Odtnhj

I can't create a pd.Series object from this dictionary for some reason. I did it with another one very similar.

Note: Updated 2018-June-04
It looks like there is a GitHub issue for this:
https://github.com/pandas-dev/pandas/issues/15457

pd.__version__
'0.23.0'


import pandas as pd
from numpy import array
import numpy as np

param_index = OrderedDict([((('criterion', 'gini'), ('max_features', 'log2'), ('min_samples_leaf', 1)), array([ 0, 40, 80, 120, 160, 200])), ((('criterion', 'gini'), ('max_features', 'log2'), ('min_samples_leaf', 2)), array([ 1, 41, 81, 121, 161, 201])), ((('criterion', 'gini'), ('max_features', 'log2'), ('min_samples_leaf', 3)), array([ 2, 42, 82, 122, 162, 202])), ((('criterion', 'gini'), ('max_features', 'log2'), ('min_samples_leaf', 5)), array([ 3, 43, 83, 123, 163, 203])), ((('criterion', 'gini'), ('max_features', 'log2'), ('min_samples_leaf', 8)), array([ 4, 44, 84, 124, 164, 204])), ((('criterion', 'gini'), ('max_features', 'sqrt'), ('min_samples_leaf', 1)), array([ 5, 45, 85, 125, 165, 205])), ((('criterion', 'gini'), ('max_features', 'sqrt'), ('min_samples_leaf', 2)), array([ 6, 46, 86, 126, 166, 206])), ((('criterion', 'gini'), ('max_features', 'sqrt'), ('min_samples_leaf', 3)), array([ 7, 47, 87, 127, 167, 207])), ((('criterion', 'gini'), ('max_features', 'sqrt'), ('min_samples_leaf', 5)), array([ 8, 48, 88, 128, 168, 208])), ((('criterion', 'gini'), ('max_features', 'sqrt'), ('min_samples_leaf', 8)), array([ 9, 49, 89, 129, 169, 209])), ((('criterion', 'gini'), ('max_features', None), ('min_samples_leaf', 1)), array([ 10, 50, 90, 130, 170, 210])), ((('criterion', 'gini'), ('max_features', None), ('min_samples_leaf', 2)), array([ 11, 51, 91, 131, 171, 211])), ((('criterion', 'gini'), ('max_features', None), ('min_samples_leaf', 3)), array([ 12, 52, 92, 132, 172, 212])), ((('criterion', 'gini'), ('max_features', None), ('min_samples_leaf', 5)), array([ 13, 53, 93, 133, 173, 213])), ((('criterion', 'gini'), ('max_features', None), ('min_samples_leaf', 8)), array([ 14, 54, 94, 134, 174, 214])), ((('criterion', 'gini'), ('max_features', 0.382), ('min_samples_leaf', 1)), array([ 15, 55, 95, 135, 175, 215])), ((('criterion', 'gini'), ('max_features', 0.382), ('min_samples_leaf', 2)), array([ 16, 56, 96, 136, 176, 216])), ((('criterion', 'gini'), ('max_features', 0.382), ('min_samples_leaf', 3)), array([ 17, 57, 97, 137, 177, 217])), ((('criterion', 'gini'), ('max_features', 0.382), ('min_samples_leaf', 5)), array([ 18, 58, 98, 138, 178, 218])), ((('criterion', 'gini'), ('max_features', 0.382), ('min_samples_leaf', 8)), array([ 19, 59, 99, 139, 179, 219])), ((('criterion', 'entropy'), ('max_features', 'log2'), ('min_samples_leaf', 1)), array([ 20, 60, 100, 140, 180, 220])), ((('criterion', 'entropy'), ('max_features', 'log2'), ('min_samples_leaf', 2)), array([ 21, 61, 101, 141, 181, 221])), ((('criterion', 'entropy'), ('max_features', 'log2'), ('min_samples_leaf', 3)), array([ 22, 62, 102, 142, 182, 222])), ((('criterion', 'entropy'), ('max_features', 'log2'), ('min_samples_leaf', 5)), array([ 23, 63, 103, 143, 183, 223])), ((('criterion', 'entropy'), ('max_features', 'log2'), ('min_samples_leaf', 8)), array([ 24, 64, 104, 144, 184, 224])), ((('criterion', 'entropy'), ('max_features', 'sqrt'), ('min_samples_leaf', 1)), array([ 25, 65, 105, 145, 185, 225])), ((('criterion', 'entropy'), ('max_features', 'sqrt'), ('min_samples_leaf', 2)), array([ 26, 66, 106, 146, 186, 226])), ((('criterion', 'entropy'), ('max_features', 'sqrt'), ('min_samples_leaf', 3)), array([ 27, 67, 107, 147, 187, 227])), ((('criterion', 'entropy'), ('max_features', 'sqrt'), ('min_samples_leaf', 5)), array([ 28, 68, 108, 148, 188, 228])), ((('criterion', 'entropy'), ('max_features', 'sqrt'), ('min_samples_leaf', 8)), array([ 29, 69, 109, 149, 189, 229])), ((('criterion', 'entropy'), ('max_features', None), ('min_samples_leaf', 1)), array([ 30, 70, 110, 150, 190, 230])), ((('criterion', 'entropy'), ('max_features', None), ('min_samples_leaf', 2)), array([ 31, 71, 111, 151, 191, 231])), ((('criterion', 'entropy'), ('max_features', None), ('min_samples_leaf', 3)), array([ 32, 72, 112, 152, 192, 232])), ((('criterion', 'entropy'), ('max_features', None), ('min_samples_leaf', 5)), array([ 33, 73, 113, 153, 193, 233])), ((('criterion', 'entropy'), ('max_features', None), ('min_samples_leaf', 8)), array([ 34, 74, 114, 154, 194, 234])), ((('criterion', 'entropy'), ('max_features', 0.382), ('min_samples_leaf', 1)), array([ 35, 75, 115, 155, 195, 235])), ((('criterion', 'entropy'), ('max_features', 0.382), ('min_samples_leaf', 2)), array([ 36, 76, 116, 156, 196, 236])), ((('criterion', 'entropy'), ('max_features', 0.382), ('min_samples_leaf', 3)), array([ 37, 77, 117, 157, 197, 237])), ((('criterion', 'entropy'), ('max_features', 0.382), ('min_samples_leaf', 5)), array([ 38, 78, 118, 158, 198, 238])), ((('criterion', 'entropy'), ('max_features', 0.382), ('min_samples_leaf', 8)), array([ 39, 79, 119, 159, 199, 239]))])


pd.Series(list(param_index.values()), index=param_index.keys())


---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/anaconda/envs/python3/lib/python3.6/site-packages/pandas/core/algorithms.py in factorize(values, sort, order, na_sentinel, size_hint)
 634 try:
--> 635 order = uniques.argsort()
 636 order2 = order.argsort()

TypeError: '<' not supported between instances of 'NoneType' and 'str'

During handling of the above exception, another exception occurred:

TypeError Traceback (most recent call last)
~/anaconda/envs/python3/lib/python3.6/site-packages/pandas/core/sorting.py in safe_sort(values, labels, na_sentinel, assume_unique)
 445 try:
--> 446 sorter = values.argsort()
 447 ordered = values.take(sorter)

TypeError: '<' not supported between instances of 'NoneType' and 'str'

During handling of the above exception, another exception occurred:

TypeError Traceback (most recent call last)
~/anaconda/envs/python3/lib/python3.6/site-packages/pandas/core/arrays/categorical.py in __init__(self, values, categories, ordered, dtype, fastpath)
 344 try:
--> 345 codes, categories = factorize(values, sort=True)
 346 except TypeError:

~/anaconda/envs/python3/lib/python3.6/site-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
 176 kwargs[new_arg_name] = new_arg_value
--> 177 return func(*args, **kwargs)
 178 return wrapper

~/anaconda/envs/python3/lib/python3.6/site-packages/pandas/core/algorithms.py in factorize(values, sort, order, na_sentinel, size_hint)
 642 na_sentinel=na_sentinel,
--> 643 assume_unique=True)
 644 

~/anaconda/envs/python3/lib/python3.6/site-packages/pandas/core/sorting.py in safe_sort(values, labels, na_sentinel, assume_unique)
 449 # try this anyway
--> 450 ordered = sort_mixed(values)
 451 

~/anaconda/envs/python3/lib/python3.6/site-packages/pandas/core/sorting.py in sort_mixed(values)
 435 dtype=bool)
--> 436 nums = np.sort(values[~str_pos])
 437 strs = np.sort(values[str_pos])

~/anaconda/envs/python3/lib/python3.6/site-packages/numpy/core/fromnumeric.py in sort(a, axis, kind, order)
 846 a = asanyarray(a).copy(order="K")
--> 847 a.sort(axis=axis, kind=kind, order=order)
 848 return a

TypeError: '<' not supported between instances of 'NoneType' and 'str'

During handling of the above exception, another exception occurred:

