I have a multi-index DataFrame created via a groupby operation. I'm trying to do a compound sort using several levels of the index, but I can't seem to find a sort function that does what I need.
Initial dataset looks something like this (daily sales counts of various products):
Date Manufacturer Product Name Product Launch Date Sales
0 2013-01-01 Apple iPod 2001-10-23 12
1 2013-01-01 Apple iPad 2010-04-03 13
2 2013-01-01 Samsung Galaxy 2009-04-27 14
3 2013-01-01 Samsung Galaxy Tab 2010-09-02 15
4 2013-01-02 Apple iPod 2001-10-23 22
5 2013-01-02 Apple iPad 2010-04-03 17
6 2013-01-02 Samsung Galaxy 2009-04-27 10
7 2013-01-02 Samsung Galaxy Tab 2010-09-02 7
I use groupby to get a sum over the date range:
> grouped = df.groupby(['Manufacturer', 'Product Name', 'Product Launch Date']).sum()
Sales
Manufacturer Product Name Product Launch Date
Apple iPad 2010-04-03 30
iPod 2001-10-23 34
Samsung Galaxy 2009-04-27 24
Galaxy Tab 2010-09-02 22
So far so good!
Now the last thing I want to do is sort each manufacturer's products by launch date, but keep them grouped hierarchically under Manufacturer - here's all I am trying to do:
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
When I try sortlevel() I lose the nice per-company hierarchy I had before:
> grouped.sortlevel('Product Launch Date')
Sales
Manufacturer Product Name Product Launch Date
Apple iPod 2001-10-23 34
Samsung Galaxy 2009-04-27 24
Apple iPad 2010-04-03 30
Samsung Galaxy Tab 2010-09-02 22
sort() and sort_index() just fail:
grouped.sort(['Manufacturer','Product Launch Date'])
KeyError: u'no item named Manufacturer'
grouped.sort_index(by=['Manufacturer','Product Launch Date'])
KeyError: u'no item named Manufacturer'
Seems like a simple operation, but I can't quite figure it out.
I'm not tied to using a MultiIndex for this, but since that's what groupby() returns, that's what I've been working with.
BTW the code to produce the initial DataFrame is:
data = {
'Date': ['2013-01-01', '2013-01-01', '2013-01-01', '2013-01-01', '2013-01-02', '2013-01-02', '2013-01-02', '2013-01-02'],
'Manufacturer' : ['Apple', 'Apple', 'Samsung', 'Samsung', 'Apple', 'Apple', 'Samsung', 'Samsung',],
'Product Name' : ['iPod', 'iPad', 'Galaxy', 'Galaxy Tab', 'iPod', 'iPad', 'Galaxy', 'Galaxy Tab'],
'Product Launch Date' : ['2001-10-23', '2010-04-03', '2009-04-27', '2010-09-02','2001-10-23', '2010-04-03', '2009-04-27', '2010-09-02'],
'Sales' : [12, 13, 14, 15, 22, 17, 10, 7]
}
df = DataFrame(data, columns=['Date', 'Manufacturer', 'Product Name', 'Product Launch Date', 'Sales'])
A hack would be to change the order of the levels:
In [11]: g
Out[11]:
Sales
Manufacturer Product Name Product Launch Date
Apple iPad 2010-04-03 30
iPod 2001-10-23 34
Samsung Galaxy 2009-04-27 24
Galaxy Tab 2010-09-02 22
In [12]: g.index = g.index.swaplevel(1, 2)
Sortlevel, which (as you've found) sorts the MultiIndex levels in order:
In [13]: g = g.sortlevel()
And swap back:
In [14]: g.index = g.index.swaplevel(1, 2)
In [15]: g
Out[15]:
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
I'm of the opinion that sortlevel should not sort the remaining labels in order, so will create a github issue. :) Although it's worth mentioning the docnote about "the need for sortedness".
Note: you could avoid the first swaplevel by reordering the order of the initial groupby:
g = df.groupby(['Manufacturer', 'Product Launch Date', 'Product Name']).sum()
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
If you want try to avoid multiple swaps within a very deep MultiIndex, you also could try with this:
- Slicing by level X (by list comprehension + .loc + IndexSlice)
- Sort the desired level (sortlevel(2))
- Concatenate every group of level X indexes
Here you have the code:
import pandas as pd
idx = pd.IndexSlice
g = pd.concat([grouped.loc[idx[i,:,:],:].sortlevel(2) for i in grouped.index.levels[0]])
g
If you are not concerned about conserving the index (I often prefer an arbitrary integer index) you can just use the following one-liner:
grouped.reset_index().sort(["Manufacturer","Product Launch Date"])
To sort a MultiIndex by the "index columns" (aka. levels) you need to use the .sort_index() method and set its level argument. If you want to sort by multiple levels, the argument needs to be set to a list of level names in sequential order.
This should give you the DataFrame you need:
df.groupby(['Manufacturer', 'Product Name', 'Launch Date']).sum().sort_index(level=['Manufacturer','Launch Date'])
来源：https://stackoverflow.com/questions/17242970/multi-index-sorting-in-pandas