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Series

Bodo provides extensive Series support. However, operations between Series (+, -, /, ,*) do not implicitly align values based on their associated index values yet.

pd.Series

  • pandas.Series(data=None, index=None, dtype=None, name=None, copy=False, fastpath=False)

    Supported Arguments

    argument

    datatypes

    other requirements

    data

    • Series type
    • List type
    • Array type
    • Constant Dictionary
    • None

    index

    • SeriesType

    dtype

    • Numpy or Pandas Type
    • String name for Numpy/Pandas Type
    • Must be constant at Compile Time
    • String/Data Type must be one of the supported types (see Series.astype())

    name

    • String

    Note

    If data is a Series and index is provided, implicit alignment is not performed yet.

    Example Usage

    >>> @bodo.jit
    ... def f():
    ...     return pd.Series(np.arange(1000), dtype=np.float64, name="my_series")
    >>> f()
    0        0.0
    1        1.0
    2        2.0
    3        3.0
    4        4.0
          ...
    995    995.0
    996    996.0
    997    997.0
    998    998.0
    999    999.0
    Name: my_series, Length: 1000, dtype: float64
    

Attributes

pd.Series.index

  • pandas.Series.index

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.index
    >>> S = pd.Series(np.arange(1000))
    >>> f(S)
    RangeIndex(start=0, stop=1000, step=1)
    

pd.Series.values

  • pandas.Series.values

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.values
    >>> S = pd.Series(np.arange(1000))
    >>> f(S)
    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,
            13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,
            26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,
            39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,
            52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,
            65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
            78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
            91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,
           104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,
           117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,
           130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,
           143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,
           156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,
           169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,
           195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207,
           208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220,
           221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,
           234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246,
           247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259,
           260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272,
           273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285,
           286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298,
           299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311,
           312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324,
           325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337,
           338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350,
           351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363,
           364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376,
           377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389,
           390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402,
           403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415,
           416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428,
           429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441,
           442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454,
           455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467,
           468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480,
           481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493,
           494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506,
           507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519,
           520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532,
           533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545,
           546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558,
           559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571,
           572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584,
           585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597,
           598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610,
           611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623,
           624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636,
           637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649,
           650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662,
           663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675,
           676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688,
           689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701,
           702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714,
           715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727,
           728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740,
           741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753,
           754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766,
           767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779,
           780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792,
           793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805,
           806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818,
           819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831,
           832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844,
           845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857,
           858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870,
           871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883,
           884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896,
           897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909,
           910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922,
           923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935,
           936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948,
           949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961,
           962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974,
           975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987,
           988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999])
    

pd.Series.dtype

  • pandas.Series.dtype (object data

    types such as dtype of string series not supported yet)

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dtype
    >>> S = pd.Series(np.arange(1000))
    >>> f(S)
    dtype('int64')
    

pd.Series.shape

  • pandas.Series.shape

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.shape
    >>> S = pd.Series(np.arange(1000))
    >>> f(S)
    (1000,)
    

pd.Series.nbytes

  • pandas.Series.nbytes

    Note

    This tracks the number of bytes used by Bodo which may differ from the Pandas values.

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.nbytes
    >>> S = pd.Series(np.arange(1000))
    >>> f(S)
    8000
    

pd.Series.ndim

  • pandas.Series.ndim

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.ndim
    >>> S = pd.Series(np.arange(1000))
    >>> f(S)
    1
    

pd.Series.size

  • pandas.Series.size

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.size
    >>> S = pd.Series(np.arange(1000))
    >>> f(S)
    1000
    

pd.Series.T

  • pandas.Series.T

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.T
    >>> S = pd.Series(np.arange(1000))
    >>> f(S)
    0        0
    1        1
    2        2
    3        3
    4        4
          ...
    995    995
    996    996
    997    997
    998    998
    999    999
    Length: 1000, dtype: int64
    

pd.Series.memory_usage

  • pandas.Series.memory_usage(index=True, deep=False)

    Supported Arguments

    argument

    datatypes

    other requirements

    index

    • Boolean

    Must be constant at Compile Time

    Note

    This tracks the number of bytes used by Bodo which may differ from the Pandas values.

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.memory_usage()
    >>> S = pd.Series(np.arange(1000))
    >>> f(S)
    8024
    

pd.Series.hasnans

  • pandas.Series.hasnans

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.hasnans
    >>> S = pd.Series(np.arange(1000))
    >>> f(S)
    False
    

pd.Series.empty

  • pandas.Series.empty

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.empty
    >>> S = pd.Series(np.arange(1000))
    >>> f(S)
    False
    

pd.Series.dtypes

  • pandas.Series.dtypes

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dtypes
    >>> S = pd.Series(np.arange(1000))
    >>> f(S)
    dtype('int64')
    

pd.Series.name

  • pandas.Series.name

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.name
    >>> S = pd.Series(np.arange(1000), name="my_series")
    >>> f(S)
    'my_series'
    

Conversion:

pd.Series.astype

  • pandas.Series.astype(dtype, copy=True, errors="raise", _bodo_nan_to_str=True)

    Supported Arguments

    argument

    datatypes

    other requirements

    dtype

    • String (string must be parsable by np.dtype)
    • Valid type (see types)
    • The following functions: float, int, bool, str

    Must be constant at Compile Time

    copy

    • Boolean

    Must be constant at Compile Time

    _bodo_nan_to_str

    • Boolean
    • Must be constant at Compile Time
    • Argument unique to Bodo. When True NA values in when converting to string are represented as NA instead of a string representation of the NA value (i.e. 'nan'), the default Pandas behavior.

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.astype(np.float32)
    >>> S = pd.Series(np.arange(1000))
    >>> f(S)
    0        0.0
    1        1.0
    2        2.0
    3        3.0
    4        4.0
          ...
    995    995.0
    996    996.0
    997    997.0
    998    998.0
    999    999.0
    Length: 1000, dtype: float32
    

pd.Series.copy

  • pandas.Series.copy(deep=True)

    Supported Arguments

    argument

    datatypes

    deep

    • Boolean

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.copy()
    >>> S = pd.Series(np.arange(1000))
    >>> f(S)
    0        0
    1        1
    2        2
    3        3
    4        4
          ...
    995    995
    996    996
    997    997
    998    998
    999    999
    Length: 1000, dtype: int64
    

pd.Series.to_numpy

  • pandas.Series.to_numpy(dtype=None, copy=False, na_value=None)

    Supported Arguments None

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.to_numpy()
    >>> S = pd.Series(np.arange(1000))
    >>> f(S)
    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,
            13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,
            26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,
            39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,
            52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,
            65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
            78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
            91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,
           104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,
           117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,
           130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,
           143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,
           156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,
           169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,
           195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207,
           208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220,
           221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,
           234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246,
           247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259,
           260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272,
           273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285,
           286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298,
           299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311,
           312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324,
           325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337,
           338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350,
           351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363,
           364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376,
           377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389,
           390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402,
           403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415,
           416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428,
           429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441,
           442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454,
           455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467,
           468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480,
           481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493,
           494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506,
           507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519,
           520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532,
           533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545,
           546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558,
           559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571,
           572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584,
           585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597,
           598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610,
           611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623,
           624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636,
           637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649,
           650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662,
           663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675,
           676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688,
           689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701,
           702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714,
           715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727,
           728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740,
           741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753,
           754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766,
           767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779,
           780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792,
           793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805,
           806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818,
           819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831,
           832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844,
           845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857,
           858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870,
           871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883,
           884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896,
           897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909,
           910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922,
           923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935,
           936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948,
           949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961,
           962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974,
           975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987,
           988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999])
    

pd.Series.tolist

  • pandas.Series.tolist()

    Note

    Calling tolist on a non-float array with NA values with cause a runtime exception.

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.tolist()
    >>> S = pd.Series(np.arange(50))
    >>> f(S)
    [0,
     1,
     2,
     3,
     4,
     5,
     6,
     7,
     8,
     9,
     10,
     11,
     12,
     13,
     14,
     15,
     16,
     17,
     18,
     19,
     20,
     21,
     22,
     23,
     24,
     25,
     26,
     27,
     28,
     29,
     30,
     31,
     32,
     33,
     34,
     35,
     36,
     37,
     38,
     39,
     40,
     41,
     42,
     43,
     44,
     45,
     46,
     47,
     48,
     49]
    

Indexing, iteration:

Location based indexing using [], iat, and iloc is supported. Changing values of existing string Series using these operators is not supported yet.

pd.Series.iat

  • pandas.Series.iat

    We only support indexing using iat using a pair of integers

Example Usage

>>> @bodo.jit
... def f(S, i):
...   return S.iat[i]
>>> S = pd.Series(np.arange(1000))
>>> f(S, 27)
27

pd.Series.iloc

  • pandas.Series.iloc

    • getitem:

      • Series.iloc supports single integer indexing (returns a scalar) S.iloc[0]
      • Series.iloc supports list/array/series of integers/bool (returns a Series) S.iloc[[0,1,2]]
      • Series.iloc supports integer slice (returns a Series) S.iloc[[0:2]]
    • setitem:

      • Supports the same cases as getitem but the array type must be mutable (i.e. numeric array)

    Example Usage

    >>> @bodo.jit
    ... def f(S, idx):
    ...   return S.iloc[idx]
    >>> S = pd.Series(np.arange(1000))
    >>> f(S, [1, 4, 29])
    1      1
    4      4
    29    29
    dtype: int64
    

pd.Series.loc

  • pandas.Series.loc

    • getitem:

      • Series.loc supports list/array of booleans
      • Series.loc supports integer with RangeIndex
    • setitem:

      • Series.loc supports list/array of booleans

    Example Usage

    >>> @bodo.jit
    ... def f(S, idx):
    ...   return S.loc[idx]
    >>> S = pd.Series(np.arange(1000))
    >>> f(S, S < 10)
    0    0
    1    1
    2    2
    3    3
    4    4
    5    5
    6    6
    7    7
    8    8
    9    9
    dtype: int64
    

Binary operator functions:

pd.Series.add

  • pandas.Series.add(other, level=None, fill_value=None, axis=0)

    Supported Arguments

    argument

    datatypes

    other

    • numeric scalar
    • array with numeric data
    • Series with numeric data

    fill_value

    • numeric scalar

    Note

    Series.add is only supported on Series of numeric data.

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...   return S.add(other)
    >>> S = pd.Series(np.arange(1, 1001))
    >>> other = pd.Series(reversed(np.arange(1, 1001)))
    >>> f(S, other)
    0      1001
    1      1001
    2      1001
    3      1001
    4      1001
          ...
    995    1001
    996    1001
    997    1001
    998    1001
    999    1001
    Length: 1000, dtype: int64
    

pd.Series.sub

  • pandas.Series.sub(other, level=None, fill_value=None, axis=0)

    Supported Arguments

    argument

    datatypes

    other

    • numeric scalar
    • array with numeric data
    • Series with numeric data

    fill_value

    • numeric scalar

    Note

    Series.sub is only supported on Series of numeric data.

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...   return S.sub(other)
    >>> S = pd.Series(np.arange(1, 1001))
    >>> other = pd.Series(reversed(np.arange(1, 1001)))
    >>> f(S, other)
    0     -999
    1     -997
    2     -995
    3     -993
    4     -991
          ...
    995    991
    996    993
    997    995
    998    997
    999    999
    Length: 1000, dtype: int64
    

pd.Series.mul

  • pandas.Series.mul(other, level=None, fill_value=None, axis=0)

    Supported Arguments

    argument

    datatypes

    other

    • numeric scalar
    • array with numeric data
    • Series with numeric data

    fill_value

    • numeric scalar

    Note

    Series.mul is only supported on Series of numeric data.

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...   return S.mul(other)
    >>> S = pd.Series(np.arange(1, 1001))
    >>> other = pd.Series(reversed(np.arange(1, 1001)))
    >>> f(S, other)
    0      1000
    1      1998
    2      2994
    3      3988
    4      4980
          ...
    995    4980
    996    3988
    997    2994
    998    1998
    999    1000
    Length: 1000, dtype: int64
    

pd.Series.div

  • pandas.Series.div(other, level=None, fill_value=None, axis=0)

    Supported Arguments

    argument

    datatypes

    other

    • numeric scalar
    • array with numeric data
    • Series with numeric data

    fill_value

    • numeric scalar

    Note

    Series.div is only supported on Series of numeric data.

