax.hist2d(dist1_E6, dist2_kangla, bins=20)
In [19]:
#延伸率 fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True, tight_layout=True) ax.set_ylabel("延伸率特性均值", fontproperties=myfont) ax.set_title("化学成分E1与延伸率特性的关系", fontproperties=myfont) ax.set_xlabel("E1 %", fontproperties=myfont) hist_yanshen_E1 = ax.hist2d(dist1_E1, dist2_yanshen, bins=20)
In [20]:
fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True, tight_layout=True) ax.set_ylabel("延伸率特性均值", fontproperties=myfont) ax.set_title("化学成分E2与延伸率特性的关系", fontproperties=myfont) ax.set_xlabel("E2 %", fontproperties=myfont) hist_yanshen_E2 = ax.hist2d(dist1_E2, dist2_yanshen, bins=20)
In [21]:
fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True, tight_layout=True) ax.set_ylabel("延伸率特性均值", fontproperties=myfont) ax.set_title("化学成分E3与延伸率特性的关系", fontproperties=myfont) ax.set_xlabel("E3 %", fontproperties=myfont) hist_yanshen_E3 = ax.hist2d(dist1_E3, dist2_yanshen, bins=20)
In [22]:
fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True, tight_layout=True) ax.set_ylabel("延伸率特性均值", fontproperties=myfont) ax.set_title("化学成分E4与延伸率特性的关系", fontproperties=myfont) ax.set_xlabel("E4 %", fontproperties=myfont) hist_yanshen_E4 = ax.hist2d(dist1_E4, dist2_yanshen, bins=20)
In [23]:
fig, ax = plt.subplots(figsize=(10, 10), sharex=True, sharey=True, tight_layout=True) ax.set_ylabel("延伸率特性均值", fontproperties=myfont) ax.set_title("化学成分E5与延伸率特性的关系", fontproperties=myfont) ax.set_xlabel("E5 %", fontproperties=myfont) hist_yanshen_E5 = ax.hist2d(dist1_E5, dist2_yanshen, bins=20)
In [24]:
fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True, tight_layout=True) ax.set_ylabel("延伸率特性均值", fontproperties=myfont) ax.set_title("化学成分E6与延伸率特性的关系", fontproperties=myfont) ax.set_xlabel("E6 %", fontproperties=myfont) hist_yanshen_E6 = ax.hist2d(dist1_E6, dist2_yanshen, bins=20)
In [25]:
import seaborn as sns dataset = pd.DataFrame(data={'E1': dist1_E1, 'E2': dist1_E2, 'E3': dist1_E3, 'E4': dist1_E4, 'E5': dist1_E5, 'E6': dist1_E6, 'qufu': dist2_qufu}) sns.set(rc = {'figure.figsize':(10,10)}) sns.lmplot(x="E1", y="qufu", data=dataset, x_estimator=np.mean)
Out[25]:
<seaborn.axisgrid.FacetGrid at 0x7f0fc4ea1a80>
In [26]:
sns.lmplot(x="E2", y="qufu", data=dataset, x_estimator=np.mean)
Out[26]:
<seaborn.axisgrid.FacetGrid at 0x7f0fc4bab8e0>
In [27]:
sns.lmplot(x="E3", y="qufu", data=dataset, x_estimator=np.mean)
Out[27]:
<seaborn.axisgrid.FacetGrid at 0x7f0fc762ca30>
In [28]:
sns.lmplot(x="E4", y="qufu", data=dataset, x_estimator=np.mean)
Out[28]:
<seaborn.axisgrid.FacetGrid at 0x7f0fc4a6ab30>
In [29]:
sns.lmplot(x="E5", y="qufu", data=dataset, x_estimator=np.mean)
Out[29]:
<seaborn.axisgrid.FacetGrid at 0x7f0fc4881240>
In [30]:
sns.lmplot(x="E6", y="qufu", data=dataset, x_estimator=np.mean)
Out[30]:
<seaborn.axisgrid.FacetGrid at 0x7f0fc477eaa0>
In [31]:
sns.jointplot(x="E1", y="qufu", data=dataset, kind="reg")
Out[31]:
<seaborn.axisgrid.JointGrid at 0x7f0fc482a170>
In [ ]:
In [8]:
import statsmodels.api as sm x = sm.add_constant(np.array([dist1_E1, dist1_E2, dist1_E3, dist1_E4, dist1_E5, dist1_E6]).transpose()) # 材料与屈服特性均值回归方程 y = np.array(dist2_qufu) model = sm.OLS(y, x) results = model.fit() print(results.summary())
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.049
Model: OLS Adj. R-squared: 0.027
Method: Least Squares F-statistic: 2.247
Date: Sun, 03 Jul 2022 Prob (F-statistic): 0.0393
Time: 14:20:56 Log-Likelihood: -742.34
No. Observations: 270 AIC: 1499.
