Issue 1:
Code Sample, a copy-pastable example if possible

In []:
df = pd.DataFrame({'test':1, 1:2, 2:3}, index=[0])
df.loc[:, 'test':]
Out[]:
test 1 2
0 1 2 3

In []:
df.loc[:, 1:]
Out[]:
TypeError Traceback (most recent call last)
<ipython-input-655-3b97c047dcab> in <module>()
1 df = pd.DataFrame({'test':1, 1:2, 2:3}, index=[0])
----> 2 df.loc[:, 1:]
[snip]
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pandas/core/indexes/base.py in _invalid_indexer(self, form, key)
1574 "indexers [{key}] of {kind}".format(
1575 form=form, klass=type(self), key=key,
-> 1576 kind=type(key)))
1577
1578 def get_duplicates(self):

TypeError: cannot do slice indexing on <class 'pandas.core.indexes.base.Index'> with these indexers [1] of <class 'int'>

In []:
df = pd.DataFrame({0:1, 1:2, 2:3}, index=[0])
df.loc[:, 1:]
Out[]:
1 2
0 2 3

Problem description

[this should explain why the current behaviour is a problem and why the expected output is a better solution.]

When having a columns with mixed types, e.g. <str>, <int> any slicing using the int columns causes a ValueError.

When you have just int columns slicing by int does not cause an issue.

Note: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!

Note: Many problems can be resolved by simply upgrading pandas to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if master addresses this issue, but that is not necessary.

For documentation-related issues, you can check the latest versions of the docs on master here:

https://pandas-docs.github.io/pandas-docs-travis/

If the issue has not been resolved there, go ahead and file it in the issue tracker.

Expected Output

Output of pd.show_versions()

[paste the output of pd.show_versions() here below this line]

INSTALLED VERSIONS

commit: None

python: 3.6.1.final.0

python-bits: 64

OS: Darwin

OS-release: 17.5.0

machine: x86_64

processor: i386

byteorder: little

LC_ALL: None

LANG: en_US.UTF-8

LOCALE: en_US.UTF-8

pandas: 0.22.0

pytest: 3.2.2

pip: 10.0.1

setuptools: 39.0.1

Cython: 0.28.1

numpy: 1.14.2

scipy: 1.0.0

pyarrow: None

xarray: None

IPython: 6.2.1

sphinx: 1.7.2

patsy: None

dateutil: 2.6.1

pytz: 2018.4

blosc: None

bottleneck: None

tables: None

numexpr: None

feather: None

matplotlib: 2.2.2

openpyxl: None

xlrd: None

xlwt: 1.3.0

xlsxwriter: None

lxml: None

bs4: 4.6.0

html5lib: 0.9999999

sqlalchemy: None

pymysql: None

psycopg2: None

jinja2: 2.10

s3fs: None

fastparquet: None

pandas_gbq: None

pandas_datareader: 0.6.0

Issue 2:
How would people feel about me adding a "select_all" option to duplicates / duplicated that returns all duplicated rows, not just all but first or all but last?

I often check to see if I have duplicates by some primary key, and if I do, I then want to look at ALL rows with the same primary key. Right now, duplicates / duplicated won't do so and I need to use the following hack:

df[df.duplicated('key',take_last=True) | df.duplicated('key')]

It seems I"m not the only person with this issue:

http://stackoverflow.com/questions/26244309/how-to-analyze-all-duplicate-entries-in-this-pandas-dataframe

http://stackoverflow.com/questions/14657241/how-do-i-get-a-list-of-all-the-duplicate-items-using-pandas-in-python

http://stackoverflow.com/questions/23667369/drop-all-duplicate-rows-in-python-pandas

Happy to write PR if people support.