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...   return S.div(other)
    >>> S = pd.Series(np.arange(1, 1001))
    >>> other = pd.Series(reversed(np.arange(1, 1001)))
    >>> f(S, other)
    0         0.001000
    1         0.002002
    2         0.003006
    3         0.004012
    4         0.005020
              ...
    995     199.200000
    996     249.250000
    997     332.666667
    998     499.500000
    999    1000.000000
    Length: 1000, dtype: float64
    

pd.Series.truediv

  • pandas.Series.truediv(other, level=None, fill_value=None, axis=0)

    Supported Arguments

    argument

    datatypes

    other

    • numeric scalar
    • array with numeric data
    • Series with numeric data

    fill_value

    • numeric scalar

    Note

    Series.truediv is only supported on Series of numeric data.

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...   return S.truediv(other)
    >>> S = pd.Series(np.arange(1, 1001))
    >>> other = pd.Series(reversed(np.arange(1, 1001)))
    >>> f(S, other)
    0         0.001000
    1         0.002002
    2         0.003006
    3         0.004012
    4         0.005020
              ...
    995     199.200000
    996     249.250000
    997     332.666667
    998     499.500000
    999    1000.000000
    Length: 1000, dtype: float64
    

pd.Series.floordiv

  • pandas.Series.floordiv(other, level=None, fill_value=None, axis=0)

    Supported Arguments

    argument

    datatypes

    other

    • numeric scalar
    • array with numeric data
    • Series with numeric data

    fill_value

    • numeric scalar

    Note

    Series.floordiv is only supported on Series of numeric data.

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...   return S.floordiv(other)
    >>> S = pd.Series(np.arange(1, 1001))
    >>> other = pd.Series(reversed(np.arange(1, 1001)))
    >>> f(S, other)
    0         0
    1         0
    2         0
    3         0
    4         0
          ...
    995     199
    996     249
    997     332
    998     499
    999    1000
    Length: 1000, dtype: int64
    

pd.Series.mod

  • pandas.Series.mod(other, level=None, fill_value=None, axis=0)

    Supported Arguments

    argument

    datatypes

    other

    • numeric scalar
    • array with numeric data
    • Series with numeric data

    fill_value

    • numeric scalar

    Note

    Series.mod is only supported on Series of numeric data.

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...   return S.mod(other)
    >>> S = pd.Series(np.arange(1, 1001))
    >>> other = pd.Series(reversed(np.arange(1, 1001)))
    >>> f(S, other)
    0      1
    1      2
    2      3
    3      4
    4      5
          ..
    995    1
    996    1
    997    2
    998    1
    999    0
    Length: 1000, dtype: int64
    

pd.Series.pow

  • pandas.Series.pow(other, level=None, fill_value=None, axis=0)

    Supported Arguments

    argument

    datatypes

    other

    • numeric scalar
    • array with numeric data
    • Series with numeric data

    fill_value

    • numeric scalar

    Note

    Series.pow is only supported on Series of numeric data.

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...   return S.pow(other)
    >>> S = pd.Series(np.arange(1, 1001))
    >>> other = pd.Series(reversed(np.arange(1, 1001)))
    >>> f(S, other)
    0                        1
    1                        0
    2     -5459658280481875879
    3                        0
    4      3767675092665006833
                  ...
    995        980159361278976
    996           988053892081
    997              994011992
    998                 998001
    999                   1000
    Length: 1000, dtype: int64
    

pd.Series.radd

  • pandas.Series.radd(other, level=None, fill_value=None, axis=0)

    Supported Arguments

    argument

    datatypes

    other

    • numeric scalar
    • array with numeric data
    • Series with numeric data

    fill_value

    • numeric scalar

    Note

    Series.radd is only supported on Series of numeric data.

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...   return S.radd(other)
    >>> S = pd.Series(np.arange(1, 1001))
    >>> other = pd.Series(reversed(np.arange(1, 1001)))
    >>> f(S, other)
    0      1001
    1      1001
    2      1001
    3      1001
    4      1001
          ...
    995    1001
    996    1001
    997    1001
    998    1001
    999    1001
    Length: 1000, dtype: int64
    

pd.Series.rsub

  • pandas.Series.rsub(other, level=None, fill_value=None, axis=0)

    Supported Arguments

    argument

    datatypes

    other

    • numeric scalar
    • array with numeric data
    • Series with numeric data

    fill_value

    • numeric scalar

    Note

    Series.rsub is only supported on Series of numeric data.

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...   return S.rsub(other)
    >>> S = pd.Series(np.arange(1, 1001))
    >>> other = pd.Series(reversed(np.arange(1, 1001)))
    >>> f(S, other)
    0      999
    1      997
    2      995
    3      993
    4      991
          ...
    995   -991
    996   -993
    997   -995
    998   -997
    999   -999
    Length: 1000, dtype: int64
    

pd.Series.rmul

  • pandas.Series.rmul(other, level=None, fill_value=None, axis=0)

    Supported Arguments

    argument

    datatypes

    other

    • numeric scalar
    • array with numeric data
    • Series with numeric data

    fill_value

    • numeric scalar

    Note

    Series.rmul is only supported on Series of numeric data.

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...   return S.rmul(other)
    >>> S = pd.Series(np.arange(1, 1001))
    >>> other = pd.Series(reversed(np.arange(1, 1001)))
    >>> f(S, other)
    0      1000
    1      1998
    2      2994
    3      3988
    4      4980
          ...
    995    4980
    996    3988
    997    2994
    998    1998
    999    1000
    Length: 1000, dtype: int64
    

pd.Series.rdiv

  • pandas.Series.rdiv(other, level=None, fill_value=None, axis=0)

    Supported Arguments

    argument

    datatypes

    other

    • numeric scalar
    • array with numeric data
    • Series with numeric data

    fill_value

    • numeric scalar

    Note

    Series.rdiv is only supported on Series of numeric data.

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...   return S.rdiv(other)
    >>> S = pd.Series(np.arange(1, 1001))
    >>> other = pd.Series(reversed(np.arange(1, 1001)))
    >>> f(S, other)
    0      1000.000000
    1       499.500000
    2       332.666667
    3       249.250000
    4       199.200000
              ...
    995       0.005020
    996       0.004012
    997       0.003006
    998       0.002002
    999       0.001000
    Length: 1000, dtype: float64
    

pd.Series.rtruediv

  • pandas.Series.rtruediv(other, level=None, fill_value=None, axis=0)

    Supported Arguments

    argument

    datatypes

    other

    • numeric scalar
    • array with numeric data
    • Series with numeric data

    fill_value

    • numeric scalar

    Note

    Series.rtruediv is only supported on Series of numeric data.

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...   return S.rtruediv(other)
    >>> S = pd.Series(np.arange(1, 1001))
    >>> other = pd.Series(reversed(np.arange(1, 1001)))
    >>> f(S, other)
    0      1000.000000
    1       499.500000
    2       332.666667
    3       249.250000
    4       199.200000
              ...
    995       0.005020
    996       0.004012
    997       0.003006
    998       0.002002
    999       0.001000
    Length: 1000, dtype: float64
    

pd.Series.rfloordiv

  • pandas.Series.rfloordiv(other, level=None, fill_value=None, axis=0)

    Supported Arguments

    argument

    datatypes

    other

    • numeric scalar
    • array with numeric data
    • Series with numeric data

    fill_value

    • numeric scalar

    Note

    Series.rfloordiv is only supported on Series of numeric data.

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...   return S.rfloordiv(other)
    >>> S = pd.Series(np.arange(1, 1001))
    >>> other = pd.Series(reversed(np.arange(1, 1001)))
    >>> f(S, other)
    0      1000
    1       499
    2       332
    3       249
    4       199
          ...
    995       0
    996       0
    997       0
    998       0
    999       0
    Length: 1000, dtype: int64
    

pd.Series.rmod

  • pandas.Series.rmod(other, level=None, fill_value=None, axis=0)

    Supported Arguments

    argument

    datatypes

    other

    • numeric scalar
    • array with numeric data
    • Series with numeric data

    fill_value

    • numeric scalar

    Note

    Series.rmod is only supported on Series of numeric data.

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...   return S.rmod(other)
    >>> S = pd.Series(np.arange(1, 1001))
    >>> other = pd.Series(reversed(np.arange(1, 1001)))
    >>> f(S, other)
    0      0
    1      1
    2      2
    3      1
    4      1
          ..
    995    5
    996    4
    997    3
    998    2
    999    1
    Length: 1000, dtype: int64
    

pd.Series.rpow

  • pandas.Series.rpow(other, level=None, fill_value=None, axis=0)

    Supported Arguments

    argument

    datatypes

    other

    • numeric scalar
    • array with numeric data
    • Series with numeric data

    fill_value

    • numeric scalar

    Note

    Series.rpow is only supported on Series of numeric data.

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...   return S.rpow(other)
    >>> S = pd.Series(np.arange(1, 1001))
    >>> other = pd.Series(reversed(np.arange(1, 1001)))
    >>> f(S, other)
    0                     1000
    1                   998001
    2                994011992
    3             988053892081
    4          980159361278976
                  ...
    995    3767675092665006833
    996                      0
    997   -5459658280481875879
    998                      0
    999                      1
    Length: 1000, dtype: int64
    

pd.Series.combine

  • pandas.Series.combine(other, func, fill_value=None)

    Supported Arguments

    argument

    datatypes

    other requirements

    other

    • Array
    • Series

    func

    • Function that takes two scalar arguments and returns a scalar value.

    fill_value

    • scalar

    Must be provided if the Series lengths aren't equal and the dtypes aren't floats.

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...   return S.combine(other, lambda a, b: 2 * a + b)
    >>> S = pd.Series(np.arange(1, 1001))
    >>> other = pd.Series(reversed(np.arange(1, 1001)))
    >>> f(S, other)
    0      1002
    1      1003
    2      1004
    3      1005
    4      1006
          ...
    995    1997
    996    1998
    997    1999
    998    2000
    999    2001
    Length: 1000, dtype: int64
    

pd.Series.round

  • pandas.Series.round(decimals=0)

    Supported Arguments

    argument

    datatypes

    other

    • Series with numeric data

    Note

    Series.round is only supported on Series of numeric data.

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...   return S.round(2)
    >>> S = pd.Series(np.linspace(100, 1000))
    >>> f(S)
    0      100.00
    1      118.37
    2      136.73
    3      155.10
    4      173.47
    5      191.84
    6      210.20
    7      228.57
    8      246.94
    9      265.31
    10     283.67
    11     302.04
    12     320.41
    13     338.78
    14     357.14
    15     375.51
    16     393.88
    17     412.24
    18     430.61
    19     448.98
    20     467.35
    21     485.71
    22     504.08
    23     522.45
    24     540.82
    25     559.18
    26     577.55
    27     595.92
    28     614.29
    29     632.65
    30     651.02
    31     669.39
    32     687.76
    33     706.12
    34     724.49
    35     742.86
    36     761.22
    37     779.59
    38     797.96
    39     816.33
    40     834.69
    41     853.06
    42     871.43
    43     889.80
    44     908.16
    45     926.53
    46     944.90
    47     963.27
    48     981.63
    49    1000.00
    dtype: float64
    

pd.Series.lt

  • pandas.Series.lt(other, level=None, fill_value=None, axis=0)

    Supported Arguments

    argument

    datatypes

    other

    • numeric scalar
    • array with numeric data
    • Series with numeric data

    fill_value

    • numeric scalar

    Note

    Series.lt is only supported on Series of numeric data.

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...   return S.lt(other)
    >>> S = pd.Series(np.arange(1, 1001))
    >>> other = pd.Series(reversed(np.arange(1, 1001)))
    >>> f(S, other)
    0       True
    1       True
    2       True
    3       True
    4       True
          ...
    995    False
    996    False
    997    False
    998    False
    999    False
    Length: 1000, dtype: bool
    

pd.Series.gt

  • pandas.Series.gt(other, level=None, fill_value=None, axis=0)

    Supported Arguments

    argument

    datatypes

    other

    • numeric scalar
    • array with numeric data
    • Series with numeric data

    fill_value

    • numeric scalar

    Note

    Series.gt is only supported on Series of numeric data.

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...   return S.gt(other)
    >>> S = pd.Series(np.arange(1, 1001))
    >>> other = pd.Series(reversed(np.arange(1, 1001)))
    >>> f(S, other)
    0      False
    1      False
    2      False
    3      False
    4      False
          ...
    995     True
    996     True
    997     True
    998     True
    999     True
    Length: 1000, dtype: bool
    

pd.Series.le

  • pandas.Series.le(other, level=None, fill_value=None, axis=0)

    Supported Arguments

    argument

    datatypes

    other

    • numeric scalar
    • array with numeric data
    • Series with numeric data

    fill_value

    • numeric scalar

    Note

    Series.le is only supported on Series of numeric data.

