BUG: filter with a multi-grouping including a datetimelike fails · Issue #10114 · pandas-dev/pandas (original) (raw)

Skip to content

Navigation Menu

• • GitHub Copilot Write better code with AI
  • GitHub Advanced Security Find and fix vulnerabilities
  • Actions Automate any workflow
  • Codespaces Instant dev environments
  • Issues Plan and track work
  • Code Review Manage code changes
  • Discussions Collaborate outside of code
  • Code Search Find more, search less
• Explore
  • Learning Pathways
  • Events & Webinars
  • Ebooks & Whitepapers
  • Customer Stories
  • Partners
  • Executive Insights
• • GitHub Sponsors Fund open source developers
  • The ReadME Project GitHub community articles
• • Enterprise platform AI-powered developer platform
• Pricing

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Sign up

Appearance settings

Description

this was changed in #9921 (worked prior to 0.16.0)
cc @evanpw

so using a multiple grouper where one item is a datetimelike fails as the name in self.indices fails. Instead .get_group() can be used to fix

In [1]: df = DataFrame({'A' : np.arange(5), 'B' : ['foo','bar','foo','bar','bar'], 'C' : Timestamp('20130101') })

In [2]: df
Out[2]: 
   A    B          C
0  0  foo 2013-01-01
1  1  bar 2013-01-01
2  2  foo 2013-01-01
3  3  bar 2013-01-01
4  4  bar 2013-01-01

In [3]: df.groupby(['B','C']).filter(lambda x: True)
Out[3]: 
Empty DataFrame
Columns: [A, B, C]
Index: []

In [4]: df.groupby(['B']).filter(lambda x: True)
Out[4]: 
   A    B          C
0  0  foo 2013-01-01
1  1  bar 2013-01-01
2  2  foo 2013-01-01
3  3  bar 2013-01-01
4  4  bar 2013-01-01

In [5]: df.groupby(['C']).filter(lambda x: True)
Out[5]: 
   A    B          C
0  0  foo 2013-01-01
1  1  bar 2013-01-01
2  2  foo 2013-01-01
3  3  bar 2013-01-01
4  4  bar 2013-01-01