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 NaNfor 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:
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 scipy.interpolate.LinearNDInterpolator 即可执行所需的操作.如果数据框是具有列"a"，"b"和"c"的MultiIndex，则:
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
这篇关于内插多索引 pandas 数据帧的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！