Df Residuals: 263 BIC: 1524.
Df Model: 6
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 203.3528 34.505 5.893 0.000 135.412 271.293
x1 -39.8312 36.665 -1.086 0.278 -112.025 32.363
x2 44.6137 25.255 1.767 0.078 -5.114 94.342
x3 1.2704 36.149 0.035 0.972 -69.907 72.448
x4 244.7439 83.223 2.941 0.004 80.876 408.612
x5 57.4122 74.462 0.771 0.441 -89.206 204.030
x6 27.2312 134.699 0.202 0.840 -237.995 292.457
==============================================================================
Omnibus: 94.154 Durbin-Watson: 1.109
Prob(Omnibus): 0.000 Jarque-Bera (JB): 435.794
Skew: -1.358 Prob(JB): 2.34e-95
Kurtosis: 8.600 Cond. No. 916.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
$ y=203.3528-39.8312x_1+44.6137x_2+1.2704x_3+244.7439x_4+57.4122x_5+27.2312x_6 $
In [33]:
# 材料与抗拉特性均值回归方程 y = np.array(dist2_kangla) model = sm.OLS(y, x) results = model.fit() print(results.summary())
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.052
Model: OLS Adj. R-squared: 0.031
Method: Least Squares F-statistic: 2.425
Date: Sat, 02 Jul 2022 Prob (F-statistic): 0.0268
Time: 22:12:18 Log-Likelihood: -699.25
No. Observations: 270 AIC: 1412.
Df Residuals: 263 BIC: 1438.
Df Model: 6
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 234.8010 29.415 7.982 0.000 176.883 292.719
x1 -25.4511 31.256 -0.814 0.416 -86.996 36.093
x2 33.0086 21.530 1.533 0.126 -9.384 75.401
x3 -3.9719 30.816 -0.129 0.898 -64.650 56.706
x4 216.7327 70.946 3.055 0.002 77.038 356.428
x5 63.1518 63.478 0.995 0.321 -61.838 188.142
x6 23.3812 114.829 0.204 0.839 -202.720 249.482
==============================================================================
Omnibus: 91.168 Durbin-Watson: 1.176
Prob(Omnibus): 0.000 Jarque-Bera (JB): 461.240
Skew: -1.275 Prob(JB): 6.97e-101
Kurtosis: 8.873 Cond. No. 916.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
$ y=234.8010-25.4511x_1+33.0086x_2-3.9719x_3+216.7327x_4+63.1518x_5+23.3812x_6 $
In [34]:
# 材料与延伸率特性均值回归方程 y = np.array(dist2_yanshen) model = sm.OLS(y, x) results = model.fit() print(results.summary())
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.039
Model: OLS Adj. R-squared: 0.017
Method: Least Squares F-statistic: 1.793
Date: Sat, 02 Jul 2022 Prob (F-statistic): 0.101
Time: 22:12:18 Log-Likelihood: -124.81
No. Observations: 270 AIC: 263.6
Df Residuals: 263 BIC: 288.8
Df Model: 6
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 9.9892 3.504 2.851 0.005 3.090 16.889
x1 -7.6164 3.724 -2.045 0.042 -14.948 -0.285
x2 3.8024 2.565 1.483 0.139 -1.248 8.853
x3 5.2722 3.671 1.436 0.152 -1.956 12.501
x4 -3.0590 8.452 -0.362 0.718 -19.701 13.583
x5 1.5053 7.562 0.199 0.842 -13.385 16.395
x6 15.4639 13.679 1.130 0.259 -11.471 42.399
==============================================================================
Omnibus: 4.194 Durbin-Watson: 0.778
Prob(Omnibus): 0.123 Jarque-Bera (JB): 4.281
Skew: -0.291 Prob(JB): 0.118
Kurtosis: 2.798 Cond. No. 916.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
$ y=9.9892-7.6164_1+3.8024_2+5.2722_3-3.0590_4+1.5053_5+15.4639x_6 $
In [35]:
# 材料与屈服特性标准差回归方程 y = np.array(dist3_qufu) model = sm.OLS(y, x) results = model.fit() print(results.summary())
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.022
