内插多索引 pandas 数据帧 [英] Interpolating multi index a pandas dataframe

问题描述

我需要插入多索引数据框:

I need to interpolate multi index dataframe:

例如:

这是主要数据帧:

a    b    c    result
1    1    1    6
1    1    2    9
1    2    1    8
1    2    2    11
2    1    1    7
2    1    2    10
2    2    1    9
2    2    2    12

我需要找到以下结果:

1.3    1.7    1.55    

到目前为止，我一直在用NaN在内部添加pd.Series 分别针对每个索引.

What I've been doing so far is appending a pd.Series inside with NaN for each index individually.

如您所见.这似乎是一种非常低效的方式.

As you can see. this seems like a VERY inefficient way.

如果有人能够丰富我，我会很高兴.

I would be happy if someone can enrich me.

P.S. 我花了一些时间查看SO，如果答案在那里，我会错过它:

P.S. I spent some time looking over SO, and if the answer is in there, I missed it:

通过插值填充多索引Pandas DataFrame

在熊猫MultiIndex内重新采样

熊猫多索引数据框，缺少值的ND插值

通过插值填充多索引Pandas DataFrame

算法:

阶段1:

a    b    c    result
1    1    1    6
1    1    2    9
1    2    1    8
1    2    2    11
1.3    1    1    6.3
1.3    1    2    9.3
1.3    2    1    8.3
1.3    2    2    11.3
2    1    1    7
2    1    2    10
2    2    1    9
2    2    2    12

阶段2:

a    b    c    result
1    1    1    6
1    1    2    9
1    2    1    8
1    2    2    11
1.3    1    1    6.3
1.3    1    2    9.3
1.3    1.7    1    7.7
1.3    1.7    2    10.7
1.3    2    1    8.3
1.3    2    2    11.3
2    1    1    7
2    1    2    10
2    2    1    9
2    2    2    12

阶段3:

a    b    c    result
1    1    1    6
1    1    2    9
1    2    1    8
1    2    2    11
1.3    1    1    6.3
1.3    1    2    9.3
1.3    1.7    1    7.7
1.3    1.7    1.55    9.35
1.3    1.7    2    10.7
1.3    2    1    8.3
1.3    2    2    11.3
2    1    1    7
2    1    2    10
2    2    1    9
2    2    2    12

推荐答案

您可以使用

You can use scipy.interpolate.LinearNDInterpolator to do what you want. If the dataframe is a MultiIndex with the column 'a','b' and 'c', then:

from scipy.interpolate import LinearNDInterpolator as lNDI
print (lNDI(points=df.index.to_frame().values, values=df.result.values)([1.3, 1.7, 1.55]))

现在，如果您具有将所有元组(a，b，c)作为要计算的索引的数据框，则可以执行以下操作:

now if you have dataframe with all the tuples (a, b, c) as index you want to calculate, you can do for example:

def pd_interpolate_MI (df_input, df_toInterpolate):
    from scipy.interpolate import LinearNDInterpolator as lNDI
    #create the function of interpolation
    func_interp = lNDI(points=df_input.index.to_frame().values, values=df_input.result.values)
    #calculate the value for the unknown index
    df_toInterpolate['result'] = func_interp(df_toInterpolate.index.to_frame().values)
    #return the dataframe with the new values
    return pd.concat([df_input, df_toInterpolate]).sort_index()

然后例如使用您的df和df_toI = pd.DataFrame(index=pd.MultiIndex.from_tuples([(1.3, 1.7, 1.55),(1.7, 1.4, 1.9)],names=df.index.names)) 然后你会得到

Then for example with your df and df_toI = pd.DataFrame(index=pd.MultiIndex.from_tuples([(1.3, 1.7, 1.55),(1.7, 1.4, 1.9)],names=df.index.names)) then you get

print (pd_interpolate_MI(df, df_toI))
              result
a   b   c           
1.0 1.0 1.00    6.00
        2.00    9.00
    2.0 1.00    8.00
        2.00   11.00
1.3 1.7 1.55    9.35
1.7 1.4 1.90   10.20
2.0 1.0 1.00    7.00
        2.00   10.00
    2.0 1.00    9.00
        2.00   12.00

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