pandas 熊猫分配新的列名作为字符串
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pandas assign with new column name as string
提问by FLab
I recently discovered pandas "assign" methodwhich I find very elegant. My issue is that the name of the new column is assigned as keyword, so it cannot have spaces or dashes in it.
我最近发现了Pandas的“分配”方法，我觉得它非常优雅。我的问题是新列的名称被指定为关键字，因此其中不能有空格或破折号。
df = DataFrame({'A': range(1, 11), 'B': np.random.randn(10)})
df.assign(ln_A = lambda x: np.log(x.A))
A B ln_A
0 1 0.426905 0.000000
1 2 -0.780949 0.693147
2 3 -0.418711 1.098612
3 4 -0.269708 1.386294
4 5 -0.274002 1.609438
5 6 -0.500792 1.791759
6 7 1.649697 1.945910
7 8 -1.495604 2.079442
8 9 0.549296 2.197225
9 10 -0.758542 2.302585
but what if I want to name the new column "ln(A)" for example? E.g.
但是，例如，如果我想将新列命名为“ln(A)”怎么办？例如
df.assign(ln(A) = lambda x: np.log(x.A))
df.assign("ln(A)" = lambda x: np.log(x.A))
File "<ipython-input-7-de0da86dce68>", line 1
df.assign(ln(A) = lambda x: np.log(x.A))
SyntaxError: keyword can't be an expression
I know I could rename the column right after the .assign call, but I want to understand more about this method and its syntax.
我知道我可以在 .assign 调用后立即重命名该列，但我想更多地了解此方法及其语法。
回答by Piotr
You can pass the keyword arguments to assignas a dictionary, like so:
您可以将关键字参数assign作为字典传递给，如下所示：
kwargs = {"ln(A)" : lambda x: np.log(x.A)}
df.assign(**kwargs)
A B ln(A)
0 1 0.500033 0.000000
1 2 -0.392229 0.693147
2 3 0.385512 1.098612
3 4 -0.029816 1.386294
4 5 -2.386748 1.609438
5 6 -1.828487 1.791759
6 7 0.096117 1.945910
7 8 -2.867469 2.079442
8 9 -0.731787 2.197225
9 10 -0.686110 2.302585
回答by piRSquared
assignexpects a bunch of key word arguments. It will, in turn, assign columns with the names of the key words. That's handy, but you can't pass an expression as the key word. This is spelled out by @EdChum in the comments with this link
assign期望一堆关键字参数。反过来，它会为列分配关键字的名称。这很方便，但您不能将表达式作为关键字传递。@EdChum 在此链接的评论中详细说明了这一点
use insertinstead for inplace transformation
使用insert代替就地转换
df.insert(2, 'ln(A)', np.log(df.A))
df
use concatif you don't want inplace
使用concat如果你不想就地
pd.concat([df, np.log(df.A).rename('log(A)')], axis=1)
