 pandas: powerful Python data analysis toolkit - 1.3.3Series.sort_index() and DataFrame.sort_index() methods are used to sort a pandas object by its index levels. In [300]: df = pd.DataFrame( .....: { .....: "one": pd.Series(np.random.randn(3), index=["a" function to apply to the index being sorted. For MultiIndex objects, the key is applied per-level to the levels specified by level. In [307]: s1 = pd.DataFrame({"a": ["B", "a", "C"], "b": [1, 2, 3], "c": [2, column You must be explicit about sorting when the column is a MultiIndex, and fully specify all levels to by. In [345]: df1.columns = pd.MultiIndex.from_tuples( .....: [("a", "one"), ("a", "two"), ("b"0 码力 | 3603 页 | 14.65 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 1.3.3Series.sort_index() and DataFrame.sort_index() methods are used to sort a pandas object by its index levels. In [300]: df = pd.DataFrame( .....: { .....: "one": pd.Series(np.random.randn(3), index=["a" function to apply to the index being sorted. For MultiIndex objects, the key is applied per-level to the levels specified by level. In [307]: s1 = pd.DataFrame({"a": ["B", "a", "C"], "b": [1, 2, 3], "c": [2, column You must be explicit about sorting when the column is a MultiIndex, and fully specify all levels to by. In [345]: df1.columns = pd.MultiIndex.from_tuples( .....: [("a", "one"), ("a", "two"), ("b"0 码力 | 3603 页 | 14.65 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 1.3.4Series.sort_index() and DataFrame.sort_index() methods are used to sort a pandas object by its index levels. In [300]: df = pd.DataFrame( .....: { .....: "one": pd.Series(np.random.randn(3), index=["a" function to apply to the index being sorted. For MultiIndex objects, the key is applied per-level to the levels specified by level. In [307]: s1 = pd.DataFrame({"a": ["B", "a", "C"], "b": [1, 2, 3], "c": [2, column You must be explicit about sorting when the column is a MultiIndex, and fully specify all levels to by. In [345]: df1.columns = pd.MultiIndex.from_tuples( .....: [("a", "one"), ("a", "two"), ("b"0 码力 | 3605 页 | 14.68 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 1.3.4Series.sort_index() and DataFrame.sort_index() methods are used to sort a pandas object by its index levels. In [300]: df = pd.DataFrame( .....: { .....: "one": pd.Series(np.random.randn(3), index=["a" function to apply to the index being sorted. For MultiIndex objects, the key is applied per-level to the levels specified by level. In [307]: s1 = pd.DataFrame({"a": ["B", "a", "C"], "b": [1, 2, 3], "c": [2, column You must be explicit about sorting when the column is a MultiIndex, and fully specify all levels to by. In [345]: df1.columns = pd.MultiIndex.from_tuples( .....: [("a", "one"), ("a", "two"), ("b"0 码力 | 3605 页 | 14.68 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 1.3.2Series.sort_index() and DataFrame.sort_index() methods are used to sort a pandas object by its index levels. In [300]: df = pd.DataFrame( .....: { .....: "one": pd.Series(np.random.randn(3), index=["a" function to apply to the index being sorted. For MultiIndex objects, the key is applied per-level to the levels specified by level. In [307]: s1 = pd.DataFrame({"a": ["B", "a", "C"], "b": [1, 2, 3], "c": [2, column You must be explicit about sorting when the column is a MultiIndex, and fully specify all levels to by. In [345]: df1.columns = pd.MultiIndex.from_tuples( .....: [("a", "one"), ("a", "two"), ("b"0 码力 | 3509 页 | 14.01 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 1.3.2Series.sort_index() and DataFrame.sort_index() methods are used to sort a pandas object by its index levels. In [300]: df = pd.DataFrame( .....: { .....: "one": pd.Series(np.random.randn(3), index=["a" function to apply to the index being sorted. For MultiIndex objects, the key is applied per-level to the levels specified by level. In [307]: s1 = pd.DataFrame({"a": ["B", "a", "C"], "b": [1, 2, 3], "c": [2, column You must be explicit about sorting when the column is a MultiIndex, and fully specify all levels to by. In [345]: df1.columns = pd.MultiIndex.from_tuples( .....: [("a", "one"), ("a", "two"), ("b"0 码力 | 3509 页 | 14.01 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 1.4.2Series.sort_index() and DataFrame.sort_index() methods are used to sort a pandas object by its index levels. In [300]: df = pd.DataFrame( .....: { .....: "one": pd.Series(np.random.randn(3), index=["a" function to apply to the index being sorted. For MultiIndex objects, the key is applied per-level to the levels specified by level. 252 Chapter 2. User Guide pandas: powerful Python data analysis toolkit, Release column You must be explicit about sorting when the column is a MultiIndex, and fully specify all levels to by. In [345]: df1.columns = pd.MultiIndex.from_tuples( .....: [("a", "one"), ("a", "two"), ("b"0 码力 | 3739 页 | 15.24 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 1.4.2Series.sort_index() and DataFrame.sort_index() methods are used to sort a pandas object by its index levels. In [300]: df = pd.DataFrame( .....: { .....: "one": pd.Series(np.random.randn(3), index=["a" function to apply to the index being sorted. For MultiIndex objects, the key is applied per-level to the levels specified by level. 252 Chapter 2. User Guide pandas: powerful Python data analysis toolkit, Release column You must be explicit about sorting when the column is a MultiIndex, and fully specify all levels to by. In [345]: df1.columns = pd.MultiIndex.from_tuples( .....: [("a", "one"), ("a", "two"), ("b"0 码力 | 3739 页 | 15.24 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 1.4.4Series.sort_index() and DataFrame.sort_index() methods are used to sort a pandas object by its index levels. In [300]: df = pd.DataFrame( .....: { .....: "one": pd.Series(np.random.randn(3), index=["a" function to apply to the index being sorted. For MultiIndex objects, the key is applied per-level to the levels specified by level. 252 Chapter 2. User Guide pandas: powerful Python data analysis toolkit, Release column You must be explicit about sorting when the column is a MultiIndex, and fully specify all levels to by. In [345]: df1.columns = pd.MultiIndex.from_tuples( .....: [("a", "one"), ("a", "two"), ("b"0 码力 | 3743 页 | 15.26 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 1.4.4Series.sort_index() and DataFrame.sort_index() methods are used to sort a pandas object by its index levels. In [300]: df = pd.DataFrame( .....: { .....: "one": pd.Series(np.random.randn(3), index=["a" function to apply to the index being sorted. For MultiIndex objects, the key is applied per-level to the levels specified by level. 