Python 如何将列和行的 Pandas DataFrame 子集转换为 numpy 数组？
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How to convert a pandas DataFrame subset of columns AND rows into a numpy array?
提问by John Prior
I'm wondering if there is a simpler, memory efficient way to select a subset of rows and columns from a pandas DataFrame.
我想知道是否有一种更简单、内存高效的方法来从 Pandas DataFrame 中选择行和列的子集。
For instance, given this dataframe:
例如，给定这个数据框：
df = DataFrame(np.random.rand(4,5), columns = list('abcde'))
print df
a b c d e
0 0.945686 0.000710 0.909158 0.892892 0.326670
1 0.919359 0.667057 0.462478 0.008204 0.473096
2 0.976163 0.621712 0.208423 0.980471 0.048334
3 0.459039 0.788318 0.309892 0.100539 0.753992
I want only those rows in which the value for column 'c' is greater than 0.5, but I only need columns 'b' and 'e' for those rows.
我只需要列 'c' 的值大于 0.5 的那些行，但我只需要这些行的列 'b' 和 'e'。
This is the method that I've come up with - perhaps there is a better "pandas" way?
这是我想出的方法 - 也许有更好的“熊猫”方法？
locs = [df.columns.get_loc(_) for _ in ['a', 'd']]
print df[df.c > 0.5][locs]
a d
0 0.945686 0.892892
My final goal is to convert the result to a numpy array to pass into an sklearn regression algorithm, so I will use the code above like this:
我的最终目标是将结果转换为 numpy 数组以传递给 sklearn 回归算法，因此我将像这样使用上面的代码：
training_set = array(df[df.c > 0.5][locs])
... and that peeves me since I end up with a huge array copy in memory. Perhaps there's a better way for that too?
...这让我很恼火，因为我最终在内存中得到了一个巨大的数组副本。也许还有更好的方法？
采纳答案by Jeff
.locaccept row and column selectors simultaneously (as do .ix/.ilocFYI)
This is done in a single pass as well.
.loc同时接受行和列选择器（.ix/.iloc仅供参考）这也是一次性完成的。
In [1]: df = DataFrame(np.random.rand(4,5), columns = list('abcde'))
In [2]: df
Out[2]:
a b c d e
0 0.669701 0.780497 0.955690 0.451573 0.232194
1 0.952762 0.585579 0.890801 0.643251 0.556220
2 0.900713 0.790938 0.952628 0.505775 0.582365
3 0.994205 0.330560 0.286694 0.125061 0.575153
In [5]: df.loc[df['c']>0.5,['a','d']]
Out[5]:
a d
0 0.669701 0.451573
1 0.952762 0.643251
2 0.900713 0.505775
And if you want the values (though this should pass directly to sklearn as is); frames support the array interface
如果你想要这些值（尽管这应该直接传递给 sklearn）；框架支持阵列接口
In [6]: df.loc[df['c']>0.5,['a','d']].values
Out[6]:
array([[ 0.66970138, 0.45157274],
[ 0.95276167, 0.64325143],
[ 0.90071271, 0.50577509]])
回答by waitingkuo
Use its value directly:
直接使用它的值：
In [79]: df[df.c > 0.5][['b', 'e']].values
Out[79]:
array([[ 0.98836259, 0.82403141],
[ 0.337358 , 0.02054435],
[ 0.29271728, 0.37813099],
[ 0.70033513, 0.69919695]])
回答by Daniel
Perhaps something like this for the first problem, you can simply access the columns by their names:
对于第一个问题，也许像这样，您可以简单地按列名访问列：
>>> df = pd.DataFrame(np.random.rand(4,5), columns = list('abcde'))
>>> df[df['c']>.5][['b','e']]
b e
1 0.071146 0.132145
2 0.495152 0.420219
For the second problem:
对于第二个问题：
>>> df[df['c']>.5][['b','e']].values
array([[ 0.07114556, 0.13214495],
[ 0.49515157, 0.42021946]])