TypeError Traceback (most recent call last)
<ipython-input-72-1f46691300f8> in <module>()
 1 param_index = OrderedDict([((('criterion', 'gini'), ('max_features', 'log2'), ('min_samples_leaf', 1)), array([ 0, 40, 80, 120, 160, 200])), ((('criterion', 'gini'), ('max_features', 'log2'), ('min_samples_leaf', 2)), array([ 1, 41, 81, 121, 161, 201])), ((('criterion', 'gini'), ('max_features', 'log2'), ('min_samples_leaf', 3)), array([ 2, 42, 82, 122, 162, 202])), ((('criterion', 'gini'), ('max_features', 'log2'), ('min_samples_leaf', 5)), array([ 3, 43, 83, 123, 163, 203])), ((('criterion', 'gini'), ('max_features', 'log2'), ('min_samples_leaf', 8)), array([ 4, 44, 84, 124, 164, 204])), ((('criterion', 'gini'), ('max_features', 'sqrt'), ('min_samples_leaf', 1)), array([ 5, 45, 85, 125, 165, 205])), ((('criterion', 'gini'), ('max_features', 'sqrt'), ('min_samples_leaf', 2)), array([ 6, 46, 86, 126, 166, 206])), ((('criterion', 'gini'), ('max_features', 'sqrt'), ('min_samples_leaf', 3)), array([ 7, 47, 87, 127, 167, 207])), ((('criterion', 'gini'), ('max_features', 'sqrt'), ('min_samples_leaf', 5)), array([ 8, 48, 88, 128, 168, 208])), ((('criterion', 'gini'), ('max_features', 'sqrt'), ('min_samples_leaf', 8)), array([ 9, 49, 89, 129, 169, 209])), ((('criterion', 'gini'), ('max_features', None), ('min_samples_leaf', 1)), array([ 10, 50, 90, 130, 170, 210])), ((('criterion', 'gini'), ('max_features', None), ('min_samples_leaf', 2)), array([ 11, 51, 91, 131, 171, 211])), ((('criterion', 'gini'), ('max_features', None), ('min_samples_leaf', 3)), array([ 12, 52, 92, 132, 172, 212])), ((('criterion', 'gini'), ('max_features', None), ('min_samples_leaf', 5)), array([ 13, 53, 93, 133, 173, 213])), ((('criterion', 'gini'), ('max_features', None), ('min_samples_leaf', 8)), array([ 14, 54, 94, 134, 174, 214])), ((('criterion', 'gini'), ('max_features', 0.382), ('min_samples_leaf', 1)), array([ 15, 55, 95, 135, 175, 215])), ((('criterion', 'gini'), ('max_features', 0.382), ('min_samples_leaf', 2)), array([ 16, 56, 96, 136, 176, 216])), ((('criterion', 'gini'), ('max_features', 0.382), ('min_samples_leaf', 3)), array([ 17, 57, 97, 137, 177, 217])), ((('criterion', 'gini'), ('max_features', 0.382), ('min_samples_leaf', 5)), array([ 18, 58, 98, 138, 178, 218])), ((('criterion', 'gini'), ('max_features', 0.382), ('min_samples_leaf', 8)), array([ 19, 59, 99, 139, 179, 219])), ((('criterion', 'entropy'), ('max_features', 'log2'), ('min_samples_leaf', 1)), array([ 20, 60, 100, 140, 180, 220])), ((('criterion', 'entropy'), ('max_features', 'log2'), ('min_samples_leaf', 2)), array([ 21, 61, 101, 141, 181, 221])), ((('criterion', 'entropy'), ('max_features', 'log2'), ('min_samples_leaf', 3)), array([ 22, 62, 102, 142, 182, 222])), ((('criterion', 'entropy'), ('max_features', 'log2'), ('min_samples_leaf', 5)), array([ 23, 63, 103, 143, 183, 223])), ((('criterion', 'entropy'), ('max_features', 'log2'), ('min_samples_leaf', 8)), array([ 24, 64, 104, 144, 184, 224])), ((('criterion', 'entropy'), ('max_features', 'sqrt'), ('min_samples_leaf', 1)), array([ 25, 65, 105, 145, 185, 225])), ((('criterion', 'entropy'), ('max_features', 'sqrt'), ('min_samples_leaf', 2)), array([ 26, 66, 106, 146, 186, 226])), ((('criterion', 'entropy'), ('max_features', 'sqrt'), ('min_samples_leaf', 3)), array([ 27, 67, 107, 147, 187, 227])), ((('criterion', 'entropy'), ('max_features', 'sqrt'), ('min_samples_leaf', 5)), array([ 28, 68, 108, 148, 188, 228])), ((('criterion', 'entropy'), ('max_features', 'sqrt'), ('min_samples_leaf', 8)), array([ 29, 69, 109, 149, 189, 229])), ((('criterion', 'entropy'), ('max_features', None), ('min_samples_leaf', 1)), array([ 30, 70, 110, 150, 190, 230])), ((('criterion', 'entropy'), ('max_features', None), ('min_samples_leaf', 2)), array([ 31, 71, 111, 151, 191, 231])), ((('criterion', 'entropy'), ('max_features', None), ('min_samples_leaf', 3)), array([ 32, 72, 112, 152, 192, 232])), ((('criterion', 'entropy'), ('max_features', None), ('min_samples_leaf', 5)), array([ 33, 73, 113, 153, 193, 233])), ((('criterion', 'entropy'), ('max_features', None), ('min_samples_leaf', 8)), array([ 34, 74, 114, 154, 194, 234])), ((('criterion', 'entropy'), ('max_features', 0.382), ('min_samples_leaf', 1)), array([ 35, 75, 115, 155, 195, 235])), ((('criterion', 'entropy'), ('max_features', 0.382), ('min_samples_leaf', 2)), array([ 36, 76, 116, 156, 196, 236])), ((('criterion', 'entropy'), ('max_features', 0.382), ('min_samples_leaf', 3)), array([ 37, 77, 117, 157, 197, 237])), ((('criterion', 'entropy'), ('max_features', 0.382), ('min_samples_leaf', 5)), array([ 38, 78, 118, 158, 198, 238])), ((('criterion', 'entropy'), ('max_features', 0.382), ('min_samples_leaf', 8)), array([ 39, 79, 119, 159, 199, 239]))])
----> 2 pd.Series(list(param_index.values()), index=param_index.keys())