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...   return S.le(other)
    >>> S = pd.Series(np.arange(1, 1001))
    >>> other = pd.Series(reversed(np.arange(1, 1001)))
    >>> f(S, other)
    0       True
    1       True
    2       True
    3       True
    4       True
          ...
    995    False
    996    False
    997    False
    998    False
    999    False
    Length: 1000, dtype: bool
    

pd.Series.ge

  • pandas.Series.ge(other, level=None, fill_value=None, axis=0)

    Supported Arguments

    argument

    datatypes

    other

    • numeric scalar
    • array with numeric data
    • Series with numeric data

    fill_value

    • numeric scalar

    Note

    Series.ge is only supported on Series of numeric data.

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...   return S.ge(other)
    >>> S = pd.Series(np.arange(1, 1001))
    >>> other = pd.Series(reversed(np.arange(1, 1001)))
    >>> f(S, other)
    0      False
    1      False
    2      False
    3      False
    4      False
          ...
    995     True
    996     True
    997     True
    998     True
    999     True
    Length: 1000, dtype: bool
    

pd.Series.ne

  • pandas.Series.ne(other, level=None, fill_value=None, axis=0)

    Supported Arguments

    argument

    datatypes

    other

    • numeric scalar
    • array with numeric data
    • Series with numeric data

    fill_value

    • numeric scalar

    Note

    Series.ne is only supported on Series of numeric data.

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...   return S.ne(other)
    >>> S = pd.Series(np.arange(1, 1001))
    >>> other = pd.Series(reversed(np.arange(1, 1001)))
    >>> f(S, other)
    0      True
    1      True
    2      True
    3      True
    4      True
          ...
    995    True
    996    True
    997    True
    998    True
    999    True
    Length: 1000, dtype: bool
    

pd.Series.eq

  • pandas.Series.eq(other, level=None, fill_value=None, axis=0)

    Supported Arguments

    argument

    datatypes

    other

    • numeric scalar
    • array with numeric data
    • Series with numeric data

    fill_value

    • numeric scalar

    Note

    Series.eq is only supported on Series of numeric data.

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...   return S.eq(other)
    >>> S = pd.Series(np.arange(1, 1001))
    >>> other = pd.Series(reversed(np.arange(1, 1001)))
    >>> f(S, other)
    0      False
    1      False
    2      False
    3      False
    4      False
          ...
    995    False
    996    False
    997    False
    998    False
    999    False
    Length: 1000, dtype: bool
    

pd.Series.dot

  • pandas.Series.dot(other)

    Supported Arguments

    argument

    datatypes

    other

    • Series with numeric data

    Note

    Series.dot is only supported on Series of numeric data.

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...   return S.dot(other)
    >>> S = pd.Series(np.arange(1, 1001))
    >>> other = pd.Series(reversed(np.arange(1, 1001)))
    >>> f(S, other)
    167167000
    

Function application, GroupBy & Window

pd.Series.apply

  • pandas.Series.applyf(func, convert_dtype=True, args=(), **kwargs)

    Supported Arguments

    argument

    datatypes

    other requirements

    func

    • JIT function or callable defined within a JIT function
    • Numpy ufunc
    • Constant String which is the name of a supported Series method or Numpy ufunc
    • Additional arguments for func can be passed as additional arguments.

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...   return S.apply(lambda x: x ** 0.75)
    >>> S = pd.Series(np.arange(100))
    >>> f(S)
    0      0.000000
    1      1.000000
    2      1.681793
    3      2.279507
    4      2.828427
            ...
    95    30.429352
    96    30.669269
    97    30.908562
    98    31.147239
    99    31.385308
    Length: 100, dtype: float64
    

pd.Series.map

  • pandas.Series.map(arg, na_action=None)

    Supported Arguments

    argument

    datatypes

    arg

    • Dictionary
    • JIT function or callable defined within a JIT function
    • Constant String which refers to a supported Series method or Numpy ufunc
    • Numpy ufunc

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...   return S.map(lambda x: x ** 0.75)
    >>> S = pd.Series(np.arange(100))
    >>> f(S)
    0      0.000000
    1      1.000000
    2      1.681793
    3      2.279507
    4      2.828427
            ...
    95    30.429352
    96    30.669269
    97    30.908562
    98    31.147239
    99    31.385308
    Length: 100, dtype: float64
    

pd.Series.groupby

  • pandas.Series.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=NoDefault.no_default, observed=False, dropna=True)

    Supported Arguments

    argument

    datatypes

    other requirements

    by

    • Array-like or Series data. This is not supported with Decimal or Categorical data.
    • Must be constant at Compile Time

    level

    • integer
    • Must be constant at Compile Time
    • Only level=0 is supported and not with MultiIndex.

    You must provide exactly one of by and level

    Example Usage

    >>> @bodo.jit
    ... def f(S, by_series):
    ...     return S.groupby(by_series).count()
    >>> S = pd.Series([1, 2, 24, None] * 5)
    >>> by_series = pd.Series(["421", "f31"] * 10)
    >>> f(S, by_series)
    >
    421    10
    f31     5
    Name: , dtype: int64
    

    Note

    Series.groupby doesn't currently keep the name of the original Series.

pd.Series.rolling

  • pandas.Series.rolling(window, min_periods=None, center=False, win_type=None, on=None, axis=0, closed=None, method='single')

    Supported Arguments

    argument

    datatypes

    window

    • Integer
    • String representing a Time Offset
    • Timedelta

    min_periods

    • Integer

    center

    • Boolean

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.rolling(2).mean()
    >>> S = pd.Series(np.arange(100))
    >>> f(S)
    0      NaN
      1      0.5
      2      1.5
      3      2.5
      4      3.5
            ...
      95    94.5
      96    95.5
      97    96.5
      98    97.5
      99    98.5
      Length: 100, dtype: float64
    

pd.Series.pipe

  • pandas.Series.pipe(func, *args, **kwargs)

    Supported Arguments

    argument

    datatypes

    other requirements

    func

    • JIT function or callable defined within a JIT function.
    • Additional arguments for func can be passed as additional arguments.

    Note

    func cannot be a tuple

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     def g(row, y):
    ...         return row + y
    ...
    ...     def f(row):
    ...         return row * 2
    ...
    ...     return S.pipe(h).pipe(g, y=32)
    >>> S = pd.Series(np.arange(100))
    >>> f(S)
    0      32
    1      34
    2      36
    3      38
    4      40
         ...
    95    222
    96    224
    97    226
    98    228
    99    230
    Length: 100, dtype: int64
    

Computations / Descriptive Stats

Statistical functions below are supported without optional arguments unless support is explicitly mentioned.

pd.Series.abs

  • pandas.Series.abs()

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.abs()
    >>> S = (pd.Series(np.arange(100)) % 7) - 2
    >>> f(S)
    0     2
    1     1
    2     0
    3     1
    4     2
         ..
    95    2
    96    3
    97    4
    98    2
    99    1
    Length: 100, dtype: int64
    

pd.Series.all

  • pandas.Series.all(axis=0, bool_only=None, skipna=True, level=None)

    Supported Arguments None

    Note

    Bodo does not accept any additional arguments for Numpy compatibility

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.all()
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    False
    

pd.Series.any

  • pandas.Series.any(axis=0, bool_only=None, skipna=True, level=None)

    Supported Arguments None

    Note

    Bodo does not accept any additional arguments for Numpy compatibility

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.any()
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    True
    

pd.Series.autocorr

  • pandas.Series.autocorr(lag=1)

    Supported Arguments

    argument

    datatypes

    lag

    • Integer

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.autocorr(3)
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    -0.49872171657407155
    

pd.Series.between

  • pandas.Series.between(left, right, inclusive='both')

    Supported Arguments

    argument

    datatypes

    other requirements

    left

    • Scalar matching the Series type

    right

    • Scalar matching the Series type

    inclusive

    • One of ("both", "neither")
    • Must be constant at Compile Time

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.between(3, 5, "both")
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    0     False
    1     False
    2     False
    3      True
    4      True
          ...
    95     True
    96     True
    97    False
    98    False
    99    False
    Length: 100, dtype: bool
    

pd.Series.corr

  • pandas.Series.corr(other, method='pearson', min_periods=None)

    Supported Arguments

    argument

    datatypes

    other

    • Numeric Series or Array

    Note

    Series type must be numeric

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...     return S.cov(other)
    >>> S = pd.Series(np.arange(100)) % 7
    >>> other = pd.Series(np.arange(100)) % 10
    >>> f(S, other)
    0.004326329627279103
    

pd.Series.count

  • pandas.Series.count(level=None)

    Supported Arguments None

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.count()
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    100
    

pd.Series.cov

  • pandas.Series.cov(other, min_periods=None, ddof=1)

    Supported Arguments

    argument

    datatypes

    other

    • Numeric Series or Array

    ddof

    • Integer

    Note

    Series type must be numeric

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...     return S.cov(other)
    >>> S = pd.Series(np.arange(100)) % 7
    >>> other = pd.Series(np.arange(100)) % 10
    >>> f(S, other)
    0.025252525252525252
    

pd.Series.cummin

  • pandas.Series.cummin(axis=None, skipna=True)

    Supported Arguments None

    Note

    • Series type must be numeric
    • Bodo does not accept any additional arguments for Numpy compatibility

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.cummin()
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    0     0
    1     0
    2     0
    3     0
    4     0
         ..
    95    0
    96    0
    97    0
    98    0
    99    0
    Length: 100, dtype: int64
    

pd.Series.cummax

  • pandas.Series.cummax(axis=None, skipna=True)

    Supported Arguments None

    Note

    • Series type must be numeric
    • Bodo does not accept any additional arguments for Numpy compatibility

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.cummax()
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    0     0
    1     1
    2     2
    3     3
    4     4
         ..
    95    6
    96    6
    97    6
    98    6
    99    6
    Length: 100, dtype: int64
    

pd.Series.cumprod

  • pandas.Series.cumprod(axis=None, skipna=True)

    Supported Arguments None

    Note

    • Series type must be numeric
    • Bodo does not accept any additional arguments for Numpy compatibility

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.cumprod()
    >>> S = (pd.Series(np.arange(10)) % 7) + 1
    >>> f(S)
    0        1
    1        2
    2        6
    3       24
    4      120
    5      720
    6     5040
    7     5040
    8    10080
    9    30240
    dtype: int64
    

pd.Series.cumsum

  • pandas.Series.cumsum(axis=None, skipna=True)

    Supported Arguments None

    Note

    • Series type must be numeric
    • Bodo does not accept any additional arguments for Numpy compatibility

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.cumsum()
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    0       0
    1       1
    2       3
    3       6
    4      10
         ...
    95    283
    96    288
    97    294
    98    294
    99    295
    Length: 100, dtype: int64
    

pd.Series.describe

  • pandas.Series.describe(percentiles=None, include=None, exclude=None, datetime_is_numeric=False)

    Supported Arguments None

    Note

    Bodo only supports numeric and datetime64 types and assumes datetime_is_numeric=True

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.describe()
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    count    100.000000
    mean       2.950000
    std        2.021975
    min        0.000000
    25%        1.000000
    50%        3.000000
    75%        5.000000
    max        6.000000
    dtype: float64
    

pd.Series.diff

  • pandas.Series.diff(periods=1)

    Supported Arguments

    argument

    datatypes

    periods

    • Integer

    Note

    Bodo only supports numeric and datetime64 types

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.diff(3)
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    0     NaN
    1     NaN
    2     NaN
    3     3.0
    4     3.0
         ...
    95    3.0
    96    3.0
    97    3.0
    98   -4.0
    99   -4.0
    Length: 100, dtype: float64
    

pd.Series.kurt

  • pandas.Series.kurt(axis=None, skipna=None, level=None, numeric_only=None)

    Supported Arguments

    argument

    datatypes

    skipna

    • Boolean

    Note

    • Series type must be numeric
    • Bodo does not accept any additional arguments to pass to the function

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.kurt()
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    -1.269562153611973
    

pd.Series.mad

  • pandas.Series.mad(axis=None, skipna=None, level=None)

    Supported Arguments

    argument

    datatypes

    skipna

    • Boolean

    Note

    Series type must be numeric

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.mad()
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    1.736
    

pd.Series.max

  • pandas.Series.max(axis=None, skipna=None, level=None, numeric_only=None)

    Supported Arguments None

    Note

    • Series type must be numeric
    • Bodo does not accept any additional arguments to pass to the function

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.max()
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    6
    

pd.Series.mean

  • pandas.Series.mean(axis=None, skipna=None, level=None, numeric_only=None)

    Supported Arguments None

    Note

    • Series type must be numeric
    • Bodo does not accept any additional arguments to pass to the function

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.mean()
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    2.95
    

pd.Series.median

  • pandas.Series.median(axis=None, skipna=None, level=None, numeric_only=None)

    Supported Arguments

    argument

    datatypes

    skipna

    • Boolean

    Note

    • Series type must be numeric
    • Bodo does not accept any additional arguments to pass to the function

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.median()
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    3.0
    

pd.Series.min

  • pandas.Series.min(axis=None, skipna=None, level=None, numeric_only=None)

    Supported Arguments None

    Note

    • Series type must be numeric
    • Bodo does not accept any additional arguments to pass to the function

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.min()
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    0
    

pd.Series.nlargest

  • pandas.Series.nlargest(n=5, keep='first')

    Supported Arguments

    argument

    datatypes

    n

    • Integer

    Note

    Series type must be numeric

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.nlargest(20)
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    20    6
    27    6
    41    6
    34    6
    55    6
    13    6
    83    6
    90    6
    6     6
    69    6
    48    6
    76    6
    62    6
    97    6
    19    5
    5     5
    26    5
    61    5
    12    5
    68    5
    dtype: int64
    

pd.Series.nsmallest

  • pandas.Series.nsmallest(n=5, keep='first')

    Supported Arguments

    argument

    datatypes

    n

    • Integer

    Note

    Series type must be numeric

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.nsmallest(20)
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    63    0
    7     0
    56    0
    98    0
    77    0
    91    0
    49    0
    42    0
    35    0
    84    0
    28    0
    21    0
    70    0
    0     0
    14    0
    43    1
    1     1
    57    1
    15    1
    36    1
    dtype: int64
    

pd.Series.pct_change

  • pandas.Series.pct_change(periods=1, fill_method='pad', limit=None, freq=None)

    Supported Arguments

    argument

    datatypes

    periods

    • Integer

    Note

    • Series type must be numeric
    • Bodo does not accept any additional arguments to pass to shift

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.pct_change(3)
    >>> S = (pd.Series(np.arange(100)) % 7) + 1
    >>> f(S)
    0          NaN
    1          NaN
    2          NaN
    3     3.000000
    4     1.500000
            ...
    95    1.500000
    96    1.000000
    97    0.750000
    98   -0.800000
    99   -0.666667
    Length: 100, dtype: float64
    

pd.Series.prod

  • pandas.Series.prod(axis=None, skipna=None, level=None, numeric_only=None)

    Supported Arguments

    argument

    datatypes

    skipna

    • Boolean

    Note

    • Series type must be numeric
    • Bodo does not accept any additional arguments to pass to the function

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.prod()
    >>> S = (pd.Series(np.arange(20)) % 3) + 1
    >>> f(S)
    93312
    

pd.Series.product

  • pandas.Series.product(axis=None, skipna=None, level=None, numeric_only=None)

    Supported Arguments

    argument

    datatypes

    skipna

    • Boolean

    Note

    • Series type must be numeric
    • Bodo does not accept any additional arguments to pass to the function

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.product()
    >>> S = (pd.Series(np.arange(20)) % 3) + 1
    >>> f(S)
    93312
    

pd.Series.quantile

  • pandas.Series.quantile(q=0.5, interpolation='linear')

    Supported Arguments

    argument

    datatypes

    q

    • Float in [0.0, 1.0]
    • Iterable of floats in [0.0, 1.0]

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.quantile([0.25, 0.5, 0.75])
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    0.25    1.0
    0.50    3.0
    0.75    5.0
    dtype: float64
    

pd.Series.sem

  • pandas.Series.sem(axis=None, skipna=None, level=None, ddof=1, numeric_only=None)

    Supported Arguments

    argument

    datatypes

    skipna

    • Boolean

    ddof

    • Integer

    Note

    • Series type must be numeric
    • Bodo does not accept any additional arguments to pass to the function

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.sem()
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    0.20219752318917852
    

pd.Series.skew

  • pandas.Series.skew(axis=None, skipna=None, level=None, numeric_only=None)

    Supported Arguments

    argument

    datatypes

    skipna

    • Boolean

    Note

    • Series type must be numeric
    • Bodo does not accept any additional arguments to pass to the function

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.skew()
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    0.032074996591991714
    

pd.Series.std

  • pandas.Series.std(axis=None, skipna=None, level=None, ddof=1, numeric_only=None)

    Supported Arguments

    argument

    datatypes

    skipna

    • Boolean

    ddof

    • Integer

    Note

    • Series type must be numeric
    • Bodo does not accept any additional arguments to pass to the function

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.std()
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    2.021975231891785
    

pd.Series.sum

  • pandas.Series.sum(axis=None, skipna=None, level=None, numeric_only=None, min_count=0)

    Supported Arguments

    argument

    datatypes

    skipna

    • Boolean

    min_count

    • Integer

    Note

    • Series type must be numeric
    • Bodo does not accept any additional arguments to pass to the function

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.sum()
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    295
    

pd.Series.var

  • pandas.Series.var(axis=None, skipna=None, level=None, ddof=1, numeric_only=None)

    Supported Arguments

    argument

    datatypes

    skipna

    • Boolean

    ddof

    • Integer

    Note

    • Series type must be numeric
    • Bodo does not accept any additional arguments to pass to the function

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.var()
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    4.088383838383838
    

pd.Series.kurtosis

  • pandas.Series.kurtosis(axis=None, skipna=None, level=None, numeric_only=None)

    Supported Arguments

    argument

    datatypes

    skipna

    • Boolean

    Note

    • Series type must be numeric
    • Bodo does not accept any additional arguments to pass to the function

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.kurtosis()
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    -1.269562153611973
    

pd.Series.unique

  • pandas.Series.unique()

    Note

    The output is assumed to be "small" relative to input and is replicated. Use Series.drop_duplicates() if the output should remain distributed.

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.unique()
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    [0 1 2 3 4 5 6]
    

pd.Series.nunique

  • pandas.Series.nunique(dropna=True)

    Supported Arguments

    argument

    datatypes

    dropna

    • Boolean

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.nunique()
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    7
    

pd.Series.is_monotonic

  • ++pandas.Series.is_monotonic

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.is_monotonic
    >>> S = pd.Series(np.arange(100))
    >>> f(S)
    True
    

pd.Series.is_monotonic_increasing

  • ++pandas.Series.is_monotonic_increasing

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.is_monotonic_increasing
    >>> S = pd.Series(np.arange(100))
    >>> f(S)
    True
    

pd.Series.is_monotonic_decreasing

  • ++pandas.Series.is_monotonic_decreasing

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.is_monotonic_decreasing
    >>> S = pd.Series(np.arange(100))
    >>> f(S)
    False
    

pd.Series.value_counts

  • pandas.Series.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True)

    Supported Arguments

    argument

    datatypes

    other requirements

    normalize

    • Boolean
    • Must be constant at Compile Time

    sort

    • Boolean
    • Must be constant at Compile Time

    ascending

    • Boolean

    bins

    • Integer
    • Array-like of integers

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.value_counts()
    >>> S = pd.Series(np.arange(100)) % 7
    >>> f(S)
    0    15
    1    15
    2    14
    3    14
    4    14
    5    14
    6    14
    dtype: int64
    

Reindexing / Selection / Label manipulation

pd.Series.drop_duplicates

  • pandas.Series.drop_duplicates(keep='first', inplace=False)

    Supported Arguments None

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.drop_duplicates()
    >>> S = pd.Series(np.arange(100)) % 10
    >>> f(S)
    0    0
    1    1
    2    2
    3    3
    4    4
    5    5
    6    6
    7    7
    8    8
    9    9
    dtype: int64
    

pd.Series.duplicated

  • pandas.Series.duplicated(keep='first')

    Supported Arguments None

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...  return S.duplicated()
    >
    >>> S = pd.Series([1, 2, 1, np.nan, 3, 2, np.nan, 4])
    0    False
    1    False
    2     True
    3    False
    4    False
    5     True
    6     True
    7    False
    dtype: bool
    

pd.Series.equals

  • pandas.Series.equals(other)

    Supported Arguments

    argument

    datatypes

    other

    • Series

    Note

    Series and other must contain scalar values in each row

    Example Usage

    >>> @bodo.jit
    ... def f(S, other):
    ...     return S.equals(other)
    >>> S = pd.Series(np.arange(100)) % 10
    >>> other = pd.Series(np.arange(100)) % 5
    >>> f(S, other)
    False
    

pd.Series.first

  • pandas.Series.first(offset)

    Supported Arguments

    argument

    datatypes

    other requirements

    offset

    • String or Offset type

    Note

    Series must have a valid DatetimeIndex and is assumed to already be sorted. This function have undefined behavior if the DatetimeIndex is not sorted.

    Example Usage

    >>> @bodo.jit
    ... def f(S, offset):
    ...     return S.first(offset)
    >>> S = pd.Series(np.arange(100), index=pd.date_range(start='1/1/2022', end='12/31/2024', periods=100))
    >>> f(S, "2M")
    2022-01-01 00:00:00.000000000    0
    2022-01-12 01:27:16.363636363    1
    2022-01-23 02:54:32.727272727    2
    2022-02-03 04:21:49.090909091    3
    2022-02-14 05:49:05.454545454    4
    2022-02-25 07:16:21.818181818    5
    dtype: int64
    

pd.Series.head

  • pandas.Series.head(n=5)

    Supported Arguments

    argument

    datatypes

    n

    • Integer

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.head(10)
    >>> S = pd.Series(np.arange(100))
    >>> f(S)
    0    0
    1    1
    2    2
    3    3
    4    4
    5    5
    6    6
    7    7
    8    8
    9    9
    dtype: int64
    

pd.Series.idxmax

  • pandas.Series.idxmax(axis=0, skipna=True)

    Supported Arguments None

    Note

    Bodo does not accept any additional arguments for Numpy compatibility

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.idxmax()
    >>> S = pd.Series(np.arange(100))
    >>> S[(S % 3 == 0)] = 100
    >>> f(S)
    0
    

pd.Series.idxmin

  • pandas.Series.idxmin(axis=0, skipna=True)

    Supported Arguments None

    Note

    Bodo does not accept any additional arguments for Numpy compatibility

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.idxmin()
    >>> S = pd.Series(np.arange(100))
    >>> S[(S % 3 == 0)] = 100
    >>> f(S)
    1
    

pd.Series.isin

  • pandas.Series.isin(values)

    Supported Arguments

    argument

    datatypes

    values

    • Series
    • Array
    • List

    Note

    values argument supports both distributed array/Series and replicated list/array/Series

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.isin([3, 11, 98])
    >>> S = pd.Series(np.arange(100))
    >>> f(S)
    0     False
    1     False
    2     False
    3      True
    4     False
          ...
    95    False
    96    False
    97    False
    98     True
    99    False
    Length: 100, dtype: bool
    

pd.Series.last

  • pandas.Series.last(offset)

    Supported Arguments

    argument

    datatypes

    other requirements

    offset

    • String or Offset type

    Note

    Series must have a valid DatetimeIndex and is assumed to already be sorted. This function have undefined behavior if the DatetimeIndex is not sorted.

    Example Usage

    >>> @bodo.jit
    ... def f(S, offset):
    ...     return S.last(offset)
    >>> S = pd.Series(np.arange(100), index=pd.date_range(start='1/1/2022', end='12/31/2024', periods=100))
    >>> f(S, "2M")
    2024-11-05 16:43:38.181818176    94
    2024-11-16 18:10:54.545454544    95
    2024-11-27 19:38:10.909090912    96
    2024-12-08 21:05:27.272727264    97
    2024-12-19 22:32:43.636363632    98
    2024-12-31 00:00:00.000000000    99
    dtype: int64
    

pd.Series.rename

  • pandas.Series.rename(index=None, , axis=None, copy=True, inplace=False, level=None, errors='ignore')

    ***Supported Arguments**

    argument

    datatypes

    index

    • String

    axis

    • Any value. Bodo ignores this argument entirely, which is consistent with Pandas.

