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

[Peter Otten]

Asif Iqbal wrote:

> 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

Can you post a self-contained example, i. e. a small script that also 
creates a -- hopefully small -- DataFrame and then triggers the ValueError?