並べ替えと検索

import numpy as np

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

print(np.sort(a))       # [1 1 2 3 4 5 6 9]
print(np.argsort(a))    # [1 3 0 6 2 4 7 5]  (indices)

# 2D sorting
b = np.array([[3, 1, 4],
              [1, 5, 9]])
print(np.sort(b, axis=0))  # sort each column
print(np.sort(b, axis=1))  # sort each row

> Output: Run NumPy 2.x in your local Python environment to see the ndarray output. The Piston server does not have NumPy pre-installed — install it locally (`pip install numpy`) and compare. Actual values may vary by NumPy version and random seed.

import numpy as np

a = np.array([1, 3, 5, 7, 9])

# Find insertion positions (array must be sorted)
print(np.searchsorted(a, 4))  # 2 (insert between 3 and 5)
print(np.searchsorted(a, [2, 6, 8]))  # [1 3 4]

# Find indices where condition is True
print(np.where(a > 4))  # (array([2, 3, 4]),)

> Output: Run NumPy 2.x in your local Python environment to see the ndarray output. The Piston server does not have NumPy pre-installed — install it locally (`pip install numpy`) and compare. Actual values may vary by NumPy version and random seed.

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

print(np.unique(a))  # [1 2 3 4 5 6 9]

# With counts
vals, counts = np.unique(a, return_counts=True)
print(vals)     # [1 2 3 4 5 6 9]
print(counts)   # [2 1 2 1 2 1 1]

> Output: Run NumPy 2.x in your local Python environment to see the ndarray output. The Piston server does not have NumPy pre-installed — install it locally (`pip install numpy`) and compare. Actual values may vary by NumPy version and random seed.

import numpy as np

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

# Sort returns a sorted copy
sorted_a = np.sort(a)
print("Original:", a)
print("Sorted:", sorted_a)

# In-place sort
a.sort()
print("In-place:", a)

# 2D sort along axis
b = np.array([[3, 1, 4], [1, 5, 9]])
print("Sort columns:\n", np.sort(b, axis=0))
print("Sort rows:\n", np.sort(b, axis=1))

import numpy as np

scores = np.array([85, 92, 78, 95, 88])
names = np.array(['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'])

# Get indices that would sort scores
idx = np.argsort(scores)
print("Indices:", idx)
print("Sorted scores:", scores[idx])

# Sort names by their scores (descending)
desc_idx = np.argsort(-scores)
print("Ranking:")
for rank, i in enumerate(desc_idx, 1):
    print(f"  {rank}. {names[i]}: {scores[i]}")

import numpy as np

grades = np.array(['A', 'B', 'A', 'C', 'B', 'A', 'B', 'B', 'C', 'A'])

unique, counts = np.unique(grades, return_counts=True)
print("Grades:", unique)
print("Counts:", counts)

# Find where to insert values into a sorted array
sorted_arr = np.array([1, 3, 5, 7, 9])
pos = np.searchsorted(sorted_arr, [2, 4, 6, 8])
print("Insertion positions:", pos)