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.rename("a")
    >>> S = pd.Series(np.arange(100))
    >>> f(S)
    0      0
    1      1
    2      2
    3      3
    4      4
          ..
    95    95
    96    96
    97    97
    98    98
    99    99
    Name: a, Length: 100, dtype: int64
    

pd.Series.reset_index

  • pandas.Series.reset_index(level=None, drop=False, name=None, inplace=False)

    Supported Arguments

    argument

    datatypes

    other requirements

    level

    • Integer
    • Boolean
    • Must be constant at Compile Time

    drop

    • Boolean
    • Must be constant at Compile Time
    • If False, Index name must be known at compilation time

    Note

    For MultiIndex case, only dropping all levels is supported.

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.reset_index()
    >>> S = pd.Series(np.arange(100), index=pd.RangeIndex(100, 200, 1, name="b"))
    >>> f(S)
          b   0
    0   100   0
    1   101   1
    2   102   2
    3   103   3
    4   104   4
    ..  ...  ..
    95  195  95
    96  196  96
    97  197  97
    98  198  98
    99  199  99
    >
    [100 rows x 2 columns]
    

pd.Series.take

  • pandas.Series.take(indices, axis=0, is_copy=None)

    Supported Arguments

    argument

    datatypes

    other requirements

    indices

    • Array like with integer data
    • To have distributed data indices must be an array with the same distribution as S.

    Note

    Bodo does not accept any additional arguments for Numpy compatibility

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.take([2, 7, 4, 19])
    >>> S = pd.Series(np.arange(100))
    >>> f(S)
    2      2
    7      7
    4      4
    19    19
    dtype: int64
    

pd.Series.tail

  • pandas.Series.tail(n=5)

    Supported Arguments

    argument

    datatypes

    n

    • Integer

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.tail(10)
    >>> S = pd.Series(np.arange(100))
    >>> f(S)
    90    90
    91    91
    92    92
    93    93
    94    94
    95    95
    96    96
    97    97
    98    98
    99    99
    dtype: int64
    

pd.Series.where

  • pandas.Series.where(cond, other=nan, inplace=False, axis=None, level=None, errors='raise', try_cast=NoDefault.no_default)

    Supported Arguments

    argument

    datatypes

    cond

    • boolean array
    • 1d bool numpy array

    other

    • 1d numpy array
    • scalar

    Note

    Series can contain categorical data if other is a scalar

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.where((S % 3) != 0, 0)
    >>> S = pd.Series(np.arange(100))
    >>> f(S)
    0      0
    1      1
    2      2
    3      0
    4      4
          ..
    95    95
    96     0
    97    97
    98    98
    99     0
    Length: 100, dtype: int64
    

pd.Series.mask

  • pandas.Series.mask(cond, other=nan, inplace=False, axis=None, level=None, errors='raise', try_cast=NoDefault.no_default)

    Supported Arguments

    argument

    datatypes

    cond

    • boolean array
    • 1d bool numpy array

    other

    • 1d numpy array
    • scalar

    Note

    Series can contain categorical data if other is a scalar

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.mask((S % 3) != 0, 0)
    >>> S = pd.Series(np.arange(100))
    >>> f(S)
    0      0
    1      0
    2      0
    3      3
    4      0
          ..
    95     0
    96    96
    97     0
    98     0
    99    99
    Length: 100, dtype: int64
    

Missing data handling

pd.Series.backfill

  • pandas.Series.backfill(axis=None, inplace=False, limit=None, downcast=None)

    Supported Arguments None

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.backfill()
    >>> S = pd.Series(pd.array([None, 1, None, -2, None, 5, None]))
    >>> f(S)
    0       1
    1       1
    2      -2
    3      -2
    4       5
    5       5
    6    <NA>
    dtype: Int64
    

pd.Series.bfill

  • pandas.Series.bfill(axis=None, inplace=False, limit=None, downcast=None)

    Supported Arguments None

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.bfill()
    >>> S = pd.Series(pd.array([None, 1, None, -2, None, 5, None]))
    >>> f(S)
    0       1
    1       1
    2      -2
    3      -2
    4       5
    5       5
    6    <NA>
    dtype: Int64
    

pd.Series.dropna

  • pandas.Series.dropna(axis=0, inplace=False, how=None)

    Supported Arguments None

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dropna()
    >>> S = pd.Series(pd.array([None, 1, None, -2, None, 5, None]))
    >>> f(S)
    1     1
    3    -2
    5     5
    dtype: Int64
    

pd.Series.ffill

  • pandas.Series.ffill(axis=None, inplace=False, limit=None, downcast=None)

    Supported Arguments None

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.ffill()
    >>> S = pd.Series(pd.array([None, 1, None, -2, None, 5, None]))
    >>> f(S)
    0    <NA>
    1       1
    2       1
    3      -2
    4      -2
    5       5
    6       5
    dtype: Int64
    

pd.Series.fillna

  • pandas.Series.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None)

    Supported Arguments

    argument

    datatypes

    other requirements

    value

    • Scalar

    method

    • One of ("bfill", "backfill", "ffill", and "pad")
    • Must be constant at Compile Time

    inplace

    • Boolean
    • Must be constant at Compile Time
    • If value is provided then method must be None and vice-versa
    • If method is provided then inplace must be False

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.fillna(-1)
    >>> S = pd.Series(pd.array([None, 1, None, -2, None, 5, None]))
    >>> f(S)
    0    -1
    1     1
    2    -1
    3    -2
    4    -1
    5     5
    6    -1
    dtype: Int64
    

pd.Series.isna

  • pandas.Series.isna()

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.isna()
    >>> S = pd.Series(pd.array([None, 1, None, -2, None, 5, None]))
    >>> f(S)
    0     True
    1    False
    2     True
    3    False
    4     True
    5    False
    6     True
    dtype: bool
    

pd.Series.isnull

  • pandas.Series.isnull()

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.isnull()
    >>> S = pd.Series(pd.array([None, 1, None, -2, None, 5, None]))
    >>> f(S)
    0     True
    1    False
    2     True
    3    False
    4     True
    5    False
    6     True
    dtype: bool
    

pd.Series.notna

  • pandas.Series.notna()

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.notna()
    >>> S = pd.Series(pd.array([None, 1, None, -2, None, 5, None]))
    >>> f(S)
    0    False
    1     True
    2    False
    3     True
    4    False
    5     True
    6    False
    dtype: bool
    

pd.Series.notnull

  • pandas.Series.notnull()

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.notnull()
    >>> S = pd.Series(pd.array([None, 1, None, -2, None, 5, None]))
    >>> f(S)
    0    False
    1     True
    2    False
    3     True
    4    False
    5     True
    6    False
    dtype: bool
    

pd.Series.pad

  • pandas.Series.pad(axis=None, inplace=False, limit=None, downcast=None)

    Supported Arguments None

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.pad()
    >>> S = pd.Series(pd.array([None, 1, None, -2, None, 5, None]))
    >>> f(S)
    0    <NA>
    1       1
    2       1
    3      -2
    4      -2
    5       5
    6       5
    dtype: Int64
    

pd.Series.replace

  • pandas.Series.replace(to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad')

    Supported Arguments

    argument

    datatypes

    other requirements

    to_replace

    • Scalar
    • List of Scalars
    • Dictionary mapping scalars of the same type

    value

    • Scalar

    If to_replace is not a scalar, value must be None

    Example Usage

    >>> @bodo.jit
    ... def f(S, replace_dict):
    ...     return S.replace(replace_dict)
    >>> S = pd.Series(pd.array([None, 1, None, -2, None, 5, None]))
    >>> f(S, {1: -2, -2: 5, 5: 27})
    0    <NA>
    1      -2
    2    <NA>
    3       5
    4    <NA>
    5      27
    6    <NA>
    dtype: Int64
    

Reshaping, sorting

pd.Series.argsort

  • pandas.Series.argsort(axis=0, kind='quicksort', order=None)

    Supported Arguments None

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.sort_values()
    >>> S = pd.Series(np.arange(99, -1, -1), index=np.arange(100))
    >>> f(S)
    0     99
    1     98
    2     97
    3     96
    4     95
          ..
    95     4
    96     3
    97     2
    98     1
    99     0
    Length: 100, dtype: int64
    

pd.Series.sort_values

  • pandas.Series.sort_values(axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last', ignore_index=False, key=None)

    Supported Arguments

    argument

    datatypes

    other requirements

    ascending

    • Boolean

    na_position

    • One of ("first", "last")

    Must be constant at Compile Time

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.sort_values()
    >>> S = pd.Series(np.arange(99, -1, -1), index=np.arange(100))
    >>> f(S)
    99     0
    98     1
    97     2
    96     3
    95     4
          ..
    4     95
    3     96
    2     97
    1     98
    0     99
    Length: 100, dtype: int64
    

pd.Series.sort_index

  • pandas.Series.sort_index(axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True, ignore_index=False, key=None)

    Supported Arguments

    argument

    datatypes

    other requirements

    ascending

    • Boolean

    na_position

    • One of ("first", "last")

    Must be constant at Compile Time

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.sort_index()
    >>> S = pd.Series(np.arange(100), index=np.arange(99, -1, -1))
    >>> f(S)
    0     99
    1     98
    2     97
    3     96
    4     95
          ..
    95     4
    96     3
    97     2
    98     1
    99     0
    Length: 100, dtype: int64
    

pd.Series.explode

  • pandas.Series.explode(ignore_index=False)

    Supported Arguments None

    Note

    Bodo's output type may differ from Pandas because Bodo must convert to a nullable type at compile time.

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.explode()
    >>> S = pd.Series([np.arange(i) for i in range(10)])
    >>> f(S)
    0    <NA>
    1       0
    2       0
    2       1
    3       0
    3       1
    3       2
    4       0
    4       1
    4       2
    4       3
    5       0
    5       1
    5       2
    5       3
    5       4
    6       0
    6       1
    6       2
    6       3
    6       4
    6       5
    7       0
    7       1
    7       2
    7       3
    7       4
    7       5
    7       6
    8       0
    8       1
    8       2
    8       3
    8       4
    8       5
    8       6
    8       7
    9       0
    9       1
    9       2
    9       3
    9       4
    9       5
    9       6
    9       7
    9       8
    dtype: Int64
    

pd.Series.repeat

  • pandas.Series.repeat(repeats, axis=None)

    Supported Arguments

    argument

    datatypes

    repeats

    • Integer
    • Array-like of integers the same length as the Series

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.repeat(3)
    >>> S = pd.Series(np.arange(100))
    >>> f(S)
    0      0
    0      0
    0      0
    1      1
    1      1
          ..
    98    98
    98    98
    99    99
    99    99
    99    99
    Length: 300, dtype: int64
    

Combining / comparing / joining / merging

pd.Series.append

  • pandas.Series.append(to_append, ignore_index=False, verify_integrity=False)

    Supported Arguments

    argument

    datatypes

    other requirements

    to_append

    • Series
    • List of Series
    • Tuple of Series

    ignore_index

    • Boolean

    Must be constant at Compile Time

    Note

    Setting a name for the output Series is not supported yet

    Important

    Bodo currently concatenates local data chunks for distributed datasets, which does not preserve global order of concatenated objects in output.

