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clean_output.txt
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--------------------------------------------------
Initial Shape and Data Type
--------------------------------------------------
--------------------------------------------------
(899164, 27)
--------------------------------------------------
--------------------------------------------------
LoanNr_ChkDgt int64
Name object
City object
State object
Zip int64
Bank object
BankState object
NAICS int64
ApprovalDate object
ApprovalFY object
Term int64
NoEmp int64
NewExist float64
CreateJob int64
RetainedJob int64
FranchiseCode int64
UrbanRural int64
RevLineCr object
LowDoc object
ChgOffDate object
DisbursementDate object
DisbursementGross object
BalanceGross object
MIS_Status object
ChgOffPrinGr object
GrAppv object
SBA_Appv object
dtype: object
--------------------------------------------------
--------------------------------------------------
LoanNr_ChkDgt 0
Name 14
City 30
State 14
Zip 0
Bank 1559
BankState 1566
NAICS 0
ApprovalDate 0
ApprovalFY 0
Term 0
NoEmp 0
NewExist 136
CreateJob 0
RetainedJob 0
FranchiseCode 0
UrbanRural 0
RevLineCr 4528
LowDoc 2582
ChgOffDate 736465
DisbursementDate 2368
DisbursementGross 0
BalanceGross 0
MIS_Status 1997
ChgOffPrinGr 0
GrAppv 0
SBA_Appv 0
dtype: int64
--------------------------------------------------
--------------------------------------------------
Initial Target Status
--------------------------------------------------
--------------------------------------------------
dict_values([739609, 157558, 1997])
--------------------------------------------------
--------------------------------------------------
dict_keys(['P I F', 'CHGOFF', nan])
--------------------------------------------------
--------------------------------------------------
State Status
--------------------------------------------------
--------------------------------------------------
count 894415.000000
mean 20.377936
std 12.389792
min 1.000000
25% 9.000000
50% 16.000000
75% 29.000000
max 51.000000
Name: State, dtype: float64
--------------------------------------------------
--------------------------------------------------
dict_values([14002, 9904, 41053, 12096, 23990, 14162, 29545, 8929, 70168, 13021, 9330, 6308, 24318, 20585, 25018, 130300, 5556, 9536, 11958, 32429, 7683, 7629, 57312, 13199, 34741, 11012, 5918, 11421, 20447, 2390, 23179, 20539, 8704, 2831, 18728, 11982, 3264, 9459, 17554, 7997, 20948, 6003, 22198, 5218, 5420, 8323, 6343, 4397, 3577, 2181, 1610])
--------------------------------------------------
--------------------------------------------------
dict_keys([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])
--------------------------------------------------
--------------------------------------------------
NAICS Status
--------------------------------------------------
--------------------------------------------------
count 894415.000000
mean 39.570217
std 26.305760
min 0.000000
25% 23.000000
50% 44.000000
75% 56.000000
max 92.000000
Name: NAICS, dtype: float64
--------------------------------------------------
--------------------------------------------------
dict_values([42218, 67312, 55111, 201632, 38076, 72196, 66154, 84385, 48565, 6346, 13526, 67525, 11749, 11298, 14539, 9421, 1838, 17845, 32328, 20036, 8973, 225, 662, 2199, 256])
--------------------------------------------------
--------------------------------------------------
dict_keys([45, 72, 62, 0, 33, 81, 23, 44, 42, 61, 53, 54, 31, 51, 71, 52, 21, 32, 56, 48, 11, 92, 22, 49, 55])
--------------------------------------------------
--------------------------------------------------
NewExist Status
--------------------------------------------------
--------------------------------------------------
count 893388.000000
mean 0.281306
std 0.449636
min 0.000000
25% 0.000000
50% 0.000000
75% 1.000000
max 1.000000
Name: NewExist, dtype: float64
--------------------------------------------------
--------------------------------------------------
dict_values([251315, 642073])
--------------------------------------------------
--------------------------------------------------
dict_keys([1.0, 0.0])
--------------------------------------------------
--------------------------------------------------
Term Status
--------------------------------------------------
--------------------------------------------------
count 893388.000000
mean 110.914703
std 78.947965
min 0.000000
25% 60.000000
50% 84.000000
75% 120.000000
max 569.000000
Name: Term, dtype: float64
--------------------------------------------------
--------------------------------------------------
dict_values([228073, 89533, 28069, 85725, 77250, 2203, 136, 201, 16878, 44608, 4957, 784, 3218, 1976, 4130, 1426, 1274, 168, 2925, 7101, 133, 1757, 19607, 2267, 1777, 2207, 9381, 7263, 1856, 2835, 5187, 112, 2273, 2160, 1022, 2853, 3927, 1569, 2041, 6822, 1542, 1459, 2098, 1815, 158, 2342, 1153, 2470, 2503, 116, 1529, 3014, 2595, 2263, 1914, 1747, 467, 2315, 1820, 2473, 1714, 15527, 187, 1570, 1111, 2400, 2483, 3031, 148, 2482, 149, 916, 2290, 3010, 2805, 2104, 2037, 2343, 1863, 2085, 199, 2452, 2379, 1787, 2436, 860, 1597, 1474, 2480, 650, 1059, 1960, 1377, 4580, 2412, 1592, 803, 752, 1836, 1476, 2155, 1562, 2234, 1004, 2545, 825, 596, 1803, 716, 2058, 234, 1866, 2696, 968, 273, 1975, 1681, 1741, 80, 2163, 1008, 1179, 1021, 110, 1553, 222, 377, 852, 2430, 92, 2691, 687, 101, 1581, 908, 1855, 103, 593, 676, 270, 407, 808, 98, 342, 1221, 436, 75, 1425, 127, 283, 139, 208, 110, 120, 160, 1927, 130, 120, 2520, 258, 366, 158, 600, 1611, 18, 108, 1194, 107, 678, 285, 231, 397, 254, 955, 2133, 736, 175, 354, 589, 976, 136, 127, 323, 176, 92, 742, 89, 674, 162, 223, 1028, 658, 115, 93, 215, 573, 165, 220, 120, 91, 48, 239, 117, 98, 218, 676, 632, 115, 87, 8, 71, 105, 108, 84, 91, 1666, 140, 64, 210, 173, 117, 86, 277, 141, 132, 215, 140, 562, 209, 291, 107, 232, 288, 153, 4, 108, 137, 4, 138, 88, 114, 251, 108, 115, 123, 109, 110, 100, 115, 125, 186, 102, 428, 93, 89, 164, 151, 148, 174, 103, 137, 548, 122, 6, 124, 100, 95, 155, 126, 121, 163, 121, 132, 132, 83, 132, 103, 180, 112, 132, 132, 145, 226, 96, 124, 155, 98, 143, 80, 169, 80, 168, 125, 6, 124, 203, 71, 133, 183, 88, 157, 89, 90, 133, 155, 104, 225, 114, 124, 138, 88, 104, 94, 90, 90, 95, 2, 12, 17, 1, 83, 2, 9, 72, 100, 6, 9, 15, 54, 3, 3, 1, 4, 18, 4, 1, 7, 1, 5, 2, 5, 8, 9, 1, 5, 6, 5, 8, 1, 1, 2, 10, 1, 1, 1, 2, 2, 1, 1, 6, 2, 2, 3, 3, 2, 4, 3, 1, 8, 2, 5, 1, 4, 1, 3, 1, 1, 2, 5, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1])
--------------------------------------------------
--------------------------------------------------
dict_keys([84, 60, 180, 240, 120, 45, 297, 162, 12, 300, 87, 114, 144, 126, 83, 102, 80, 137, 42, 96, 167, 7, 36, 37, 26, 264, 72, 24, 5, 54, 66, 161, 71, 4, 93, 288, 108, 10, 13, 90, 19, 16, 3, 27, 149, 41, 246, 57, 82, 298, 14, 61, 58, 44, 32, 85, 112, 38, 73, 47, 11, 48, 134, 15, 79, 53, 39, 6, 255, 55, 133, 95, 35, 59, 62, 68, 123, 46, 18, 70, 138, 40, 52, 25, 65, 91, 1, 74, 49, 103, 77, 31, 86, 63, 56, 22, 0, 97, 23, 17, 69, 21, 43, 89, 276, 92, 183, 2, 132, 34, 131, 9, 78, 99, 129, 216, 8, 29, 289, 30, 119, 228, 168, 208, 81, 147, 125, 94, 51, 211, 64, 111, 266, 75, 306, 28, 232, 117, 118, 309, 303, 98, 191, 116, 76, 113, 292, 88, 166, 244, 176, 258, 203, 231, 142, 33, 157, 165, 50, 210, 294, 301, 106, 20, 318, 229, 204, 269, 104, 241, 178, 115, 174, 192, 67, 100, 141, 282, 122, 156, 153, 268, 249, 238, 233, 105, 263, 124, 279, 140, 186, 107, 190, 308, 128, 243, 302, 299, 280, 223, 311, 222, 202, 257, 130, 101, 121, 278, 272, 319, 283, 221, 250, 290, 199, 252, 187, 310, 304, 136, 261, 196, 181, 175, 195, 177, 139, 110, 242, 270, 277, 184, 150, 207, 358, 213, 273, 357, 248, 275, 164, 239, 206, 215, 170, 254, 217, 172, 158, 218, 189, 256, 179, 262, 193, 146, 155, 135, 185, 307, 200, 109, 265, 349, 209, 236, 251, 145, 152, 169, 245, 230, 285, 201, 293, 171, 227, 127, 188, 225, 247, 163, 143, 235, 160, 148, 253, 159, 295, 198, 271, 234, 237, 336, 220, 291, 287, 154, 219, 197, 312, 284, 281, 151, 267, 274, 182, 212, 305, 205, 226, 194, 259, 214, 173, 224, 385, 313, 340, 461, 296, 343, 348, 286, 260, 334, 320, 315, 360, 421, 342, 351, 372, 314, 354, 435, 316, 480, 329, 387, 325, 321, 317, 352, 359, 389, 333, 330, 417, 404, 370, 324, 398, 419, 425, 414, 365, 345, 481, 350, 335, 369, 341, 355, 362, 322, 339, 375, 323, 347, 327, 505, 326, 418, 328, 363, 413, 361, 356, 396, 438, 382, 364, 367, 374, 442, 353, 527, 569, 338, 366, 386, 368, 428, 346, 388, 430, 443, 381, 409, 445, 384, 391, 511, 412, 449, 403, 434, 402, 423, 440, 429])
--------------------------------------------------
--------------------------------------------------
NoEmp Status
--------------------------------------------------
--------------------------------------------------
count 893388.000000
mean 11.416947
std 73.740818
min 0.000000
25% 2.000000
50% 4.000000
75% 10.000000
max 9999.000000
Name: NoEmp, dtype: float64
--------------------------------------------------
--------------------------------------------------
dict_values([73248, 137336, 31322, 10634, 3813, 2922, 152589, 90110, 3716, 59993, 7817, 20718, 45521, 670, 7841, 18061, 14212, 31385, 31217, 4002, 6187, 2300, 1431, 2421, 5149, 5011, 261, 1407, 8604, 9907, 634, 18262, 10, 2948, 3464, 11743, 339, 9305, 63, 2747, 1192, 1299, 1302, 3627, 92, 1142, 287, 24, 874, 1376, 376, 1437, 4999, 6516, 1233, 1347, 1287, 193, 2413, 11, 363, 1430, 80, 602, 550, 1500, 693, 11, 217, 698, 668, 22, 762, 48, 410, 474, 168, 329, 129, 409, 446, 8, 13, 103, 1062, 137, 297, 218, 68, 864, 132, 113, 51, 185, 4, 4, 418, 1, 303, 45, 314, 53, 37, 82, 277, 1, 143, 245, 2, 7, 654, 23, 55, 23, 64, 244, 183, 144, 173, 41, 128, 134, 39, 233, 43, 19, 90, 1, 101, 390, 18, 120, 228, 431, 56, 1, 1, 64, 74, 110, 86, 20, 18, 115, 27, 8, 17, 45, 13, 1, 36, 87, 126, 65, 133, 66, 158, 7, 6, 50, 25, 7, 34, 4, 1, 9, 87, 97, 4, 185, 59, 8, 115, 13, 43, 8, 16, 78, 6, 7, 40, 28, 36, 2, 22, 22, 35, 5, 26, 18, 6, 43, 22, 29, 26, 1, 24, 6, 2, 8, 