How to reorder indexed rows based on a list in Pandas data frame

Question

I have a data frame that looks like this:

company  Amazon  Apple  Yahoo
name
A             0    130      0
C           173      0      0
Z             0      0    150

It was created using this code:

import pandas as pd
df = pd.DataFrame({'name' : ['A', 'Z','C'],
                   'company' : ['Apple', 'Yahoo','Amazon'],
                   'height' : [130, 150,173]})

df = df.pivot(index="name", columns="company", values="height").fillna(0)

What I want to do is to sort the row (with index name) according to a predefined list:

["Z", "C", "A"]`

Resulting in this :

company  Amazon  Apple  Yahoo
name
Z             0      0    150
C           173      0      0
A             0    130      0

How can I achieve that?

Answer 1

You could set index on predefined order using reindex like

In [14]: df.reindex(["Z", "C", "A"])
Out[14]:
company  Amazon  Apple  Yahoo
Z             0      0    150
C           173      0      0
A             0    130      0

However, if it's alphabetical order, you could use sort_index(ascending=False)

In [12]: df.sort_index(ascending=False)
Out[12]:
company  Amazon  Apple  Yahoo
name
Z             0      0    150
C           173      0      0
A             0    130      0

Like pointed below, you need to assign it to some variable

In [13]: df = df.sort_index(ascending=False)

Answer 2

We could also use loc:

lst = ["Z", "C", "A"]
df = df.loc[lst]

Output:

company  Amazon  Apple  Yahoo
name                         
Z             0      0    150
C           173      0      0
A             0    130      0

Note that if there are values in lst that does not exist in df.index (e.g. if lst=['Z','C','A','D']), then loc throws a KeyError (whereas reindex creates a new row 'D' full of NaNs).

---

MultiIndex

If df is MultiIndex, such as:

           C3
C1 C2        
2  evelen   0
   ten      1
   twelve   2
1  evelen   3
   ten      4
   twelve   5

and if you want to sort the second level by ten,eleven andtwelve, then using loc:

out = df.loc[:, ['ten','evelen', 'twelve'],:]

Output:

           C3
C1 C2        
2  evelen   0
   ten      1
   twelve   2
1  evelen   3
   ten      4
   twelve   5

and for both levels:

out = df.loc[[1,2], ['ten','evelen','twelve'], :]

Output:

           C3
C1 C2        
1  ten      4
   evelen   3
   twelve   5
2  ten      1
   evelen   0
   twelve   2