# Drop NaN in a for loop for each column [closed]

I will try to explain my problem. So I have two DataFrames , Df1 and Df2. Each of them has 3 columns and 4 rows. I will solve a quadratic functions with np.polyfit.

M=3

for t in range(M-1,0,-1):

regs = np.polyfit(Df1[:,t],Df2[:,t+1],2)

C = np.polyval(regs,Df1[:,t])


But I want to use only the values which are smaller than 1.1

 Df1[Df1 < 1.1]


Now I have something like that as Df1

[1. , 1.09, 1.08, NaN]
[1. , 1., 1.07, 1.04]
[1. , NaN, 1.01, NaN]
[1. , 0.78, NaN,0.95]


And my Df2 looks like

[0.1 , 0., 0.08, 0.]
[0.1 , 0.11, 0., 0.09]
[0.1 , 0.33, 0.22, 0.]
[0.1 , 0.09, 0.108, 0.]


So what I want to do is for each column from Df1, if Df1 has a NaN Then I don't want to calculate it.

Here is what I tried to explain (in this case for Df1[2] and Df2[3]):

X =[1.08,1.07,1.01]
Y =[0.,0.09,0]


## closed as off-topic by skoestlmeier, Bob Jansen♦Jan 22 at 22:02

• This question does not appear to be about quantitative finance within the scope defined in the help center.
If this question can be reworded to fit the rules in the help center, please edit the question.

• I'm voting to close this question as off-topic because it is not related with Quantitative Finance. – skoestlmeier Jan 22 at 22:02