numpy

I'm taking a course in data science and they use the sklearn library, where there is a GridSearchCV method, the problem is th ... ain_fin, y_train_fin) sorted(gridsearch.cv_results_,key = lambda x: -x.mean_validation_score) What could be the problem ?

I have the original random array: np.array([0, 1, 2]). You need to get two rows of numbers (lists or one-dimensional arrays ... _2] Is there a faster way to generate such series of numbers m_1 and m_2 in another way, for example, using list generation?

You need to put a circle on the image, outside of which the image will change color to grayscale, and inside the circle the c ... = img1-41 ax_1.axis('off') ax_1.imshow(ax_1.Img) Who knows how this can be implemented? The first picture in the image: .

I can't figure out how to create an array with a certain dimension, that's what I mean, that's how I create it now: A = np.a ... ad the documentation and didn't really understand the append method and the meaning of "axis". Please help me how to do this?

Given a NumPy array: [[1 0 1 0 0 1] [1 1 1 0 0 1] [1 0 1 0 0 1] [1 0 1 0 1 1] [1 0 1 0 0 1]] a = [[1, 0, 1, 0, 0, 1], ... np.array(a) How can I delete duplicate rows? That is, the output should be: [[1 0 1 0 0 1] [1 1 1 0 0 1] [1 0 1 0 1 1]]

import numpy as np A = np.array([[1, 1, 2, -1], [2, -1, 0, -5], [-1, -1, 0, -2], ... r 5.329070518200744e-15. What am I doing wrong? Maybe I was inattentive somewhere, and if not, how is it better to calculate?

I am doing a course work assignment on Numerical Methods and Computer Modeling: find the root of a function and display the f ... ction itself, so that it searches for values only in the array, or the plotting function. Help, please, kursach is close. ;)

Let's imagine a table of two columns in a dataframe: 0 NaN 0 NaN 0 1 1 0 1 5 1 NaN 2 0 0 NaN 0 1 2 1 2 NaN 3 NaN 3 500 3 N ... y the only non - NaN value on the right? Desired result: 0 1 0 1 0 1 1 0 1 5 1 NaN 2 0 0 1 0 1 2 1 2 NaN 3 500 3 500 3 500

I work with a table of data that has both positive and negative values. How can I replace all positive values in a certain column with NaN? I tried it like this: df.loc[df['days_employed'] > 0, 'days_employed'] = "NaN" But I got an error.

How can I use sklearn or numpy to find the fraction of wrongly predicted values? There are two arrays of numbers of the same ... d to compare them, and divide the number of incorrect answers by the length. Is it possible to do this somehow in 1 function?

There is a DataFrame with columns 'floor' and 'floors_total'. I need to add a separate column with their grouping: 'First', ' ... re not correct in most cases (they do not fit the 'floor' == 'floors_total' condition). Can you tell me what I'm doing wrong?

My program should solve SLA with square matrices without FSR by the Gaussian algorithm. SLOUGH is represented as Ax = f. im ... 'VECTOR f:\n',f,'\n\n') AA = A ff = f xx = np.linalg.solve(AA, ff) print('My solution:\n',x) print('Numpy solution\n',xx)

An array is given: import numpy as np a = np.array([1, 0, 1, 1, 0]) [1 0 1 1 0] Please tell me how you can check whether all the elements in the array are the same or not?

import pandas as pd import numpy as np from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import tr ... an error. ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

I want to use the scipy.linalg.solve_banded method to solve SLOUGH, but I can't figure out how the ribbon matrix is represent ... од x[n-1] = f[n-1]/A[n-1][n-1]; for i in range(n - 2, -1, -1): x[i]=(f[i] - A[i][i + 1]*x[i+1]) / A[i][i] #Вывод print(x)

There is a code: import numpy while True: print(123) If you make an exe from it, the file will instantly close when op ... sted in why it causes the exe to close instantly. Maybe someone knows?) If you import, let's say only argmin, nothing changes

You need to sort only the first row of the two-dimensional array in ascending order so that the second row is associated with ... It turns out: [[10 20 30] [ 0 1 2]] It is necessary that the second row is not sorted, but is linked to the first row.

I need to go through all the pixels of the image. What libraries can I use to do this?

There is a lower triangular matrix of the form: 0.0 0.5 0.0 0.4 0.6 0.0 Written as a list of lists [[0.0], [0.5, 0.0], [0. ... 0.0 0.6 0.4 0.6 0.0 I guess that there is some method in numpy, but I'm not very familiar with this package, I can't find

There is a numpy array X, with dimension (num_test, D), where D is a matrix of some size. There is a numpy array X_train, wit ... trying to figure out how to do it. Therefore, it will be even better if you explain in terms of check out what you're doing.