would be inconsistent with other selection methods in pandas (as this is not a slice, nor does it resolve to one) dft[’2013-1-15 12:30:00’] To select a single row, use .loc In [61]: dft.loc[’2013-1-15 nothing passed to reindex (GH1267) • More robust NA handling in DataFrame.drop_duplicates (GH557) • Resolve locale-based and pre-epoch HDF5 timestamp deserialization issues (GH973, GH1081, GH179) • Implement

# Otherwise index.get_value will raise InvalidIndexError 983 try: 984 # For labels that don't resolve as scalars like tuples and frozensets File /pandas/pandas/core/series.py:1089, in Series._get_value(self # Otherwise index.get_value will raise InvalidIndexError 983 try: 984 # For labels that don't resolve as scalars like tuples and frozensets File /pandas/pandas/core/series.py:1089, in Series._get_value(self than just dropping the repeats, using groupby() on the index is a common trick. For example, we’ll resolve duplicates by taking the average of all rows with the same label. In [18]: df2.groupby(level=0)

# Otherwise index.get_value will raise InvalidIndexError 962 try: 963 # For labels that don't resolve as scalars like tuples and frozensets File /pandas/pandas/core/series.py:1069, in Series._get_value(self than just dropping the repeats, using groupby() on the index is a common trick. For example, we’ll resolve duplicates by taking the average of all rows with the same label. In [18]: df2.groupby(level=0) axis="columns") ....: except Exception as err: ....: print(repr(err)) ....: KeyError('a') To resolve this issue, one can make a copy so that the mutation does not apply to the container being iterated

# Otherwise index.get_value will raise InvalidIndexError 962 try: 963 # For labels that don't resolve as scalars like tuples and frozensets File /pandas/pandas/core/series.py:1069, in Series._get_value(self than just dropping the repeats, using groupby() on the index is a common trick. For example, we’ll resolve duplicates by taking the average of all rows with the same label. In [18]: df2.groupby(level=0) axis="columns") ....: except Exception as err: ....: print(repr(err)) ....: KeyError('a') To resolve this issue, one can make a copy so that the mutation does not apply to the container being iterated

than just dropping the repeats, using groupby() on the index is a common trick. For example, we’ll resolve duplicates by taking the average of all rows with the same label. In [18]: df2.groupby(level=0) axis="columns") ....: except Exception as err: ....: print(repr(err)) ....: KeyError('a') To resolve this issue, one can make a copy so that the mutation does not apply to the container being iterated of existing categoricals when used in operations that combine categoricals, e.g. astype, and will resolve to False if there is no existing ordered to maintain. See also: Categorical Represent a categorical

than just dropping the repeats, using groupby() on the index is a common trick. For example, we’ll resolve duplicates by taking the average of all rows with the same label. In [18]: df2.groupby(level=0) axis="columns") ....: except Exception as err: ....: print(repr(err)) ....: KeyError('a') To resolve this issue, one can make a copy so that the mutation does not apply to the container being iterated of existing categoricals when used in operations that combine categoricals, e.g. astype, and will resolve to False if there is no existing ordered to maintain. See also: Categorical Represent a categorical

than just dropping the repeats, using groupby() on the index is a common trick. For example, we’ll resolve duplicates by taking the average of all rows with the same label. In [18]: df2.groupby(level=0) axis="columns") ....: except Exception as err: ....: print(repr(err)) ....: KeyError('a') To resolve this issue, one can make a copy so that the mutation does not apply to the container being iterated of existing categoricals when used in operations that combine categoricals, e.g. astype, and will resolve to False if there is no existing ordered to maintain. See also: Categorical Represent a categorical

Highlights include: • Temporarily restore matplotlib datetime plotting functionality. This should resolve issues for users who implic- itly relied on pandas to plot datetimes with matplotlib. See here. your commits on top of the latest pandas git master. If this leads to merge conflicts, you must resolve these before submitting your pull request. If you have uncommitted changes, you will need to stash Highlights include: • Temporarily restore matplotlib datetime plotting functionality. This should resolve issues for users who relied implicitly on pandas to plot datetimes with matplotlib. See here. •

your commits on top of the lastest pandas git master. If this leads to merge conflicts, you must resolve these before submitting your Pull Request. If you have uncommitted changes, you will need to stash would be inconsistent with other selection methods in pandas (as this is not a slice, nor does it resolve to one) dft['2013-1-15 12:30:00'] To select a single row, use .loc In [71]: dft.loc['2013-1-15 nothing passed to reindex (GH1267) • More robust NA handling in DataFrame.drop_duplicates (GH557) • Resolve locale-based and pre-epoch HDF5 timestamp deserialization issues (GH973, GH1081, GH179) • Implement

would be inconsistent with other selection methods in pandas (as this is not a slice, nor does it resolve to one) dft[’2013-1-15 12:30:00’] To select a single row, use .loc In [63]: dft.loc[’2013-1-15 nothing passed to reindex (GH1267) • More robust NA handling in DataFrame.drop_duplicates (GH557) • Resolve locale-based and pre-epoch HDF5 timestamp deserialization issues (GH973, GH1081, GH179) • Implement