تقطيع المصفوفات وفهرستها

import numpy as np

a = np.array([10, 20, 30, 40, 50])
print(a[0])    # 10
print(a[-1])   # 50
print(a[2])    # 30

import numpy as np

a = np.arange(10)  # [0 1 2 3 4 5 6 7 8 9]
print(a[2:7])      # [2 3 4 5 6]
print(a[::2])      # [0 2 4 6 8]
print(a[::-1])     # [9 8 7 6 5 4 3 2 1 0]

import numpy as np

m = np.array([[1, 2, 3],
              [4, 5, 6],
              [7, 8, 9]])

print(m[1, 2])     # 6  (row 1, col 2)
print(m[0, :])     # [1 2 3]  first row
print(m[:, 1])     # [2 5 8]  second column

import numpy as np

m = np.arange(16).reshape(4, 4)
print(m)
# [[ 0  1  2  3]
#  [ 4  5  6  7]
#  [ 8  9 10 11]
#  [12 13 14 15]]

print(m[1:3, 1:3])
# [[ 5  6]
#  [ 9 10]]

import numpy as np

a = np.array([3, 7, 2, 9, 1, 6])
mask = a > 4
print(mask)     # [False  True False  True False  True]
print(a[mask])  # [7 9 6]

# Equivalent one-liner
print(a[a > 4])  # [7 9 6]

import numpy as np

a = np.array([10, 20, 30, 40, 50])
idx = [4, 1, 3]
print(a[idx])    # [50 20 40]

# Selecting specific rows from a 2D array
m = np.arange(20).reshape(4, 5)
print(m[[0, 2], :])  # rows 0 and 2

import numpy as np

a = np.array([5, -3, 8, -1, 2])
result = np.where(a > 0, a, 0)  # keep positives, zero negatives
print(result)  # [5 0 8 0 2]

# np.where with one argument returns indices of True elements
idx = np.where(a > 0)
print(idx)     # (array([0, 2, 4]),)

import numpy as np

original = np.arange(6)
view = original[2:5]   # view, not copy
view[:] = 99
print(original)  # [ 0  1 99 99 99  5]  -- original changed!

safe = original[2:5].copy()  # true copy
safe[:] = 0
print(original)  # [ 0  1 99 99 99  5]  -- unchanged

import numpy as np

t = np.arange(24).reshape(2, 3, 4)
print(t.shape)       # (2, 3, 4)
print(t[0, :, :])    # first 'plane' -- shape (3, 4)
print(t[1, 2, 3])    # scalar at depth 1, row 2, col 3

import numpy as np

a = np.zeros(8)
a[2:5] = 1.0          # slice assignment
print(a)  # [0. 0. 1. 1. 1. 0. 0. 0.]

a[a == 0] = -1.0      # boolean assignment
print(a)  # [-1. -1.  1.  1.  1. -1. -1. -1.]

import numpy as np

t = np.ones((2, 3, 4))
print(t[..., 0].shape)   # (2, 3) -- last axis fixed at 0

a = np.array([1, 2, 3])
print(a.shape)                 # (3,)
print(a[:, np.newaxis].shape)  # (3, 1) column vector