Question

说我有一个数组数组如下：

array([[ 7108.4,  -600. ],
       [ 7101.3,  -500. ],
       [ 7094.3,  -400. ],
       [ 7087.2,  -300. ],
       [ 7080.1,  -200. ],
       [ 7073.1,  -100. ],
       [ 7058.3,   100. ],
       [ 7051.3,   200. ],
       [ 7044.2,   300. ],
       [ 7037.1,   400. ],
       [ 7030.1,   500. ],
       [ 7023. ,   600. ]])

这是一个价格和数量清单，正数是买入，负数是卖出。

如果目前的市场价格是7050，我想要挤压＆＃39;我的价格如下：

array([[ 7108.4,  -600. ],
       [ 7101.3,  -500. ],
       [ 7094.3,  -400. ],
       [ 7087.2,  -300. ],
       [ 7080.1,  -200. ],
       [ 7073.1,  -100. ],
       [ 7050. ,   300. ], # Price & quantity changed here
       [ 7044.2,   300. ],
       [ 7037.1,   400. ],
       [ 7030.1,   500. ],
       [ 7023. ,   600. ]])

并且在另一个方向做同样的事情，所以如果市场价格是7085：

array([[ 7108.4,  -600. ],
       [ 7101.3,  -500. ],
       [ 7094.3,  -400. ],
       [ 7087.2,  -300. ],
       [ 7085. ,  -300. ], # Price & quantity change here
       [ 7058.3,   100. ],
       [ 7051.3,   200. ],
       [ 7044.2,   300. ],
       [ 7037.1,   400. ],
       [ 7030.1,   500. ],
       [ 7023. ,   600. ]])

写这样东西最干净的方法是什么？理想情况下，我想将挤压和数量分组写成两个单独的步骤。

Answer 1

pandas使这种操作更容易。

>>> import numpy as np
>>> import pandas as pd
>>> 
>>> t = np.array([[ 7108.4,  -600. ],
...        [ 7101.3,  -500. ],
...        [ 7094.3,  -400. ],
...        [ 7087.2,  -300. ],
...        [ 7080.1,  -200. ],
...        [ 7073.1,  -100. ],
...        [ 7058.3,   100. ],
...        [ 7051.3,   200. ],
...        [ 7044.2,   300. ],
...        [ 7037.1,   400. ],
...        [ 7030.1,   500. ],
...        [ 7023. ,   600. ]])
>>> 
>>> df = pd.DataFrame(t, columns=['price', 'quantity'])
>>> (df.price > 7050) & (df.quantity > 0)
0     False
1     False
2     False
3     False
4     False
5     False
6      True
7      True
8     False
9     False
10    False
11    False
dtype: bool
>>> df[(df.price > 7050) & (df.quantity > 0)]
    price  quantity
6  7058.3     100.0
7  7051.3     200.0
>>> df[(df.price > 7050) & (df.quantity > 0)].quantity.sum()
300.0
>>> df[(df.price < 7085) & (df.quantity < 0)]
    price  quantity
4  7080.1    -200.0
5  7073.1    -100.0
>>> df[(df.price < 7085) & (df.quantity < 0)].quantity.sum()
-300.0

如果你愿意，你仍然可以坚持使用numpy：

>>> import numpy as np
>>> import pandas as pd
>>> 
>>> t = np.array([[ 7108.4,  -600. ],
...        [ 7101.3,  -500. ],
...        [ 7094.3,  -400. ],
...        [ 7087.2,  -300. ],
...        [ 7080.1,  -200. ],
...        [ 7073.1,  -100. ],
...        [ 7058.3,   100. ],
...        [ 7051.3,   200. ],
...        [ 7044.2,   300. ],
...        [ 7037.1,   400. ],
...        [ 7030.1,   500. ],
...        [ 7023. ,   600. ]])
>>> 
>>> 
>>> t[:,0]
array([ 7108.4,  7101.3,  7094.3,  7087.2,  7080.1,  7073.1,  7058.3,
        7051.3,  7044.2,  7037.1,  7030.1,  7023. ])
>>> t[:,0] > 7050
array([ True,  True,  True,  True,  True,  True,  True,  True, False,
       False, False, False], dtype=bool)
>>> (t[:,0] > 7050) & (t[:,1] > 0)
array([False, False, False, False, False, False,  True,  True, False,
       False, False, False], dtype=bool)
>>> t[(t[:,0] > 7050) & (t[:,1] > 0)]
array([[ 7058.3,   100. ],
       [ 7051.3,   200. ]])
>>> t[(t[:,0] < 7085) & (t[:,1] < 0)]
array([[ 7080.1,  -200. ],
       [ 7073.1,  -100. ]])
>>> t[(t[:,0] < 7085) & (t[:,1] < 0)][:,1]
array([-200., -100.])
>>> t[(t[:,0] < 7085) & (t[:,1] < 0)][:,1].sum()
-300.0

Answer 2

因为Eric的答案很棒，但根据你的评论：

是否可以修改原始数组，以便输出为包含内部变更的订单的完整列表？

这是我的方法，没有numpy：

array=[[ 7108.4,  -600. ],
       [ 7101.3,  -500. ],
       [ 7094.3,  -400. ],
       [ 7087.2,  -300. ],
       [ 7080.1,  -200. ],
       [ 7073.1,  -100. ],
       [ 7058.3,   100. ],
       [ 7051.3,   200. ],
       [ 7044.2,   300. ],
       [ 7037.1,   400. ],
       [ 7030.1,   500. ],
       [ 7023. ,   600. ]]

price=0
positive_value=7050
updated_list=[]
track=[]
for index,item in enumerate(array):
    if item[0]>positive_value:
        if item[1]>0:
            price+=item[1]
            track.append(index)
        else:
            updated_list.append(item)
    else:
        updated_list.append(item)

updated_list.insert(track[0],[positive_value,price])
print(updated_list)

输出：

[[7108.4, -600.0], [7101.3, -500.0], [7094.3, -400.0], [7087.2, -300.0], [7080.1, -200.0], [7073.1, -100.0], [7050, 300.0], [7044.2, 300.0], [7037.1, 400.0], [7030.1, 500.0], [7023.0, 600.0]]

第二个：

array=[[ 7108.4,  -600. ],
       [ 7101.3,  -500. ],
       [ 7094.3,  -400. ],
       [ 7087.2,  -300. ],
       [ 7080.1,  -200. ],
       [ 7073.1,  -100. ],
       [ 7058.3,   100. ],
       [ 7051.3,   200. ],
       [ 7044.2,   300. ],
       [ 7037.1,   400. ],
       [ 7030.1,   500. ],
       [ 7023. ,   600. ]]

price=0
positive_value=7085
updated_list=[]
track=[]
for index,item in enumerate(array):
    if item[0] < positive_value:
        if item[1] < 0:
            price+=item[1]
            track.append(index)
        else:
            updated_list.append(item)
    else:
        updated_list.append(item)

updated_list.insert(track[0],[positive_value,price])
print(updated_list)

输出：

[[7108.4, -600.0], [7101.3, -500.0], [7094.3, -400.0], [7087.2, -300.0], [7085, -300.0], [7058.3, 100.0], [7051.3, 200.0], [7044.2, 300.0], [7037.1, 400.0], [7030.1, 500.0], [7023.0, 600.0]]

如何编写累加器来压缩中点周围的数组？

2 个答案: