Hello, Michael,
I tried to discover the problem.
df[0] yields nothingdf[1] yields nothingdf[2] yields nothing
However, df[3] gives the following:sid
-9223372036854775808 NaN
1 133738.70
4 295256.11
5 137733.09
6 409413.58
8 269600.97
9 12852.94
Can we split this back to normal? or turn it into a dictionary, so that I can
put values back properly.
I like to use sid as index, some way.
Regards.
David
On Friday, 13 May 2016, 22:58, Michael Selik <michael.se...@gmail.com>
wrote:
What have code you tried? What error message are you receiving?
On Fri, May 13, 2016, 5:54 PM David Shi <davidg...@yahoo.co.uk> wrote:
Hello, Michael,
How to convert a float type column into an integer or label or string type?
On Friday, 13 May 2016, 22:02, Michael Selik <michael.se...@gmail.com>
wrote:
To clarify that you're specifying the index as a label, use df.iloc
>>> df = pd.DataFrame({'X': range(4)}, index=list('abcd')) >>> df
X a 0 b 1 c 2 d 3 >>> df.loc['a'] X 0 Name: a,
dtype: int64 >>> df.iloc[0] X 0 Name: a, dtype: int64
On Fri, May 13, 2016 at 4:54 PM David Shi <davidg...@yahoo.co.uk> wrote:
Dear Michael,
To avoid complication, I only groupby using one column.
It is OK now. But, how to refer to new row index? 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, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 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,
51.0, 53.0, 54.0, 55.0, 56.0],
dtype='float64', name=u'StateFIPS')
Regards.
David
On Friday, 13 May 2016, 21:43, Michael Selik <michael.se...@gmail.com>
wrote:
Here's an example.
>>> import pandas as pd >>> df = pd.DataFrame({'group': list('AB') * 2,
'data': range(4)}, index=list('wxyz')) >>> df data group w 0
A x 1 B y 2 A z 3 B >>> df =
df.reset_index() >>> df index data group 0 w 0 A 1
x 1 B 2 y 2 A 3 z 3 B >>>
df.groupby('group').max() index data group A y 2
B z 3
If that doesn't help, you'll need to explain what you're trying to accomplish
in detail -- what variables you started with, what transformations you want to
do, and what variables you hope to have when finished.
On Fri, May 13, 2016 at 4:36 PM David Shi <davidg...@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='object', name=0)
How to use this index?
Regards.
David
On Friday, 13 May 2016, 21:19, David Shi <davidg...@yahoo.co.uk> wrote:
Hello, Michael,
I typed in df.index
I got the followingMultiIndex(levels=[[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, 25.0,
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, 51.0, 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'SC', u'SD', u'State',
u'TN', u'TX', u'UT', u'VA', u'VT', u'WA', u'WI', u'WV', u'WY']],
labels=[[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=[u'StateFIPS', 0])Regards.
David
On Friday, 13 May 2016, 21:11, David Shi <davidg...@yahoo.co.uk> wrote:
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
On Friday, 13 May 2016, 20:58, Michael Selik <michael.se...@gmail.com>
wrote:
Just in case I misunderstood, why don't you make a little example of before
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-separated values.
On Fri, May 13, 2016 at 3:56 PM Michael Selik <michael.se...@gmail.com> wrote:
In order to preserve your index after the aggregation, you need to make sure 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 <davidg...@yahoo.co.uk> wrote:
Hello, Michael,
Why reset_index before grouping?
Regards.
David
On Friday, 13 May 2016, 17:57, Michael Selik <michael.se...@gmail.com> wrote:
On Fri, May 13, 2016 at 12:27 PM David Shi via Python-list
<python-list@python.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 meaningless.
What you could do is reset_index before the grouping and when you aggregate
decide how to handle the formerly-known-as-index column (min, max, mean, ?).
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