Closed. This question needs debugging details. It is not currently accepting answers.
Edit the question to include desired behavior, a specific problem or error, and the shortest code necessary to reproduce the problem. This will help others answer the question.
Closed last year.
Improve this question
I’ve loaded a sample using the code:
Data = np.load(sample_1.npy)
I need to convert the data points from H/T (coin tosses) to 0/1 (binary array). Not sure what code would work for this? I’ve tried googling answers and they haven’t worked.
If it's an array with "H" and "T" as values, you could try that:
Data = np.array([0 if x == "H" else 1 for x in Data])
Related
Closed. This question needs debugging details. It is not currently accepting answers.
Edit the question to include desired behavior, a specific problem or error, and the shortest code necessary to reproduce the problem. This will help others answer the question.
Closed last month.
Improve this question
I'm trying to replace certain columns of my dataframe that contains NaN with 0's. For example, I have a column named 'CQ Exp Net Rep Liquid' that has some values in the column that are NaN.
I've tried running
df[['CQ Exp Net Rep Liquid']].fillna(0,inplace=True)
and then when I proceed to run
df[['CQ Exp Net Rep Liquid']].sample(n=10)
I am returning the column with NaNs still. I have tried df.bfill as well to no avail. Does anyone know what is going on?
Closed. This question needs debugging details. It is not currently accepting answers.
Edit the question to include desired behavior, a specific problem or error, and the shortest code necessary to reproduce the problem. This will help others answer the question.
Closed 1 year ago.
Improve this question
I have two array one with shape (320,1) and one with (850,1) how should I combine the two array. I tried np.append however it seems to be an error.
Assuming the two arrays are called arr1 and arr2, you could try the following code:
arr3 = np.append(arr1,arr2).reshape((-1,1))
The reshape is needed to make sure the final shape is (1170,1) instead of (1170,)
Closed. This question needs debugging details. It is not currently accepting answers.
Edit the question to include desired behavior, a specific problem or error, and the shortest code necessary to reproduce the problem. This will help others answer the question.
Closed 1 year ago.
Improve this question
I'm trying to get the element-wise mulitplication using normal *, and i tried np.multiply(), both give a weird answers.
now (1-y) is (100,) and np.log(1-sigmoid(np.dot(X,theta)))) is (100,1), so when i muliply them by element-wise, it should give (100,1); but it gives me (100,100) matrix(ALL are higlighted BLUE)
Here is my original function if it can help.
Can anyone help me with getting the source of erre here?
I'm not 100% sure why python does this, but the way to solve it would be to apply np.reshape((1-y),(100,1)) and then apply np.multiply(). In general it's always better to reshape your arrays and to give them a second dimension.
EDIT: This explains how numpy does the broadcasting when using an array of dimensions (n,) and (n,1).
Closed. This question needs debugging details. It is not currently accepting answers.
Edit the question to include desired behavior, a specific problem or error, and the shortest code necessary to reproduce the problem. This will help others answer the question.
Closed 2 years ago.
Improve this question
I wanted to calculate this mathematical expression in python3:
Xi = (70 + 1344736401384689745317259585614247891453883777111/315)% 115792089210356248762697446949407573529996955224135760342422259061068512044369
But I'm getting this wrong result: 4.2690044488402846e+45 . Is there something wrong in the expression? how can I fix it to give me the right result?
I think you might be confused because it is showing scientific notation? '{}'.format() should help in that case.
Xi = (70 + 1344736401384689745317259585614247891453883777111/315)% 115792089210356248762697446949407573529996955224135760342422259061068512044369
print('{0:f}'.format(Xi))
Closed. This question needs debugging details. It is not currently accepting answers.
Edit the question to include desired behavior, a specific problem or error, and the shortest code necessary to reproduce the problem. This will help others answer the question.
Closed 2 years ago.
Improve this question
Given this dictionary:
my_dict={'animal':['Dog','Cat'],'age':[4,5],'bark':[True,False],'badge_num':['234','896']}
what code will cast 'age' to float?
you could use map combined with float like the following:
my_dict['age']=list(map(float, my_dict['age']))