小编hks*_*014的帖子

import numpy as np
from keras.utils import np_utils
nsample = 100
sample_space = ["HOME","DRAW","AWAY"]
array = np.random.choice(sample_space, nsample, )
uniques, coded_id = np.unique(array, return_inverse=True)
coded_array = np_utils.to_categorical(coded_id)

例

映射字典

 ['AWAY', 'HOME', 'DRAW', 'AWAY', ...]

输入

[[ 0.  1.  0.]
 [ 0.  0.  1.]
 [ 0.  0.  1.]
 ..., 
 [ 0.  0.  1.]
 [ 0.  0.  1.]
 [ 1.  0.  0.]]

编码输出

import numpy as np
from keras.utils import np_utils
nsample = 100
sample_space = ["HOME","DRAW","AWAY"]
array = np.random.choice(sample_space, nsample, ) …

有没有简单的工具/库可以帮助我轻松计算数据帧的sma（..）？

                             GLD       SMA(5)
Date                                
2005-01-03 00:00:00+00:00  43.020000  Nan
2005-01-04 00:00:00+00:00  42.740002  Nan
2005-01-05 00:00:00+00:00  42.669998  Nan
2005-01-06 00:00:00+00:00  42.150002  Nan
2005-01-07 00:00:00+00:00  41.840000  ..
2005-01-10 00:00:00+00:00  41.950001  ..
2005-01-11 00:00:00+00:00  42.209999  ..
2005-01-12 00:00:00+00:00  42.599998  ..
2005-01-13 00:00:00+00:00  42.599998  ..
2005-01-14 00:00:00+00:00  42.320000  ..