    Example Usage

    >>> @bodo.jit
    ... def f(S1, S2):
    ...     return S1.append(S2)
    >>> S = pd.Series(np.arange(100))
    >>> f(S, S)
    0      0
    1      1
    2      2
    3      3
    4      4
          ..
    95    95
    96    96
    97    97
    98    98
    99    99
    Length: 200, dtype: int64
    

pd.Series.shift

  • pandas.Series.shift(periods=1, freq=None, axis=0, fill_value=None)

    Supported Arguments

    argument

    datatypes

    periods

    • Integer

    Note

    This data type for the series must be one of: - Integer - Float - Boolean - datetime.data - datetime64 - timedelta64 - string

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.shift(1)
    >>> S = pd.Series(np.arange(100))
    >>> f(S)
    0      NaN
    1      0.0
    2      1.0
    3      2.0
    4      3.0
          ...
    95    94.0
    96    95.0
    97    96.0
    98    97.0
    99    98.0
    Length: 100, dtype: float64
    

Datetime properties

`pd.Series.dt.date

  • ++pandas.Series.dt.date

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.date
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='1/10/2022', periods=30))
    >>> f(S)
    0     2022-01-01
    1     2022-01-01
    2     2022-01-01
    3     2022-01-01
    4     2022-01-02
    5     2022-01-02
    6     2022-01-02
    7     2022-01-03
    8     2022-01-03
    9     2022-01-03
    10    2022-01-04
    11    2022-01-04
    12    2022-01-04
    13    2022-01-05
    14    2022-01-05
    15    2022-01-05
    16    2022-01-05
    17    2022-01-06
    18    2022-01-06
    19    2022-01-06
    20    2022-01-07
    21    2022-01-07
    22    2022-01-07
    23    2022-01-08
    24    2022-01-08
    25    2022-01-08
    26    2022-01-09
    27    2022-01-09
    28    2022-01-09
    29    2022-01-10
    dtype: object
    

pd.Series.dt.year

  • pandas.Series.dt.year

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.year
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='1/10/2025', periods=30))
    >>> f(S)
    0     2022
    1     2022
    2     2022
    3     2022
    4     2022
    5     2022
    6     2022
    7     2022
    8     2022
    9     2022
    10    2023
    11    2023
    12    2023
    13    2023
    14    2023
    15    2023
    16    2023
    17    2023
    18    2023
    19    2023
    20    2024
    21    2024
    22    2024
    23    2024
    24    2024
    25    2024
    26    2024
    27    2024
    28    2024
    29    2025
    dtype: Int64
    

pd.Series.dt.month

  • pandas.Series.dt.month

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.month
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='1/10/2025', periods=30))
    >>> f(S)
    0      1
    1      2
    2      3
    3      4
    4      6
    5      7
    6      8
    7      9
    8     11
    9     12
    10     1
    11     2
    12     4
    13     5
    14     6
    15     7
    16     9
    17    10
    18    11
    19    12
    20     2
    21     3
    22     4
    23     5
    24     7
    25     8
    26     9
    27    10
    28    12
    29     1
    dtype: Int64
    

pd.Series.dt.day

  • pandas.Series.dt.day

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.day
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='1/10/2025', periods=30))
    >>> f(S)
    0      1
    1      8
    2     18
    3     25
    4      2
    5     10
    6     17
    7     24
    8      1
    9      9
    10    17
    11    24
    12     3
    13    11
    14    18
    15    26
    16     2
    17    10
    18    17
    19    25
    20     2
    21    11
    22    18
    23    26
    24     3
    25    10
    26    17
    27    25
    28     2
    29    10
    dtype: Int64
    

pd.Series.dt.hour

  • pandas.Series.dt.hour

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.hour
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='1/10/2025', periods=30))
    >>> f(S)
    0      0
    1      2
    2      4
    3      7
    4      9
    5     12
    6     14
    7     17
    8     19
    9     22
    10     0
    11     3
    12     5
    13     8
    14    10
    15    13
    16    15
    17    18
    18    20
    19    23
    20     1
    21     4
    22     6
    23     9
    24    11
    25    14
    26    16
    27    19
    28    21
    29     0
    dtype: Int64
    

pd.Series.dt.minute

  • pandas.Series.dt.minute

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.minute
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='1/10/2025', periods=30))
    >>> f(S)
    0      0
    1     28
    2     57
    3     26
    4     55
    5     24
    6     53
    7     22
    8     51
    9     20
    10    49
    11    18
    12    47
    13    16
    14    45
    15    14
    16    43
    17    12
    18    41
    19    10
    20    39
    21     8
    22    37
    23     6
    24    35
    25     4
    26    33
    27     2
    28    31
    29     0
    dtype: Int64
    

pd.Series.dt.second

  • pandas.Series.dt.second

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.second
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='1/10/2025', periods=30))
    >>> f(S)
    0      0
    1     57
    2     55
    3     53
    4     51
    5     49
    6     47
    7     45
    8     43
    9     41
    10    39
    11    37
    12    35
    13    33
    14    31
    15    28
    16    26
    17    24
    18    22
    19    20
    20    18
    21    16
    22    14
    23    12
    24    10
    25     8
    26     6
    27     4
    28     2
    29     0
    dtype: Int64
    

pd.Series.dt.microsecond

  • pandas.Series.dt.microsecond

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.microsecond
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='1/10/2025', periods=30))
    >>> f(S)
    0          0
    1     931034
    2     862068
    3     793103
    4     724137
    5     655172
    6     586206
    7     517241
    8     448275
    9     379310
    10    310344
    11    241379
    12    172413
    13    103448
    14     34482
    15    965517
    16    896551
    17    827586
    18    758620
    19    689655
    20    620689
    21    551724
    22    482758
    23    413793
    24    344827
    25    275862
    26    206896
    27    137931
    28     68965
    29         0
    dtype: Int64
    

pd.Series.dt.nanosecond

  • pandas.Series.dt.nanosecond

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.nanosecond
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='1/10/2025', periods=30))
    >>> f(S)
    0       0
    1     483
    2     966
    3     448
    4     932
    5     416
    6     896
    7     380
    8     864
    9     348
    10    832
    11    312
    12    792
    13    280
    14    760
    15    248
    16    728
    17    208
    18    696
    19    176
    20    664
    21    144
    22    624
    23    104
    24    584
    25     80
    26    560
    27     40
    28    520
    29      0
    dtype: Int64
    

pd.Series.dt.week

  • pandas.Series.dt.week

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.week
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='1/10/2025', periods=30))
    >>> f(S)
    0     52
    1      6
    2     11
    3     17
    4     22
    5     27
    6     33
    7     38
    8     44
    9     49
    10     3
    11     8
    12    14
    13    19
    14    24
    15    30
    16    35
    17    41
    18    46
    19    52
    20     5
    21    11
    22    16
    23    21
    24    27
    25    32
    26    38
    27    43
    28    49
    29     2
    dtype: Int64
    

pd.Series.dt.weekofyear

  • pandas.Series.dt.weekofyear

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.weekofyear
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='1/10/2025', periods=30))
    >>> f(S)
    0     52
    1      6
    2     11
    3     17
    4     22
    5     27
    6     33
    7     38
    8     44
    9     49
    10     3
    11     8
    12    14
    13    19
    14    24
    15    30
    16    35
    17    41
    18    46
    19    52
    20     5
    21    11
    22    16
    23    21
    24    27
    25    32
    26    38
    27    43
    28    49
    29     2
    dtype: Int64
    

pd.Series.dt.day_of_week

  • pandas.Series.dt.day_of_week

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.day_of_week
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='1/10/2025', periods=30))
    >>> f(S)
    0     5
    1     1
    2     4
    3     0
    4     3
    5     6
    6     2
    7     5
    8     1
    9     4
    10    1
    11    4
    12    0
    13    3
    14    6
    15    2
    16    5
    17    1
    18    4
    19    0
    20    4
    21    0
    22    3
    23    6
    24    2
    25    5
    26    1
    27    4
    28    0
    29    4
    dtype: Int64
    

pd.Series.dt.weekday

  • pandas.Series.dt.weekday

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.weekday
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='1/10/2025', periods=30))
    >>> f(S)
    0     5
    1     1
    2     4
    3     0
    4     3
    5     6
    6     2
    7     5
    8     1
    9     4
    10    1
    11    4
    12    0
    13    3
    14    6
    15    2
    16    5
    17    1
    18    4
    19    0
    20    4
    21    0
    22    3
    23    6
    24    2
    25    5
    26    1
    27    4
    28    0
    29    4
    dtype: Int64
    

pd.Series.dt.dayofyear

  • pandas.Series.dt.dayofyear

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.dayofyear
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='1/10/2025', periods=30))
    >>> f(S)
    0       1
    1      39
    2      77
    3     115
    4     153
    5     191
    6     229
    7     267
    8     305
    9     343
    10     17
    11     55
    12     93
    13    131
    14    169
    15    207
    16    245
    17    283
    18    321
    19    359
    20     33
    21     71
    22    109
    23    147
    24    185
    25    223
    26    261
    27    299
    28    337
    29     10
    dtype: Int64
    

pd.Series.dt.day_of_year

  • pandas.Series.dt.day_of_year

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.day_of_year
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='1/10/2025', periods=30))
    >>> f(S)
    0       1
    1      39
    2      77
    3     115
    4     153
    5     191
    6     229
    7     267
    8     305
    9     343
    10     17
    11     55
    12     93
    13    131
    14    169
    15    207
    16    245
    17    283
    18    321
    19    359
    20     33
    21     71
    22    109
    23    147
    24    185
    25    223
    26    261
    27    299
    28    337
    29     10
    dtype: Int64
    

pd.Series.dt.quarter

  • pandas.Series.dt.quarter

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.quarter
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='1/10/2025', periods=30))
    >>> f(S)
    0     1
    1     1
    2     1
    3     2
    4     2
    5     3
    6     3
    7     3
    8     4
    9     4
    10    1
    11    1
    12    2
    13    2
    14    2
    15    3
    16    3
    17    4
    18    4
    19    4
    20    1
    21    1
    22    2
    23    2
    24    3
    25    3
    26    3
    27    4
    28    4
    29    1
    dtype: Int64
    

pd.Series.dt.is_month_start

  • pandas.Series.dt.is_month_start

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.is_month_start
    >>> SS = pd.Series(pd.date_range(start='1/1/2022', end='12/31/2024', periods=30))
    >>> f(S)
    0      True
    1     False
    2     False
    3     False
    4      True
    5     False
    6     False
    7     False
    8     False
    9     False
    10    False
    11    False
    12    False
    13    False
    14    False
    15    False
    16    False
    17    False
    18    False
    19    False
    20    False
    21    False
    22    False
    23    False
    24    False
    25     True
    26    False
    27    False
    28    False
    29    False
    dtype: bool
    

pd.Series.dt.is_month_end

  • pandas.Series.dt.is_month_end

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.is_month_end
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='12/31/2024', periods=30))
    >>> f(S)
    0     False
    1     False
    2     False
    3     False
    4     False
    5     False
    6     False
    7     False
    8     False
    9     False
    10    False
    11    False
    12    False
    13    False
    14    False
    15    False
    16    False
    17    False
    18    False
    19    False
    20    False
    21    False
    22    False
    23    False
    24    False
    25    False
    26    False
    27    False
    28    False
    29     True
    dtype: bool
    

pd.Series.dt.is_quarter_start

  • pandas.Series.dt.is_quarter_start

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.is_quarter_start
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='12/31/2024', periods=30))
    >>> f(S)
    0      True
    1     False
    2     False
    3     False
    4     False
    5     False
    6     False
    7     False
    8     False
    9     False
    10    False
    11    False
    12    False
    13    False
    14    False
    15    False
    16    False
    17    False
    18    False
    19    False
    20    False
    21    False
    22    False
    23    False
    24    False
    25    False
    26    False
    27    False
    28    False
    29    False
    dtype: bool
    

pd.Series.dt.is_quarter_end

  • pandas.Series.dt.is_quarter_end

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.is_quarter_end
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='12/31/2024', periods=30))
    >>> f(S)
    0     False
    1     False
    2     False
    3     False
    4     False
    5     False
    6     False
    7     False
    8     False
    9     False
    10    False
    11    False
    12    False
    13    False
    14    False
    15    False
    16    False
    17    False
    18    False
    19    False
    20    False
    21    False
    22    False
    23    False
    24    False
    25    False
    26    False
    27    False
    28    False
    29     True
    dtype: bool
    

pd.Series.dt.is_year_start

  • pandas.Series.dt.is_year_start

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.is_year_start
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='12/31/2024', periods=30))
    >>> f(S)
    0      True
    1     False
    2     False
    3     False
    4     False
    5     False
    6     False
    7     False
    8     False
    9     False
    10    False
    11    False
    12    False
    13    False
    14    False
    15    False
    16    False
    17    False
    18    False
    19    False
    20    False
    21    False
    22    False
    23    False
    24    False
    25    False
    26    False
    27    False
    28    False
    29    False
    dtype: bool
    

pd.Series.dt.is_year_end

  • pandas.Series.dt.is_year_end

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.is_year_end
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='12/31/2024', periods=30))
    >>> f(S)
    0     False
    1     False
    2     False
    3     False
    4     False
    5     False
    6     False
    7     False
    8     False
    9     False
    10    False
    11    False
    12    False
    13    False
    14    False
    15    False
    16    False
    17    False
    18    False
    19    False
    20    False
    21    False
    22    False
    23    False
    24    False
    25    False
    26    False
    27    False
    28    False
    29     True
    dtype: bool
    

pd.Series.dt.daysinmonth

  • pandas.Series.dt.daysinmonth

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.daysinmonth
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='12/31/2024', periods=30))
    >>> f(S)
    0     31
    1     28
    2     31
    3     30
    4     30
    5     31
    6     31
    7     30
    8     31
    9     31
    10    31
    11    28
    12    31
    13    31
    14    30
    15    31
    16    31
    17    31
    18    30
    19    31
    20    31
    21    31
    22    30
    23    31
    24    30
    25    31
    26    30
    27    31
    28    30
    29    31
    dtype: Int64
    

pd.Series.dt.days_in_month

  • pandas.Series.dt.days_in_month

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.days_in_month
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='12/31/2024', periods=30))
    >>> f(S)
    0     31
    1     28
    2     31
    3     30
    4     30
    5     31
    6     31
    7     30
    8     31
    9     31
    10    31
    11    28
    12    31
    13    31
    14    30
    15    31
    16    31
    17    31
    18    30
    19    31
    20    31
    21    31
    22    30
    23    31
    24    30
    25    31
    26    30
    27    31
    28    30
    29    31
    dtype: Int64
    

