Python中NumPy二维数组元素重复次数、位置统计及行列对比需求

Got it, let's work through this problem step by step to get the exact output you need, plus a detailed distribution breakdown. Here's how to approach it:

Step 1: Setup the Original Array #

First, let's define the NumPy array we're working with:

import numpy as np

# Original 2D array
arr = np.array([
    [8, 12, 5, 2],
    [12, 15, 6, 10],
    [15, 8, 12, 5],
    [12, 15, 8, 6]
])

Step 2: Track Element Counts and Positions #

We'll use a dictionary to map each unique element to its total occurrence count and 1-based positions (matching the format in your example). Here's the code to implement this:

# Dictionary to store element details: key = element value, value = (count, list_of_1based_positions)
element_tracker = {}

# Get unique elements and their total occurrence counts
unique_elements, total_counts = np.unique(arr, return_counts=True)

# For each element, find all its positions (converted to 1-based indices)
for elem, count in zip(unique_elements, total_counts):
    # Get 0-based indices where the element appears
    row_indices, col_indices = np.where(arr == elem)
    # Convert to 1-based positions (since your example uses 1-indexed rows/columns)
    positions = [(row + 1, col + 1) for row, col in zip(row_indices, col_indices)]
    element_tracker[elem] = (count, positions)

Step 3: Output in the Requested Format #

To print the results in the syntax you provided:

# Print each element's info
for elem, (count, positions) in element_tracker.items():
    position_str = ", ".join([str(pos) for pos in positions])
    print(f"({elem}, {count}, {position_str})")

Running this code will produce:

(2, 1, (1, 4))
(5, 2, (1, 3), (3, 4))
(6, 2, (2, 3), (4, 4))
(8, 3, (1, 1), (3, 2), (4, 3))
(10, 1, (2, 4))
(12, 4, (1, 2), (2, 1), (3, 3), (4, 1))
(15, 3, (2, 2), (3, 1), (4, 2))

Step 4: Row and Column Distribution Analysis #

Let's dive into the distribution using element 15 as a focused example (as you requested):

• Total occurrences: 3
• Exact positions: (2,2), (3,1), (4,2)
• Row distribution: Appears in rows 2, 3, and 4 (one occurrence per row), with no presence in row 1. This means it's concentrated in the lower three-quarters of the array.
• Column distribution: Shows up twice in column 2 (rows 2 and 4) and once in column 1 (row 3). It never appears in columns 3 or 4, so it's clustered on the left side of the array.

For a quick comparison, take element 12 (the most frequent element with 4 occurrences):

• Row distribution: Present in all 4 rows (one per row)
• Column distribution: Shows up twice in column 1, once in column 2, and once in column 3 — spread across the left three columns.

内容的提问来源于stack exchange，提问作者S. Waleed