每三行数据框乘以不同的值

debugcn 发表于 Dev

布赖恩

我有一个9行的数据框。我想将前三行乘以一个值，将后三行乘以第二个值，将第三组3行乘以另一个值。

我正在使用这些变量：

import pandas as pd

df = pd.DataFrame([[i] * 5 for i in range(9)], columns=list('ABCDE'))

a = pd.Series(range(3))

print df

   A  B  C  D  E
0  0  0  0  0  0
1  1  1  1  1  1
2  2  2  2  2  2
3  3  3  3  3  3
4  4  4  4  4  4
5  5  5  5  5  5
6  6  6  6  6  6
7  7  7  7  7  7
8  8  8  8  8  8

我能够使它像这样工作：

for i, e in a.iteritems():
    start, end = i * len(a), (i + 1) * len(a)
    df.iloc[start:end] *= e

print df

    A   B   C   D   E
0   0   0   0   0   0
1   0   0   0   0   0
2   0   0   0   0   0
3   3   3   3   3   3
4   4   4   4   4   4
5   5   5   5   5   5
6  12  12  12  12  12
7  14  14  14  14  14
8  16  16  16  16  16

耶斯列尔

另一种解决方案多df由mul与numpy array通过扩大numpy.repeat：

print (df.mul(np.repeat(a.index.values, [3] * len(a)), axis=0))
    A   B   C   D   E
0   0   0   0   0   0
1   0   0   0   0   0
2   0   0   0   0   0
3   3   3   3   3   3
4   4   4   4   4   4
5   5   5   5   5   5
6  12  12  12  12  12
7  14  14  14  14  14
8  16  16  16  16  16

时间-（len(df)=9）：

In [20]: %timeit (df.mul(np.repeat(a.index.values, [3] * len(a)), axis=0))
The slowest run took 6.12 times longer than the fastest. This could mean that an intermediate result is being cached.
1000 loops, best of 3: 197 µs per loop

In [21]: %%timeit 
    ...: df.loc[:, :] = (df.values.reshape(3, df.size / 3) * np.arange(3)[:, None]).reshape(df.shape)

__main__:257: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
The slowest run took 6.16 times longer than the fastest. This could mean that an intermediate result is being cached.
1000 loops, best of 3: 199 µs per loop

计时代码-（len(df)=90k）：

df = pd.DataFrame([[i] * 5 for i in range(9)], columns=list('ABCDE'))
df = pd.concat([df]*10000).reset_index(drop=True)
a = pd.Series(range(3000))
print (df)

时间-（len(df)=90k）：

In [24]: %timeit (df.mul(np.repeat(a.index.values, [3] * len(a)), axis=0))
100 loops, best of 3: 3.58 ms per loop

In [33]: %%timeit
    ...: df.loc[:, :] = (df.values.reshape(3, df.size / 3) * np.arange(3)[:, None]).reshape(df.shape)
    ...: 
__main__:257: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
100 loops, best of 3: 10.9 ms per loop

In [34]: %%timeit
    ...: df.iloc[:, :] = (df.values.reshape(3, df.size / 3) * np.arange(3)[:, None]).reshape(df.shape)
    ...: 
__main__:257: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
100 loops, best of 3: 10.9 ms per loop

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