How to put back a number-based index
Michael Selik
michael.selik at gmail.com
Sat May 14 10:33:49 EDT 2016
You might also be interested in "Python for Data Analysis" for a thorough
discussion of Pandas.
http://shop.oreilly.com/product/0636920023784.do
On Sat, May 14, 2016 at 10:29 AM Michael Selik <michael.selik at gmail.com>
wrote:
> David, it sounds like you'll need a thorough introduction to the basics of
> Python.
> Check out the tutorial: https://docs.python.org/3/tutorial/
>
> On Sat, May 14, 2016 at 6:19 AM David Shi <davidgshi at yahoo.co.uk> wrote:
>
>> Hello, Michael,
>>
>> I discovered that the problem is "two columns of data are put together"
>> and "are recognised as one column".
>>
>> This is very strange. I would like to understand the subject well.
>>
>> And, how many ways are there to investigate into the nature of objects
>> dynamically?
>>
>> Some object types only get shown as an object. Are there anything to be
>> typed in Python, to reveal objects.
>>
>> Regards.
>>
>> David
>>
>>
>> On Saturday, 14 May 2016, 4:30, Michael Selik <michael.selik at gmail.com>
>> wrote:
>>
>>
>> What were you hoping to get from ``df[0]``?
>> When you say it "yields nothing" do you mean it raised an error? What was
>> the error message?
>>
>> Have you tried a Google search for "pandas set index"?
>>
>> http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.set_index.html
>>
>> On Fri, May 13, 2016 at 11:18 PM David Shi <davidgshi at yahoo.co.uk> wrote:
>>
>> Hello, Michael,
>>
>> I tried to discover the problem.
>>
>> df[0] yields nothing
>> df[1] yields nothing
>> df[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.selik at gmail.com>
>> wrote:
>>
>>
>> What have code you tried? What error message are you receiving?
>>
>> On Fri, May 13, 2016, 5:54 PM David Shi <davidgshi at 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.selik at 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 <davidgshi at 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.selik at 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 <davidgshi at 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 <davidgshi at yahoo.co.uk> wrote:
>>
>>
>> Hello, Michael,
>>
>> I typed in df.index
>>
>> I got the following
>>
>> MultiIndex(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 <davidgshi at 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.selik at 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.selik at 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 <davidgshi at yahoo.co.uk> wrote:
>>
>> Hello, Michael,
>>
>> Why reset_index before grouping?
>>
>> Regards.
>>
>> David
>>
>>
>> On Friday, 13 May 2016, 17:57, Michael Selik <michael.selik at gmail.com>
>> wrote:
>>
>>
>>
>>
>> On Fri, May 13, 2016 at 12:27 PM David Shi via Python-list <
>> python-list at 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, ?).
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
More information about the Python-list
mailing list