I've got a DataFrame
like below:
ex = pd.DataFrame({'speed': {(1252540, 0): 0.0,
(1252540, 1): 0.0,
(1252540, 2): 0.0,
(1252541, 0): 0.0,
(1252541, 1): 0.0,
(1252541, 2): 0.0,
(1252543, 0): 0.0,
(1252543, 1): 0.0,
(1252543, 2): 0.0,
(1252544, 0): 0.0,
(1252544, 1): 0.0,
(1252544, 2): 0.0,
(1252545, 0): 0.0,
(1252545, 1): 0.0,
(1252545, 2): 0.0,
(1252546, 3): 0.0,
(1252546, 4): 0.0,
(1252546, 5): 0.0,
(1252547, 3): 0.0,
(1252547, 4): 0.0},
'unknown': {(1252540, 0): np.nan,
(1252540, 1): np.nan,
(1252540, 2): np.nan,
(1252541, 0): np.nan,
(1252541, 1): np.nan,
(1252541, 2): np.nan,
(1252543, 0): np.nan,
(1252543, 1): np.nan,
(1252543, 2): np.nan,
(1252544, 0): np.nan,
(1252544, 1): np.nan,
(1252544, 2): np.nan,
(1252545, 0): np.nan,
(1252545, 1): np.nan,
(1252545, 2): np.nan,
(1252546, 3): np.nan,
(1252546, 4): np.nan,
(1252546, 5): np.nan,
(1252547, 3): np.nan,
(1252547, 4): np.nan}})
ex.index.names = ['id', 'id2']
I'd like to set a first level of MultiIndex
to (0, 0, 0, 1, 1, 1, 2, 2, 2, ...)
so that each new value in level 0 is assigned with next integer. Normally, I could do simple shift with something like:
idx = ex.index.get_level_values(0).to_numeric()
idx -= idx.min()
but as you can see, some values ( 1252542
) may be missing from original index, while there shouldn't be any gap in new indexing. How can I accomplish that? If I could preserve the mapping (like 1252540 -> 0, 1252541 -> 1, 1252543 -> 2...
), possibly in a form of dict, it'd great, but it's not mandatory.
Let me know if this helps:
indices = ex.index.get_level_values('id').unique().sort_values()
dict = {}
for key,value in (zip(indices,range(0,len(indices)))):
dict[key] = value
ex.rename(index=dict)
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