6, 3, 1, 7, 18, 2, 6, 7, 8, 11, 14, 38, 9, 15, 7, 30, 1, 1, 118, 37, 18, 16, 10, 8, 5, 4, 1, 7, 3, 12, 1, 4, 4, 1, 10, 1, 4, 4, 24, 18, 10, 1, 6, 1, 1, 2, 1, 2, 7, 3, 9, 1, 1, 2, 2, 22, 1, 1, 2, 6, 8, 2, 8, 5, 1, 16, 3, 32, 19, 37, 39, 1, 10, 3, 2, 3, 13, 10, 4, 10, 1, 1, 45, 5, 20, 9, 11, 3, 12, 4, 1, 1, 5, 11, 36, 5, 3, 3, 5, 1, 5, 9, 17, 10, 3, 8, 2, 3, 4, 2, 6, 2, 14, 4, 10, 1, 9, 1, 6, 11, 11, 25, 3, 1, 3, 1, 7, 3, 1, 1, 9, 1, 2, 1, 3, 5, 1, 2, 1, 1, 1, 3, 2, 1, 1, 4, 1, 6, 1, 1, 3, 2, 2, 4, 2, 1, 2, 1, 1, 1, 10, 1, 1, 3, 4, 1, 1, 1, 1, 2, 1, 2, 5, 4, 2, 3, 4, 1, 1, 2, 4, 2, 6, 2, 4, 1, 2, 2, 2, 4, 2, 4, 3, 11, 1, 2, 6, 3, 2, 3, 2, 1, 1, 4, 1, 1, 6, 6, 1, 1, 3, 1, 3, 4, 3, 1, 1, 2, 2, 3, 3, 1, 1, 1, 3, 1, 1, 1, 1, 3, 1, 2, 2, 4, 4, 1, 5, 3, 1, 5, 5, 1, 3, 1, 2, 13, 1, 1, 1, 2, 4, 1, 2, 1, 1, 1, 3, 3, 1, 2, 1, 2, 1, 1, 1, 1, 1, 3, 1, 1, 2, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1, 3, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 6, 1, 1, 1, 2, 2, 4, 3, 2, 2, 1, 3, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 5, 1, 1, 2, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
--------------------------------------------------
--------------------------------------------------
dict_keys([4, 2, 7, 14, 19, 45, 1, 3, 24, 5, 16, 12, 6, 90, 18, 9, 20, 10, 8, 50, 17, 32, 31, 60, 22, 40, 72, 55, 30, 25, 46, 15, 214, 28, 23, 11, 57, 13, 112, 26, 80, 42, 65, 21, 97, 100, 200, 126, 48, 33, 58, 38, 35, 0, 75, 36, 70, 66, 27, 2000, 56, 34, 93, 85, 150, 29, 41, 290, 67, 44, 47, 119, 39, 155, 54, 49, 82, 95, 300, 51, 120, 265, 133, 86, 37, 160, 68, 61, 220, 43, 71, 98, 350, 78, 233, 263, 62, 7941, 63, 210, 125, 107, 450, 165, 130, 9992, 77, 64, 424, 257, 52, 600, 190, 142, 99, 59, 73, 135, 74, 109, 250, 69, 500, 140, 116, 260, 96, 339, 87, 110, 161, 88, 105, 53, 400, 2725, 605, 103, 91, 81, 94, 147, 144, 175, 136, 182, 156, 118, 375, 345, 121, 145, 180, 101, 84, 89, 76, 550, 216, 108, 138, 171, 117, 249, 279, 208, 170, 79, 9999, 115, 104, 900, 83, 153, 106, 191, 270, 102, 295, 3000, 230, 195, 127, 313, 205, 141, 128, 199, 124, 162, 305, 185, 152, 129, 275, 576, 132, 196, 640, 178, 750, 720, 414, 315, 143, 299, 217, 179, 320, 4000, 167, 114, 203, 134, 8000, 111, 5511, 5921, 92, 240, 280, 148, 340, 285, 1300, 1800, 680, 258, 231, 173, 1980, 510, 5812, 401, 192, 5084, 480, 186, 131, 146, 168, 1050, 189, 343, 7216, 312, 8041, 394, 307, 7389, 174, 828, 485, 266, 2100, 1000, 5947, 368, 1600, 5000, 1500, 298, 6000, 221, 4953, 360, 355, 137, 325, 123, 122, 344, 163, 386, 530, 237, 330, 188, 387, 1003, 761, 421, 113, 308, 158, 310, 187, 238, 235, 222, 7111, 2112, 254, 1200, 225, 228, 2400, 317, 425, 8018, 430, 197, 157, 176, 198, 256, 3200, 183, 277, 362, 202, 289, 139, 520, 154, 2151, 177, 316, 224, 149, 151, 215, 395, 426, 262, 6501, 169, 282, 4100, 383, 172, 688, 287, 1250, 390, 318, 4847, 7231, 5149, 3900, 967, 3500, 314, 602, 476, 206, 1461, 274, 408, 441, 246, 336, 570, 227, 1940, 735, 523, 1233, 3170, 1711, 255, 351, 1451, 207, 365, 454, 1550, 823, 544, 1150, 294, 9000, 226, 7000, 463, 211, 370, 2900, 4005, 1900, 241, 288, 194, 213, 332, 484, 281, 322, 650, 181, 521, 218, 800, 420, 3400, 1400, 700, 242, 2200, 2500, 625, 495, 358, 243, 466, 354, 223, 184, 479, 269, 435, 342, 385, 193, 232, 827, 2401, 273, 713, 253, 261, 296, 2501, 1629, 1700, 429, 2010, 455, 660, 164, 1235, 252, 376, 