我有一个带有open,high,low,close,key1和key2列的熊猫数据集。现在,我想按key1和key2对数据集进行分组,并使用公式-(高+低+闭合)/ 3计算枢轴。到目前为止,我已经能够做到。但是要求是将计算出的数据移到我无法编码的下一组。
我能够按key1和key2列对数据集进行分组,并能够通过以下代码计算数据透视表,但是无法将值移至下一组。
import pandas as pd
data = pd.DataFrame([[110, 115, 105, 111, 1, 2],[11, 16, 6, 12, 1, 2],[12, 17, 7, 13, 1, 3],[22, 25, 17, 20, 1, 3],[12, 16, 6, 11, 2, 4],[32, 36, 26, 28, 2, 4],[9, 13, 4, 13, 2, 5],[49, 53, 40, 45, 2, 5],[13, 18, 9, 12, 3, 6],[14, 16, 10, 13, 3, 6]], columns=["open","high","low","close","key1", "key2"])
s = (data.high.groupby([data.key1, data.key2]).max() + data.low.groupby([data.key1, data.key2]).min() + data.close.groupby([data.key1, data.key2]).last()) / 3
#data['pivot'] = data['key1', 'key2'].map(s.shift())
print(data)
当我使用以下代码时,
import pandas as pd
data = pd.DataFrame([[110, 115, 105, 111, 1, 2],[11, 16, 6, 12, 1, 2],[12, 17, 7, 13, 1, 3],[22, 25, 17, 20, 1, 3],[12, 16, 6, 11, 2, 4],[32, 36, 26, 28, 2, 4],[9, 13, 4, 13, 2, 5],[49, 53, 40, 45, 2, 5],[13, 18, 9, 12, 3, 6],[14, 16, 10, 13, 3, 6]], columns=["open","high","low","close","key1", "key2"])
data['pivot'] = (data.high.groupby([data.key1, data.key2]).transform('max') + data.low.groupby([data.key1, data.key2]).transform('min') + data.close.groupby([data.key1, data.key2]).transform('last')) / 3
print(data)
我得到了低于输出的结果。
open high low close key1 key2 pivot
0 110 115 105 111 1 2 44.333333
1 11 16 6 12 1 2 44.333333
2 12 17 7 13 1 3 17.333333
3 22 25 17 20 1 3 17.333333
4 12 16 6 11 2 4 23.333333
5 32 36 26 28 2 4 23.333333
6 9 13 4 13 2 5 34.000000
7 49 53 40 45 2 5 34.000000
8 13 18 9 12 3 6 13.333333
9 14 16 10 13 3 6 13.333333
但预期输出:
open high low close key1 key2 pivot
0 110 115 105 111 1 2 NaN
1 11 16 6 12 1 2 NaN
2 12 17 7 13 1 3 44.333333
3 22 25 17 20 1 3 44.333333
4 12 16 6 11 2 4 17.333333
5 32 36 26 28 2 4 17.333333
6 9 13 4 13 2 5 23.333333
7 49 53 40 45 2 5 23.333333
8 13 18 9 12 3 6 34.000000
9 14 16 10 13 3 6 34.000000
答案 0 :(得分:0)
首先对字典和GroupBy.agg
使用聚合函数,然后对shift
使用新列DataFrame.join
:
s = data.groupby(['key1','key2']).agg({'low':'min','high':'max','close':'last'}).sum(axis=1)/3
data = data.join(s.rename('pivot').shift(), on=['key1','key2'])
print (data)
open high low close key1 key2 pivot
0 110 115 105 111 1 2 NaN
1 11 16 6 12 1 2 NaN
2 12 17 7 13 1 3 44.333333
3 22 25 17 20 1 3 44.333333
4 12 16 6 11 2 4 17.333333
5 32 36 26 28 2 4 17.333333
6 9 13 4 13 2 5 23.333333
7 49 53 40 45 2 5 23.333333
8 13 18 9 12 3 6 34.000000
9 14 16 10 13 3 6 34.000000