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.
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