380, 204, 464, 245, 1100, 608, 209, 247, 5555, 329, 604, 456, 166, 1280, 3089, 985, 1020, 505, 1502, 234, 5200, 284, 609, 259, 475, 324, 5680, 1981, 323, 251, 740, 575, 396, 1030, 229, 2610, 515, 328, 442, 433, 2232, 341, 306, 3732, 346, 447, 850, 427, 407, 782, 293, 236, 356, 4685, 7241, 363, 1005, 369, 458, 267, 7999, 2020, 445, 2121, 1125, 1010, 4658, 712, 212, 271, 377, 1718, 1515, 560, 404, 302, 276, 248, 1015, 268, 3737, 319, 2120, 304, 512, 585, 292, 808, 244, 9090, 3030, 606, 840, 301, 2300, 3600, 159, 525, 353, 7991, 5211, 4012, 1112, 1440, 413, 410, 488, 4501, 4800, 3100, 3334, 538, 1603, 1706, 2520, 283, 1520, 2202, 357, 201, 1012, 499, 423, 635, 1073, 465, 2510, 1644, 1101, 403, 4300, 382, 498, 448, 3009, 685, 1340, 2700, 367, 535, 760, 1524, 309, 7007, 384, 327, 1960, 540, 5013, 780, 348, 717, 8500, 7538, 405, 2005, 1382, 858, 9945, 1542, 1920, 3713])
--------------------------------------------------
--------------------------------------------------
CreateJob Status
--------------------------------------------------
--------------------------------------------------
count 893388.000000
mean 8.433891
std 236.879318
min 0.000000
25% 0.000000
50% 0.000000
75% 1.000000
max 8800.000000
Name: CreateJob, dtype: float64
--------------------------------------------------
--------------------------------------------------
dict_values([625642, 6343, 2021, 18569, 20372, 62552, 4446, 11513, 28577, 7333, 1432, 5361, 10925, 2072, 57317, 967, 136, 2329, 4264, 635, 923, 3321, 1817, 44, 1437, 999, 1894, 353, 590, 761, 82, 144, 942, 300, 335, 112, 753, 37, 86, 19, 161, 442, 627, 50, 38, 15, 20, 55, 112, 112, 184, 13, 256, 357, 180, 137, 140, 258, 34, 287, 59, 11, 12, 86, 56, 26, 40, 33, 18, 20, 89, 8, 112, 26, 6, 51, 3, 41, 33, 42, 24, 1, 44, 5, 4, 16, 31, 2, 7, 8, 29, 2, 23, 30, 6, 3, 13, 1, 21, 1, 15, 14, 4, 17, 3, 2, 20, 10, 2, 1, 1, 4, 3, 26, 1, 4, 1, 3, 5, 1, 1, 11, 5, 2, 645, 1, 6, 1, 1, 12, 2, 4, 2, 11, 3, 1, 1, 10, 1, 5, 1, 15, 3, 1, 3, 3, 8, 10, 4, 2, 1, 9, 3, 2, 15, 8, 1, 1, 2, 1, 1, 1, 1, 6, 1, 2, 3, 1, 11, 1, 1, 1, 1, 4, 4, 1, 1, 8, 1, 3, 2, 1, 1, 1, 2, 1, 1, 1, 1, 11, 1, 2, 1, 2, 1, 1, 1, 1, 1, 3, 3, 1, 5, 4, 1, 5, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 2, 1, 1, 4, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1])
--------------------------------------------------
--------------------------------------------------
dict_keys([0, 7, 30, 5, 4, 1, 20, 10, 3, 8, 16, 15, 6, 11, 2, 40, 55, 25, 12, 21, 50, 9, 13, 47, 18, 17, 14, 29, 23, 35, 43, 75, 22, 45, 27, 65, 19, 58, 48, 72, 38, 28, 24, 150, 200, 82, 68, 41, 80, 70, 33, 97, 32, 26, 34, 36, 31, 100, 56, 60, 90, 77, 99, 39, 44, 51, 120, 85, 69, 95, 42, 160, 37, 57, 600, 49, 1000, 53, 54, 46, 59, 163, 450, 456, 3000, 452, 451, 198, 79, 454, 62, 136, 64, 52, 126, 180, 74, 303, 63, 386, 78, 98, 455, 76, 152, 221, 110, 84, 153, 127, 2020, 225, 453, 125, 458, 457, 174, 104, 89, 320, 154, 300, 102, 149, 8800, 800, 130, 235, 5199, 250, 137, 500, 121, 105, 96, 360, 255, 140, 122, 175, 1200, 66, 112, 3500, 118, 220, 115, 73, 93, 151, 195, 67, 138, 400, 61, 124, 91, 1711, 131, 184, 409, 1618, 1150, 88, 1530, 157, 145, 166, 135, 210, 226, 183, 252, 116, 71, 129, 223, 81, 569, 139, 144, 1011, 179, 214, 83, 146, 171, 141, 350, 92, 101, 119, 280, 123, 205, 1229, 128, 103, 189, 114, 108, 158, 167, 87, 186, 86, 134, 1100, 750, 206, 375, 109, 433, 2140, 177, 264, 168, 240, 5621, 170, 169, 165, 222, 106, 148, 363, 1118, 310, 164, 5085, 143, 480, 256, 365, 155, 190, 397, 1027, 270, 94, 2515, 162, 182, 1016, 860])
