internal HDF5 data arrangement for DataFrames has been transposed. Legacy files will still be readable by HDFStore (GH1834, GH1824) • Legacy cruft removed: pandas.stats.misc.quantileTS • Use ISO8601 format “porting” guide listing potential issues for users migrating legacy codebases from pandas 0.7 or earlier to 0.8.0. Bug fixes to the 0.7.x series for legacy NumPy < 1.6 users will be provided as they arise. There analysis toolkit, Release 0.12.0 1.8.3 Time series changes and improvements Note: With this release, legacy scikits.timeseries users should be able to port their code to use pandas. Note: See documentation

internal HDF5 data arrangement for DataFrames has been transposed. Legacy files will still be readable by HDFStore (GH1834, GH1824) • Legacy cruft removed: pandas.stats.misc.quantileTS • Use ISO8601 format “porting” guide listing potential issues for users migrating legacy codebases from pandas 0.7 or earlier to 0.8.0. Bug fixes to the 0.7.x series for legacy NumPy < 1.6 users will be provided as they arise. There in 0.7.x beyond bug fixes. 1.10.3 Time series changes and improvements Note: With this release, legacy scikits.timeseries users should be able to port their code to use pandas. Note: See documentation

internal HDF5 data arrangement for DataFrames has been transposed. Legacy files will still be readable by HDFStore (GH1834, GH1824) • Legacy cruft removed: pandas.stats.misc.quantileTS • Use ISO8601 format “porting” guide listing potential issues for users migrating legacy codebases from pandas 0.7 or earlier to 0.8.0. Bug fixes to the 0.7.x series for legacy NumPy < 1.6 users will be provided as they arise. There in 0.7.x beyond bug fixes. 1.11.3 Time series changes and improvements Note: With this release, legacy scikits.timeseries users should be able to port their code to use pandas. Note: See documentation

internal HDF5 data arrangement for DataFrames has been transposed. Legacy files will still be readable by HDFStore (GH1834, GH1824) • Legacy cruft removed: pandas.stats.misc.quantileTS • Use ISO8601 format “porting” guide listing potential issues for users migrating legacy codebases from pandas 0.7 or earlier to 0.8.0. Bug fixes to the 0.7.x series for legacy NumPy < 1.6 users will be provided as they arise. There in 0.7.x beyond bug fixes. 1.15.3 Time series changes and improvements Note: With this release, legacy scikits.timeseries users should be able to port their code to use pandas. Note: See documentation

internal HDF5 data arrangement for DataFrames has been transposed. Legacy files will still be readable by HDFStore (GH1834, GH1824) • Legacy cruft removed: pandas.stats.misc.quantileTS • Use ISO8601 format “porting” guide listing potential issues for users migrating legacy codebases from pandas 0.7 or earlier to 0.8.0. Bug fixes to the 0.7.x series for legacy NumPy < 1.6 users will be provided as they arise. There in 0.7.x beyond bug fixes. 1.14.3 Time series changes and improvements Note: With this release, legacy scikits.timeseries users should be able to port their code to use pandas. Note: See documentation

(GH11157). • Series.is_time_series deprecated in favor of Series.index.is_all_dates (GH11135) • Legacy offsets (like ’A@JAN’) listed in here are deprecated (note that this has been alias since 0.8.0) internal HDF5 data arrangement for DataFrames has been transposed. Legacy files will still be readable by HDFStore (GH1834, GH1824) • Legacy cruft removed: pandas.stats.misc.quantileTS • Use ISO8601 format “porting” guide listing potential issues for users migrating legacy codebases from pandas 0.7 or earlier to 0.8.0. Bug fixes to the 0.7.x series for legacy NumPy < 1.6 users will be provided as they arise. There

read_gbq() method has gained the dialect argument to allow users to specify whether to use Big- Query’s legacy SQL or BigQuery’s standard SQL. See the docs for more details (GH13615). • The to_gbq() method now of expand (GH13701) • Removal of the legacy time rules (offset aliases), deprecated since 0.17.0 (this has been alias since 0.8.0) (GH13590, GH13868). Now legacy time rules raises ValueError. For the • Pretty printing sets (e.g. in DataFrame cells) now uses set literal syntax ({x,y}) instead of Legacy Python syntax (set([x,y])) (GH11215) • Improve the error message in pandas.io.gbq.to_gbq() when

read_gbq() method has gained the dialect argument to allow users to specify whether to use Big- Query’s legacy SQL or BigQuery’s standard SQL. See the docs for more details (GH13615). • The to_gbq() method now of expand (GH13701) • Removal of the legacy time rules (offset aliases), deprecated since 0.17.0 (this has been alias since 0.8.0) (GH13590, GH13868). Now legacy time rules raises ValueError. For the • Pretty printing sets (e.g. in DataFrame cells) now uses set literal syntax ({x,y}) instead of Legacy Python syntax (set([x,y])) (GH11215) • Improve the error message in pandas.io.gbq.to_gbq() when

read_gbq() method has gained the dialect argument to allow users to specify whether to use Big- Query’s legacy SQL or BigQuery’s standard SQL. See the docs for more details (GH13615). • The to_gbq() method now of expand (GH13701) • Removal of the legacy time rules (offset aliases), deprecated since 0.17.0 (this has been alias since 0.8.0) (GH13590, GH13868). Now legacy time rules raises ValueError. For the • Pretty printing sets (e.g. in DataFrame cells) now uses set literal syntax ({x, y}) instead of Legacy Python syntax (set([x, y])) (GH11215) • Improve the error message in pandas.io.gbq.to_gbq() when

read_gbq() method has gained the dialect argument to allow users to specify whether to use Big- Query’s legacy SQL or BigQuery’s standard SQL. See the docs for more details (GH13615). • The to_gbq() method now of expand (GH13701) • Removal of the legacy time rules (offset aliases), deprecated since 0.17.0 (this has been alias since 0.8.0) (GH13590, GH13868). Now legacy time rules raises ValueError. For the • Pretty printing sets (e.g. in DataFrame cells) now uses set literal syntax ({x, y}) instead of Legacy Python syntax (set([x, y])) (GH11215) • Improve the error message in pandas.io.gbq.to_gbq() when