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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)
0    False
1    False
2     True
3    False
4    False
5    False
6    False
dtype: boolean

pd.Series.str.isdecimal

  • pandas.Series.str.isdecimal() 

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

Categorical accessor

pd.Series.cat.codes

  • pandas.Series.cat.codes

    Note

    If categories cannot be determined at compile time, then Bodo defaults to creating codes with an int64, which may differ from Pandas.

    Example Usage

    >>> @bodo.jit
... def f(S):
...     return S.cat.codes
>>> S = pd.Series(["a", "ce", "Erw", "a3", "@"] * 10).astype("category")
>>> f(S)
0     2
1     4
2     1
3     3
4     0
5     2
6     4
7     1
8     3
9     0
10    2
11    4
12    1
13    3
14    0
15    2
16    4
17    1
18    3
19    0
20    2
21    4
22    1
23    3
24    0
25    2
26    4
27    1
28    3
29    0
30    2
31    4
32    1
33    3
34    0
35    2
36    4
37    1
38    3
39    0
40    2
41    4
42    1
43    3
44    0
45    2
46    4
47    1
48    3
49    0
dtype: int8

Serialization / IO / Conversion

pd.Series.to_csv

  • pandas.Series.to_csv(path_or_buf=None, sep=',', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, mode='w', encoding=None, compression='infer', quoting=None, quotechar='"', line_terminator=None, chunksize=None, date_format=None, doublequote=True, escapechar=None, decimal='.', errors='strict', storage_options=None)

pd.Series.to_dict

  • pandas.Series.to_dict(into=)

    Supported Arguments None

    Note

    • This method is not parallelized since dictionaries are not parallelized.

    • This method returns a typedDict, which maintains typing information if passing the dictionary between JIT code and regular Python. This can be converted to a regular Python dictionary by using the dict constructor.

    Example Usage

    >>> @bodo.jit
... def f(S):
...     return S.to_dict()
>>> S = pd.Series(np.arange(10))
>>> dict(f(S))
{0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9}

pd.Series.to_frame

  • pandas.Series.to_frame(name=None)

    Supported Arguments

    argument

    datatypes

    other requirements

    name

    • String

    Must be constant at Compile Time

    Note

    If name is not provided Series name must be a known constant

    Example Usage

    >>> @bodo.jit
... def f(S):
...     return S.to_frame("my_column")
>>> S = pd.Series(np.arange(1000))
>>> f(S)
     my_column
0            0
1            1
2            2
3            3
4            4
..         ...
995        995
996        996
997        997
998        998
999        999

[1000 rows x 1 columns]

Heterogeneous Series

Bodo's Series implementation requires all elements to share a common data type. However, in situations where the size and types of the elements are constant at compile time, Bodo has some mixed type handling with its Heterogeneous Series type.

Warning

This type's primary purpose is for iterating through the rows of a DataFrame with different column types. You should not attempt to directly create Series with mixed types.

Heterogeneous Series operations are a subset of those supported for Series and the supported operations are listed below. Please refer to series for detailed usage.

Attributes

pd.Series.index

  • pandas.Series.index

    Example Usage

    >>> @bodo.jit
... def f(df):
...     return df.apply(lambda row: len(row.index), axis=1)
>>> df = pd.DataFrame({"A": np.arange(100), "B": ["A", "b"] * 50})
>>> f(df)
0     2
1     2
2     2
3     2
4     2
    ..
95    2
96    2
97    2
98    2
99    2
Length: 100, dtype: int64

pd.Series.values

  • pandas.Series.values

    Example Usage

    >>> @bodo.jit
... def f(df):
...     return df.apply(lambda row: row.values, axis=1)
>>> df = pd.DataFrame({"A": np.arange(100), "B": ["A", "b"] * 50})
>>> f(df)
0      (0, A)
1      (1, b)
2      (2, A)
3      (3, b)
4      (4, A)
      ...
95    (95, b)
96    (96, A)
97    (97, b)
98    (98, A)
99    (99, b)
Length: 100, dtype: object

pd.Series.shape

  • pandas.Series.shape

    Example Usage

    >>> @bodo.jit
... def f(df):
...     return df.apply(lambda row: row.shape, axis=1)
>>> df = pd.DataFrame({"A": np.arange(100), "B": ["A", "b"] * 50})
>>> f(df)
0     (2,)
1     (2,)
2     (2,)
3     (2,)
4     (2,)
      ...
95    (2,)
96    (2,)
97    (2,)
98    (2,)
99    (2,)
Length: 100, dtype: object

pd.Series.ndim

  • pandas.Series.ndim

    Example Usage
>>> @bodo.jit
... def f(df):
...     return df.apply(lambda row: row.ndim, axis=1)
>>> df = pd.DataFrame({"A": np.arange(100), "B": ["A", "b"] * 50})
>>> f(df)
0     1
1     1
2     1
3     1
4     1
    ..
95    1
96    1
97    1
98    1
99    1
Length: 100, dtype: int64

pd.Series.size

  • pandas.Series.size

    Example Usage

    >>> @bodo.jit
... def f(df):
...     return df.apply(lambda row: row.size, axis=1)
>>> df = pd.DataFrame({"A": np.arange(100), "B": ["A", "b"] * 50})
>>> f(df)
0     2
1     2
2     2
3     2
4     2
..
95    2
96    2
97    2
98    2
99    2
Length: 100, dtype: int64

pd.Series.T

  • pandas.Series.T

    Example Usage

    >>> @bodo.jit
... def f(df):
...     return df.apply(lambda row: row.T.size, axis=1)
>>> df = pd.DataFrame({"A": np.arange(100), "B": ["A", "b"] * 50})
>>> f(df)
0     2
1     2
2     2
3     2
4     2
    ..
95    2
96    2
97    2
98    2
99    2
Length: 100, dtype: int64

pd.Series.empty

  • pandas.Series.empty

    Example Usage

    >>> @bodo.jit
... def f(df):
...     return df.apply(lambda row: row.empty, axis=1)
>>> df = pd.DataFrame({"A": np.arange(100), "B": ["A", "b"] * 50})
>>> f(df)
0     False
1     False
2     False
3     False
4     False
      ...
95    False
96    False
97    False
98    False
99    False
Length: 100, dtype: boolean

pd.Series.name

  • pandas.Series.name

    Example Usage

    >>> @bodo.jit
... def f(df):
...     return df.apply(lambda row: row.name, axis=1)
>>> df = pd.DataFrame({"A": np.arange(100), "B": ["A", "b"] * 50})
>>> f(df)
0      0
1      1
2      2
3      3
4      4
      ..
95    95
96    96
97    97
98    98
99    99
Length: 100, dtype: int64
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