[英]Fill Dataframe with values from another Dataframe (not the same column names)
I'm trying to fill a empty dataframe (OutputData) in Python with values from another dataframe (InputData).我正在尝试用另一个 dataframe (InputData) 中的值填充 Python 中的空 dataframe (OutputData)。
InputData has four columns ("Strike", "DTE", "IV", "Pred_IV") OutputData has as an index all unique Strikes from InputData and as Column names all unique DTE from Input Data. InputData 有四列(“Strike”、“DTE”、“IV”、“Pred_IV”)OutputData 具有来自 InputData 的所有唯一 Strikes 作为索引,并且作为列名称,所有来自 Input Data 的唯一 DTE。
My goal is to fill the OutputData with the corresponding "Pred_IV" values from InputData.我的目标是用来自 InputData 的相应“Pred_IV”值填充 OutputData。 As it needs to match both the index and the column name I'm not getting my head around on how to do it with any known function.因为它需要同时匹配索引和列名,所以我没有考虑如何使用任何已知的 function 进行匹配。
If there is no value in InputData which matches both the index and column name the value can remain NaN如果 InputData 中没有与索引和列名匹配的值,则该值可以保持为 NaN
Find below the dataframes I use with the df.to_dict() extract for additional detail.在下面找到我使用 df.to_dict() 提取的数据帧以获取更多详细信息。
Many thanks for your help.非常感谢您的帮助。
Best, Flo最好的,弗洛
InputData.head()输入数据.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() 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}})
OutputData.head()输出数据.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() 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}}
Here is a way to do what I believe your question is asking:这是一种方法来做我认为你的问题是问:
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: Output:
DTE 2.784 3.781 4.778
Strike
0.5131 0.733360 0.735295 NaN
0.5864 0.733476 0.735357 0.736143
If you prefer unnamed indexes, you can do this instead:如果你更喜欢未命名的索引,你可以这样做:
OutputData = pd.DataFrame(data=np.NaN,
columns=list(set(InputData.DTE.to_list())),
index=list(set(InputData.Strike.to_list())))
Output: Output:
2.784 3.781 4.778
0.5131 0.733360 0.735295 NaN
0.5864 0.733476 0.735357 0.736143
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.