使用索引在 Pandas 中查找两个系列之间的交集
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Finding the intersection between two series in Pandas using index
提问by Boss1295
I have two series of different lengths, and I am attempting to find the intersection of the two series based on the index, where the index is a string. The end result is, hopefully, a series that has the elements of the intersection based on the common string indexes.
我有两个不同长度的系列，我试图根据索引找到两个系列的交集，其中索引是一个字符串。希望最终结果是一个具有基于公共字符串索引的交集元素的系列。
Any ideas?
有任何想法吗？
回答by Alex Riley
Pandas indexes have an intersection methodwhich you can use. If you have two Series, s1and s2, then
Pandas 索引有一个可以使用的交集方法。如果你有两个系列，s1和s2，然后
s1.index.intersection(s2.index)
or, equivalently:
或者，等效地：
s1.index & s2.index
gives you the index values which are in both s1and s2.
给你这是在这两个指数值s1和s2。
You can then use this list of indexes to view the corresponding elements of a series. For example:
然后，您可以使用此索引列表查看系列的相应元素。例如：
>>> ixs = s1.index.intersection(s2.index)
>>> s1.loc[ixs]
# subset of s1 with only the indexes also found in s2 appears here
回答by nurp
Both my data increments so I wrote a function to get the indices then filtered the data based on their indexes.
我的两个数据都会增加，所以我编写了一个函数来获取索引，然后根据它们的索引过滤数据。
np.shape(data1) # (1330, 8)
np.shape(data2) # (2490, 9)
index_1, index_2 = overlap(data1, data2)
data1 = data1[index1]
data2 = data2[index2]
np.shape(data1) # (540, 8)
np.shape(data2) # (540, 9)
def overlap(data1, data2):
'''both data is assumed to be incrementing'''
mask1 = np.array([False] * len(data1))
mask2 = np.array([False] * len(data2))
idx_1 = 0
idx_2 = 0
while idx_1 < len(data1) and idx_2 < len(data2):
if data1[idx_1] < data2[idx_2]:
mask1[idx_1] = False
mask2[idx_2] = False
idx_1 += 1
elif data1[idx_1] > data2[idx_2]:
mask1[idx_1] = False
mask2[idx_2] = False
idx_2 += 1
else:
mask1[idx_1] = True
mask2[idx_2] = True
idx_1 += 1
idx_2 += 1
return mask1, mask2
