Pandas: how can I create multi-level columns
-
16-10-2019 - |
Pergunta
I have a pandas DataFrame which has the following columns:
n_0
n_1
p_0
p_1
e_0
e_1
I want to transform it to have columns and sub-columns:
0
n
p
e
1
n
p
e
I've searched in the documentation, and I'm completely lost on how to implement this. Does anyone have any suggestions?
Solução
Finally, I found a solution.
You can find the example script below.
#!/usr/bin/env python3
import pickle
import pandas as pd
import itertools
import numpy as np
data = pd.DataFrame(np.random.randn(10, 5), columns=('0_n', '1_n', '0_p', '1_p', 'x'))
indices = set()
groups = set()
others = set()
for c in data.columns:
if '_' in c:
(i, g) = c.split('_')
c2 = pd.MultiIndex.from_tuples((i, g),)
indices.add(int(i))
groups.add(g)
else:
others.add(c)
columns = list(itertools.product(groups, indices))
columns = pd.MultiIndex.from_tuples(columns)
ret = pd.DataFrame(columns=columns)
for c in columns:
ret[c] = data['%d_%s' % (int(c[1]), c[0])]
for c in others:
ret[c] = data['%s' % c]
ret.rename(columns={'total': 'total_indices'}, inplace=True)
print("Before:")
print(data)
print("")
print("After:")
print(ret)
Sorry for this...
Outras dicas
I had to adjust victor's sort to get OP's specific column format:
df = df.sort_index(level=0, axis=1)
0 1
e n p e n p
0 -0.995452 -3.237846 1.298927 -0.269253 -0.857724 -0.461103
There is a simpler solution:
data.columns = data.columns.str.split('_', expand=True)
To arrange column names one can also do:
data.sort_index(axis=1, inplace=True)
To change column levels:
data = data.reorder_levels([1,0], axis=1)
Licenciado em: CC-BY-SA com atribuição
Não afiliado a datascience.stackexchange