Storing data in different variable during a FOR loop-CodePudding

I have this code:

import math
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

sens_fac = [0.8, 1, 1.2]
A = 13;
B = 5;
C = 7/2;
D = 3*1.2;
par = [A,B,C,D]
data = pd.DataFrame(index=range(len(sens_fac)),columns=range(len(par)))
term1 = pd.DataFrame(index=range(len(sens_fac)),columns=range(len(par)))
term2 = pd.DataFrame(index=range(len(sens_fac)),columns=range(len(par)))
term3 = pd.DataFrame(index=range(len(sens_fac)),columns=range(len(par)))
term4 = pd.DataFrame(index=range(len(sens_fac)),columns=range(len(par)))
sol = []

for i in range(len(par)):
    factors = [1, 1, 1, 1]
    for j in range(len(sens_fac)):
        factors[i] = sens_fac[j]
        print(factors)
        x=25
        t1 = np.log(x)**math.sin(x/(A*factors[0]))
        t2 = (B*factors[1])*math.sqrt(x)
        t3 = (factors[2]*C)**math.exp(1/x)
        t4 = x/(factors[3]*D)*2
        res = t1   t2   t3   t4
        term1[i][j] = t1
        term2[i][j] = t2
        term3[i][j] = t3
        term4[i][j] = t4
        data[i][j] = res

print(term1)
print(term2)
print(term3)
print(term4)

The dataframes term1, term2, ... , termX only are different in a specific step of the FOR loop. For instance, term1 have different values only when i=0, but are the same for i=1, i=2 or i=3. On the other hand, term2 have different values when i=1 but keep the same values for the rest of the iterations. Actually, you can see this in the DataFrame:

term1 =

          0         1         2         3
0  2.195296  2.995886  2.995886  2.995886
1  2.995886  2.995886  2.995886  2.995886
2  3.216978  2.995886  2.995886  2.995886

term2 =

      0     1     2     3
0  25.0  20.0  25.0  25.0
1  25.0  25.0  25.0  25.0
2  25.0  30.0  25.0  25.0

I would like to obtain something like this:

term1 =

          0         
0  2.195296 
1  2.995886  
2  3.216978

or term2 =

How can I do it?

CodePudding user response：

You can drop the columns with only one unique element (keep columns with more than one unique element):

term1.loc[:, term1.nunique().gt(1)].squeeze()

output:

0    2.195296
1    2.995886
2    3.216978
Name: 0, dtype: float64

NB. you can use squeeze to convert the unique matching column to Series and thus be able to use them in operations without worrying about index alignment