Drop even levels from multi-index dataset

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Here is sample dataset:

                          |Count|
|-------------------------|     |
|Name |ID_1  |Level |ID_2 |     |
|-----|------|------|-----|-----|
|Kate |91978 |Junior|3    |13   |
|Lucy |47992 |Junior|3    |11   |
|John |37005 |Middle|2    |8    |
|Peter|42235 |Senior|1    |21   |

Let's say ['Name', 'ID_1', 'Level','ID_2'] are multi-index names

I want to drop all even multi-index names, meaning get rid of ['ID_1',ID_2'] So that end result looks like this:

              |Count|
|-------------|     |
|Name  |Level |     |
|------|------|-----|
|Kate  |Junior|13   |
|Lucy  |Junior|11   |
|John  |Middle|8    |
|Peter |Senior|21   |

I found this method: df.index = df.index.droplevel(1)

But the thing is the real dataset is too big, and dropping each second column manually is not an option. How to drop all even columns at once?

multi-index pandas python
2 Answers

You can pass a list of index levels to DataFrame.droplevel.

For instance, given the following DataFrame

import pandas as pd

df = (
    pd.DataFrame(np.random.randint(5, size=(5,5)), 
                 columns=list('abcde'))
      .set_index(list('abcd'))
)

>>> df

         e
a b c d   
0 4 2 0  2
3 2 3 1  1
4 2 2 3  4
0 0 1 4  2
  4 3 4  4

You can do something like

res = df.droplevel(list(range(1, len(df.index.names), 2)))

>>> res

     e
a c   
0 2  2
3 3  1
4 2  4
0 1  2
  3  4
user_171206920

Df

import pandas.util.testing
df =pd.DataFrame(np.random.rand(4,8),columns=list('abcdfrtl'))

drop even indices; -

df.droplevel(df.index.names[1::2])

drop even columns; - Or use the loc accessor

new=df.iloc[:,0::2]



     a         c         f         t
0  0.069244  0.747373  0.217126  0.779653
1  0.303143  0.288040  0.234507  0.923503
2  0.195276  0.131842  0.447319  0.511451
3  0.821173  0.776493  0.827540  0.679356
user_89869750
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