用另一個 Dataframe 的值填充 Dataframe（不是相同的列名）

Question

我正在嘗試用另一個 dataframe (InputData) 中的值填充 Python 中的空 dataframe (OutputData)。

InputData 有四列（“Strike”、“DTE”、“IV”、“Pred_IV”）OutputData 具有來自 InputData 的所有唯一 Strikes 作為索引，並且作為列名稱，所有來自 Input Data 的唯一 DTE。

我的目標是用來自 InputData 的相應“Pred_IV”值填充 OutputData。 因為它需要同時匹配索引和列名，所以我沒有考慮如何使用任何已知的 function 進行匹配。

如果 InputData 中沒有與索引和列名匹配的值，則該值可以保持為 NaN

在下面找到我使用 df.to_dict() 提取的數據幀以獲取更多詳細信息。

非常感謝您的幫助。

最好的，弗洛

輸入數據.head()

    Strike  DTE     IV      Pred_IV
8   0.5131  2.784   0.3366  0.733360
9   0.5131  3.781   0.3291  0.735295
20  0.5864  2.784   0.3178  0.733476
21  0.5864  3.781   0.3129  0.735357
22  0.5864  4.778   0.3008  0.736143

InputData.head().to_dict()

{'Strike': {8: 0.5131, 9: 0.5131, 20: 0.5864, 21: 0.5864, 22: 0.5864},
 'DTE': {8: 2.784, 9: 3.781, 20: 2.784, 21: 3.781, 22: 4.778},
 'IV': {8: 0.33659999999999995,
  9: 0.32909999999999995,
  20: 0.3178,
  21: 0.3129,
  22: 0.30079999999999996},
 'Pred_IV': {8: 0.7333602770095773,
  9: 0.7352946387206533,
  20: 0.7334762408944806,
  21: 0.7353567361456718,
  22: 0.7361431377881676}})

輸出數據.head()

        0.025   0.101   0.197   0.274   0.523   0.772   1.769   2.267   2.784   3.781   4.778   5.774
0.5131  NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN
0.5864  NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN
0.6597  NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN
0.7330  NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN
0.7697  NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN

OutputData.head(2).to_dict()

{0.025: {0.5131: nan,
  0.5864: nan,
  0.6597: nan,
  0.733: nan,
  0.7696999999999999: nan},
 0.101: {0.5131: nan,
  0.5864: nan,
  0.6597: nan,
  0.733: nan,
  0.7696999999999999: nan},
 0.197: {0.5131: nan,
  0.5864: nan,
  0.6597: nan,
  0.733: nan,
  0.7696999999999999: nan},
 0.274: {0.5131: nan,
  0.5864: nan,
  0.6597: nan,
  0.733: nan,
  0.7696999999999999: nan},
 0.523: {0.5131: nan,
  0.5864: nan,
  0.6597: nan,
  0.733: nan,
  0.7696999999999999: nan},
 0.772: {0.5131: nan,
  0.5864: nan,
  0.6597: nan,
  0.733: nan,
  0.7696999999999999: nan},
 1.769: {0.5131: nan,
  0.5864: nan,
  0.6597: nan,
  0.733: nan,
  0.7696999999999999: nan},
 2.267: {0.5131: nan,
  0.5864: nan,
  0.6597: nan,
  0.733: nan,
  0.7696999999999999: nan},
 2.784: {0.5131: nan,
  0.5864: nan,
  0.6597: nan,
  0.733: nan,
  0.7696999999999999: nan},
 3.781: {0.5131: nan,
  0.5864: nan,
  0.6597: nan,
  0.733: nan,
  0.7696999999999999: nan},
 4.778: {0.5131: nan,
  0.5864: nan,
  0.6597: nan,
  0.733: nan,
  0.7696999999999999: nan},
 5.774: {0.5131: nan,
  0.5864: nan,
  0.6597: nan,
  0.733: nan,
  0.7696999999999999: nan}}

Answer 1

這是一種方法來做我認為你的問題是問：

import pandas as pd
import numpy as np
InputData = pd.DataFrame(
    columns='Strike,DTE,IV,Pred_IV'.split(','),
    index=[8,9,20,21,22],
    data=[[0.5131,  2.784,   0.3366,  0.733360],
    [0.5131,  3.781,   0.3291,  0.735295],
    [0.5864,  2.784,   0.3178,  0.733476],
    [0.5864,  3.781,   0.3129,  0.735357],
    [0.5864,  4.778,   0.3008,  0.736143]])

OutputData = pd.DataFrame(data=np.NaN,
    columns=pd.Index(name='DTE', data=list(set(InputData.DTE.to_list()))), 
    index=pd.Index(name='Strike', data=list(set(InputData.Strike.to_list()))))
def foo(x):
    OutputData.loc[x.Strike, x.DTE] = x.Pred_IV
InputData.apply(foo, axis=1)
print(OutputData)

Output：

DTE        2.784     3.781     4.778
Strike
0.5131  0.733360  0.735295       NaN
0.5864  0.733476  0.735357  0.736143

如果你更喜歡未命名的索引，你可以這樣做：

OutputData = pd.DataFrame(data=np.NaN,
    columns=list(set(InputData.DTE.to_list())),
    index=list(set(InputData.Strike.to_list())))

Output：

           2.784     3.781     4.778
0.5131  0.733360  0.735295       NaN
0.5864  0.733476  0.735357  0.736143