import numpy as np
import numpy.lib.recfunctions
# Define the starting arrays as structured arrays with two fields ('key' and 'field')
dtype = [('key', np.int), ('field', np.float)]
x = np.array([(1, 2),
(2, 4),
(3, 6),
(4, np.NaN),
(5, 10)],
dtype=dtype)
y = np.array([(0, -5),
(1, 0),
(2, 5),
(5, 20),
(6, 25)],
dtype=dtype)
# You want an outer join, rather than the default inner join
# (all values are returned, not just ones with a common key)
join = np.lib.recfunctions.join_by('key', x, y, jointype='outer')
# Now we have a structured array with three fields: 'key', 'field1', and 'field2'
# (since 'field' was in both arrays, it renamed x['field'] to 'field1', and
# y['field'] to 'field2')
# This returns a masked array, if you want it filled with
# NaN's, do the following...
join.fill_value = np.NaN
join = join.filled()
# Just displaying it... Keep in mind that as a structured array,
# it has one dimension, where each row contains the 3 fields
for row in join:
print row
见numpy.lib.recfunctions.join_by
它只适用于结构化数组或重排数组,因此存在一些问题。在
首先,您至少需要对结构化数组有所了解。如果不是,请参阅here。在
该输出:
^{2}$希望有帮助!在
编辑1:添加示例 编辑2:真的不应该加入浮动。。。将“key”字段更改为int
相关问题 更多 >
编程相关推荐