使用python拟合二元曲线系数

```python
import numpy as np
import pandas as pd注：
xlsx 表格中 有 压力P，流量值Q，温度值 K；
df = pd.read_excel("./i100-10000slm.xlsx",usecols=['p1','molboxQm','Dek'])
#print(df.head())#column_data = df['p1']
# 获取行数和列数
#rows, cols = df.shape
#print(rows,cols)
#column_data = pd.DataFrame(df)
#print(column_data)# 提取第一行数据
#row1 = column_data[0, :]
#print("第一行数据：", row1)#print(df.iloc[2])
#for nt(rows.Column1, cols.Column2)
#column_data = pd.read_excel('./i100-10000slm.xlsx', usecols='A', index_col=2)#for
#df = pd.re ad_excel("./i100-10000slm.xlsx", "测量数据", header=3, usecols="A", skiprows=[0,], nrows=62)
# 指定初始值
a = 0
b = 0
c = 0
d = 0
e = 0
f = 0
g = 0
h = 0
k = 0
# y = 0  # 在2)处说y初始值为0， 但在1)计算y有初始值，根据需要选择此处要不要指定初始值
#K = 0 # 总的迭代次数， 由于k为从0开始的迭代，所以无需指定k的初始值   den = 0
anum = 0
bnum = 0
cnum = 0# 逐行迭代数据
for index, row in df.iterrows():# 获取当前行的数据P = row['p1']Q = row['molboxQm']T = row['Dek']# 处理数据print(P, Q ,T)# 指定迭代次数#T = 325.#Q = 450.#P = 15.# 计算x, y, z初始值x = np.sqrt(T) * Qy = 100.24 + 0.2881*(T - 275.15) * np.sqrt(Q)z = P# 指定T, Q, P值
# for k in range(0, K):a = a + xb = b + yc = c + x**2d = d + x*ye = e + y**2f = f + zg = g + z*xh = h + z*yy = y + z**2k = k + 1print(k)
den += k*(c*e - d**2) + a*(d*b - a*e) + b*(a*d - c*b)
anum += f*(c*e - d**2) + a*(d*h - g*e) + b*(g*d - c*h)
bnum += k*(g*e - d*h) + f*(d*b - a*e) + b*(a*h - g*b)
cnum += k*(c*h - g*d) + a*(g*b - a*h) + f*(a*d -c*b)# 计算A, B, C
A = anum / den
B = bnum / den
C = cnum / den# 求平方和
sse = A**2 * k + 2*A*C*b - 2*A*f + B**2*C + C**2 * e + y + 2*B*C*d - 2*B*g - 2*C*g# 求均值误差和
ssm = y - f**2/k# 求R的相关系数
R = 1.0 - sse/ssmprint("A 数据：", A)
print("B 数据：", B)
print("C 数据：", C)
#print(A,B,C)