Model: OLS Adj. R-squared: 0.000
Method: Least Squares F-statistic: 1.005
Date: Sat, 02 Jul 2022 Prob (F-statistic): 0.423
Time: 22:12:18 Log-Likelihood: -398.92
No. Observations: 270 AIC: 811.8
Df Residuals: 263 BIC: 837.0
Df Model: 6
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 2.8216 9.671 0.292 0.771 -16.222 21.865
x1 -3.6748 10.277 -0.358 0.721 -23.910 16.561
x2 9.2533 7.079 1.307 0.192 -4.685 23.192
x3 -6.5848 10.132 -0.650 0.516 -26.535 13.366
x4 -21.5972 23.327 -0.926 0.355 -67.528 24.334
x5 -11.2795 20.871 -0.540 0.589 -52.375 29.816
x6 2.4244 37.755 0.064 0.949 -71.916 76.765
==============================================================================
Omnibus: 238.636 Durbin-Watson: 1.608
Prob(Omnibus): 0.000 Jarque-Bera (JB): 6096.254
Skew: 3.424 Prob(JB): 0.00
Kurtosis: 25.249 Cond. No. 916.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
$ y=2.8216-3.6748x_1+9.2533x_2-6.5848x_3-21.5972x_4-11.2795x_5+2.4244x_6 $
In [36]:
# 材料与抗拉特性标准差回归方程 y = np.array(dist3_kangla) model = sm.OLS(y, x) results = model.fit() print(results.summary())
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.019
Model: OLS Adj. R-squared: -0.003
Method: Least Squares F-statistic: 0.8562
Date: Sat, 02 Jul 2022 Prob (F-statistic): 0.528
Time: 22:12:18 Log-Likelihood: -343.91
No. Observations: 270 AIC: 701.8
Df Residuals: 263 BIC: 727.0
Df Model: 6
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const -0.7273 7.889 -0.092 0.927 -16.260 14.806
x1 -0.3171 8.382 -0.038 0.970 -16.822 16.188
x2 8.1385 5.774 1.410 0.160 -3.230 19.508
x3 -4.1692 8.264 -0.504 0.614 -20.442 12.104
x4 -18.5395 19.027 -0.974 0.331 -56.004 18.925
x5 -5.8194 17.024 -0.342 0.733 -39.340 27.701
x6 16.7100 30.795 0.543 0.588 -43.927 77.347
==============================================================================
Omnibus: 201.569 Durbin-Watson: 1.642
Prob(Omnibus): 0.000 Jarque-Bera (JB): 3303.665
Skew: 2.815 Prob(JB): 0.00
Kurtosis: 19.185 Cond. No. 916.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
$ y=-0.7273-0.3171x_1+8.1385x_2-4.1692x_3-18.5395x_4-5.8194x_5+16.7100x_6 $
In [37]:
# 材料与延伸特性标准差回归方程 y = np.array(dist3_yanshen) model = sm.OLS(y, x) results = model.fit() print(results.summary())
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.018
Model: OLS Adj. R-squared: -0.005
Method: Least Squares F-statistic: 0.7814
Date: Sat, 02 Jul 2022 Prob (F-statistic): 0.585
Time: 22:12:18 Log-Likelihood: 151.91
No. Observations: 270 AIC: -289.8
Df Residuals: 263 BIC: -264.6
Df Model: 6
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 1.5023 1.257 1.195 0.233 -0.974 3.978
x1 0.1300 1.336 0.097 0.923 -2.501 2.761
x2 -0.3995 0.920 -0.434 0.665 -2.212 1.413
x3 0.3247 1.317 0.246 0.806 -2.269 2.919
x4 -0.3529 3.033 -0.116 0.907 -6.325 5.619
x5 -5.6869 2.714 -2.096 0.037 -11.030 -0.344
x6 1.8671 4.909 0.380 0.704 -7.798 11.532
==============================================================================
Omnibus: 223.455 Durbin-Watson: 1.792
Prob(Omnibus): 0.000 Jarque-Bera (JB): 6610.393
Skew: 3.018 Prob(JB): 0.00
Kurtosis: 26.477 Cond. No. 916.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
