如何将列和行的熊猫DataFrame子集转换为numpy数组?

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、更节省内存的方法可以从熊猫数据存储器中选择行和列的子集。

For instance, given this dataframe:

例如，给定这个dataframe:

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.

我只需要那些列“c”的值大于0.5的行，但对于那些行，我只需要列“b”和“e”。

This is the method that I've come up with - perhaps there is a better "pandas" way?

这就是我提出的方法——也许有更好的“熊猫”方式?

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成一个numpy数组，并传递给sklearn回归算法，所以我将使用上面的代码:

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?

…这让我很不爽，因为我最终得到了一个巨大的数组拷贝。也许还有更好的办法?

3 个解决方案

#1

.loc accept row and column selectors simultaneously (as do .ix/.iloc FYI) This is done in a single pass as well.

.loc同时接受行和列选择器(如.ix/)。iloc FYI)这是在单次传递中完成的。

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

如果你想要值(尽管这应该直接通过sklearn);帧支持数组接口

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]])

#2

Use its value directly:

直接使用它的价值:

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]])

#3

Perhaps something like this for the first problem, you can simply access the columns by their names:

也许对于第一个问题，您可以简单地按列名访问它们:

>>> 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:

第二个问题:

>>> df[df['c']>.5][['b','e']].values
array([[ 0.07114556,  0.13214495],
       [ 0.49515157,  0.42021946]])

#1