Path: csiph.com!fu-berlin.de!uni-berlin.de!not-for-mail
From: David Shi <davidgshi@yahoo.co.uk>
Newsgroups: comp.lang.python
Subject: Re: How to put back a number-based index
Date: Fri, 13 May 2016 21:51:58 +0000 (UTC)
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Message-ID: <mailman.653.1463176501.32212.python-list@python.org>
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Hello, Michael,
How to convert a float type column into an integer or label or string type?=
=20

    On Friday, 13 May 2016, 22:02, Michael Selik <michael.selik@gmail.com> =
wrote:
=20

 To clarify that you're specifying the index as a label, use df.iloc
=C2=A0 =C2=A0 >>> df =3D pd.DataFrame({'X': range(4)}, index=3Dlist('abcd')=
)=C2=A0 =C2=A0 >>> df=C2=A0 =C2=A0 =C2=A0 =C2=A0X=C2=A0 =C2=A0 a =C2=A00=C2=
=A0 =C2=A0 b =C2=A01=C2=A0 =C2=A0 c =C2=A02=C2=A0 =C2=A0 d =C2=A03=C2=A0 =
=C2=A0 >>> df.loc['a']=C2=A0 =C2=A0 X =C2=A0 =C2=A00=C2=A0 =C2=A0 Name: a, =
dtype: int64=C2=A0 =C2=A0 >>> df.iloc[0]=C2=A0 =C2=A0 X =C2=A0 =C2=A00=C2=
=A0 =C2=A0 Name: a, dtype: int64
On Fri, May 13, 2016 at 4:54 PM David Shi <davidgshi@yahoo.co.uk> wrote:

Dear Michael,
To avoid complication, I only groupby using one column.
It is OK now.=C2=A0 But, how to refer to new row index?=C2=A0 How do I use =
floating index?
Float64Index([ 1.0,  4.0,  5.0,  6.0,  8.0,  9.0, 10.0, 11.0, 12.0, 13.0, 1=
6.0,
              17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 2=
7.0,
              28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 3=
8.0,
              39.0, 40.0, 41.0, 42.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 5=
0.0,
              51.0, 53.0, 54.0, 55.0, 56.0],
             dtype=3D'float64', name=3Du'StateFIPS')
Regards.
David=20

    On Friday, 13 May 2016, 21:43, Michael Selik <michael.selik@gmail.com> =
wrote:
=20

 Here's an example.
=C2=A0 =C2=A0 >>> import pandas as pd=C2=A0 =C2=A0 >>> df =3D pd.DataFrame(=
{'group': list('AB') * 2, 'data': range(4)}, index=3Dlist('wxyz'))=C2=A0 =
=C2=A0 >>> df=C2=A0 =C2=A0 =C2=A0 =C2=A0data group=C2=A0 =C2=A0 w =C2=A0 =
=C2=A0 0 =C2=A0 =C2=A0 A=C2=A0 =C2=A0 x =C2=A0 =C2=A0 1 =C2=A0 =C2=A0 B=C2=
=A0 =C2=A0 y =C2=A0 =C2=A0 2 =C2=A0 =C2=A0 A=C2=A0 =C2=A0 z =C2=A0 =C2=A0 3=
 =C2=A0 =C2=A0 B=C2=A0 =C2=A0 >>> df =3D df.reset_index()=C2=A0 =C2=A0 >>> =
df=C2=A0 =C2=A0 =C2=A0 index =C2=A0data group=C2=A0 =C2=A0 0 =C2=A0 =C2=A0 =
w =C2=A0 =C2=A0 0 =C2=A0 =C2=A0 A=C2=A0 =C2=A0 1 =C2=A0 =C2=A0 x =C2=A0 =C2=
=A0 1 =C2=A0 =C2=A0 B=C2=A0 =C2=A0 2 =C2=A0 =C2=A0 y =C2=A0 =C2=A0 2 =C2=A0=
 =C2=A0 A=C2=A0 =C2=A0 3 =C2=A0 =C2=A0 z =C2=A0 =C2=A0 3 =C2=A0 =C2=A0 B=C2=
=A0 =C2=A0 >>> df.groupby('group').max()=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =
index =C2=A0data=C2=A0 =C2=A0 group=C2=A0 =C2=A0 A =C2=A0 =C2=A0 =C2=A0 =C2=
=A0 y =C2=A0 =C2=A0 2=C2=A0 =C2=A0 B =C2=A0 =C2=A0 =C2=A0 =C2=A0 z =C2=A0 =
=C2=A0 3
If that doesn't help, you'll need to explain what you're trying to accompli=
sh in detail -- what variables you started with, what transformations you w=
ant to do, and what variables you hope to have when finished.
On Fri, May 13, 2016 at 4:36 PM David Shi <davidgshi@yahoo.co.uk> wrote:

Hello, Michael,
I changed groupby with one column.
The index is different.
Index([   u'AL',    u'AR',    u'AZ',    u'CA',    u'CO',    u'CT',    u'DC'=
,
          u'DE',    u'FL',    u'GA',    u'IA',    u'ID',    u'IL',    u'IN'=
,
          u'KS',    u'KY',    u'LA',    u'MA',    u'MD',    u'ME',    u'MI'=
,
          u'MN',    u'MO',    u'MS',    u'MT',    u'NC',    u'ND',    u'NE'=
,
          u'NH',    u'NJ',    u'NM',    u'NV',    u'NY',    u'OH',    u'OK'=
,
          u'OR',    u'PA',    u'RI',    u'SC',    u'SD', u'State',    u'TN'=
,
          u'TX',    u'UT',    u'VA',    u'VT',    u'WA',    u'WI',    u'WV'=
,
          u'WY'],
      dtype=3D'object', name=3D0)
How to use this index?
Regards.
David=20

    On Friday, 13 May 2016, 21:19, David Shi <davidgshi@yahoo.co.uk> wrote:
=20

 Hello, Michael,
I typed in df.index
I got the followingMultiIndex(levels=3D[[1.0, 4.0, 5.0, 6.0, 8.0, 9.0, 10.0=
, 11.0, 12.0, 13.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 2=
5.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0=
, 38.0, 39.0, 40.0, 41.0, 42.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 5=
1.0, 53.0, 54.0, 55.0, 56.0], [u'AL', u'AR', u'AZ', u'CA', u'CO', u'CT', u'=
DC', u'DE', u'FL', u'GA', u'IA', u'ID', u'IL', u'IN', u'KS', u'KY', u'LA', =
u'MA', u'MD', u'ME', u'MI', u'MN', u'MO', u'MS', u'MT', u'NC', u'ND', u'NE'=
, u'NH', u'NJ', u'NM', u'NV', u'NY', u'OH', u'OK', u'OR', u'PA', u'RI', u'S=
C', u'SD', u'State', u'TN', u'TX', u'UT', u'VA', u'VT', u'WA', u'WI', u'WV'=
, u'WY']],
           labels=3D[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,=
 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34=
, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48], [0, 2, 1, 3, 4, =
5, 7, 6, 8, 9, 11, 12, 13, 10, 14, 15, 16, 19, 18, 17, 20, 21, 23, 22, 24, =
27, 31, 28, 29, 30, 32, 25, 26, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 45,=
 44, 46, 48, 47, 49]],
           names=3D[u'StateFIPS', 0])Regards.
David=20

    On Friday, 13 May 2016, 21:11, David Shi <davidgshi@yahoo.co.uk> wrote:
=20

 Dear Michael,
I have done a number of operation in between.
Providing that information does not help you
How to reset index after grouping and various operations is of interest.
How to type in a command to find out its current dataframe?
Regards.
David=20

    On Friday, 13 May 2016, 20:58, Michael Selik <michael.selik@gmail.com> =
wrote:
=20

 Just in case I misunderstood, why don't you make a little example of befor=
e and after the grouping? This mailing list does not accept attachments, so=
 you'll have to make do with pasting a few rows of comma-separated or tab-s=
eparated values.
On Fri, May 13, 2016 at 3:56 PM Michael Selik <michael.selik@gmail.com> wro=
te:

In order to preserve your index after the aggregation, you need to make sur=
e it is considered a data column (via reset_index) and then choose how your=
 aggregation will operate on that column.
On Fri, May 13, 2016 at 3:29 PM David Shi <davidgshi@yahoo.co.uk> wrote:

Hello, Michael,
Why reset_index before grouping?
Regards.
David=20

  On Friday, 13 May 2016, 17:57, Michael Selik <michael.selik@gmail.com> wr=
ote:
=20

=20

On Fri, May 13, 2016 at 12:27 PM David Shi via Python-list <python-list@pyt=
hon.org> wrote:

I lost my indexes after grouping in Pandas.
I managed to rest_index and got back the index column.
But How can I get back a index row?


Was the grouping an aggregation? If so, the original indexes are meaningles=
s. What you could do is reset_index before the grouping and when you aggreg=
ate decide how to handle the formerly-known-as-index column (min, max, mean=
, ?).

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