where assigning a arrays.PandasArray to a pandas.core.frame.DataFrame would raise error (GH26390) • Allow keyword arguments for callable local reference used in the DataFrame.query() string (GH26426) • Fixed DataFrame.to_parquet() which would raise a ValueError when the dataframe had no columns (GH27339) • Allow parsing of PeriodDtype columns when using read_csv() (GH26934) 1.6.13 Plotting • Fixed bug where (GH27083) 1.6.19 Other • Removed unused C functions from vendored UltraJSON implementation (GH26198) • Allow Index and RangeIndex to be passed to numpy min and max functions (GH26125) • Use actual class name

where assigning a arrays.PandasArray to a pandas.core.frame.DataFrame would raise error (GH26390) • Allow keyword arguments for callable local reference used in the DataFrame.query() string (GH26426) • Fixed DataFrame.to_parquet() which would raise a ValueError when the dataframe had no columns (GH27339) • Allow parsing of PeriodDtype columns when using read_csv() (GH26934) 1.6.13 Plotting • Fixed bug where (GH27083) 1.6.19 Other • Removed unused C functions from vendored UltraJSON implementation (GH26198) • Allow Index and RangeIndex to be passed to numpy min and max functions (GH26125) • Use actual class name

to_gbq . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 994 25.10.3 Authentication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 995 25.10 match rows ONLY with prior data, and not an exact match. In [6]: pd.merge_asof(left, right, on='a', allow_exact_matches=False) Out[6]: 1.1. v0.19.0 (October 2, 2016) 5 pandas: powerful Python data analysis 0 2013-01-01 09:00:03 3.0 2013-01-01 09:00:05 NaN 2013-01-01 09:00:06 4.0 Furthermore, we now allow an optional on parameter to specify a column (rather than the default of the index) in a DataFrame

to_gbq . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 996 25.10.3 Authentication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 997 25.10 match rows ONLY with prior data, and not an exact match. In [6]: pd.merge_asof(left, right, on='a', allow_exact_matches=False) Out[6]: a left_val right_val 0 1 a NaN 1 5 b 3.0 2 10 c 7.0 In a typical 0 2013-01-01 09:00:03 3.0 2013-01-01 09:00:05 NaN 2013-01-01 09:00:06 4.0 Furthermore, we now allow an optional on parameter to specify a column (rather than the default of the index) in a DataFrame

fashion as with a DatetimeIndex. (GH23882) • pandas.api.types.is_list_like() has gained a keyword allow_sets which is True by default; if False, all instances of set will not be considered “list-like” anymore parameter (GH8839) • DataFrame.to_records() now accepts index_dtypes and column_dtypes parameters to allow different data types in stored column and index records (GH18146) • IntervalIndex has gained the matches the API of pandas.api.extensions.ExtensionArray. take() (GH19506): – The default value of allow_fill has changed from False to True. – The out and mode parameters are now longer accepted (previously

2 Limiting output Spreadsheet programs will only show one screenful of data at a time and then allow you to scroll, so there isn’t really a need to limit output. In pandas, you’ll need to put a little data analysis toolkit, Release 1.3.2 Transforming with a dict Passing a dict of functions will allow selective transforming per column. In [195]: tsdf.transform({"A": np.abs, "B": lambda x: x + 1}) Name: scalar-name, dtype: float64 The methods DataFrame.rename_axis() and Series.rename_axis() allow specific names of a MultiIndex to be changed (as opposed to the labels). In [250]: df = pd.DataFrame(

pandas! Limiting output Spreadsheet programs will only show one screenful of data at a time and then allow you to scroll, so there isn’t really a need to limit output. In pandas, you’ll need to put a little 842205 2000-01-10 0.030876 0.969124 Transforming with a dict Passing a dict of functions will allow selective transforming per column. In [195]: tsdf.transform({"A": np.abs, "B": lambda x: x + 1}) Name: scalar-name, dtype: float64 The methods DataFrame.rename_axis() and Series.rename_axis() allow specific names of a MultiIndex to be changed (as opposed to the labels). In [250]: df = pd.DataFrame(

pandas! Limiting output Spreadsheet programs will only show one screenful of data at a time and then allow you to scroll, so there isn’t really a need to limit output. In pandas, you’ll need to put a little 842205 2000-01-10 0.030876 0.969124 Transforming with a dict Passing a dict of functions will allow selective transforming per column. In [195]: tsdf.transform({"A": np.abs, "B": lambda x: x + 1}) Name: scalar-name, dtype: float64 The methods DataFrame.rename_axis() and Series.rename_axis() allow specific names of a MultiIndex to be changed (as opposed to the labels). In [250]: df = pd.DataFrame(

no longer support the all and any aggregation functions and will now raise TypeError. (GH8302). • Allow equality comparisons of Series with a categorical dtype and object dtype; previously these would raise context manager to HDFStore for automatic closing (GH8791). • to_datetime gains an exact keyword to allow for a format to not require an exact match for a provided for- mat string (if its False). exact defaults returns data for valid symbols and np.nan for invalid (GH8494) • Bug in get_quote_yahoo that wouldn’t allow non-float return values (GH5229). 1.3 v0.15.0 (October 18, 2014) This is a major release from 0

returns data for valid symbols and np.nan for invalid (GH8494) • Bug in get_quote_yahoo that wouldn’t allow non-float return values (GH5229). 1.2 v0.15.0 (October 18, 2014) This is a major release from 0 and behaves in a similar manner, but allows compatibility with np.timedelta64 types as well as a host of custom representation, parsing, and attributes. This type is very similar to how Timestamp works 1 1 days 00:00:02 2 dtype: int32 Finally, the combination of TimedeltaIndex with DatetimeIndex allow certain combination operations that are NaT preserving: In [30]: tdi = TimedeltaIndex([’1 days’,pd