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.rank`¶

pandas.Series.rank(axis=0, method='average', numeric_only=NoDefault.no_default, na_option='keep', ascending=True, pct=False)

Supported Arguments
argument

datatypes
method
String in {'average', 'min', 'max', 'first', 'dense'}
na_option
String in {'keep', 'top', 'bottom'}
ascending
Boolean
pct
Boolean
Note
- Using method='first' with ascending=False is currently unsupported.
Example Usage
```
>>> @bodo.jit
... def f(S):
...     return S.rank(method='dense', na_option='bottom', pct=True)
>>> S = pd.Series([np.nan, 4, 2, 4, 8, np.nan])
>>> f(S)
0    1.00
1    0.50
2    0.25
3    0.50
4    0.75
5    1.00
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	String argument be a valid frequency alias

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	String argument be a valid frequency alias

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.cat`¶

pandas.Series.str.cat(others=None, sep=None, na_rep=None, join='left')

Supported Arguments

argument	datatypes
`others`	DataFrame
`sep`	String

Example Usage

>>> @bodo.jit
... def f(S, df):
...     return S.str.cat(df, ",")
>>> S = pd.Series(["s1", "s2", "s3", None, "s5"])
>>> df = pd.DataFrame({"A": ["a1", "a2", "a3", "a4", "a5"], "B": ["b1", "b2", None, "b4", "b5"]})
>>> f(S, df)
0    s1,a1,b1
1    s2,a2,b2
2         NaN
3         NaN
4    s5,a5,b5
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

argument	datatypes
`method`	String in {'average', 'min', 'max', 'first', 'dense'}
`na_option`	String in {'keep', 'top', 'bottom'}
`ascending`	Boolean
`pct`	Boolean

argument	datatypes	other requirements
`to_append`	Series List of Series Tuple of Series
`ignore_index`	Boolean	Must be constant at Compile Time

Series¶

pd.Series¶

Attributes¶

pd.Series.index¶

pd.Series.values¶

pd.Series.dtype¶

pd.Series.shape¶

pd.Series.nbytes¶

pd.Series.ndim¶

pd.Series.size¶

pd.Series.T¶

pd.Series.memory_usage¶

pd.Series.hasnans¶

pd.Series.empty¶

pd.Series.dtypes¶

pd.Series.name¶

Conversion:¶

pd.Series.astype¶

pd.Series.copy¶

pd.Series.to_numpy¶

pd.Series.tolist¶

Indexing, iteration:¶

pd.Series.iat¶

pd.Series.iloc¶

pd.Series.loc¶

Binary operator functions:¶

pd.Series.add¶

pd.Series.sub¶

pd.Series.mul¶

pd.Series.div¶

pd.Series.truediv¶

pd.Series.floordiv¶

pd.Series.mod¶

pd.Series.pow¶

pd.Series.radd¶

pd.Series.rsub¶

pd.Series.rmul¶

pd.Series.rdiv¶

pd.Series.rtruediv¶

pd.Series.rfloordiv¶

pd.Series.rmod¶

pd.Series.rpow¶

pd.Series.combine¶

pd.Series.round¶

pd.Series.lt¶

pd.Series.gt¶

pd.Series.le¶

pd.Series.ge¶

pd.Series.ne¶

pd.Series.eq¶

pd.Series.dot¶

Function application, GroupBy & Window¶

pd.Series.apply¶

pd.Series.map¶

pd.Series.groupby¶

pd.Series.rolling¶

pd.Series.pipe¶

Computations / Descriptive Stats¶

pd.Series.abs¶

pd.Series.all¶

pd.Series.any¶

pd.Series.autocorr¶

pd.Series.between¶

pd.Series.corr¶

pd.Series.count¶

pd.Series.cov¶

pd.Series.cummin¶

pd.Series.cummax¶

pd.Series.cumprod¶

pd.Series.cumsum¶

pd.Series.describe¶

pd.Series.diff¶

pd.Series.kurt¶

pd.Series.mad¶

pd.Series.max¶

pd.Series.mean¶

pd.Series.median¶

pd.Series.min¶

pd.Series.nlargest¶

pd.Series.nsmallest¶

`pd.Series`¶

`pd.Series.index`¶

`pd.Series.values`¶

`pd.Series.dtype`¶

`pd.Series.shape`¶

`pd.Series.nbytes`¶

`pd.Series.ndim`¶

`pd.Series.size`¶

`pd.Series.T`¶

`pd.Series.memory_usage`¶

`pd.Series.hasnans`¶

`pd.Series.empty`¶

`pd.Series.dtypes`¶

`pd.Series.name`¶

`pd.Series.astype`¶

`pd.Series.copy`¶

`pd.Series.to_numpy`¶

`pd.Series.tolist`¶

`pd.Series.iat`¶

`pd.Series.iloc`¶

`pd.Series.loc`¶

`pd.Series.add`¶

`pd.Series.sub`¶

`pd.Series.mul`¶

`pd.Series.div`¶

`pd.Series.truediv`¶

`pd.Series.floordiv`¶

`pd.Series.mod`¶

`pd.Series.pow`¶

`pd.Series.radd`¶

`pd.Series.rsub`¶

`pd.Series.rmul`¶

`pd.Series.rdiv`¶

`pd.Series.rtruediv`¶

`pd.Series.rfloordiv`¶

`pd.Series.rmod`¶

`pd.Series.rpow`¶

`pd.Series.combine`¶

`pd.Series.round`¶

`pd.Series.lt`¶

`pd.Series.gt`¶

`pd.Series.le`¶

`pd.Series.ge`¶

`pd.Series.ne`¶

`pd.Series.eq`¶

`pd.Series.dot`¶

`pd.Series.apply`¶

`pd.Series.map`¶

`pd.Series.groupby`¶

`pd.Series.rolling`¶

`pd.Series.pipe`¶

`pd.Series.abs`¶

`pd.Series.all`¶

`pd.Series.any`¶

`pd.Series.autocorr`¶

`pd.Series.between`¶

`pd.Series.corr`¶

`pd.Series.count`¶

`pd.Series.cov`¶

`pd.Series.cummin`¶

`pd.Series.cummax`¶

`pd.Series.cumprod`¶

`pd.Series.cumsum`¶

`pd.Series.describe`¶

`pd.Series.diff`¶

`pd.Series.kurt`¶

`pd.Series.mad`¶

`pd.Series.max`¶

`pd.Series.mean`¶

`pd.Series.median`¶

`pd.Series.min`¶

`pd.Series.nlargest`¶

`pd.Series.nsmallest`¶

`pd.Series.pct_change`¶

`pd.Series.prod`¶

`pd.Series.product`¶

`pd.Series.quantile`¶

`pd.Series.rank`¶

`pd.Series.sem`¶

`pd.Series.skew`¶