--------------------------------------------------
--------------------------------------------------
RetainedJob Status
--------------------------------------------------
--------------------------------------------------
count 893388.000000
mean 10.802368
std 237.336447
min 0.000000
25% 0.000000
50% 1.000000
75% 4.000000
max 9500.000000
Name: RetainedJob, dtype: float64
--------------------------------------------------
--------------------------------------------------
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dict_keys([0, 7, 23, 4, 6, 1, 9, 20, 2, 5, 19, 8, 3, 10, 24, 12, 15, 11, 25, 44, 17, 14, 65, 28, 38, 16, 42, 26, 18, 13, 50, 93, 40, 37, 60, 21, 30, 31, 34, 35, 150, 73, 41, 45, 100, 180, 58, 75, 165, 36, 130, 29, 27, 125, 99, 22, 46, 32, 257, 43, 47, 80, 70, 54, 62, 33, 39, 400, 55, 95, 48, 120, 71, 63, 81, 52, 94, 78, 160, 109, 86, 77, 155, 85, 90, 64, 3225, 61, 69, 66, 210, 107, 97, 51, 83, 112, 53, 72, 76, 87, 68, 118, 138, 67, 57, 56, 117, 171, 229, 115, 275, 153, 300, 105, 140, 135, 59, 79, 200, 295, 205, 206, 128, 186, 137, 250, 89, 49, 131, 92, 404, 110, 320, 139, 82, 108, 88, 104, 114, 134, 230, 102, 103, 96, 98, 84, 101, 220, 233, 74, 267, 91, 9500, 355, 123, 175, 550, 500, 450, 170, 195, 116, 305, 147, 610, 187, 235, 157, 124, 127, 106, 254, 4441, 277, 225, 207, 111, 312, 317, 173, 350, 216, 143, 430, 197, 176, 145, 126, 133, 256, 2200, 362, 202, 148, 316, 8800, 215, 146, 185, 154, 212, 141, 163, 184, 5000, 3200, 132, 194, 113, 161, 172, 330, 366, 190, 1300, 390, 4000, 476, 3900, 967, 268, 136, 602, 121, 240, 122, 162, 523, 159, 1711, 119, 251, 152, 417, 291, 544, 129, 142, 231, 189, 203, 360, 213, 280, 484, 260, 177, 281, 675, 226, 263, 700, 247, 600, 245, 750, 151, 270, 375, 191, 182, 223, 7250, 214, 169, 342, 221, 217, 232, 815, 287, 285, 188, 1000, 1700, 428, 660, 156, 1500, 318, 265, 167, 236, 370, 310, 204, 609, 475, 322, 208, 515, 259, 328, 497, 356, 255, 158, 192, 166, 219, 363, 274, 144, 262, 315, 178, 420, 286, 585, 325, 710, 196, 384, 237, 940, 302, 371, 394, 1600, 3860, 244, 393, 410, 472, 720, 168, 252, 290, 297, 548, 485, 183, 800, 149, 387, 298, 480, 266, 164, 403, 369, 498, 448, 685, 535, 292, 327, 911, 3100, 540, 304, 1111, 243, 199, 900, 198])
--------------------------------------------------
--------------------------------------------------
isFranchise Status
--------------------------------------------------
--------------------------------------------------
count 893388.000000
mean 0.942289
std 0.233196
min 0.000000
25% 1.000000
50% 1.000000
75% 1.000000
max 1.000000
Name: isFranchise, dtype: float64
--------------------------------------------------
--------------------------------------------------
dict_values([841830, 51558])
--------------------------------------------------
--------------------------------------------------
dict_keys([1, 0])
--------------------------------------------------
--------------------------------------------------
UrbanRural Status
--------------------------------------------------
--------------------------------------------------
count 893388.000000
mean 0.756010
std 0.646489
min 0.000000
25% 0.000000
50% 1.000000
75% 1.000000
max 2.000000