$ y=1.5023+0.1300x_1-0.3995x_2+0.3247x_3-0.3529x_4-5.6869x_5+1.8671x_6 $
In [38]:
def cal_qufu_mean(x1, x2, x3, x4, x5, x6): count = 203.3528 - 39.8312*x1 + 44.6137*x2 + 1.2704*x3 + 244.7439*x4 + 57.4122*x5 + 27.2312*x6 return count def cal_kangla_mean(x1, x2, x3, x4, x5, x6): count = 234.8010 - 25.4511*x1 + 33.0086*x2 - 3.9719*x3 + 216.7327*x4 + 63.1518*x5 + 23.3812*x6 return count def cal_yanshen_mean(x1, x2, x3, x4, x5, x6): count = 9.9892 - 7.6164*x1 + 3.8024*x2 + 5.2722*x3 - 3.0590*x4 + 1.5053*x5 + 15.4639*x6 return count def cal_qufu_std(x1, x2, x3, x4, x5, x6): count = 2.8216 - 3.6748*x1 + 9.2533*x2 - 6.5848*x3 - 21.5972*x4 - 11.2795*x5 + 2.4244*x6 return count def cal_kangla_std(x1, x2, x3, x4, x5, x6): count = -0.7273 + 0.3171*x1 + 8.1385*x2 - 4.1692*x3 - 18.5395*x4 - 5.8194*x5 + 16.7100*x6 return count def cal_yanshen_std(x1, x2, x3, x4, x5, x6): count = 1.5023 + 0.1300*x1 - 0.3995*x2 + 0.3247*x3 - 0.3529*x4 - 5.6869*x5 + 1.8671*x6 return count def calcutate_all(ronglianhao): x1 = pd_chem_E1[ronglianhao] x2 = pd_chem_E2[ronglianhao] x3 = pd_chem_E3[ronglianhao] x4 = pd_chem_E4[ronglianhao] x5 = pd_chem_E5[ronglianhao] x6 = pd_chem_E6[ronglianhao] print("屈服均值: " + str(cal_qufu_mean(x1, x2, x3, x4, x5, x6)) + "\n" "抗拉均值: " + str(cal_kangla_mean(x1, x2, x3, x4, x5, x6)) + "\n" "延伸率均值: " + str(cal_yanshen_mean(x1, x2, x3, x4, x5, x6)) + "\n" "屈服标准差: " + str(cal_qufu_std(x1, x2, x3, x4, x5, x6)) + "\n" "抗拉标准差: " + str(cal_kangla_std(x1, x2, x3, x4, x5, x6)) + "\n" "延伸率标准差: " + str(cal_yanshen_std(x1, x2, x3, x4, x5, x6)) + "\n" )
In [39]:
#熔炼号90624 calcutate_all(90624)
屈服均值: 281.0436817499999 抗拉均值: 302.12715380000003 延伸率均值: 11.72966825 屈服标准差: 4.044883449999997 抗拉标准差: 3.9242447999999994 延伸率标准差: 0.6836438499999999
In [40]:
#熔炼号90626 calcutate_all(90626)
屈服均值: 280.14576220000004 抗拉均值: 301.2504561 延伸率均值: 11.75250665 屈服标准差: 4.09420725 抗拉标准差: 3.896615800000003 延伸率标准差: 0.7077119000000001
In [41]:
#熔炼号90627 calcutate_all(90627)
屈服均值: 281.1477789 抗拉均值: 302.06638649999996 延伸率均值: 11.8371067 屈服标准差: 4.187677149999999 抗拉标准差: 3.9974252999999997 延伸率标准差: 0.6685442000000003
In [42]:
#熔炼号90628 calcutate_all(90628)
屈服均值: 281.59236580000004 抗拉均值: 302.4213721 延伸率均值: 11.84703395 屈服标准差: 4.235370449999999 抗拉标准差: 4.034019550000003 延伸率标准差: 0.66078115
In [43]:
#熔炼号90629 calcutate_all(90629)
屈服均值: 280.6431412 抗拉均值: 301.66065395000004 延伸率均值: 11.799698150000001 屈服标准差: 4.13804585 抗拉标准差: 3.9504264000000022 延伸率标准差: 0.6839600000000002
In [44]:
#熔炼号90630 calcutate_all(90630)
屈服均值: 282.0879321 抗拉均值: 302.9229411999999 延伸率均值: 11.77570985 屈服标准差: 4.159440649999999 抗拉标准差: 4.0227504 延伸率标准差: 0.6695417499999997
In [45]:
#熔炼号90631 calcutate_all(90631)
屈服均值: 281.16844065 抗拉均值: 302.0872306499999 延伸率均值: 11.8406746 屈服标准差: 4.1261977000000005 抗拉标准差: 3.898947100000001 延伸率标准差: 0.6666137999999998
In [46]:
