Python Logistic regression with 2 features data X and label Y - Training accuracy

# import sklearn and necessary libraries
from sklearn.linear_model import LogisticRegression

# Apply sklearn logistic regression on the given data X and labels Y
X_skl = np.vstack((df1,df2))   # 10000 x 2 array
Y_skl = Y   # 10000 x 1 array
LogR = LogisticRegression()
LogR.fit(X_skl,Y_skl)
Y_skl_hat = LogR.predict(X_skl)

# Calculate the accuracy
# Check the number of points where Y_skl is not equal to Y_skl_hat
error_count_skl = 0   # Count the number of error points
for i in range(N):
    if Y_skl[i] == Y_skl_hat[i]:
        error_count_skl = error_count_skl
    else:
        error_count_skl = error_count_skl + 1

# Calculate the accuracy 
Accuracy = 100*(N - error_count_skl)/N
print("Accuracy(%):")
print(Accuracy)

Output:
Accuracy(%):
99.48

Hello,

I'm trying to apply logistic regression model on array X (with size of 10000 x 2) and label Y (10000 x 1)
using sklearn library in Python. I'm completely lost cause I've never used this library before. Can anyone help me with the coding?

Edited:
Sorry for the vague question, the goal is to find the training accuracy using the entire dataset of X. Above is what I came up with, can anyone take a look and see if it makes sense?

To calculate accuracy you can simply use this sklearn method.

sklearn.metrics.accuracy_score(y_true, y_pred)

In your case

sklearn.metrics.accuracy_score(Y_skl, Y_skl_hat)

If you want to take a look at
sklearn documentation for accuracy_score

And also you should train your model on some data and test it on others to check if the model can be generalized and to avoid overfitting.
To split your data in train and test datasets you could use:

sklearn.model_selection.train_test_split

If you want to take a look at
sklearn documentation for train_test_split