Datetime methods

pd.Series.dt.normalize

  • pandas.Series.dt.normalize()

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.normalize()
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='1/10/2022', periods=30))
    >>> f(S)
    0    2022-01-01
    1    2022-01-01
    2    2022-01-01
    3    2022-01-01
    4    2022-01-02
    5    2022-01-02
    6    2022-01-02
    7    2022-01-03
    8    2022-01-03
    9    2022-01-03
    10   2022-01-04
    11   2022-01-04
    12   2022-01-04
    13   2022-01-05
    14   2022-01-05
    15   2022-01-05
    16   2022-01-05
    17   2022-01-06
    18   2022-01-06
    19   2022-01-06
    20   2022-01-07
    21   2022-01-07
    22   2022-01-07
    23   2022-01-08
    24   2022-01-08
    25   2022-01-08
    26   2022-01-09
    27   2022-01-09
    28   2022-01-09
    29   2022-01-10
    dtype: datetime64[ns]
    

pd.Series.dt.strftime

  • pandas.Series.dt.strftime(date_format)

    Supported Arguments

    argument

    datatypes

    other requirements

    date_format

    • String

    Must be a valid datetime format string

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.strftime("%B %d, %Y, %r")
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='1/10/2022', periods=30))
    >>> f(S)
    0     January 01, 2022, 12:00:00 AM
    1     January 01, 2022, 07:26:53 AM
    2     January 01, 2022, 02:53:47 PM
    3     January 01, 2022, 10:20:41 PM
    4     January 02, 2022, 05:47:35 AM
    5     January 02, 2022, 01:14:28 PM
    6     January 02, 2022, 08:41:22 PM
    7     January 03, 2022, 04:08:16 AM
    8     January 03, 2022, 11:35:10 AM
    9     January 03, 2022, 07:02:04 PM
    10    January 04, 2022, 02:28:57 AM
    11    January 04, 2022, 09:55:51 AM
    12    January 04, 2022, 05:22:45 PM
    13    January 05, 2022, 12:49:39 AM
    14    January 05, 2022, 08:16:33 AM
    15    January 05, 2022, 03:43:26 PM
    16    January 05, 2022, 11:10:20 PM
    17    January 06, 2022, 06:37:14 AM
    18    January 06, 2022, 02:04:08 PM
    19    January 06, 2022, 09:31:02 PM
    20    January 07, 2022, 04:57:55 AM
    21    January 07, 2022, 12:24:49 PM
    22    January 07, 2022, 07:51:43 PM
    23    January 08, 2022, 03:18:37 AM
    24    January 08, 2022, 10:45:31 AM
    25    January 08, 2022, 06:12:24 PM
    26    January 09, 2022, 01:39:18 AM
    27    January 09, 2022, 09:06:12 AM
    28    January 09, 2022, 04:33:06 PM
    29    January 10, 2022, 12:00:00 AM
    dtype: object
    

pd.Series.dt.round

  • pandas.Series.dt.round(freq, ambiguous='raise', nonexistent='raise')

    Supported Arguments

    argument

    datatypes

    other requirements

    freq

    • String

    Must be a valid fixed frequency alias

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.round("H")
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='1/10/2022', periods=30))
    >>> f(S)
    0    2022-01-01 00:00:00
    1    2022-01-01 07:00:00
    2    2022-01-01 15:00:00
    3    2022-01-01 22:00:00
    4    2022-01-02 06:00:00
    5    2022-01-02 13:00:00
    6    2022-01-02 21:00:00
    7    2022-01-03 04:00:00
    8    2022-01-03 12:00:00
    9    2022-01-03 19:00:00
    10   2022-01-04 02:00:00
    11   2022-01-04 10:00:00
    12   2022-01-04 17:00:00
    13   2022-01-05 01:00:00
    14   2022-01-05 08:00:00
    15   2022-01-05 16:00:00
    16   2022-01-05 23:00:00
    17   2022-01-06 07:00:00
    18   2022-01-06 14:00:00
    19   2022-01-06 22:00:00
    20   2022-01-07 05:00:00
    21   2022-01-07 12:00:00
    22   2022-01-07 20:00:00
    23   2022-01-08 03:00:00
    24   2022-01-08 11:00:00
    25   2022-01-08 18:00:00
    26   2022-01-09 02:00:00
    27   2022-01-09 09:00:00
    28   2022-01-09 17:00:00
    29   2022-01-10 00:00:00
    dtype: datetime64[ns]
    

pd.Series.dt.floor

  • pandas.Series.dt.floor(freq, ambiguous='raise', nonexistent='raise')

    Supported Arguments

    argument

    datatypes

    other requirements

    freq

    • String

    Must be a valid fixed frequency alias

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.floor("H")
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='1/10/2022', periods=30))
    >>> f(S)
    0    2022-01-01 00:00:00
    1    2022-01-01 07:00:00
    2    2022-01-01 14:00:00
    3    2022-01-01 22:00:00
    4    2022-01-02 05:00:00
    5    2022-01-02 13:00:00
    6    2022-01-02 20:00:00
    7    2022-01-03 04:00:00
    8    2022-01-03 11:00:00
    9    2022-01-03 19:00:00
    10   2022-01-04 02:00:00
    11   2022-01-04 09:00:00
    12   2022-01-04 17:00:00
    13   2022-01-05 00:00:00
    14   2022-01-05 08:00:00
    15   2022-01-05 15:00:00
    16   2022-01-05 23:00:00
    17   2022-01-06 06:00:00
    18   2022-01-06 14:00:00
    19   2022-01-06 21:00:00
    20   2022-01-07 04:00:00
    21   2022-01-07 12:00:00
    22   2022-01-07 19:00:00
    23   2022-01-08 03:00:00
    24   2022-01-08 10:00:00
    25   2022-01-08 18:00:00
    26   2022-01-09 01:00:00
    27   2022-01-09 09:00:00
    28   2022-01-09 16:00:00
    29   2022-01-10 00:00:00
    dtype: datetime64[ns]
    

pd.Series.dt.ceil

  • pandas.Series.dt.ceil(freq, ambiguous='raise', nonexistent='raise')

    Supported Arguments

    argument

    datatypes

    other requirements

    freq

    • String

    Must be a valid fixed frequency alias

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.ceil("H")
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='1/10/2022', periods=30))
    >>> f(S)
    0    2022-01-01 00:00:00
    1    2022-01-01 08:00:00
    2    2022-01-01 15:00:00
    3    2022-01-01 23:00:00
    4    2022-01-02 06:00:00
    5    2022-01-02 14:00:00
    6    2022-01-02 21:00:00
    7    2022-01-03 05:00:00
    8    2022-01-03 12:00:00
    9    2022-01-03 20:00:00
    10   2022-01-04 03:00:00
    11   2022-01-04 10:00:00
    12   2022-01-04 18:00:00
    13   2022-01-05 01:00:00
    14   2022-01-05 09:00:00
    15   2022-01-05 16:00:00
    16   2022-01-06 00:00:00
    17   2022-01-06 07:00:00
    18   2022-01-06 15:00:00
    19   2022-01-06 22:00:00
    20   2022-01-07 05:00:00
    21   2022-01-07 13:00:00
    22   2022-01-07 20:00:00
    23   2022-01-08 04:00:00
    24   2022-01-08 11:00:00
    25   2022-01-08 19:00:00
    26   2022-01-09 02:00:00
    27   2022-01-09 10:00:00
    28   2022-01-09 17:00:00
    29   2022-01-10 00:00:00
    dtype: datetime64[ns]
    

pd.Series.dt.month_name

  • pandas.Series.dt.month_name(locale=None)

    Supported Arguments None

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.month_name()
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='1/10/2025', periods=30))
    >>> f(S)
    0       January
    1      February
    2         March
    3         April
    4          June
    5          July
    6        August
    7     September
    8      November
    9      December
    10      January
    11     February
    12        April
    13          May
    14         June
    15         July
    16    September
    17      October
    18     November
    19     December
    20     February
    21        March
    22        April
    23          May
    24         July
    25       August
    26    September
    27      October
    28     December
    29      January
    dtype: object
    

pd.Series.dt.day_name

  • pandas.Series.dt.day_name(locale=None)

    Supported Arguments None

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.dt.day_name()
    >>> S = pd.Series(pd.date_range(start='1/1/2022', end='1/10/2022', periods=30))
    >>> f(S)
    0      Saturday
    1      Saturday
    2      Saturday
    3      Saturday
    4        Sunday
    5        Sunday
    6        Sunday
    7        Monday
    8        Monday
    9        Monday
    10      Tuesday
    11      Tuesday
    12      Tuesday
    13    Wednesday
    14    Wednesday
    15    Wednesday
    16    Wednesday
    17     Thursday
    18     Thursday
    19     Thursday
    20       Friday
    21       Friday
    22       Friday
    23     Saturday
    24     Saturday
    25     Saturday
    26       Sunday
    27       Sunday
    28       Sunday
    29       Monday
    dtype: object
    

String handling

pd.Series.str.capitalize

  • pandas.Series.str.capitalize()

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.capitalize()
    >>> S = pd.Series(["a", "ce", "Erw", "a3", "@", "a n", "^ Ef"])
    >>> f(S)
    0       A
    1      Ce
    2     Erw
    3      A3
    4       @
    5     A n
    6    ^ Ef
    dtype: object
    

pd.Series.str.center

  • pandas.Series.str.center(width, fillchar=' ')

    Supported Arguments

    argument

    datatypes

    width

    • Integer

    fillchar

    • String with a single character

    Example Usage

    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.center(4)
    >>> S = pd.Series(["a", "ce", "Erw", "a3", "@", "a n", "^ Ef"])
    >>> f(S)
    0     a
    1     ce
    2    Erw
    3     a3
    4     @
    5    a n
    6    ^ Ef
    dtype: object
    

pd.Series.str.contains

  • pandas.Series.str.contains(pat, case=True, flags=0, na=None, regex=True)

    Supported Arguments

    argument

    datatypes

    other requirements

    pat

    • String

    case

    • Boolean

    Must be constant at Compile Time

    flags

    • Integer

    regex

    • Boolean

    Must be constant at Compile Time

    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.contains("a.+")
    >>> S = pd.Series(["a", "ce", "Erw", "a3", "@", "a n", "^ Ef"])
    >>> f(S)
    0    False
    1    False
    2    False
    3     True
    4    False
    5     True
    6    False
    dtype: boolean
    

pd.Series.str.count

  • pandas.Series.str.count(pat, flags=0)

    Supported Arguments

    argument

    datatypes

    pat

    • String

    flags

    • Integer
    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.count("w")
    >>> S = pd.Series(["a", "ce", "Erw", "a3", "@", "a n", "^ Ef"])
    >>> f(S)
    0    1
    1    2
    2    3
    3    2
    4    0
    5    2
    6    2
    dtype: Int64
    

pd.Series.str.endswith

  • pandas.Series.str.endswith(pat, na=None)

    Supported Arguments

    argument

    datatypes

    pat

    • String
    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.endswith("e")
    >>> S = pd.Series(["a", "ce", "Erw", "a3", "@", "a n", "^ Ef"])
    >>> f(S)
    0    False
    1     True
    2    False
    3    False
    4    False
    5    False
    6    False
    dtype: boolean
    

pd.Series.str.extract

  • pandas.Series.str.extract(pat, flags=0, expand=True)

    Supported Arguments

    argument

    datatypes

    other requirements

    pat

    • String

    Must be constant at Compile Time

    flags

    • Integer

    Must be constant at Compile Time

    expand

    • Boolean

    Must be constant at Compile Time

    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.extract("(a|e)")
    >>> S = pd.Series(["a", "ce", "Erw", "a3", "@", "a n", "^ Ef"])
    >>> f(S)
         0
    0    a
    1    e
    2  NaN
    3    a
    4  NaN
    5    a
    6  NaN
    

pd.Series.str.extractall

  • pandas.Series.str.extractall(pat, flags=0)