# for i in range(len(dist2_qufu)): # print(dist2_qufu[i]) # for key in pd_chem_E1: # if key in phy_dict: # E1_list.append(pd_chem_E1[key]) # E2_list.append(pd_chem_E2[key]) # E3_list.append(pd_chem_E3[key]) # E4_list.append(pd_chem_E4[key]) # E5_list.append(pd_chem_E5[key]) # E6_list.append(pd_chem_E6[key]) # phy_dict_qufu_mean_list.append(phy_dict_qufu_mean[key]) # phy_dict_qufu_std_list.append(phy_dict_qufu_std[key]) # phy_dict_kangla_mean_list.append(phy_dict_kangla_mean[key]) # phy_dict_kangla_std_list.append(phy_dict_kangla_std[key]) # phy_dict_yanshen_mean_list.append(phy_dict_yanshen_mean[key]) # phy_dict_yanshen_std_list.append(phy_dict_yanshen_std[key]) # np_E1 = np.array(E1_list) # np_E2 = np.array(E2_list) # np_E3 = np.array(E3_list) # np_E4 = np.array(E4_list) # np_E5 = np.array(E5_list) # np_E6 = np.array(E6_list) new_ronglian = [] for key in pd_chem_E1: if key in phy_dict: new_ronglian.append(key) # data = pd.DataFrame({'熔炼号': new_ronglian, '屈服均值': phy_dict_qufu_mean_list, '抗拉均值': phy_dict_kangla_mean_list, '延伸率均值': phy_dict_yanshen_mean_list, # '屈服标准差': phy_dict_qufu_std_list, '抗拉标准差': phy_dict_kangla_std_list, '延伸率标准差': phy_dict_yanshen_std_list, 'E1': np_E1, 'E2': np_E2, # 'E3': np_E3, 'E4': np_E4, 'E5': np_E5, 'E6': np_E6}) # data.to_excel("/home/bobmaster/Downloads/数学建模/data.xlsx", sheet_name="Sheet1")
In [47]:
from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split x = np.array([dist1_E1, dist1_E2, dist1_E3, dist1_E4, dist1_E5, dist1_E6]).transpose() y = np.array(dist2_qufu) X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42) model = LinearRegression().fit(X_train, y_train) ronglianhao = 90623 y_pred = model.predict(np.array([pd_chem_E1[ronglianhao], pd_chem_E2[ronglianhao], pd_chem_E3[ronglianhao], pd_chem_E4[ronglianhao], pd_chem_E5[ronglianhao], pd_chem_E6[ronglianhao]]).reshape(-1,6)) y_pred
Out[47]:
array([282.11678074])
In [ ]:
In [48]:
model.coef_
Out[48]:
array([ -21.09158051, 24.71549855, -5.01065116, 226.01198632,
25.40913652, -139.41772546])
In [49]:
model.intercept_
Out[49]:
232.98837814693027
In [50]:
# 材料与屈服特性均值多元线性回归方程 x = np.array([dist1_E1, dist1_E2, dist1_E3, dist1_E4, dist1_E5, dist1_E6]).transpose() y = np.array(dist2_qufu) X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42) model = LinearRegression().fit(X_train, y_train) model.coef_
Out[50]:
array([ -21.09158051, 24.71549855, -5.01065116, 226.01198632,
25.40913652, -139.41772546])
In [51]:
model.intercept_
Out[51]:
232.98837814693027
In [52]:
# 材料与抗拉特性均值多元线性回归方程 x = np.array([dist1_E1, dist1_E2, dist1_E3, dist1_E4, dist1_E5, dist1_E6]).transpose() y = np.array(dist2_kangla) X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42) model = LinearRegression().fit(X_train, y_train) model.coef_
Out[52]:
array([ -8.33255893, 10.87035246, -4.29381787, 197.08886361,
30.81475037, -133.2003945 ])
In [53]:
model.intercept_
Out[53]:
265.8372810566513
In [54]:
# 材料与延伸特性均值多元线性回归方程 x = np.array([dist1_E1, dist1_E2, dist1_E3, dist1_E4, dist1_E5, dist1_E6]).transpose() y = np.array(dist2_yanshen) X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42) model = LinearRegression().fit(X_train, y_train) model.coef_
Out[54]:
array([-5.07378937, 5.7620131 , 1.5875269 , -8.09637312, 9.06779244,
22.52728787])
In [55]:
model.intercept_
Out[55]:
7.172527937298794
In [56]:
