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Pandas: create new (sub-level) columns in a multi-index dataframe and assign values

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1

Let's be given a data-frame like the following one:

import pandas as pd 
import numpy as np

a = ['a', 'b']
b = ['i', 'ii']
mi = pd.MultiIndex.from_product([a,b], names=['first', 'second'])
A = pd.DataFrame(np.zeros([3,4]), columns=mi)

first a b
second i ii i ii
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0

I would like to create new columns iii for all first-level columns and assign the value of a new array (of matching size). I tried the following, to no avail.

A.loc[:,pd.IndexSlice[:,'iii']] = np.arange(6).reshape(3,-1)

The result should look like this:

 a b
 i ii iii i ii iii
0 0.0 0.0 0.0 0.0 0.0 1.0
1 0.0 0.0 2.0 0.0 0.0 3.0
2 0.0 0.0 4.0 0.0 0.0 5.0

python pandas multi-index

share|improve this question

asked Mar 8 at 2:31

normaniusnormanius

1,6491332

add a comment | 

1

Let's be given a data-frame like the following one:

import pandas as pd 
import numpy as np

a = ['a', 'b']
b = ['i', 'ii']
mi = pd.MultiIndex.from_product([a,b], names=['first', 'second'])
A = pd.DataFrame(np.zeros([3,4]), columns=mi)

first a b
second i ii i ii
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0

I would like to create new columns iii for all first-level columns and assign the value of a new array (of matching size). I tried the following, to no avail.

A.loc[:,pd.IndexSlice[:,'iii']] = np.arange(6).reshape(3,-1)

The result should look like this:

 a b
 i ii iii i ii iii
0 0.0 0.0 0.0 0.0 0.0 1.0
1 0.0 0.0 2.0 0.0 0.0 3.0
2 0.0 0.0 4.0 0.0 0.0 5.0

python pandas multi-index

share|improve this question

asked Mar 8 at 2:31

normaniusnormanius

1,6491332

add a comment | 

Let's be given a data-frame like the following one:

import pandas as pd 
import numpy as np

a = ['a', 'b']
b = ['i', 'ii']
mi = pd.MultiIndex.from_product([a,b], names=['first', 'second'])
A = pd.DataFrame(np.zeros([3,4]), columns=mi)

first a b
second i ii i ii
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0

I would like to create new columns iii for all first-level columns and assign the value of a new array (of matching size). I tried the following, to no avail.

A.loc[:,pd.IndexSlice[:,'iii']] = np.arange(6).reshape(3,-1)

The result should look like this:

 a b
 i ii iii i ii iii
0 0.0 0.0 0.0 0.0 0.0 1.0
1 0.0 0.0 2.0 0.0 0.0 3.0
2 0.0 0.0 4.0 0.0 0.0 5.0

python pandas multi-index

share|improve this question

asked Mar 8 at 2:31

normaniusnormanius

1,6491332

import pandas as pd 
import numpy as np

a = ['a', 'b']
b = ['i', 'ii']
mi = pd.MultiIndex.from_product([a,b], names=['first', 'second'])
A = pd.DataFrame(np.zeros([3,4]), columns=mi)

first a b
second i ii i ii
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0

A.loc[:,pd.IndexSlice[:,'iii']] = np.arange(6).reshape(3,-1)

 a b
 i ii iii i ii iii
0 0.0 0.0 0.0 0.0 0.0 1.0
1 0.0 0.0 2.0 0.0 0.0 3.0
2 0.0 0.0 4.0 0.0 0.0 5.0

share|improve this question

asked Mar 8 at 2:31

normaniusnormanius

1,6491332

share|improve this question

asked Mar 8 at 2:31

normaniusnormanius

1,6491332

asked Mar 8 at 2:31

normaniusnormanius

1,6491332

asked Mar 8 at 2:31

normaniusnormanius

1,6491332

1 Answer
1

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oldest

votes

1

Since you have multiple index in columns , I recommend create the additional append df , then concat it back

appenddf=pd.DataFrame(np.arange(6).reshape(3,-1), 
 index=A.index,
 columns=pd.MultiIndex.from_product([A.columns.levels[0],['iii']]))

appenddf
 a b
 iii iii
0 0 1
1 2 3
2 4 5
A=pd.concat([A,appenddf],axis=1).sort_index(level=0,axis=1)
A
first a b 
second i ii iii i ii iii
0 0.0 0.0 0 0.0 0.0 1
1 0.0 0.0 2 0.0 0.0 3
2 0.0 0.0 4 0.0 0.0 5

