[Tutor] Adding a new row to the dataframe with datetime as index

[Asif Iqbal]

On Mon, May 21, 2018 at 9:28 AM, Peter Otten <__peter__ at web.de> wrote:

> Asif Iqbal wrote:
>
> > Hi,
> >
> > I am trying to add a new row to a new date in the dataframe like below
> >
> >    df.loc['2018-01-24'] = [0,1,2,3,4,5]
> >
> > And I am getting the following error
> >
> >   ValueError: cannot set using a list-like indexer with a different
> length
> > than the value
> >
> > I do have the right number of columns and I can lookup a row by the date
> >
> >   df.loc['2018-01-23']
> >
> >   df.shape
> >   (8034, 6)
> >
> >   df.index
> >   DatetimeIndex(['2018-01-23', '2018-01-22', '2018-01-19', '2018-01-18',
> >                '2018-01-17', '2018-01-16', '2018-01-12', '2018-01-11',
> >                '2018-01-10', '2018-01-09',
> >                ...
> >                '1986-03-25', '1986-03-24', '1986-03-21', '1986-03-20',
> >                '1986-03-19', '1986-03-18', '1986-03-17', '1986-03-14',
> >                '1986-03-13', '2018-01-24'],
> >               dtype='datetime64[ns]', name='date', length=8034,
> freq=None)
> >
> > Any idea how to add a new row to a new date?
>
> My experiments indicate that there may be multiple values with the same
> key:
>
> > >>> import pandas as pd
> >>> df = pd.DataFrame([[1,2], [3,4], [5,6], [7,8]], index=["a", "b", "a",
> "a"])
> >>> df.loc["a"]
>    0  1
> a  1  2
> a  5  6
> a  7  8
>
> [3 rows x 2 columns]
> >>> df.loc["a"] = [10, 20]
> Traceback (most recent call last):
>   File "<stdin>", line 1, in <module>
>   File "/usr/lib/python3/dist-packages/pandas/core/indexing.py", line 98,
> in
> __setitem__
>     self._setitem_with_indexer(indexer, value)
>   File "/usr/lib/python3/dist-packages/pandas/core/indexing.py", line
> 422,
> in _setitem_with_indexer
>     self.obj._data = self.obj._data.setitem(indexer, value)
>   File "/usr/lib/python3/dist-packages/pandas/core/internals.py", line
> 2396,
> in setitem
>     return self.apply('setitem', *args, **kwargs)
>   File "/usr/lib/python3/dist-packages/pandas/core/internals.py", line
> 2376,
> in apply
>     applied = getattr(blk, f)(*args, **kwargs)
>   File "/usr/lib/python3/dist-packages/pandas/core/internals.py", line
> 615,
> in setitem
>     raise ValueError("cannot set using a list-like indexer "
> ValueError: cannot set using a list-like indexer with a different length
> than the value
>
> If found two ways to resolve this,
>
> (1) the obvious, ensure that the lengths are the same:
>
> >>> df.loc["a"] = [[10, 20], [30, 40], [50, 60]]
> >>> df
>     0   1
> a  10  20
> b   3   4
> a  30  40
> a  50  60
>
> (2) pass the key as a tuple:
>
> >>> df.loc["a",] = [1000, 2000]
> >>> df
>       0     1
> a  1000  2000
> b     3     4
> a  1000  2000
> a  1000  2000
>
> [4 rows x 2 columns]
>
> I suspect that you want neither, and instead avoid duplicate keys.



I want to overwrite the row

  print ( df.loc['2018-01-24'] )
  2018-01-24 0.0 1.0 2.0 3.0 4.0 NaN


  df.loc['2018-01-24'] = [0,1,2,3,4,5]
  ValueError: cannot set using a list-like indexer with a different length
than the value





>
> _______________________________________________
> Tutor maillist  -  Tutor at python.org
> To unsubscribe or change subscription options:
> https://mail.python.org/mailman/listinfo/tutor
>



-- 
Asif Iqbal
PGP Key: 0xE62693C5 KeyServer: pgp.mit.edu
A: Because it messes up the order in which people normally read text.
Q: Why is top-posting such a bad thing?