Question

说我有这个数据数组

data = np.asarray([[1, 2, 3, 4], ['a', 'b', 'c', 'd'], ['A', 'B', 'C', 'D'], ['x', 'y', 'z', 'zz']])

和这些索引，每个“对”对应于数据矩阵中的一个单元格

indices = np.asarray([[0, 0], [3, 0], [2, 2], [3, 2]])

现在，我想从指定的单元格中检索数据。我可以通过以下方式做到这一点：

searched_data = []
for x, y in coords:
    searched_data.append(data[y][x])

是否存在更多的Python风格或更多的numpy-ish变体，可以通过花式数组索引或其他方式在一行中完成此操作？

我尝试过（受this post的启发）：

x_indexed1 = data[indices[:, 1]][:,[indices[:, 0]]]

但这给了我

[[['1' '4' '3' '4']]

 [['1' '4' '3' '4']]

 [['A' 'D' 'C' 'D']]

 [['A' 'D' 'C' 'D']]]

还有这个

x_indexed = data[np.ix_(indices[:, 1],indices[:, 0])]

给出

[['1' '4' '3' '4']
 ['1' '4' '3' '4']
 ['A' 'D' 'C' 'D']
 ['A' 'D' 'C' 'D']]

Answer 1

您已经接近了，但是当您想像这样的具有对齐索引的library(highcharter) data = data.frame(date = seq(as.Date("2019-01-01"), as.Date("2020-01-01"), by = "day")) data$y = 1 data$y2 = 2 # base highchart() %>% hc_yAxis_multiples( list(top = "0%", height = "47%", title = list(text = "y0"), opposite=FALSE, labels = list(format = "£{value}"), min=0), list(top = "53%", height = "47%", title = list(text = "y1"), opposite=FALSE, labels = list(format = "£{value}"), offset=0, min=0)) %>% hc_xAxis(type = "datetime", dateTimeLabelFormats = list(day = '%d of %b')) %>% hc_add_theme(hc_theme_flat(chart = list(backgroundColor = "#FFF"))) %>% hc_add_series(data, "area", stacking="normal", yAxis = 0, hcaes(x=date, y=y)) %>% hc_add_series(data, "area", stacking="normal", yAxis = 1, hcaes(x=date, y=y2)) # type = stock highchart(type="stock") %>% hc_yAxis_multiples( list(top = "0%", height = "47%", title = list(text = "y0"), opposite=FALSE, labels = list(format = "£{value}"), min=0), list(top = "53%", height = "47%", title = list(text = "y1"), opposite=FALSE, labels = list(format = "£{value}"), offset=0, min=0)) %>% hc_xAxis(type = "datetime", dateTimeLabelFormats = list(day = '%d of %b')) %>% hc_add_theme(hc_theme_flat(chart = list(backgroundColor = "#FFF"))) %>% hc_add_series(data, "area", stacking="normal", yAxis = 0, hcaes(x=date, y=y)) %>% hc_add_series(data, "area", stacking="normal", yAxis = 1, hcaes(x=date, y=y2))索引时，请勿使用numpy.ndarray 。使用 tuple 进行多维索引：

[][]

为了更加清楚：

>>> data[indices[:, 1], indices[:,0]]
array(['1', '4', 'C', 'D'], dtype='<U21')

您的第一次尝试是这样的：

>>> ys = indices[:, 1]
>>> xs = indices[:, 0]
>>> data[ys, xs]
array(['1', '4', 'C', 'D'], dtype='<U21')

因此，要进行分解，“部分索引”将假设您在剩下的维度中是一个完整切片>>> data[ys][:,[xs]] array([[['1', '4', '3', '4']], [['1', '4', '3', '4']], [['A', 'D', 'C', 'D']], [['A', 'D', 'C', 'D']]], dtype='<U21')，因此对于您:来说，将选择以下行：

data[ys, :]

然后这就是您用>>> data[ys] array([['1', '2', '3', '4'], ['1', '2', '3', '4'], ['A', 'B', 'C', 'D'], ['A', 'B', 'C', 'D']], dtype='<U21')进行索引的过程，该过程基本上选择所有行和xs的列，这些列和您包装在列表中的列基本上不压缩维度：

[:, [xs]]

通过索引检索2D数组元素的Python方法

1 个答案: