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从数据框创建查找表

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我想基于多个条件从数据框创建查找表 . 我有以下df：

N = 100
L = ['AR1', 'PO1', 'RU1']

np.random.seed(0)

df3 = pd.DataFrame(
    {'X':np.random.uniform(1,4,N),
     'Y':np.random.uniform(1,4,N),
     'Z':np.random.uniform(1,4,N),
     'LG':np.random.choice(L,N),
    })

df3['bins_X'] = df3.groupby('LG')['X'].apply(pd.qcut, q=5, labels=np.arange(5))
df3['bins_Y'] = df3.groupby('LG')['Y'].apply(pd.qcut, q=5, labels=np.arange(5))
df3['bins_Z'] = df3.groupby('LG')['Z'].apply(pd.qcut, q=5, labels=np.arange(5))
df3['bins_X_int'] = df3.groupby('LG')['X'].apply(pd.qcut, q=5)
df3['bins_Y_int'] = df3.groupby('LG')['Y'].apply(pd.qcut, q=5)
df3['bins_Z_int'] = df3.groupby('LG')['Z'].apply(pd.qcut, q=5)

df3.head()

enter image description here

我想从中创建以下lookup_table：

enter image description here

因此，按“LG”和从0到4排序的分组进行分组 . 我需要的是示例lookup_table，其中填充了来自数据帧的关联bin_intervals .

python pandas

2 回答

  • 2

    IIUC：

    def get_ints(s, q):
    return pd.Series(pd.qcut(s, q).sort_values().unique())

d1 = df3.set_index('LG')[list('XYZ')].stack()
g = d1.groupby(level=[0, 1])
g.apply(get_ints, q=5).unstack(1).rename_axis(['LG', 'bin_number']).reset_index()

     LG  bin_number                X                Y                Z
0   AR1           0   [1.306, 1.926]  [1.0556, 1.875]  [1.0493, 1.819]
1   AR1           1   (1.926, 2.447]   (1.875, 2.757]   (1.819, 2.595]
2   AR1           2   (2.447, 2.812]  (2.757, 3.0724]    (2.595, 2.95]
3   AR1           3  (2.812, 3.0744]  (3.0724, 3.376]    (2.95, 3.334]
4   AR1           4  (3.0744, 3.936]   (3.376, 3.803]   (3.334, 3.885]
5   PO1           0  [1.0564, 1.286]  [1.0955, 1.566]   [1.074, 1.596]
6   PO1           1   (1.286, 1.868]   (1.566, 1.911]   (1.596, 1.895]
7   PO1           2   (1.868, 2.682]   (1.911, 2.669]   (1.895, 2.426]
8   PO1           3    (2.682, 3.29]   (2.669, 2.958]   (2.426, 3.283]
9   PO1           4    (3.29, 3.965]   (2.958, 3.676]   (3.283, 3.848]
10  RU1           0  [1.0141, 1.452]  [1.0351, 2.158]  [1.0397, 1.632]
11  RU1           1   (1.452, 1.983]    (2.158, 2.49]   (1.632, 2.223]
12  RU1           2   (1.983, 2.622]   (2.49, 3.0893]  (2.223, 3.0732]
13  RU1           3   (2.622, 3.226]  (3.0893, 3.673]  (3.0732, 3.729]
14  RU1           4   (3.226, 3.929]   (3.673, 3.997]   (3.729, 3.971]
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  • 1

    IIUC你可以这样做：

    In [55]: lkp = df3[['LG']].sort_values('LG').copy()

In [56]: lkp['bin_number'] = lkp.groupby('LG').cumcount()

In [57]: lkp
Out[57]:
     LG  bin_number
0   AR1           0
46  AR1           1
25  AR1           2
26  AR1           3
57  AR1           4
28  AR1           5
29  AR1           6
56  AR1           7
31  AR1           8
32  AR1           9
..  ...         ...
45  RU1          24
98  RU1          25
55  RU1          26
58  RU1          27
60  RU1          28
61  RU1          29
63  RU1          30
64  RU1          31
39  RU1          32
99  RU1          33

[100 rows x 2 columns]
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