x = np.array([dist1_E1, dist1_E2, dist1_E3, dist1_E4, dist1_E5, dist1_E6]).transpose() def cal_qufu_mean(ronglianhao): y = np.array(dist2_qufu) X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42) model = LinearRegression().fit(X_train, y_train) count = model.predict(np.array([pd_chem_E1[ronglianhao], pd_chem_E2[ronglianhao], pd_chem_E3[ronglianhao], pd_chem_E4[ronglianhao], pd_chem_E5[ronglianhao], pd_chem_E6[ronglianhao]]).reshape(-1,6)) return count def cal_kangla_mean(ronglianhao): y = np.array(dist2_kangla) X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42) model = LinearRegression().fit(X_train, y_train) count = model.predict(np.array([pd_chem_E1[ronglianhao], pd_chem_E2[ronglianhao], pd_chem_E3[ronglianhao], pd_chem_E4[ronglianhao], pd_chem_E5[ronglianhao], pd_chem_E6[ronglianhao]]).reshape(-1,6)) return count def cal_yanshen_mean(ronglianhao): y = np.array(dist2_yanshen) X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42) model = LinearRegression().fit(X_train, y_train) count = model.predict(np.array([pd_chem_E1[ronglianhao], pd_chem_E2[ronglianhao], pd_chem_E3[ronglianhao], pd_chem_E4[ronglianhao], pd_chem_E5[ronglianhao], pd_chem_E6[ronglianhao]]).reshape(-1,6)) return count def cal_qufu_std(ronglianhao): y = np.array(dist3_qufu) X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42) model = LinearRegression().fit(X_train, y_train) count = model.predict(np.array([pd_chem_E1[ronglianhao], pd_chem_E2[ronglianhao], pd_chem_E3[ronglianhao], pd_chem_E4[ronglianhao], pd_chem_E5[ronglianhao], pd_chem_E6[ronglianhao]]).reshape(-1,6)) return count def cal_kangla_std(ronglianhao): y = np.array(dist3_kangla) X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42) model = LinearRegression().fit(X_train, y_train) count = model.predict(np.array([pd_chem_E1[ronglianhao], pd_chem_E2[ronglianhao], pd_chem_E3[ronglianhao], pd_chem_E4[ronglianhao], pd_chem_E5[ronglianhao], pd_chem_E6[ronglianhao]]).reshape(-1,6)) return count def cal_yanshen_std(ronglianhao): y = np.array(dist3_yanshen) X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42) model = LinearRegression().fit(X_train, y_train) count = model.predict(np.array([pd_chem_E1[ronglianhao], pd_chem_E2[ronglianhao], pd_chem_E3[ronglianhao], pd_chem_E4[ronglianhao], pd_chem_E5[ronglianhao], pd_chem_E6[ronglianhao]]).reshape(-1,6)) return count def calcutate_all(ronglianhao): print("屈服均值: " + str(cal_qufu_mean(ronglianhao)) + "\n" "抗拉均值: " + str(cal_kangla_mean(ronglianhao)) + "\n" "延伸率均值: " + str(cal_yanshen_mean(ronglianhao)) + "\n" "屈服标准差: " + str(cal_qufu_std(ronglianhao)) + "\n" "抗拉标准差: " + str(cal_kangla_std(ronglianhao)) + "\n" "延伸率标准差: " + str(cal_yanshen_std(ronglianhao)) + "\n" )
In [57]:
# 熔炼号 90624 calcutate_all(90624)
屈服均值: [281.04919773] 抗拉均值: [302.13923671] 延伸率均值: [11.75333675] 屈服标准差: [4.06391763] 抗拉标准差: [3.6079243] 延伸率标准差: [0.68167218]
In [58]:
# 熔炼号 90626 calcutate_all(90626)
屈服均值: [280.5012662] 抗拉均值: [301.6395381] 延伸率均值: [11.70884635] 屈服标准差: [4.0805444] 抗拉标准差: [3.56479364] 延伸率标准差: [0.7105368]
In [59]:
# 熔炼号 90627 calcutate_all(90627)
屈服均值: [281.1057409] 抗拉均值: [302.02799232] 延伸率均值: [11.85466945] 屈服标准差: [4.19329091] 抗拉标准差: [3.67943761] 延伸率标准差: [0.65688161]
In [60]:
# 熔炼号 90628 calcutate_all(90628)
屈服均值: [281.5443029] 抗拉均值: [302.35074987] 延伸率均值: [11.87558182] 屈服标准差: [4.27624867] 抗拉标准差: [3.74579568] 延伸率标准差: [0.64559413]
