-
-
Notifications
You must be signed in to change notification settings - Fork 18k
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
API: support "unique=True" in MultiIndex.get_level_values() #17896
Comments
By the way: this is in principle related to #2770, which however is being tackled in a different and complementary way. |
how is this not just |
#2770 is handled by |
|
ok, you are adding it there, ok!. I am not sure |
|
I agree it would be nice to have a clean way to get those unique values, but IMO it does not belong in (not directly a good idea for alternative though) |
Code Sample, a copy-pastable example if possible
I often find my self doing
Problem description
The above is very inefficient, because first a
Series
is built which includes a copy of the entire level (possibly using way more memory than the index itself), and only then duplicates are stripped. Other people on SO have faced the same problem, and this is also blocking a fix I wrote for #17845.I'm pushing a simple PR in seconds.
Expected Output
Same as above, but in an efficient way.
Output of
pd.show_versions()
INSTALLED VERSIONS
commit: None
python: 3.5.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.0-3-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.UTF-8
LOCALE: it_IT.UTF-8
pandas: 0.21.0rc1+19.gb15d92d14
pytest: 3.0.6
pip: 9.0.1
setuptools: None
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.19.0
pyarrow: None
xarray: None
IPython: 5.1.0.dev
sphinx: 1.5.6
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.3.0
numexpr: 2.6.1
feather: 0.3.1
matplotlib: 2.0.0
openpyxl: None
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.6
lxml: None
bs4: 4.5.3
html5lib: 0.999999999
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: 0.2.1
The text was updated successfully, but these errors were encountered: