pandas 在熊猫中设置联合
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Set Union in pandas
提问by cppgnlearner
I have two columns which I stored sets in my dataframe.
我有两列存储在我的数据框中。
I want to perform set union on the two columns using fast vectorized operation
我想使用快速矢量化操作对两列执行集合并集
df['union'] = df.set1 | df.set2
but the error TypeError: unsupported operand type(s) for |: 'set' and 'bool'is preventing me from doing so as I have type np.nanin both columns.
但错误TypeError: unsupported operand type(s) for |: 'set' and 'bool'阻止我这样做，因为我np.nan在两列中都输入了内容。
Is there a good solution to overcome this?
有没有好的解决方案来克服这个问题？
采纳答案by ayhan
For these operations pure Python may be more efficient.
对于这些操作，纯 Python 可能更高效。
%timeit pd.Series([set1.union(set2) for set1, set2 in zip(df['A'], df['B'])])
10 loops, best of 3: 43.3 ms per loop
%timeit df.apply(lambda x: x.A.union(x.B), axis=1)
1 loop, best of 3: 2.6 s per loop
If we could use +, it would probably take half the time (inheritance may not worth it):
如果我们可以使用+，它可能需要一半的时间（继承可能不值得）：
%timeit df['A'] - df['B']
10 loops, best of 3: 22.1 ms per loop
%timeit pd.Series([set1.difference(set2) for set1, set2 in zip(df['A'], df['B'])])
10 loops, best of 3: 35.7 ms per loop
DataFrame for timings:
用于计时的数据帧：
import pandas as pd
import numpy as np
l1 = [set(np.random.choice(list('abcdefg'), np.random.randint(1, 5))) for _ in range(100000)]
l2 = [set(np.random.choice(list('abcdefg'), np.random.randint(1, 5))) for _ in range(100000)]
df = pd.DataFrame({'A': l1, 'B': l2})
回答by piRSquared
This is the best I could come up with:
这是我能想到的最好的：
# method 1
df.apply(lambda x: x.set1.union(x.set2), axis=1)
# method 2
df.applymap(list).sum(1).apply(set)
Wow!
哇！
I expected the method 2 to be quicker. Not so!
我希望方法 2 更快。不是这样！
Example
例子
df = pd.DataFrame([[{1, 2, 3}, {3, 4, 5}] for _ in range(3)],
columns=list('AB'))
df
df.apply(lambda x: x.set1.union(x.set2), axis=1)
0 {1, 2, 3, 4, 5}
1 {1, 2, 3, 4, 5}
2 {1, 2, 3, 4, 5}
