Revisions to Multi-Index Sorting in Pandas

added 1 character in body

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edited Jun 20, 2019 at 14:32

577
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This one liner works for me:

In [1]: groupdgrouped.sortlevel(["Manufacturer","Product Launch Date"], sort_remaining=False)

                                               Sales
Manufacturer Product Name Product Launch Date       
Apple        iPod         2001-10-23              34
             iPad         2010-04-03              30
Samsung      Galaxy       2009-04-27              24
             Galaxy Tab   2010-09-02              22

Note this works too:

groups.sortlevel([0,2], sort_remaining=False)

This wouldn't have worked when you originally posted over two years ago, because sortlevel by default sorted on ALL indices which mucked up your company hierarchy. sort_remaining which disables that behavior was added last year. Here's the commit link for reference: https://github.com/pydata/pandas/commit/3ad64b11e8e4bef47e3767f1d31cc26e39593277

This one liner works for me:

In [1]: groupd.sortlevel(["Manufacturer","Product Launch Date"], sort_remaining=False)

                                               Sales
Manufacturer Product Name Product Launch Date       
Apple        iPod         2001-10-23              34
             iPad         2010-04-03              30
Samsung      Galaxy       2009-04-27              24
             Galaxy Tab   2010-09-02              22

Note this works too:

groups.sortlevel([0,2], sort_remaining=False)

This wouldn't have worked when you originally posted over two years ago, because sortlevel by default sorted on ALL indices which mucked up your company hierarchy. sort_remaining which disables that behavior was added last year. Here's the commit link for reference: https://github.com/pydata/pandas/commit/3ad64b11e8e4bef47e3767f1d31cc26e39593277

This one liner works for me:

In [1]: grouped.sortlevel(["Manufacturer","Product Launch Date"], sort_remaining=False)

                                               Sales
Manufacturer Product Name Product Launch Date       
Apple        iPod         2001-10-23              34
             iPad         2010-04-03              30
Samsung      Galaxy       2009-04-27              24
             Galaxy Tab   2010-09-02              22

Note this works too:

groups.sortlevel([0,2], sort_remaining=False)

This wouldn't have worked when you originally posted over two years ago, because sortlevel by default sorted on ALL indices which mucked up your company hierarchy. sort_remaining which disables that behavior was added last year. Here's the commit link for reference: https://github.com/pydata/pandas/commit/3ad64b11e8e4bef47e3767f1d31cc26e39593277

Dropped an unnecessary parenthesis and improved grammar.

Source Link

edited Oct 9, 2015 at 20:46

Jim

577
1
7
14

This one liner works for me:

In [1]: groupd.sortlevel(["Manufacturer","Product Launch Date"], sort_remaining=False)

                                               Sales
Manufacturer Product Name Product Launch Date       
Apple        iPod         2001-10-23              34
             iPad         2010-04-03              30
Samsung      Galaxy       2009-04-27              24
             Galaxy Tab   2010-09-02              22

Note this works too:

groups.sortlevel([0,2], sort_remaining=False)

This wouldn't have worked when you originally posted over two years ago, because sortlevel by default sorted on ALL indices which mucked up your company hierarchy. sort_remaining which disables that behavior was added last year). CommitHere's the commit link for reference: https://github.com/pydata/pandas/commit/3ad64b11e8e4bef47e3767f1d31cc26e39593277

This one liner works for me:

In [1]: groupd.sortlevel(["Manufacturer","Product Launch Date"], sort_remaining=False)

                                               Sales
Manufacturer Product Name Product Launch Date       
Apple        iPod         2001-10-23              34
             iPad         2010-04-03              30
Samsung      Galaxy       2009-04-27              24
             Galaxy Tab   2010-09-02              22

Note this works too:

groups.sortlevel([0,2], sort_remaining=False)

This wouldn't have worked when you originally posted over two years ago, because sortlevel by default sorted on ALL indices which mucked up your company hierarchy. sort_remaining which disables that behavior was added last year). Commit link for reference: https://github.com/pydata/pandas/commit/3ad64b11e8e4bef47e3767f1d31cc26e39593277

This one liner works for me:

In [1]: groupd.sortlevel(["Manufacturer","Product Launch Date"], sort_remaining=False)

                                               Sales
Manufacturer Product Name Product Launch Date       
Apple        iPod         2001-10-23              34
             iPad         2010-04-03              30
Samsung      Galaxy       2009-04-27              24
             Galaxy Tab   2010-09-02              22

Note this works too:

groups.sortlevel([0,2], sort_remaining=False)

This wouldn't have worked when you originally posted over two years ago, because sortlevel by default sorted on ALL indices which mucked up your company hierarchy. sort_remaining which disables that behavior was added last year. Here's the commit link for reference: https://github.com/pydata/pandas/commit/3ad64b11e8e4bef47e3767f1d31cc26e39593277

Source Link

answered Oct 9, 2015 at 20:39

Jim

577
1
7
14

This one liner works for me:

In [1]: groupd.sortlevel(["Manufacturer","Product Launch Date"], sort_remaining=False)

                                               Sales
Manufacturer Product Name Product Launch Date       
Apple        iPod         2001-10-23              34
             iPad         2010-04-03              30
Samsung      Galaxy       2009-04-27              24
             Galaxy Tab   2010-09-02              22

Note this works too:

groups.sortlevel([0,2], sort_remaining=False)

This wouldn't have worked when you originally posted over two years ago, because sortlevel by default sorted on ALL indices which mucked up your company hierarchy. sort_remaining which disables that behavior was added last year). Commit link for reference: https://github.com/pydata/pandas/commit/3ad64b11e8e4bef47e3767f1d31cc26e39593277

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