In [61]:
# 熔炼号 90629 calcutate_all(90629)
屈服均值: [280.77029025] 抗拉均值: [301.80837018] 延伸率均值: [11.7935581] 屈服标准差: [4.13601804] 抗拉标准差: [3.62497317] 延伸率标准差: [0.67978443]
In [62]:
# 熔炼号 90630 calcutate_all(90630)
屈服均值: [282.06368924] 抗拉均值: [302.8621545] 延伸率均值: [11.81286397] 屈服标准差: [4.22813292] 抗拉标准差: [3.73535752] 延伸率标准差: [0.65672621]
In [63]:
# 熔炼号 90631 calcutate_all(90631)
屈服均值: [281.24719713] 抗拉均值: [302.18315812] 延伸率均值: [11.82315402] 屈服标准差: [4.12274657] 抗拉标准差: [3.59635772] 延伸率标准差: [0.66175219]
In [64]:
# 材料与屈服特性标准差多元线性回归方程 x = np.array([dist1_E1, dist1_E2, dist1_E3, dist1_E4, dist1_E5, dist1_E6]).transpose() y = np.array(dist3_qufu) X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42) model = LinearRegression().fit(X_train, y_train) model.coef_
Out[64]:
array([ 4.61087137, 7.89819425, -15.91215619, -26.69428153,
-5.1575353 , -40.74486056])
In [65]:
model.intercept_
Out[65]:
5.062939344879167
In [66]:
# 材料与抗拉特性标准差多元线性回归方程 x = np.array([dist1_E1, dist1_E2, dist1_E3, dist1_E4, dist1_E5, dist1_E6]).transpose() y = np.array(dist3_kangla) X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42) model = LinearRegression().fit(X_train, y_train) model.coef_
Out[66]:
array([ 5.52211393, 7.22300547, -13.92408713, -20.73062805,
-0.08048425, -21.0089931 ])
In [67]:
model.intercept_
Out[67]:
1.5298723495190005
In [68]:
# 材料与延伸率特性标准差多元线性回归方程 x = np.array([dist1_E1, dist1_E2, dist1_E3, dist1_E4, dist1_E5, dist1_E6]).transpose() y = np.array(dist3_yanshen) X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42) model = LinearRegression().fit(X_train, y_train) model.coef_
Out[68]:
array([ 1.31314118, -1.7168275 , 0.19307053, -0.86744266, -4.87384482,
-4.01341483])
In [69]:
model.intercept_
Out[69]:
2.6570980963371644
In [ ]:
In [70]:
dataset = pd.DataFrame({'熔炼号': new_ronglian, '屈服均值': phy_dict_qufu_mean_list, '抗拉均值': phy_dict_kangla_mean_list, '延伸率均值': phy_dict_yanshen_mean_list, '屈服标准差': phy_dict_qufu_std_list, '抗拉标准差': phy_dict_kangla_std_list, '延伸率标准差': phy_dict_yanshen_std_list, 'E1': np_E1, 'E2': np_E2, 'E3': np_E3, 'E4': np_E4, 'E5': np_E5, 'E6': np_E6})
In [ ]:
In [71]:
from matplotlib.colors import LogNorm fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True, tight_layout=True) ax.set_ylabel("E1 %", fontproperties=myfont) ax.set_title("化学成分E1与屈服特性的关系", fontproperties=myfont) ax.set_xlabel("屈服特性均值", fontproperties=myfont) hist_qufu_E1 = ax.hist2d(dist2_qufu, dist1_E1, bins=20, norm=LogNorm(), cmap = "YlGn")
In [72]:
import numpy as np import matplotlib.pyplot as plt plt.step(dataset.iloc[:,7], dataset.iloc[:,1], label='pre (default)') plt.plot(dataset.iloc[:,7], dataset.iloc[:,1], 'o--', color='grey', alpha=0.3) plt.step(dataset.iloc[:,7], dataset.iloc[:,2], where='mid', label='mid') plt.plot(dataset.iloc[:,7], dataset.iloc[:,2], 'o--', color='grey', alpha=0.3) plt.grid(axis='x', color='0.95') plt.legend(title='Parameter where:') plt.title('plt.step(where=...)') plt.show()
In [9]:
