英文:
Concatenate results of `apply` in pandas
问题
我想对pandas Series/DataFrame的每个元素/行应用一个函数，并将结果连接/堆叠成一个单独的DataFrame。
例如，我可以从一个Series开始，s = pd.Series(["a", "b,c", "d,e,f"]), 然后我想得到最终的结果 res = pd.Series(["a", "b", "c", "d", "e", "f"])
一个较慢的方法是:
res = []
for _, x in s.items():
res.append(pd.Series(x.split(",")))
res = pd.concat(res, ignore_index=True)
我想探索pandas的内部功能。似乎可以通过类似于 s.apply(lambda x: x.split(",")) 或 s.str.split() 的方式来实现这一点，它会给出一个包含列表的Series...
备注：
-
上面的简单例子实际上可以通过类似于
pd.Series(",".join(s.tolist()).split(","))的方式解决，但我正在寻找一个通用的解决方案。 -
请注意，这不是 Apply elementwise, concatenate resulting rows into a DataFrame 的重复（在该问题中，“apply”是以一种通用的方式使用，而不是作为pandas函数的名称）。
英文:
I would like to apply a function to each element/row of a pandas Series/DataFrame and concatenate/stack the results into a single DataFrame.
E.g., I may start with a Series s = pd.Series(["a", "b,c", "d,e,f"]), and I would like to obtain as a final result res = pd.Series(["a", "b", "c", "d", "e", "f"])
A slow way of doing this would be:
res = []
for _, x in s.items():
res.append(pd.Series(s.split(",")))
res = pd.concat(res, ignore_index=True)
I would like to explore the internal functionality of pandas. It seems that there should be a way of doing this by starting with something like s.apply(lambda x: x.split(",")) or s.str.split(), which gives a series of lists...
Remarks:
-
The simple example above could be actually solved using something like
pd.Series(",".join(s.tolist()).split(",")), but I am looking for a generalizable solution. -
Note that this is not a duplicate of Apply elementwise, concatenate resulting rows into a DataFrame (where apply is used in a generic sense rather than as a name of pandas function.)
答案1
得分: 2
可以在两个步骤中完成此操作：
- 将每个字符串拆分为由逗号分隔的字符串列表
- 将由字符串列表组成的列展开为一系列字符串
这可以在一行中完成，结合使用map和flatmap：
s.str.split(',').explode()
英文:
You can achieve this in two steps:
- split each string into a list of strings delimiting by comma
- explode the column made up of lists of strings into a series of strings
This can be achieved in a single line that combines the map with the flatmap:
s.str.split(',').explode()
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