~/anaconda/envs/python3/lib/python3.6/site-packages/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
 180 
 181 if index is not None:
--> 182 index = _ensure_index(index)
 183 
 184 if data is None:

~/anaconda/envs/python3/lib/python3.6/site-packages/pandas/core/indexes/base.py in _ensure_index(index_like, copy)
 4955 index_like = copy(index_like)
 4956 
-> 4957 return Index(index_like)
 4958 
 4959 

~/anaconda/envs/python3/lib/python3.6/site-packages/pandas/core/indexes/base.py in __new__(cls, data, dtype, copy, name, fastpath, tupleize_cols, **kwargs)
 433 from .multi import MultiIndex
 434 return MultiIndex.from_tuples(
--> 435 data, names=name or kwargs.get('names'))
 436 # other iterable of some kind
 437 subarr = com._asarray_tuplesafe(data, dtype=object)

~/anaconda/envs/python3/lib/python3.6/site-packages/pandas/core/indexes/multi.py in from_tuples(cls, tuples, sortorder, names)
 1354 arrays = lzip(*tuples)
 1355 
-> 1356 return MultiIndex.from_arrays(arrays, sortorder=sortorder, names=names)
 1357 
 1358 @classmethod

~/anaconda/envs/python3/lib/python3.6/site-packages/pandas/core/indexes/multi.py in from_arrays(cls, arrays, sortorder, names)
 1298 from pandas.core.arrays.categorical import _factorize_from_iterables
 1299 
-> 1300 labels, levels = _factorize_from_iterables(arrays)
 1301 if names is None:
 1302 names = [getattr(arr, "name", None) for arr in arrays]

~/anaconda/envs/python3/lib/python3.6/site-packages/pandas/core/arrays/categorical.py in _factorize_from_iterables(iterables)
 2541 # For consistency, it should return a list of 2 lists.
 2542 return [, ]
-> 2543 return map(list, lzip(*[_factorize_from_iterable(it) for it in iterables]))

~/anaconda/envs/python3/lib/python3.6/site-packages/pandas/core/arrays/categorical.py in <listcomp>(.0)
 2541 # For consistency, it should return a list of 2 lists.
 2542 return [, ]
-> 2543 return map(list, lzip(*[_factorize_from_iterable(it) for it in iterables]))

~/anaconda/envs/python3/lib/python3.6/site-packages/pandas/core/arrays/categorical.py in _factorize_from_iterable(values)
 2513 codes = values.codes
 2514 else:
-> 2515 cat = Categorical(values, ordered=True)
 2516 categories = cat.categories
 2517 codes = cat.codes

~/anaconda/envs/python3/lib/python3.6/site-packages/pandas/core/arrays/categorical.py in __init__(self, values, categories, ordered, dtype, fastpath)
 349 # raise, as we don't have a sortable data structure and so
 350 # the user should give us one by specifying categories
--> 351 raise TypeError("'values' is not ordered, please "
 352 "explicitly specify the categories order "
 353 "by passing in a categories argument.")

TypeError: 'values' is not ordered, please explicitly specify the categories order by passing in a categories argument.

I think you just need to flatten your list of indexes.

In [1]: s = pd.Series(data=param_index.values(), index=[x[0] for x in param_index.keys()])
In [2]: s.head()
Out[2]:
 (criterion, gini) [0, 40, 80, 120, 160, 200]
 (criterion, gini) [1, 41, 81, 121, 161, 201]
 (criterion, gini) [2, 42, 82, 122, 162, 202]
 (criterion, gini) [3, 43, 83, 123, 163, 203]
 (criterion, gini) [4, 44, 84, 124, 164, 204]
 dtype: object