从数组中删除一行

Question

我正在处理一个名为 numbers 的数组，该数组将创建 4 列，分别称为 (x)、(y)、(z)，第四列用于程序中。

我希望如果两行的 x 和 y 值重合，那么根据它们的 c，其中一个将从主数组中删除（“0”z 值删除“1 ”，“1”z 值删除“2”，“2”z 值删除“0”）。

原始数组如下所示：

[[12 15  2  0]
 [65 23  0  0]
 [24 66  2  0]
 [65 23  1  0]
 [24 66  0  0]]

问题是当我尝试运行以下程序时，最后没有得到所需的数组。预期的输出数组如下所示：

[[12 15  2  0]
 [65 23  0  0]
 [24 66  2  0]]

我已经从下面的程序中摘录了一段

import numpy as np

#Array
numbers = np.array([[12,15,2,0],[65,23,0,0],[24,66,2,0],[65,23,1,0],[24,66,0,0]])

#Original Array
print(numbers)

#Lists to store x, y and z values
xs = []
ys = []
zs = []

#Any removed row is added into this list
removed = []

#Code to delete a row
for line1 in numbers:
    for line2 in numbers:
        if line1[0] == line2[0]:
            if line2[1] == line2[1]:
                if line1[2] == 1 and line2[2] == 0:    
                    removed.append(line1)
                if line1[2] == 0 and line2[2] == 2:    
                    removed.append(line1)
                if line1[2] == 2 and line2[2] == 1:    
                    removed.append(line1)

for i in removed:
    numbers = np.delete(numbers,i,axis=0)

for line in numbers:                        
    xs.append(line[0])
    ys.append(line[1])
    zs.append(line[2])

#Update the original Array
for i in removed:
    print(removed)

print()
print("x\n", xs)
print("y\n", ys)
print("z\n", zs)
print()
#Updated Array
print(numbers)

Answer 1

测试数组

a = lifeforms = np.array([[12,15,2,0],
                          [13,13,0,0],
                          [13,13,1,0],
                          [13,13,2,0],
                          [65,23,1,0],
                          [24,66,2,0],
                          [14,14,1,0],
                          [14,14,1,1],
                          [14,14,1,2],
                          [14,14,2,0],
                          [15,15,3,2],
                          [15,15,2,0],
                          [65,23,0,0],
                          [24,66,0,0]])

实现颜色选择的函数。

test_one = np.array([[0,1],[1,0],[1,2],[2,1]])
test_two = np.array([[0,2],[2,0]])

def f(g):
    a = g.loc[:,2].unique()
    if np.any(np.all(a == test_one, axis=1)):
        idx = (g[2] == g[2].min()).idxmax()
    elif np.any(np.all(a == test_two, axis=1)):
        idx = (g[2] == g[2].max()).idxmax()
    else:
        raise ValueError('group colors outside bounds')
    return idx

Groupby 前两列；迭代组；保存所需行的索引；使用这些索引从 DataFrame 中选择行。

df = pd.DataFrame(a)
gb = df.groupby([0,1])

indices = []
for k,g in gb:
    if g.loc[:,2].unique().shape[0] > 2:
        #print(f'(0,1,2) - dropping indices {g.index}')
        continue
    if g.shape[0] == 1:
        indices.extend(g.index.to_list())
        #print(f'unique - keeping index {g.index.values}')
        continue
    #print(g.loc[:,2])
    try:
        idx = f(g)
    except ValueError as e:
        print(sep)
        print(e)
        print(g)
        print(sep)
        continue 
    #print(f'keeping index {idx}')
    indices.append(idx)
    #print(sep)

print(df.loc[indices,:])

Answer 2

如果您可以使用 pandas，您可以执行以下操作：

x = np.array([[12,15,2,0],[65,23,0,1],[24,66,2,0],[65,23,1,0],[24,66,0,0]])
df = pd.DataFrame(x)
new_df = df.iloc[df.loc[:,(0,1)].drop_duplicates().index]
print(new_df)

    0   1  2  3
0  12  15  2  0
1  65  23  0  1
2  24  66  2  0

它的作用如下：

1. 将数组转换为 pandas 数据帧
2. df.loc[:,(0,1)].drop_duplicates().index 将返回您希望保留的行的索引（基于第一列和第二列）
3. df.iloc 将返回切片的数据帧。

根据 cmets 中的 OP 问题和@wwii 评论进行编辑：

1. 您可以使用.to_numpy() 返回到numpy 数组，所以只需使用arr = new_df.to_numpy()

2. 您可以尝试以下方法：

   xx = np.array([[12,15,2,0],[65,23,1,0],[24,66,2,0],[65,23,0,0],[24,66,0,0]])
   df = pd.DataFrame(xx)
   df_new = df.groupby([0,1], group_keys=False).apply(lambda x: x.loc[x[2].idxmin()])
   df_new.reset_index(drop=True, inplace=True)
   
       0   1  2  3
   0  12  15  2  0
   1  24  66  0  0
   2  65  23  0  0
   

当需要考虑特殊的启发式时，可以执行以下操作：

import pandas as pd
import numpy as np

def f_(x):
    vals = x[2].tolist()
    if len(vals)==2:
        # print(vals)
        if vals[0] == 0 and vals[1] == 1:
            return vals[0]
        elif vals[0] == 1 and vals[1] == 0:
            return vals[1]
        elif vals[0] == 1 and vals[1] == 2:
            return vals[0]
        elif vals[0] == 2 and vals[1] == 0:
            return vals[0]
    elif len(vals) > 2:
        return -1
    else:
        return x[2]

xx = np.array([[12,15,2,0],[65,23,1,0],[24,66,2,0],[65,23,0,0],[24,66,0,0]])
df = pd.DataFrame(xx)
df_new = df.groupby([0,1], group_keys=False).apply(lambda x: x.loc[x[2] == f_(x)])
df_new.reset_index(drop=True, inplace=True)
print(df_new)

    0   1  2  3
0  12  15  2  0
1  24  66  2  0
2  65  23  0  0