Thanks for the report - definitely strange! I actually think they both should raise since the slice provided is for position and not labels (.iloc expects the former and .loc the latter).

Let's see what others think

Has this been addressed? Having this issue in pands 3.6.6. and pandas 0.23.4

df.loc[0:1, 'col1'] = 0

raises now

/home/developer/.config/JetBrains/PyCharm2020.2/scratches/scratch_4.py:43: FutureWarning: Slicing a positional slice with .loc is not supported, and will raise TypeError in a future version.  Use .loc with labels or .iloc with positions instead.
  df.loc[0:1, 'col1'] = 0

which seems appropriate.

While

df.loc[0, 'col1'] = 0

raises

Traceback (most recent call last):
  File "/home/developer/.config/JetBrains/PyCharm2020.2/scratches/scratch_4.py", line 44, in <module>
    df.loc[0, 'col1'] = 0
  File "/home/developer/PycharmProjects/pandas/pandas/core/indexing.py", line 684, in __setitem__
    iloc._setitem_with_indexer(indexer, value, self.name)
  File "/home/developer/PycharmProjects/pandas/pandas/core/indexing.py", line 1613, in _setitem_with_indexer
    labels = index.insert(len(index), key)
  File "/home/developer/PycharmProjects/pandas/pandas/core/indexes/datetimelike.py", line 935, in insert
    return DatetimeIndexOpsMixin.insert(self, loc, item)
  File "/home/developer/PycharmProjects/pandas/pandas/core/indexes/datetimelike.py", line 613, in insert
    result = super().insert(loc, item)
  File "/home/developer/PycharmProjects/pandas/pandas/core/indexes/extension.py", line 340, in insert
    code = arr._validate_scalar(item)
  File "/home/developer/PycharmProjects/pandas/pandas/core/arrays/datetimelike.py", line 567, in _validate_scalar
    raise TypeError(msg)
TypeError: value should be a 'Timestamp' or 'NaT'. Got 'int' instead.

Process finished with exit code 1

which does seem appropriate too?

Any update on df.loc[0, 'col1'] = 0 getting the error TypeError: value should be a 'Timestamp' or 'NaT'. Got 'int' instead. ? I use Python 3.7.2.

yep this looks closable with a test in master @phofl as the above is correct (raising)

If this raises, is there a suggestion how to achieve what we want - i.e., assign a value to a particular cell?

u can use labels with loc or iloc with positional indexers

you cannot mix these

@jreback there are already tests for slices in main:
https://github.com/pandas-dev/pandas/blob/main/pandas/tests/indexing/test_loc.py#L2707
That should close issue 1 of 2.

The other case no longer throws an error in main. See script and results below:

import numpy as np
import pandas as pd
print(pd.__version__)

def get_df():
    arr = np.random.rand(2, 2)
    colnames = ['col1', 'col2']
    index = pd.date_range('1-1-2018', periods=2)
    df = pd.DataFrame(data=arr, index=index, columns=colnames)
    return df

print('ex2')

df = get_df()
print(df)
df.loc[0, 'col1'] = 1
print(df)

And results:

$ python /mnt/c/tmp/test_py.py
0.9.0+25706.g9289c46e16.dirty
ex2
                col1      col2
2018-01-01  0.816587  0.962762
2018-01-02  0.259267  0.076658
                         col1      col2
2018-01-01 00:00:00  0.816587  0.962762
2018-01-02 00:00:00  0.259267  0.076658
0                    1.000000       NaN

Comparing stack trace above to stack trace when TypeError is raised in main, I see that we now go through base.py which catches the TypeError and performs a cast:

https://github.com/pandas-dev/pandas/blob/main/pandas/core/indexes/base.py#L6873

So questions to @jreback are:

• Do we intend for df[0, 'col1'] = 0 to throw here? If so I'm happy to try to fix that, but lmk if this is a minor case of some larger project in works that will just clobber a fix to this bug.
• Based on my understanding above, type confusion in loc is far broader just datetimes, and the "cast to common" on TypeError in Index.insert strategy should be more nuanced (e.g. not casting to Object). Is there an opinion on correct behavior here?

take

take

Hello, is the issue still open? I am a beginner and I would like to help. Thanks!

1.df.loc[0, 'col1'] = 0

• Fails because .loc[] is label-based.
• DataFrame has a DatetimeIndex
  When you write .loc[0, 'col1'], Pandas looks for index label 0, not the first row.
  0 is not a datetime, so Pandas inserts a new row with index 0 or it causes a TypeError.

2. df.loc[0:1, 'col1'] = 0

• Fails because .loc[0:1] is trying to slice by position, which is not allowed with .loc.
• .loc expects label slices, not integer positions.
  Results in:TypeError: Slicing a positional slice with .loc is not

Solutions:
1.Use .loc (Label-Based Indexing)
.loc : label-based indexing, accesses rows and columns using the actual labels (not numeric positions).

df.loc['2018-01-01', 'col1'] = 0

2. Use .iloc (Position-Based Indexing)
   iloc : integer-location based indexing,accesses elements of a DataFrame by row and column positions (integers), not by their labels.

df.iloc[0, 0] = 0
(first row,first col)

Hi there, is this still an issue? Not seeing many responses to previous comments. Willing to help if possible.