Name: UrbanRural, dtype: float64
--------------------------------------------------
--------------------------------------------------
dict_values([322276, 466814, 104298])
--------------------------------------------------
--------------------------------------------------
dict_keys([0, 1, 2])
--------------------------------------------------
--------------------------------------------------
LowDoc Status
--------------------------------------------------
--------------------------------------------------
count 889973.000000
mean 0.123649
std 0.329181
min 0.000000
25% 0.000000
50% 0.000000
75% 0.000000
max 1.000000
Name: LowDoc, dtype: float64
--------------------------------------------------
--------------------------------------------------
dict_values([110044, 779929])
--------------------------------------------------
--------------------------------------------------
dict_keys([1.0, 0.0])
--------------------------------------------------
--------------------------------------------------
count 889973.000000
mean 0.824821
std 0.380121
min 0.000000
25% 1.000000
50% 1.000000
75% 1.000000
max 1.000000
Name: MIS_Status, dtype: float64
--------------------------------------------------
--------------------------------------------------
MIS_Status
--------------------------------------------------
--------------------------------------------------
dict_values([734068, 155905])
--------------------------------------------------
--------------------------------------------------
dict_keys([1, 0])
--------------------------------------------------
--------------------------------------------------
GrAppv Status
--------------------------------------------------
--------------------------------------------------
count 8.899730e+05
mean 1.929204e+05
std 2.834306e+05
min 1.000000e+03
25% 3.500000e+04
50% 9.000000e+04
75% 2.250000e+05
max 5.472000e+06
Name: GrAppv, dtype: float64
--------------------------------------------------
--------------------------------------------------
count 889973.000000
mean 11.384386
std 1.293664
min 6.907755
25% 10.463103
50% 11.407565
75% 12.323856
max 15.515155
Name: GrAppv, dtype: float64
--------------------------------------------------
--------------------------------------------------
Final Target Status
--------------------------------------------------
--------------------------------------------------
dict_values([734068, 155905])
--------------------------------------------------
--------------------------------------------------
dict_keys([1, 0])
--------------------------------------------------
--------------------------------------------------
Final Data Status
--------------------------------------------------
--------------------------------------------------
State 0
NAICS 0
Term 0
NoEmp 0
NewExist 0
CreateJob 0
RetainedJob 0
isFranchise 0
UrbanRural 0
LowDoc 0
MIS_Status 0
GrAppv 0
dtype: int64
--------------------------------------------------
--------------------------------------------------
(889973, 12)
--------------------------------------------------
--------------------------------------------------
State int64
NAICS int64
Term int64
NoEmp int64
NewExist float64
CreateJob int64
RetainedJob int64
isFranchise int64
UrbanRural int64
LowDoc float64
MIS_Status int64
GrAppv float64
dtype: object
--------------------------------------------------