I've been trying to figure out how to make these tuples index keys in pandas but I'm getting an error.
How can I use the suggestion from the error with pd.Categorical below to fix this error?
I am aware that I can convert to a string but I am curious to see what is meant by the suggestion in the error message?
This works perfectly fine when I run it with 0.22.0. I've opened a GitHub issue for this if anyone wants to see the proper output from 0.22.0.
I want to update my pandas and handle this problem appropriately.
Running this with the current pandas 0.23.4:import sys; sys.version
# '3.6.4 |Anaconda, Inc.| (default, Jan 16 2018, 12:04:33) \n[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]'
import pandas as pd; pd.__version__
# '0.23.4'
index = [(('criterion', 'gini'), ('max_features', 'log2'), ('min_samples_leaf', 1)), (('criterion', 'gini'), ('max_features', 'log2'), ('min_samples_leaf', 2)), (('criterion', 'gini'), ('max_features', 'log2'), ('min_samples_leaf', 3)), (('criterion', 'gini'), ('max_features', 'log2'), ('min_samples_leaf', 5)), (('criterion', 'gini'), ('max_features', 'log2'), ('min_samples_leaf', 8)), (('criterion', 'gini'), ('max_features', 'sqrt'), ('min_samples_leaf', 1)), (('criterion', 'gini'), ('max_features', 'sqrt'), ('min_samples_leaf', 2)), (('criterion', 'gini'), ('max_features', 'sqrt'), ('min_samples_leaf', 3)), (('criterion', 'gini'), ('max_features', 'sqrt'), ('min_samples_leaf', 5)), (('criterion', 'gini'), ('max_features', 'sqrt'), ('min_samples_leaf', 8)), (('criterion', 'gini'), ('max_features', None), ('min_samples_leaf', 1)), (('criterion', 'gini'), ('max_features', None), ('min_samples_leaf', 2)), (('criterion', 'gini'), ('max_features', None), ('min_samples_leaf', 3)), (('criterion', 'gini'), ('max_features', None), ('min_samples_leaf', 5)), (('criterion', 'gini'), ('max_features', None), ('min_samples_leaf', 8)), (('criterion', 'gini'), ('max_features', 0.382), ('min_samples_leaf', 1)), (('criterion', 'gini'), ('max_features', 0.382), ('min_samples_leaf', 2)), (('criterion', 'gini'), ('max_features', 0.382), ('min_samples_leaf', 3)), (('criterion', 'gini'), ('max_features', 0.382), ('min_samples_leaf', 5)), (('criterion', 'gini'), ('max_features', 0.382), ('min_samples_leaf', 8)), (('criterion', 'entropy'), ('max_features', 'log2'), ('min_samples_leaf', 1)), (('criterion', 'entropy'), ('max_features', 'log2'), ('min_samples_leaf', 2)), (('criterion', 'entropy'), ('max_features', 'log2'), ('min_samples_leaf', 3)), (('criterion', 'entropy'), ('max_features', 'log2'), ('min_samples_leaf', 5)), (('criterion', 'entropy'), ('max_features', 'log2'), ('min_samples_leaf', 8)), (('criterion', 'entropy'), ('max_features', 'sqrt'), ('min_samples_leaf', 1)), (('criterion', 'entropy'), ('max_features', 'sqrt'), ('min_samples_leaf', 2)), (('criterion', 'entropy'), ('max_features', 'sqrt'), ('min_samples_leaf', 3)), (('criterion', 'entropy'), ('max_features', 'sqrt'), ('min_samples_leaf', 5)), (('criterion', 'entropy'), ('max_features', 'sqrt'), ('min_samples_leaf', 8)), (('criterion', 'entropy'), ('max_features', None), ('min_samples_leaf', 1)), (('criterion', 'entropy'), ('max_features', None), ('min_samples_leaf', 2)), (('criterion', 'entropy'), ('max_features', None), ('min_samples_leaf', 3)), (('criterion', 'entropy'), ('max_features', None), ('min_samples_leaf', 5)), (('criterion', 'entropy'), ('max_features', None), ('min_samples_leaf', 8)), (('criterion', 'entropy'), ('max_features', 0.382), ('min_samples_leaf', 1)), (('criterion', 'entropy'), ('max_features', 0.382), ('min_samples_leaf', 2)), (('criterion', 'entropy'), ('max_features', 0.382), ('min_samples_leaf', 3)), (('criterion', 'entropy'), ('max_features', 0.382), ('min_samples_leaf', 5)), (('criterion', 'entropy'), ('max_features', 0.382), ('min_samples_leaf', 8))]