252 Chapter 2. User Guide pandas: powerful Python data analysis toolkit, Release column You must be explicit about sorting when the column is a MultiIndex, and fully specify all levels to by. In [345]: df1.columns = pd.MultiIndex.from_tuples( .....: [("a", "one"), ("a", "two"), ("b"0 码力 | 3743 页 | 15.26 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 1.5.0rc0Series.sort_index() and DataFrame.sort_index() methods are used to sort a pandas object by its index levels. In [300]: df = pd.DataFrame( .....: { .....: "one": pd.Series(np.random.randn(3), index=["a" function to apply to the index being sorted. For MultiIndex objects, the key is applied per-level to the levels specified by level. 256 Chapter 2. User Guide pandas: powerful Python data analysis toolkit, Release column You must be explicit about sorting when the column is a MultiIndex, and fully specify all levels to by. In [345]: df1.columns = pd.MultiIndex.from_tuples( .....: [("a", "one"), ("a", "two"), ("b"0 码力 | 3943 页 | 15.73 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 1.5.0rc0Series.sort_index() and DataFrame.sort_index() methods are used to sort a pandas object by its index levels. In [300]: df = pd.DataFrame( .....: { .....: "one": pd.Series(np.random.randn(3), index=["a" function to apply to the index being sorted. For MultiIndex objects, the key is applied per-level to the levels specified by level. 256 Chapter 2. User Guide pandas: powerful Python data analysis toolkit, Release column You must be explicit about sorting when the column is a MultiIndex, and fully specify all levels to by. In [345]: df1.columns = pd.MultiIndex.from_tuples( .....: [("a", "one"), ("a", "two"), ("b"0 码力 | 3943 页 | 15.73 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 0.20.3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438 7.3.4 Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438 axis with MultiIndex . . . . . . . . . . . . . . . . . . . . . . . . . . . . 641 13.1.4 Defined Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 641 13.1.5 Data . . . . . . 651 13.2.4 Swapping levels with swaplevel() . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 652 13.2.5 Reordering levels with reorder_levels() . . . . . . . . . . . . . . . .0 码力 | 2045 页 | 9.18 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 0.20.3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438 7.3.4 Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438 axis with MultiIndex . . . . . . . . . . . . . . . . . . . . . . . . . . . . 641 13.1.4 Defined Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 641 13.1.5 Data . . . . . . 651 13.2.4 Swapping levels with swaplevel() . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 652 13.2.5 Reordering levels with reorder_levels() . . . . . . . . . . . . . . . .0 码力 | 2045 页 | 9.18 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 0.21.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 466 7.3.4 Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 466 axis with MultiIndex . . . . . . . . . . . . . . . . . . . . . . . . . . . . 671 13.1.4 Defined Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 671 13.1.5 Data . . . . . . 680 13.2.4 Swapping levels with swaplevel() . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 681 13.2.5 Reordering levels with reorder_levels() . . . . . . . . . . . . . . . .0 码力 | 2207 页 | 8.59 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 0.21.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 466 7.3.4 Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 466 axis with MultiIndex . . . . . . . . . . . . . . . . . . . . . . . . . . . . 671 13.1.4 Defined Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 671 13.1.5 Data . . . . . . 680 13.2.4 Swapping levels with swaplevel() . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 681 13.2.5 Reordering levels with reorder_levels() . . . . . . . . . . . . . . . .0 码力 | 2207 页 | 8.59 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 0.19.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 392 8.3.4 Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 392 . . . . . . 588 14.2.4 Swapping levels with swaplevel() . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 589 14.2.5 Reordering levels with reorder_levels() . . . . . . . . . . . . . . . . unstacking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 708 19.2.1 Multiple Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 710 19.2.2 Missing0 码力 | 1943 页 | 12.06 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 0.19.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 392 8.3.4 Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 392 . . . . . . 588 14.2.4 Swapping levels with swaplevel() . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 589 14.2.5 Reordering levels with reorder_levels() . . . . . . . . . . . . . . . . unstacking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 708 19.2.1 Multiple Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 710 19.2.2 Missing0 码力 | 1943 页 | 12.06 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 0.19.0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390 8.3.4 Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390 . . . . . . 586 14.2.4 Swapping levels with swaplevel() . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587 14.2.5 Reordering levels with reorder_levels() . . . . . . . . . . . . . . . . unstacking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 706 19.2.1 Multiple Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 708 19.2.2 Missing0 码力 | 1937 页 | 12.03 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 0.19.0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390 8.3.4 Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390 . . . . . . 586 14.2.4 Swapping levels with swaplevel() . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587 14.2.5 Reordering levels with reorder_levels() . . . . . . . . . . . . . . . . unstacking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 706 19.2.1 Multiple Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 708 19.2.2 Missing0 码力 | 1937 页 | 12.03 MB | 1 年前3
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