---

Another workable solution

for i,x in enumerate(A.columns.levels[0]):
 A[x,'iii']=np.arange(6).reshape(3,-1)[:,i]
A
first a b a b
second i ii i ii iii iii
0 0.0 0.0 0.0 0.0 0 1
1 0.0 0.0 0.0 0.0 2 3
2 0.0 0.0 0.0 0.0 4 5
# here I did not add `sort_index` 

share|improve this answer

answered Mar 8 at 2:39

Wen-BenWen-Ben

119k83569

add a comment | 

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1

Since you have multiple index in columns , I recommend create the additional append df , then concat it back

appenddf=pd.DataFrame(np.arange(6).reshape(3,-1), 
 index=A.index,
 columns=pd.MultiIndex.from_product([A.columns.levels[0],['iii']]))

appenddf
 a b
 iii iii
0 0 1
1 2 3
2 4 5
A=pd.concat([A,appenddf],axis=1).sort_index(level=0,axis=1)
A
first a b 
second i ii iii i ii iii
0 0.0 0.0 0 0.0 0.0 1
1 0.0 0.0 2 0.0 0.0 3
2 0.0 0.0 4 0.0 0.0 5

---

Another workable solution

for i,x in enumerate(A.columns.levels[0]):
 A[x,'iii']=np.arange(6).reshape(3,-1)[:,i]
A
first a b a b
second i ii i ii iii iii
0 0.0 0.0 0.0 0.0 0 1
1 0.0 0.0 0.0 0.0 2 3
2 0.0 0.0 0.0 0.0 4 5
# here I did not add `sort_index` 

share|improve this answer

answered Mar 8 at 2:39

Wen-BenWen-Ben

119k83569

add a comment | 

1

Since you have multiple index in columns , I recommend create the additional append df , then concat it back

appenddf=pd.DataFrame(np.arange(6).reshape(3,-1), 
 index=A.index,
 columns=pd.MultiIndex.from_product([A.columns.levels[0],['iii']]))

appenddf
 a b
 iii iii
0 0 1
1 2 3
2 4 5
A=pd.concat([A,appenddf],axis=1).sort_index(level=0,axis=1)
A
first a b 
second i ii iii i ii iii
0 0.0 0.0 0 0.0 0.0 1
1 0.0 0.0 2 0.0 0.0 3
2 0.0 0.0 4 0.0 0.0 5

---

Another workable solution

for i,x in enumerate(A.columns.levels[0]):
 A[x,'iii']=np.arange(6).reshape(3,-1)[:,i]
A
first a b a b
second i ii i ii iii iii
0 0.0 0.0 0.0 0.0 0 1
1 0.0 0.0 0.0 0.0 2 3
2 0.0 0.0 0.0 0.0 4 5
# here I did not add `sort_index` 

share|improve this answer

answered Mar 8 at 2:39

Wen-BenWen-Ben

119k83569

add a comment | 

Since you have multiple index in columns , I recommend create the additional append df , then concat it back

appenddf=pd.DataFrame(np.arange(6).reshape(3,-1), 
 index=A.index,
 columns=pd.MultiIndex.from_product([A.columns.levels[0],['iii']]))

appenddf
 a b
 iii iii
0 0 1
1 2 3
2 4 5
A=pd.concat([A,appenddf],axis=1).sort_index(level=0,axis=1)
A
first a b 
second i ii iii i ii iii
0 0.0 0.0 0 0.0 0.0 1
1 0.0 0.0 2 0.0 0.0 3
2 0.0 0.0 4 0.0 0.0 5

---

Another workable solution

for i,x in enumerate(A.columns.levels[0]):
 A[x,'iii']=np.arange(6).reshape(3,-1)[:,i]
A
first a b a b
second i ii i ii iii iii
0 0.0 0.0 0.0 0.0 0 1
1 0.0 0.0 0.0 0.0 2 3
2 0.0 0.0 0.0 0.0 4 5
# here I did not add `sort_index` 

share|improve this answer

answered Mar 8 at 2:39

Wen-BenWen-Ben

119k83569

appenddf=pd.DataFrame(np.arange(6).reshape(3,-1), 
 index=A.index,
 columns=pd.MultiIndex.from_product([A.columns.levels[0],['iii']]))

appenddf
 a b
 iii iii
0 0 1
1 2 3
2 4 5
A=pd.concat([A,appenddf],axis=1).sort_index(level=0,axis=1)
A
first a b 
second i ii iii i ii iii
0 0.0 0.0 0 0.0 0.0 1
1 0.0 0.0 2 0.0 0.0 3
2 0.0 0.0 4 0.0 0.0 5

for i,x in enumerate(A.columns.levels[0]):
 A[x,'iii']=np.arange(6).reshape(3,-1)[:,i]
A
first a b a b
second i ii i ii iii iii
0 0.0 0.0 0.0 0.0 0 1
1 0.0 0.0 0.0 0.0 2 3
2 0.0 0.0 0.0 0.0 4 5
# here I did not add `sort_index` 

share|improve this answer

answered Mar 8 at 2:39

Wen-BenWen-Ben

119k83569

answered Mar 8 at 2:39

Wen-BenWen-Ben

119k83569

answered Mar 8 at 2:39

Wen-BenWen-Ben

119k83569