len(index)
# 40
pd.Index(index)
Traceback (most recent call last):
File "/Users/jespinoz/anaconda/envs/py3_testing/lib/python3.6/site-packages/pandas/core/algorithms.py", line 635, in factorize
order = uniques.argsort()
TypeError: '<' not supported between instances of 'NoneType' and 'str'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/jespinoz/anaconda/envs/py3_testing/lib/python3.6/site-packages/pandas/core/sorting.py", line 451, in safe_sort
sorter = values.argsort()
TypeError: '<' not supported between instances of 'NoneType' and 'str'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/jespinoz/anaconda/envs/py3_testing/lib/python3.6/site-packages/pandas/core/arrays/categorical.py", line 345, in __init__
codes, categories = factorize(values, sort=True)
File "/Users/jespinoz/anaconda/envs/py3_testing/lib/python3.6/site-packages/pandas/util/_decorators.py", line 178, in wrapper
return func(*args, **kwargs)
File "/Users/jespinoz/anaconda/envs/py3_testing/lib/python3.6/site-packages/pandas/core/algorithms.py", line 643, in factorize
assume_unique=True)
File "/Users/jespinoz/anaconda/envs/py3_testing/lib/python3.6/site-packages/pandas/core/sorting.py", line 455, in safe_sort
ordered = sort_mixed(values)
File "/Users/jespinoz/anaconda/envs/py3_testing/lib/python3.6/site-packages/pandas/core/sorting.py", line 441, in sort_mixed
nums = np.sort(values[~str_pos])
File "/Users/jespinoz/anaconda/envs/py3_testing/lib/python3.6/site-packages/numpy/core/fromnumeric.py", line 847, in sort
a.sort(axis=axis, kind=kind, order=order)
TypeError: '<' not supported between instances of 'NoneType' and 'str'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/jespinoz/anaconda/envs/py3_testing/lib/python3.6/site-packages/pandas/core/indexes/base.py", line 449, in __new__
data, names=name or kwargs.get('names'))
File "/Users/jespinoz/anaconda/envs/py3_testing/lib/python3.6/site-packages/pandas/core/indexes/multi.py", line 1330, in from_tuples
return MultiIndex.from_arrays(arrays, sortorder=sortorder, names=names)
File "/Users/jespinoz/anaconda/envs/py3_testing/lib/python3.6/site-packages/pandas/core/indexes/multi.py", line 1274, in from_arrays
labels, levels = _factorize_from_iterables(arrays)
File "/Users/jespinoz/anaconda/envs/py3_testing/lib/python3.6/site-packages/pandas/core/arrays/categorical.py", line 2543, in _factorize_from_iterables
return map(list, lzip(*[_factorize_from_iterable(it) for it in iterables]))
File "/Users/jespinoz/anaconda/envs/py3_testing/lib/python3.6/site-packages/pandas/core/arrays/categorical.py", line 2543, in <listcomp>
return map(list, lzip(*[_factorize_from_iterable(it) for it in iterables]))
File "/Users/jespinoz/anaconda/envs/py3_testing/lib/python3.6/site-packages/pandas/core/arrays/categorical.py", line 2515, in _factorize_from_iterable
cat = Categorical(values, ordered=True)
File "/Users/jespinoz/anaconda/envs/py3_testing/lib/python3.6/site-packages/pandas/core/arrays/categorical.py", line 351, in __init__
raise TypeError("'values' is not ordered, please "
TypeError: 'values' is not ordered, please explicitly specify the categories order by passing in a categories argument
I wish the error message was a little more informative. Thanks to the above answers I was able to figure out the issue. I ended up doing this which is compatible with both versions:
>>> pd.__version__
'0.23.4'
>>> index_categorical = pd.Index([*map(frozenset, index)], dtype="category")
>>> dict(index_categorical[0])
{'criterion': 'gini', 'max_features': 'log2', 'min_samples_leaf': 1}
>>> pd.__version__
'0.22.0'
>>> index_categorical = pd.Index([*map(frozenset, index)], dtype="category")
>>> dict(index_categorical[0])
{'min_samples_leaf': 1, 'criterion': 'gini', 'max_features': 'log2'}