from string import ascii_letters import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt plt.rc('axes', unicode_minus=False) sns.set_theme(style="white") sns.set(font="WenQuanYi Zen Hei") dataset = pd.DataFrame({'屈服': phy_dict_qufu_mean_list, '抗拉': phy_dict_kangla_mean_list, '延伸率': phy_dict_yanshen_mean_list, 'E1': np_E1, 'E2': np_E2, 'E3': np_E3, 'E4': np_E4, 'E5': np_E5, 'E6': np_E6}) # Compute the correlation matrix corr = dataset.corr() # Generate a mask for the upper triangle mask = np.triu(np.ones_like(corr, dtype=bool)) # Set up the matplotlib figure f, ax = plt.subplots(figsize=(11, 9)) # Generate a custom diverging colormap cmap = sns.diverging_palette(230, 20, as_cmap=True) # Draw the heatmap with the mask and correct aspect ratio sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0, square=True, linewidths=.5, cbar_kws={"shrink": .5})
Out[9]:
<AxesSubplot:>
In [74]:
sns.set(font="WenQuanYi Zen Hei") dataset = pd.DataFrame({'熔炼号': new_ronglian, '屈服均值': phy_dict_qufu_mean_list, '抗拉均值': phy_dict_kangla_mean_list, '延伸率均值': phy_dict_yanshen_mean_list, '屈服标准差': phy_dict_qufu_std_list, '抗拉标准差': phy_dict_kangla_std_list, '延伸率标准差': phy_dict_yanshen_std_list, 'E1': np_E1, 'E2': np_E2, 'E3': np_E3, 'E4': np_E4, 'E5': np_E5, 'E6': np_E6})
In [75]:
sns.lineplot(x="E1", y="屈服均值", data=dataset)
Out[75]:
<AxesSubplot:xlabel='E1', ylabel='屈服均值'>
In [76]:
sns.lineplot(x="E1", y="抗拉均值", data=dataset)
Out[76]:
<AxesSubplot:xlabel='E1', ylabel='抗拉均值'>
In [77]:
sns.lineplot(x="E1", y="延伸率均值", data=dataset)
Out[77]:
<AxesSubplot:xlabel='E1', ylabel='延伸率均值'>
In [78]:
sns.lineplot(x="E2", y="屈服均值", data=dataset)
Out[78]:
<AxesSubplot:xlabel='E2', ylabel='屈服均值'>
In [79]:
sns.lineplot(x="E2", y="抗拉均值", data=dataset)
Out[79]:
<AxesSubplot:xlabel='E2', ylabel='抗拉均值'>
In [80]:
sns.lineplot(x="E2", y="延伸率均值", data=dataset)
Out[80]:
<AxesSubplot:xlabel='E2', ylabel='延伸率均值'>
In [81]:
sns.lineplot(x="E3", y="屈服均值", data=dataset)
Out[81]:
<AxesSubplot:xlabel='E3', ylabel='屈服均值'>
In [82]:
sns.lineplot(x="E3", y="抗拉均值", data=dataset)
Out[82]:
<AxesSubplot:xlabel='E3', ylabel='抗拉均值'>
In [83]:
sns.lineplot(x="E3", y="延伸率均值", data=dataset)
Out[83]:
<AxesSubplot:xlabel='E3', ylabel='延伸率均值'>
In [84]:
sns.lineplot(x="E4", y="屈服均值", data=dataset)
Out[84]:
<AxesSubplot:xlabel='E4', ylabel='屈服均值'>
In [85]:
sns.lineplot(x="E4", y="抗拉均值", data=dataset)
Out[85]:
<AxesSubplot:xlabel='E4', ylabel='抗拉均值'>
In [86]:
sns.lineplot(x="E4", y="延伸率均值", data=dataset)
Out[86]:
<AxesSubplot:xlabel='E4', ylabel='延伸率均值'>
In [87]:
sns.lineplot(x="E5", y="屈服均值", data=dataset)
Out[87]:
<AxesSubplot:xlabel='E5', ylabel='屈服均值'>
In [88]:
sns.lineplot(x="E5", y="抗拉均值", data=dataset)
Out[88]:
<AxesSubplot:xlabel='E5', ylabel='抗拉均值'>
In [89]:
sns.lineplot(x="E5", y="延伸率均值", data=dataset)
Out[89]:
<AxesSubplot:xlabel='E5', ylabel='延伸率均值'>
In [90]:
sns.lineplot(x="E6", y="屈服均值", data=dataset)
Out[90]:
<AxesSubplot:xlabel='E6', ylabel='屈服均值'>
In [91]:
sns.lineplot(x="E6", y="抗拉均值", data=dataset)
Out[91]:
<AxesSubplot:xlabel='E6', ylabel='抗拉均值'>
In [92]:
sns.lineplot(x="E6", y="延伸率均值", data=dataset)
Out[92]:
<AxesSubplot:xlabel='E6', ylabel='延伸率均值'>
In [97]:
sns.set(rc={"figure.figsize": (5, 5)}, font="WenQuanYi Zen Hei")
In [98]:
sns.lineplot(x="E6", y="延伸率均值", data=dataset)
Out[98]:
<AxesSubplot:xlabel='E6', ylabel='延伸率均值'>
In [ ]: