Pandas: Get unique MultiIndex level values by label

Pandas 0.23.0 finally introduced a much cleaner solution to this problem: the level argument to Index.unique():

In [3]: df.index.unique(level='co')
Out[3]: Index(['DE', 'FR'], dtype='object', name='co')

This is now the recommended solution. It is far more efficient because it avoids creating a complete representation of the level values in memory, and re-scanning it.

I guess u want unique values in a certain level (and by level names) of a multiindex. I usually do the following, which is a bit long.

In [11]: df.index.get_level_values('co').unique()
Out[11]: array(['DE', 'FR'], dtype=object)

An alternative approach is to find the number of levels by calling df.index.levels[level_index] where level_index can be inferred from df.index.names.index(level_name). In the above example level_name = 'co'.

The proposed answer by @Happy001 computes the unique which may be computationally intensive.

If you're going to do the level lookup repeatedly, you could create a map of your index level names to level unique values with:

df_level_value_map = {
    name: level 
    for name, level in zip(df.index.names, df.index.levels)
}
df_level_value_map['']

But this is not in any way more efficient (or shorter) than your original attempts if you're only going to do this lookup once.

I really wish there was a method on indexes that returned such a dictionary (or series?) with a name like:

df.index.get_level_map(levels={...})

Where the levels parameter can limit the map to a subset of the existing levels. I could do without the parameter if it could be a property like:

df.index.level_map