How to read specific column with specific row in x_test using python I have a dataset with four input. here I am trying to predict X1 input in next time period using lstm model. 

Here I put x_train 80% and x_test 20% .
In x_test first input some of rows are with 0 values.

What I want is I want to read that 0 value separately. I tried and it didn't work for me. Can anyone help me to solve this problem.
Here my code:

data=data.values
scaler_x = preprocessing.MinMaxScaler(feature_range =(0, 1))
x = np.array(x).reshape ((len(x),4 ))
x = scaler_x.fit_transform(x)
scaler_y = preprocessing.MinMaxScaler(feature_range =(0, 1))
y = np.array(y).reshape ((len(y), 1))
y = scaler_y.fit_transform(y)
train_end = 80
x_train=x[0: train_end ,]
x_test=x[train_end : ,]
y_train=y[0: train_end]
y_test=y[train_end :] 
x_train=x_train.reshape(x_train.shape +(1,))
x_test=x_test.reshape(x_test.shape + (1,))


After writing code for lstm I tried to predict value for x_test. Before that I want to specify that rows with 0 values.

x_test_n = np.flip(x_test_n, axis=0)
curr_frame = x_test_n[0]


Then it just read first row with four input together.
What I want is to read first input with 0 values.

Image for what I got:



What I want to read



After  changing the code:

pred=[]
for index, row in x_test_n.iterrows():
  if index_row, index_col = np.where(x_test_n == 0)
    x_test_n.iloc[index] = pred[-1]
    row['X_test_n'] = pred[-1]
    pred1 = model.predict(x_test_n)
    pred.append(pred1)


It gave me an error:


  if index_row, index_col = np.where(x_test_n == 0)
For a 2D np array x_test_n, you could use index_row, index_col = np.where(x_test_n == 0) to return the indices with 0 values.