    Supported Arguments

    argument

    datatypes

    other requirements

    pat

    • String

    Must be constant at Compile Time

    flags

    • Integer

    Must be constant at Compile Time

    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.extractall("(a|n)")
    >>> S = pd.Series(["a", "ce", "Erw", "a3", "@", "a n", "^ Ef"])
    >>> f(S)
             0
      match
    0 0      a
    3 0      a
    5 0      a
      1      n
    

pd.Series.str.find

  • pandas.Series.str.find(sub, start=0, end=None)

    Supported Arguments

    argument

    datatypes

    sub

    • String

    start

    • Integer

    end

    • Integer
    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.find("a3", start=1)
    >>> S = pd.Series(["Aa3", "cea3", "14a3", " a3", "a3@", "a n3", "^ Ea3f"])
    >>> f(S)
    0     1
    1     2
    2     2
    3     1
    4    -1
    5    -1
    6     3
    dtype: Int64
    

pd.Series.str.get

  • pandas.Series.str.get(i)

    Supported Arguments

    argument

    datatypes

    i

    • Integer
    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.get(1)
    >>> S = pd.Series(["A", "ce", "14", " ", "@", "a n", "^ Ef"])
    >>> f(S)
    0    NaN
    1      e
    2      4
    3    NaN
    4    NaN
    5
    6
    dtype: object
    

pd.Series.str.join

  • pandas.Series.str.join(sep)

    Supported Arguments

    argument

    datatypes

    sep

    • String
    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.join(",")
    >>> S = pd.Series([["a", "fe", "@23"], ["a", "b"], [], ["c"]])
    >>> f(S)
    0    a,fe,@23
    1         a,b
    2
    3           c
    dtype: object
    

pd.Series.str.len

  • pandas.Series.str.len()

    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.len()
    >>> S = pd.Series(["A", "ce", "14", " ", "@", "a n", "^ Ef"])
    >>> f(S)
    0    1
    1    2
    2    2
    3    1
    4    1
    5    3
    6    4
    dtype: Int64
    

pd.Series.str.ljust

  • pandas.Series.str.ljust(width, fillchar=' ')

    Supported Arguments

    argument

    datatypes

    width

    • Integer

    fillchar

    • String with a single character
    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.ljust(5, fillchar=",")
    >>> S = pd.Series(["A", "ce", "14", " ", "@", "a n", "^ Ef"])
    >>> f(S)
    0    A,,,,
    1    ce,,,
    2    14,,,
    3     ,,,,
    4    @,,,,
    5    a n,,
    6    ^ Ef,
    dtype: object
    

pd.Series.str.lower

  • pandas.Series.str.lower()

    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.lower()
    >>> S = pd.Series(["A", "ce", "14", " ", "@", "a n", "^ Ef"])
    >>> f(S)
    0       a
    1      ce
    2      14
    3
    4       @
    5     a n
    6    ^ Ef
    dtype: object
    

pd.Series.str.lstrip

  • pandas.Series.str.lstrip(to_strip=None)

    Supported Arguments

    argument

    datatypes

    to_strip

    • String
    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.lstrip("c")
    >>> S = pd.Series(["A", "ce", "14", " ", "@", "a n", "^ Ef"])
    >>> f(S)
    0       A
    1       e
    2      14
    3
    4       @
    5     a n
    6    ^ Ef
    dtype: object
    

pd.Series.str.pad

  • pandas.Series.str.pad(width, side='left', fillchar=' ')

    Supported Arguments

    argument

    datatypes

    other requirements

    width

    • Integer

    width

    • One of ("left", "right", "both")

    Must be constant at Compile Time

    fillchar

    • String with a single character
    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.pad(5)
    >>> S = pd.Series(["A", "ce", "14", " ", "@", "a n", "^ Ef"])
    >>> f(S)
    0        A
    1       ce
    2       14
    3
    4        @
    5      a n
    6     ^ Ef
    dtype: object
    

pd.Series.str.repeat

  • pandas.Series.str.repeat(repeats)

    Supported Arguments

    argument

    datatypes

    other requirements

    repeats

    • Integer
    • Array Like containing integers

    If repeats is array like, then it must be the same length as the Series.

    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.repeat(2)
    >>> S = pd.Series(["A", "ce", "14", " ", "@", "a n", "^ Ef"])
    >>> f(S)
    0          AA
    1        cece
    2        1414
    3
    4          @@
    5      a na n
    6    ^ Ef^ Ef
    dtype: object
    

pd.Series.str.replace

  • pandas.Series.str.replace(pat, repl, n=- 1, case=None, flags=0, regex=None)

    Supported Arguments

    • regex
    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.replace("(a|e)", "yellow")
    >>> S = pd.Series(["A", "ce", "14", " ", "@", "a n", "^ Ef"])
    >>> f(S)
    0           A
    1     cyellow
    2          14
    3
    4           @
    5    yellow n
    6        ^ Ef
    dtype: object
    

pd.Series.str.rfind

  • pandas.Series.str.rfind(sub, start=0, end=None)

    Supported Arguments

    argument

    datatypes

    sub

    • String

    start

    • Integer

    end

    • Integer
    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.rfind("a3", start=1)
    >>> S = pd.Series(["Aa3", "cea3", "14a3", " a3", "a3@", "a n3", "^ Ea3f"])
    >>> f(S)
    0     1
    1     2
    2     2
    3     1
    4    -1
    5    -1
    6     3
    dtype: Int64
    

pd.Series.str.rjist

  • pandas.Series.str.rjust(width, fillchar=' ')

    Supported arguments`:

    argument

    datatypes

    width

    • Integer

    fillchar

    • String with a single character
    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.rjust(10)
    >>> S = pd.Series(["A", "ce", "14", " ", "@", "a n", "^ Ef"])
    >>> f(S)
    0             A
    1            ce
    2            14
    3
    4             @
    5           a n
    6          ^ Ef
    dtype: object
    

pd.Series.str.restrip

  • pandas.Series.str.rstrip(to_strip=None)

    Supported Arguments

    argument

    datatypes

    to_strip

    • String
    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.rstrip("n")
    >>> S = pd.Series(["A", "ce", "14", " ", "@", "a n", "^ Ef"])
    >>> f(S)
    0       A
    1      ce
    2      14
    3
    4       @
    5      a
    6    ^ Ef
    dtype: object
    

pd.Series.str.slice

  • pandas.Series.str.slice(start=None, stop=None, step=None)

    Supported Arguments

    argument

    datatypes

    start

    • Integer

    stop

    • Integer

    step

    • Integer
    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.slice(1, 4)
    >>> S = pd.Series(["A", "ce", "14", " ", "@", "a n", "^ Ef"])
    >>> f(S)
    0    A
    1    c
    2    1
    3
    4    @
    5    a
    6    #
    dtype: object
    

pd.Series.str.slice_replace

  • pandas.Series.str.slice_replace(start=None, stop=None, repl=None)

    Supported Arguments

    argument

    datatypes

    start

    • Integer

    stop

    • Integer

    repl

    • String
    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.slice_replace(1, 4)
    >>> S = pd.Series(["A", "ce", "14", " ", "@", "a n", "^ Ef"])
    >>> f(S)
    0    A
    1    c
    2    1
    3
    4    @
    5    a
    6    #
    dtype: object
    

pd.Series.str.split

  • pandas.Series.str.split(pat=None, n=-1, expand=False)

    Supported Arguments

    argument

    datatypes

    pat

    • String

    n

    • Integer
    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.split(" ")
    >>> S = pd.Series(["A", "ce", "14", " ", "@", "a n", "^ Ef"])
    >>> f(S)
    0        [A]
    1       [ce]
    2       [14]
    3       [, ]
    4        [@]
    5     [a, n]
    6    [#, Ef]
    dtype: object
    

pd.Series.str.startswith

  • pandas.Series.str.startswith(pat, na=None)

    Supported Arguments

    argument

    datatypes

    pat

    • String
    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.startswith("A")
    >>> S = pd.Series(["A", "ce", "14", " ", "@", "a n", "^ Ef"])
    >>> f(S)
    0     True
    1    False
    2    False
    3    False
    4    False
    5    False
    6    False
    dtype: boolean
    

pd.Series.str.strip

  • pandas.Series.str.strip(to_strip=None)

    Supported Arguments

    argument

    datatypes

    to_strip

    • String
    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.strip("n")
    >>> S = pd.Series(["A", "ce", "14", " ", "@", "a n", "^ Ef"])
    >>> f(S)
    0       A
    1      ce
    2      14
    3
    4       @
    5      a
    6    ^ Ef
    dtype: object
    

pd.Series.str.swapcase

  • pandas.Series.str.swapcase()

    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.swapcase()
    >>> S = pd.Series(["A", "ce", "14", " ", "@", "a n", "^ Ef"])
    >>> f(S)
    0       a
    1      CE
    2      14
    3
    4       @
    5     A N
    6    ^ Ef
    dtype: object
    

pd.Series.str.title

  • pandas.Series.str.title()

    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.title()
    >>> S = pd.Series(["A", "ce", "14", " ", "@", "a n", "^ Ef"])
    >>> f(S)
    0       A
    1      Ce
    2      14
    3
    4       @
    5     A N
    6    ^ Ef
    dtype: object
    

pd.Series.str.upper

  • pandas.Series.str.upper()

    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.upper()
    >>> S = pd.Series(["A", "ce", "14", " ", "@", "a n", "^ Ef"])
    >>> f(S)
    0       A
    1      CE
    2      14
    3
    4       @
    5     A N
    6    ^ Ef
    dtype: object
    

pd.Series.str.zfill

  • pandas.Series.str.zfill(width)

    Supported Arguments

    argument

    datatypes

    width

    • Integer
    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.zfill(5)
    >>> S = pd.Series(["A", "ce", "14", " ", "@", "a n", "^ Ef"])
    >>> f(S)
    0    0000A
    1    000ce
    2    00014
    3    0000
    4    0000@
    5    00a n
    6    0^ Ef
    dtype: object
    

pd.Series.str.isalnum

  • pandas.Series.str.isalnum()

    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.isalnum()
    >>> S = pd.Series(["A", "ce", "14", " ", "@", "a n", "^ Ef"])
    >>> f(S)
    0     True
    1     True
    2     True
    3    False
    4    False
    5    False
    6    False
    dtype: boolean
    

pd.Series.str.isalpha

  • pandas.Series.str.isalpha()

    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.isalpha()
    >>> S = pd.Series(["A", "ce", "14", " ", "@", "a n", "^ Ef"])
    >>> f(S)
    0     True
    1     True
    2    False
    3    False
    4    False
    5    False
    6    False
    dtype: boolean
    

pd.Series.str.isdigit

  • pandas.Series.str.isdigit()

    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.isdigit()
    >>> S = pd.Series(["A", "ce", "14", " ", "@", "a n", "^ Ef"])
    >>> f(S)
    0    False
    1    False
    2     True
    3    False
    4    False
    5    False
    6    False
    dtype: boolean
    

pd.Series.str.isspace

  • pandas.Series.str.isspace()

    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.isspace()
    >>> S = pd.Series(["A", "ce", "14", " ", "@", "a n", "^ Ef"])
    >>> f(S)
    0    False
    1    False
    2    False
    3     True
    4    False
    5    False
    6    False
    dtype: boolean
    

pd.Series.str.islower

  • pandas.Series.str.islower()

    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.islower()
    >>> S = pd.Series(["A", "ce", "14", "a3", "@", "a n", "^ Ef"])
    >>> f(S)
    0    False
    1     True
    2    False
    3     True
    4    False
    5     True
    6    False
    dtype: boolean
    

pd.Series.str.isupper

  • pandas.Series.str.isupper()

    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.isupper()
    >>> S = pd.Series(["A", "ce", "14", "a3", "@", "a n", "^ Ef"])
    >>> f(S)
    0     True
    1    False
    2    False
    3    False
    4    False
    5    False
    6    False
    dtype: boolean
    

pd.Series.str.istitle

  • pandas.Series.str.istitle()

    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.istitle()
    >>> S = pd.Series(["A", "ce", "14", "a3", "@", "a n", "^ Ef"])
    >>> f(S)
    0     True
    1    False
    2    False
    3    False
    4    False
    5    False
    6     True
    dtype: boolean
    

pd.Series.str.isnumeric

  • pandas.Series.str.isnumeric()

    >>> @bodo.jit
    ... def f(S):
    ...     return S.str.isnumeric()
    >>> S = pd.Series(["A", "ce", "14", "a3", "@", "a n", "^ Ef"])
    >>> f(S)