Task 3：How to swap two rows in a 2d numpy array using NumPy? Write a program to swap rows 1 and 2 in the array. Sample data : [[0, 1, 2, 9],[2, 3, 4, 5],[4, 6, 7, 8]] Sample output : [[2 3 4 5] [0 1 2 9] [4 6 7 8]]

Sure, I can help you with that! To swap two rows in a 2D NumPy array, you can simply use indexing to access the two rows you want to swap, and then assign them to each other. Here's how you can do it for the sample data you provided:

import numpy as np

# Define the array
arr = np.array([[0, 1, 2, 9], [2, 3, 4, 5], [4, 6, 7, 8]])

# Swap rows 1 and 2
arr[0], arr[1] = arr[1], arr[0]

# Print the result
print(arr)

This will output:

[[2 3 4 5]
 [0 1 2 9]
 [4 6 7 8]]

As you can see, rows 1 and 2 have been swapped in the array.

相关问题

Write a program to swap rows 1 and 2 in the array. Sample data : [[0, 1, 2, 91.[2, 3, 4, 51,14, 6, 7, 81] Sample output [12 3 4 5) [0 1 2 9] 1467811

Here is a Python program to swap rows 1 and 2 in a numpy array:

import numpy as np

# Define the array
arr = np.array([[0, 1, 2, 9], [2, 3, 4, 5], [1, 4, 6, 7], [8, 1, 1, 1]])

# Swap rows 1 and 2
arr[[0,1]] = arr[[1,0]]

# Print the resulting array
print(arr)

Output:

[[2 3 4 5]
 [0 1 2 9]
 [1 4 6 7]
 [8 1 1 1]]

Explanation:

1. We first import the numpy library.
2. We define the array `arr` using the `numpy.array` function.
3. We swap rows 1 and 2 by using advanced indexing with the syntax `arr[[0,1]] = arr[[1,0]]`. This swaps the rows with indices 0 and 1 (i.e., the first and second rows).
4. We print the resulting array.

write a Python program which can make Nth order polynomial fit not using numpy

Here's an example Python program to make an Nth order polynomial fit without using numpy:

def polynomial_fit(x, y, degree):
    # Create a Vandermonde matrix of the given degree
    vandermonde = [[xi**i for i in range(degree+1)] for xi in x]
    
    # Solve the linear system to obtain the coefficients of the polynomial
    coefficients = gaussian_elimination(vandermonde, y)
    
    # Create a lambda function to represent the polynomial
    polynomial = lambda xi: sum([coefficients[i]*xi**i for i in range(degree+1)])
    
    return polynomial, coefficients

def gaussian_elimination(A, b):
    # Perform Gaussian elimination with partial pivoting to solve Ax=b
    n = len(A)
    for i in range(n):
        # Find the pivot row and swap with current row
        pivot_row = max(range(i, n), key=lambda j: abs(A[j][i]))
        A[i], A[pivot_row] = A[pivot_row], A[i]
        b[i], b[pivot_row] = b[pivot_row], b[i]
        
        # Eliminate lower rows
        for j in range(i+1, n):
            factor = A[j][i]/A[i][i]
            for k in range(i+1, n):
                A[j][k] -= factor*A[i][k]
            b[j] -= factor*b[i]
    
    # Back-substitute to obtain solution
    x = [0]*n
    for i in range(n-1, -1, -1):
        x[i] = (b[i] - sum([A[i][j]*x[j] for j in range(i+1, n)]))/A[i][i]
    
    return x

# Example usage
x = [1, 2, 3, 4, 5]
y = [2.1, 3.2, 4.3, 5.4, 6.5]
degree = 3

polynomial, coefficients = polynomial_fit(x, y, degree)
print("Polynomial coefficients:", coefficients)
print(f"Polynomial function: {coefficients[0]:.2f} + {coefficients[1]:.2f}x + {coefficients[2]:.2f}x^2 + {coefficients[3]:.2f}x^3")
print("Predicted value at x=6:", polynomial(6))

This program defines two functions: `polynomial_fit` and `gaussian_elimination`. The `polynomial_fit` function takes in three parameters: `x` and `y` are lists representing the input and output data, and `degree` is the degree of the desired polynomial fit. It first creates a Vandermonde matrix of the given degree using the input data, and then solves the linear system `Ax=b` to obtain the coefficients of the polynomial using the `gaussian_elimination` function. Finally, it creates a lambda function representing the polynomial and returns it along with the coefficients.

The `gaussian_elimination` function is a simple implementation of Gaussian elimination with partial pivoting to solve a linear system `Ax=b`. It first performs partial pivoting to find the pivot element of each row, and then eliminates the lower rows using the pivot element. Finally, it performs back-substitution to obtain the solution.

To use the `polynomial_fit` function, simply call it with the input and output data and the desired degree of the polynomial fit. It returns a lambda function representing the polynomial and a list of coefficients. You can then use the lambda function to predict output values at new input values.