‘BACKTICK_QUOTED_STRING__AT_key’ is not defined for Pandas Quering function

Trying to get a hold of pd.query function Getting a UndefinedVariableError: name ‘BACKTICK_QUOTED_STRING__AT_key’ is not defined for the below pandas python code. Where am i going wrong? Answer Try this : or

How to extract elements from a filename and move them to different columns?

I have a filenames which I converted into a list. The list has the following elements: My goal is to extract elements from this list and fill out a dataframe, which should look like this: LINK TO THE GOOGLE SHEETS CONTAINING THE IMAGE ABOVE: https://docs.google.com/spreadsheets/d/1kuX3M4RFCNWtNoE7Hm1ejxWMwF-Cs4p8SsjA3JzdidA/edit?usp=sharing WHAT I’VE DONE SO FAR is the following code: But, this one does not leave empty spaces thus not doing what I needed. Thank you very much in advanced. Answer As per number of the comments. It’s a pain because the tokens in the filename are not fully fixed format. Quite a lot of conditional

How to replace values in a np 2d array based on condition for every row

I have a numpy 2d array (named lda_fit) with probabilities, where I want to replace the probabilities with 0 or 1, based on the max value in each line. So after all the first line should look like [0,1,0,0], the second like [1,0,0,0] and so on. I have tried, and this works, but only for a given threshold (0.5): But as I might not have the largest value being greater than 0.5, I want to specify a new threshold for each line. Unfortunately this gives me the max value of the whole array. Answer You can use np.max with specifying

AttributeError: ‘numpy.ndarray’ object has no attribute ‘self’

I started implementing the backend of neural network but got stuck in a code of python. The below is the code for neural Network. While i was making use of the userdefined class in one of the application to be made, i got an error by name attributeError. Please help me out solving it. I tried all the indentation syntax but nothing works. The Below is the error log which was popped as soon as i run used the defined class in a classification problem. Answer According to the Neural Network Back-end theory, the error must be multiplied to the

Python uniform random number generation to a triangle shape

I have three data points which I performed a linear fit and obtained the 1 sigma uncertainty lines. Now I would like to generate 100k data point uniformly distributed between the 1 sigma error bars (the big triangle on the left side) but I do not have any idea how am I able to do that. Here is my code In NumPy there is a Numpy random triangle function, however, I was not able to implement that in my case and I am not even sure if that is the right approach. I appreciate any help. Answer You can use

I cannot understand the module of the python

Thankyou for helping me in advance. What I am curious is this. I made the File A, and in there exist module named B, and there is function C. Then to use the C, I should type from A import B B.C() Something like this. but when I use the module numpy, there is file named numpy, and there are many files and module in the numpy. And if I just type Import numpy then I can use numpy.array and everything even though I didnt import any module. I also want If I just import the file A, then I

Combination of rows in numpy.ndarray

I have the following numpy.ndarray I want to find all the possible combinations of sum of each row (sum of individual elements of a row except the last column) of S[0,:,:] with each row of S[1,:,:], i.e., my desired result is (order does not matter): which is a 9-by-2 array resulting from 9 possible combinations of S[0,:,:] and S[1,:,:]. Although I have used a particular shape of S here, the shape may vary, i.e., for in the above problem, x=2, y=3, and z=3, but these values may vary. Therefore, I am seeking for a generalized version. Your help will be

Element-wise random choice of a Series of lists (without a loop)

I want to randomly select an element from each list in a Series of lists. So s is: I know I can do the following: Which does work: But I am wondering if there is a non-loop approach to do this? For instance, (assuming each list is equal size) you could make an array of random indices to try and pick a different element from each list: But doing say s[i] doesn’t work because that is indexing s rather than applying to each list: My motivation is to have something that would work on a large amount of lists, hence

What’s the difference b/w np.random.randint() and np.random.uniform()?

What’s the difference b/w np.random.randint() and np.random.uniform()? I have gone through the numpy documentation but have not gotten a satisfactory understanding of the difference b/w them, except that the default precision of np.random.uniform() is much greater than the integer values generated by np.random.randint() Answer np.random.randint() always returns integer value even when we decide to pick random numbers from a narrow spectrum – At other end, np.random.uniform() always return a unique number (floating numbers) for the same range.

How to create a mask of substrings using np.where or list comprehensions?

I have two python lists of strings that I would like to compare. The first is my main list, containing a series of long codes. The second is a list of partial strings. The desired result is a mask of list 1, populated by the substrings from list two. If no match is found, list3 can return 0, np.nan, ‘-‘, or similar. In other words, I’m looking for the following: With help from folks in another thread, I was able to get close. However, these results return the values in list1, but I would like my result to return the