Numpy：从 pandas 数据帧创建可变长度序列

Question

假设我有以下数据框：

df_raw = pd.DataFrame({"person_id": [101, 101, 102, 102, 102, 103], "date": [0, 5, 0, 7, 11, 0], "val1": [99, 11, 22, 33, 44, 22], "val2": [77, 88, 22, 66, 55, 33]})

我想要实现的是创建一个 3 维 numpy 数组，结果应如下所示：

np_pros = np.array([[[0, 99, 77], [5, 11, 88]], [[0, 22, 22], [7, 33, 66], [11, 44, 55]], [[0, 22, 33]]])

换句话说，3D数组应该具有以下形状[unique_ids, None, feature_size]。在我的例子中，unique_ids 的数量是 3，feature size 是 3（除 person_id 之外的所有列），y 列的长度可变，它表示person_id.

的测量次数

我很清楚我可以创建一个 np.zeros((unique_ids, max_num_features, feature_size)) 数组，填充它然后删除我不需要但我想要更快的东西的元素。原因是我的实际数据框很大（大约 [50000, 455]），这将导致一个大约 [12500, 200, 455].

的 numpy 数组

期待您的回答！

Answer 1

这是一种方法：

ix = np.flatnonzero(df1.person_id != df1.person_id.shift(1))
np.split(df1.drop('person_id', axis=1).values, ix[1:])

[array([[ 0, 99, 77],
        [ 5, 11, 88]], dtype=int64), 
 array([[ 0, 22, 22],
        [ 7, 33, 66],
        [11, 44, 55]], dtype=int64), 
 array([[ 0, 22, 33]], dtype=int64)]

详情

使用 np.flatnonzero after comparing df1 with a shifted version of itself (pd.shift) 以获得 person_id 发生变化的索引：

ix = np.flatnonzero(df1.person_id != df1.person_id.shift(1))
#array([0, 2, 5])

使用np.split根据获取的索引拆分数据框的感兴趣列：

np.split(df1.drop('person_id', axis=1).values, ix[1:])

[array([[ 0, 99, 77],
        [ 5, 11, 88]], dtype=int64), 
 array([[ 0, 22, 22],
        [ 7, 33, 66],
        [11, 44, 55]], dtype=int64), 
 array([[ 0, 22, 33]], dtype=int64)]

Answer 2

您可以使用 groupby:

import pandas as pd

df_raw = pd.DataFrame({"person_id": [101, 101, 102, 102, 102, 103], "date": [0, 5, 0, 7, 11, 0], "val1": [99, 11, 22, 33, 44, 22], "val2": [77, 88, 22, 66, 55, 33]})

result = [group.values for _, group in df_raw.groupby('person_id')[['date', 'val1', 'val2']]]
print(result)

输出

[array([[  0, 101,  99,  77],
       [  5, 101,  11,  88]]), array([[  0, 102,  22,  22],
       [  7, 102,  33,  66],
       [ 11, 102,  44,  55]]), array([[  0, 103,  22,  33]])]

Answer 3

另一个解决方案 xarray

让我们 创建由 person_id

的口是心非暗示的维度

>>> df['newdim'] = df.person_id.duplicated()
>>> df.newdim    = df.groupby('person_id').newdim.cumsum()
>>> df           = df.set_index(["newdim", "person_id"])
>>> df
                  date  val1  val2
newdim person_id                  
0.0    101           0    99    77
1.0    101           5    11    88
0.0    102           0    22    22
1.0    102           7    33    66
2.0    102          11    44    55
0.0    103           0    22    33

为了可读性，我们可能想把df变成一个xarray.Dataset对象

>>> xa = df.to_xarray()
>>> xa
<xarray.Dataset>
Dimensions:    (newdim: 3, person_id: 3)
Coordinates:
  * newdim     (newdim) float64 0.0 1.0 2.0
  * person_id  (person_id) int64 101 102 103
Data variables:
    date       (newdim, person_id) float64 0.0 0.0 0.0 5.0 7.0 nan nan 11.0 nan
    val1       (newdim, person_id) float64 99.0 22.0 22.0 11.0 33.0 nan nan ...
    val2       (newdim, person_id) float64 77.0 22.0 33.0 88.0 66.0 nan nan ...

然后进入维度健康 numpy 数组

>>> ar = xa.to_array().T.values
>>> ar
array([[[ 0., 99., 77.],
        [ 5., 11., 88.],
        [nan, nan, nan]],

       [[ 0., 22., 22.],
        [ 7., 33., 66.],
        [11., 44., 55.]],

       [[ 0., 22., 33.],
        [nan, nan, nan],
        [nan, nan, nan]]])

请注意，nan-值是通过强制引入的。

Numpy：从 pandas 数据帧创建可变长度序列

Numpy: Creation of a variable length sequence from a pandas data-frame

python

numpy

pandas

numpy-ndarray