pandas 高效连接多个熊猫系列
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Concatenate multiple pandas series efficiently
提问by Kane Chew
I understand that I can use combine_firstto merge two series:
我知道我可以combine_first用来合并两个系列：
series1 = pd.Series([1,2,3,4,5],index=['a','b','c','d','e'])
series2 = pd.Series([1,2,3,4,5],index=['f','g','h','i','j'])
series3 = pd.Series([1,2,3,4,5],index=['k','l','m','n','o'])
Combine1 = series1.combine_first(series2)
print(Combine1
Output:
输出：
a 1.0
b 2.0
c 3.0
d 4.0
e 5.0
f 1.0
g 2.0
h 3.0
i 4.0
j 5.0
dtype: float64
What if I need to merge 3 or more series?
如果我需要合并 3 个或更多系列怎么办？
I understand that using the following code: print(series1 + series2 + series3)yields:
我了解使用以下代码：print(series1 + series2 + series3)产生：
a NaN
b NaN
c NaN
d NaN
e NaN
f NaN
...
dtype: float64
Can I merge multiple series efficiently without using combine_firstmultiple times?
我可以在不combine_first多次使用的情况下有效地合并多个系列吗？
Thanks
谢谢
回答by cs95
Combine Series with Non-Overlapping Indexes
将系列与非重叠索引相结合
To combine series vertically, use pd.concat.
要垂直组合系列，请使用pd.concat.
# Setup
series_list = [
pd.Series(range(1, 6), index=list('abcde')),
pd.Series(range(1, 6), index=list('fghij')),
pd.Series(range(1, 6), index=list('klmno'))
]
pd.concat(series_list)
a 1
b 2
c 3
d 4
e 5
f 1
g 2
h 3
i 4
j 5
k 1
l 2
m 3
n 4
o 5
dtype: int64
Combine with Overlapping Indexes
结合重叠索引
series_list = [
pd.Series(range(1, 6), index=list('abcde')),
pd.Series(range(1, 6), index=list('abcde')),
pd.Series(range(1, 6), index=list('kbmdf'))
]
If the Series have overlapping indices, you can either combine (add) the keys,
如果系列具有重叠索引，您可以组合（添加）键，
pd.concat(series_list, axis=1, sort=False).sum(axis=1)
a 2.0
b 6.0
c 6.0
d 12.0
e 10.0
k 1.0
m 3.0
f 5.0
dtype: float64
Alternatively, just drop duplicates values on the index if you want to take only the first/last value (when there are duplicates).
或者，如果您只想获取第一个/最后一个值（当有重复时），只需删除索引上的重复值。
res = pd.concat(series_list, axis=0)
# keep first value
res[~res.index.duplicated(keep='first')]
# keep last value
res[~res.index.duplicated(keep='last')]
回答by miradulo
Presuming that you were using the behavior of combine_firstto prioritize the values of the series in order as combine_firstis meant for, you could succinctly make multiple calls to it with a lambda expression.
假设您正在使用 的行为combine_first按预期的顺序对系列的值进行优先级排序combine_first，您可以使用 lambda 表达式简洁地多次调用它。
from functools import reduce
l_series = [series1, series2, series3]
reduce(lambda s1, s2: s1.combine_first(s2), l_series)
Of course if the indices are unique as in your current example, you can simply use pd.concatinstead.
当然，如果索引在您当前的示例中是唯一的，您可以简单地使用pd.concat。
Demo
演示
series1 = pd.Series(list(range(5)),index=['a','b','c','d','e'])
series2 = pd.Series(list(range(5, 10)),index=['a','g','h','i','j'])
series3 = pd.Series(list(range(10, 15)),index=['k','b','m','c','o'])
from functools import reduce
l_series = [series1, series2, series3]
print(reduce(lambda s1, s2: s1.combine_first(s2), l_series))
# a 0.0
# b 1.0
# c 2.0
# d 3.0
# e 4.0
# g 6.0
# h 7.0
# i 8.0
# j 9.0
# k 10.0
# m 12.0
# o 14.0
# dtype: float64
回答by White
Agree with what @codespeed has pointed out in his answer.
同意@codespeed 在他的回答中指出的内容。
I think it will depend on user needs. If series index are confirmed with no overlapping, concat will be a better option. (as original question posted, there is no index overlapping, then concat will be a better option)
我认为这将取决于用户的需求。如果确定系列索引没有重叠，则 concat 将是更好的选择。（作为原始问题发布，没有索引重叠，然后 concat 将是更好的选择）
If there is index overlapping, you might need to consider how to handle overlapping, which value to be overwritten. (as example provided by codespeed, if index are matching to different values, need to be careful about combine_first)
如果有索引重叠，可能需要考虑如何处理重叠，覆盖哪个值。（以codespeed提供的例子，如果索引匹配到不同的值，需要注意combine_first）
i.e. (note series3 is same as series1, series2 is same as series4)
即（注意series3与series1相同，series2与series4相同）
import pandas as pd
import numpy as np
series1 = pd.Series([1,2,3,4,5],index=['a','b','c','d','e'])
series2 = pd.Series([2,3,4,4,5],index=['a','b','c','i','j'])
series3 = pd.Series([1,2,3,4,5],index=['a','b','c','d','e'])
series4 = pd.Series([2,3,4,4,5],index=['a','b','c','i','j'])
print(series1.combine_first(series2))
a 1.0
b 2.0
c 3.0
d 4.0
e 5.0
i 4.0
j 5.0
dtype: float64
print(series4.combine_first(series3))
a 2.0
b 3.0
c 4.0
d 4.0
e 5.0
i 4.0
j 5.0
dtype: float64
回答by Vaishali
You would use combine_first if you want one series's values prioritized over the other. Its usually used to fill the missing values in the first series. I am not sure whats the expected output in your example but looks like you can use concat
如果您希望一个系列的值优先于另一个系列，您可以使用 combine_first。它通常用于填充第一个系列中的缺失值。我不确定您的示例中的预期输出是什么，但看起来您可以使用 concat
pd.concat([series1, series2, series3])
You get
你得到
a 1
b 2
c 3
d 4
e 5
f 1
g 2
h 3
i 4
j 5
k 1
l 2
m 3
n 4
o 5
