pandas: powerful Python data analysis toolkit - 1.0.0allow “M”, “y”, or “Y” for the “unit” argument (GH23264) • Removed the previously deprecated keyword “time_rule” from (non-public) offsets.generate_range, which has been moved to core.arrays._ranges.generate_range() toolkit, Release 1.0.0 • Bug in DataFrame.rolling() not allowing rolling on monotonic decreasing time indexes (GH19248). • Bug in DataFrame.groupby() not offering selection by column name when axis=1 contributed patches to this release. People with a “+” by their names contributed a patch for the first time. • Aaditya Panikath + • Abdullah ˙Ihsan Seçer • Abhijeet Krishnan + • Adam J. Stewart • Adam0 码力 | 3015 页 | 10.78 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 0.25.1argument to disambiguate DST transition times (GH25017) • DataFrame.at_time() and Series.at_time() now support datetime.time objects with time- zones (GH24043) • DataFrame.pivot_table() now accepts an observed (GH2936, GH2656, GH7739, GH10519, GH12155, GH20084, GH21417) Now every group is evaluated only a single time. In [20]: df = pd.DataFrame({"a": ["x", "y"], "b": [1, 2]}) In [21]: df Out[21]: a b 0 x 1 (continues queries. IntervalIndex methods previously matched on any overlapping Interval. Behavior with scalar points, e.g. querying with an integer, is unchanged (GH16316). In [42]: ii = pd.IntervalIndex.from_tuples([(00 码力 | 2833 页 | 9.65 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 0.25.0argument to disambiguate DST transition times (GH25017) • DataFrame.at_time() and Series.at_time() now support datetime.time objects with time- zones (GH24043) • DataFrame.pivot_table() now accepts an observed (GH2936, GH2656, GH7739, GH10519, GH12155, GH20084, GH21417) Now every group is evaluated only a single time. In [20]: df = pd.DataFrame({"a": ["x", "y"], "b": [1, 2]}) In [21]: df Out[21]: a b 0 x 1 (continues queries. IntervalIndex methods previously matched on any overlapping Interval. Behavior with scalar points, e.g. querying with an integer, is unchanged (GH16316). In [42]: ii = pd.IntervalIndex.from_tuples([(00 码力 | 2827 页 | 9.62 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 1.1.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 2.1.9 Time series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 729 2.17 Time series / date functionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 734 2.17.2 Timestamps vs. time spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 735 2.17.3 Converting0 码力 | 3231 页 | 10.87 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 1.1.0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 2.1.9 Time series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 729 2.17 Time series / date functionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 734 2.17.2 Timestamps vs. time spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 735 2.17.3 Converting0 码力 | 3229 页 | 10.87 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 1.0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 715 2.14 Time series / date functionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 720 2.14.2 Timestamps vs. Time Spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 721 2.14.3 Converting to . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 730 2.14.7 Time/date components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 738 2.140 码力 | 3091 页 | 10.16 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 1.0.4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 718 2.14 Time series / date functionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 723 2.14.2 Timestamps vs. Time Spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 724 2.14.3 Converting to . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 733 2.14.7 Time/date components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 741 2.140 码力 | 3081 页 | 10.24 MB | 1 年前3
pandas: powerful Python data analysis toolkit -1.0.3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 716 3.14 Time series / date functionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 721 3.14.2 Timestamps vs. Time Spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 722 3.14.3 Converting to . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 731 3.14.7 Time/date components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 739 3.140 码力 | 3071 页 | 10.10 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 1.3.4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 2.1.9 Time series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 Exponentially Weighted window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 832 2.20 Time series / date functionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 836 2.20.2 Timestamps vs. time spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 837 2.20.3 Converting0 码力 | 3605 页 | 14.68 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 1.3.2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 2.1.9 Time series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 Exponentially Weighted window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 798 2.20 Time series / date functionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 802 2.20.2 Timestamps vs. time spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 803 2.20.3 Converting0 码力 | 3509 页 | 14.01 MB | 1 年前3
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