Python Pandas 根据列的最大值删除列
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Python Pandas drop columns based on max value of column
提问by professorDante
Im just getting going with Pandas as a tool for munging two dimensional arrays of data. It's super overwhelming, even after reading the docs. You can do so much that I can't figure out how to do anything, if that makes any sense.
我刚刚开始使用 Pandas 作为处理二维数据数组的工具。即使在阅读了文档之后，它也是超级压倒性的。你可以做的太多，我无法弄清楚如何做任何事情，如果这有意义的话。
My dataframe (simplified):
我的数据框（简化）：
Date Stock1 Stock2 Stock3
2014.10.10 74.75 NaN NaN
2014.9.9 NaN 100.95 NaN
2010.8.8 NaN NaN 120.45
So each column only has one value.
所以每一列只有一个值。
I want to remove all columns that have a max value less than x. So say here as an example, if x = 80, then I want a new DataFrame:
我想删除最大值小于 x 的所有列。所以在这里举个例子，如果 x = 80，那么我想要一个新的 DataFrame：
Date Stock2 Stock3
2014.10.10 NaN NaN
2014.9.9 100.95 NaN
2010.8.8 NaN 120.45
How can this be acheived? I've looked at dataframe.max() which gives me a series. Can I use that, or have a lambda function somehow in select()?
怎样才能做到这一点？我看过 dataframe.max() 这给了我一个系列。我可以使用它，或者在 select() 中有一个 lambda 函数吗？
回答by Adam Hughes
Use the df.max()to index with.
使用df.max()来索引。
In [19]: from pandas import DataFrame
In [23]: df = DataFrame(np.random.randn(3,3), columns=['a','b','c'])
In [36]: df
Out[36]:
a b c
0 -0.928912 0.220573 1.948065
1 -0.310504 0.847638 -0.541496
2 -0.743000 -1.099226 -1.183567
In [24]: df.max()
Out[24]:
a -0.310504
b 0.847638
c 1.948065
dtype: float64
Next, we make a boolean expression out of this:
接下来，我们从中创建一个布尔表达式：
In [31]: df.max() > 0
Out[31]:
a False
b True
c True
dtype: bool
Next, you can index df.columns by this (this is called boolean indexing):
接下来，您可以通过此索引 df.columns （这称为布尔索引）：
In [34]: df.columns[df.max() > 0]
Out[34]: Index([u'b', u'c'], dtype='object')
Which you can finally pass to DF:
您最终可以传递给 DF：
In [35]: df[df.columns[df.max() > 0]]
Out[35]:
b c
0 0.220573 1.948065
1 0.847638 -0.541496
2 -1.099226 -1.183567
Of course, instead of 0, you use any value that you want as the cutoff for dropping.
当然，您可以使用任何您想要的值作为丢弃的截止值，而不是 0。
