85625373985124742 This is all exactly identical to the behavior before. However, if you ask for a key not contained in the Series, in versions 0.6.1 and prior, Series would fall back on a location-based for Series containing objects (PR241) • Added inner join option to DataFrame.join when joining on key(s) (GH248) • Implemented selecting DataFrame columns by passing a list to __getitem__ (GH253) • Implemented should be sent to: support@lambdafoundry.com 4.4 Credits pandas development began at AQR Capital Management in April 2008. It was open-sourced at the end of 2009. AQR continued to provide resources for development

85625373985124742 This is all exactly identical to the behavior before. However, if you ask for a key not contained in the Series, in versions 0.6.1 and prior, Series would fall back on a location-based for Series containing objects (PR241) • Added inner join option to DataFrame.join when joining on key(s) (GH248) • Implemented selecting DataFrame columns by passing a list to __getitem__ (GH253) • Implemented should be sent to: support@lambdafoundry.com 4.4 Credits pandas development began at AQR Capital Management in April 2008. It was open-sourced at the end of 2009. AQR continued to provide resources for development

2963101333219374 This is all exactly identical to the behavior before. However, if you ask for a key not contained in the Series, in versions 0.6.1 and prior, Series would fall back on a location-based for Series containing objects (PR241) • Added inner join option to DataFrame.join when joining on key(s) (GH248) • Implemented selecting DataFrame columns by passing a list to __getitem__ (GH253) • Implemented should be sent to: support@lambdafoundry.com 4.4 Credits pandas development began at AQR Capital Management in April 2008. It was open-sourced at the end of 2009. AQR continued to provide resources for development

column from a table as a Series. – deprecated the unique method, can be replicated by select_column(key,column).unique() – min_itemsize parameter to append will now automatically create data_columns for (GH2694) • Fixed performance issues while aggregating boolean data (GH2692) • When given a boolean mask key and a Series of new values, Series __setitem__ will now align the incoming values with the original longer sorts the group keys (sort=False) by default. This was done for performance reasons: the group-key sorting is often one of the more expensive parts of the computation and is often unnec- essary. •

column from a table as a Series. – deprecated the unique method, can be replicated by select_column(key,column).unique() – min_itemsize parameter to append will now automatically create data_columns for (GH2694) • Fixed performance issues while aggregating boolean data (GH2692) • When given a boolean mask key and a Series of new values, Series __setitem__ will now align the incoming values with the original longer sorts the group keys (sort=False) by default. This was done for performance reasons: the group-key sorting is often one of the more expensive parts of the computation and is often unnec- essary. •

implemented for bool dtypes • In HDFStore, select_as_multiple will always raise a KeyError, when a key or the selector is not found (GH6177) • df[’col’] = value and df.loc[:,’col’] = value are now completely 10:00:00 2013-09-05 10:00:00 1 In [78]: pivot_table(df, index=Grouper(freq=’M’, key=’Date’), ....: columns=Grouper(freq=’M’, key=’PayDay’), ....: values=’Quantity’, aggfunc=np.sum) ....: Out[78]: PayDay column from a table as a Series. – deprecated the unique method, can be replicated by select_column(key,column).unique() – min_itemsize parameter to append will now automatically create data_columns for

TypeError rather than ValueError (a couple of edge cases only), (GH8865) • Bug in using a pd.Grouper(key=...) with no level/axis or level only (GH8795, GH8866) • Report a TypeError when invalid/no paramaters powerful Python data analysis toolkit, Release 0.15.2 • Bug in DatetimeIndex when using time object as key (GH8667) • Bug in merge where how=’left’ and sort=False would not preserve left frame order (GH7331) StataWriter when writing large files (GH8079) • Performance and memory usage improvements in multi-key groupby (GH8128) • Performance improvements in groupby .agg and .apply where builtins max/min were

StataWriter when writing large files (GH8079) • Performance and memory usage improvements in multi-key groupby (GH8128) • Performance improvements in groupby .agg and .apply where builtins max/min were passing a where (GH8014) • Bug in DataFrameGroupby.transform when transforming with a passed non-sorted key (GH8046, GH8430) • Bug in repeated timeseries line and area plot may result in ValueError or incorrect fill_method was ignored if you passed how (GH2073) • Bug in TimeGrouper doesn’t exclude column specified by key (GH7227) • Bug in DataFrame and Series bar and barh plot raises TypeError when bottom and left keyword

df.groupby(’key’) as well as the .sum() operation. N = 1000000 ngroups = 10 df = DataFrame({'key' : np.random.randint(0,ngroups,size=N), 'data' : np.random.randn(N) }) df.groupby('key')['data'].sum() values (GH8790) Observation Origin _merge value Merge key only in ’left’ frame left_only Merge key only in ’right’ frame right_only Merge key in both frames both In [40]: df1 = pd.DataFrame({'col1':[0 HDFStores when using the table format (GH10447) • Enable pd.read_hdf to be used without specifying a key when the HDF file contains a single dataset (GH10443) • pd.read_stata will now read Stata 118 type

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 696 18.2.4 Joining key columns on an index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 697 18.2.5 Joining column and the index level (:issue‘14327‘) • Bug in df.groupby where TypeError raised when pd.Grouper(key=...) is passed in a list (GH14334) • Bug in pd.pivot_table may raise TypeError or ValueError when merge_asof() performs an asof merge, which is similar to a left-join except that we match on nearest key rather than equal keys. In [1]: left = pd.DataFrame({'a': [1, 5, 10], ...: 'left_val': ['a', 'b'