Overview

Dataset statistics

Number of variables168
Number of observations522902
Missing cells22982407
Missing cells (%)26.2%
Total size in memory666.7 MiB
Average record size in memory1.3 KiB

Variable types

Numeric129
Text33
Boolean6

Alerts

bankacctype_710L has constant value ""Constant
commnoinclast6m_3546845L has constant value ""Constant
deferredmnthsnum_166L has constant value ""Constant
interestrategrace_34L has constant value ""Constant
isbidproductrequest_292L has constant value ""Constant
isdebitcard_729L has constant value ""Constant
mastercontrelectronic_519L has constant value ""Constant
mastercontrexist_109L has constant value ""Constant
paytype1st_925L has constant value ""Constant
paytype_783L has constant value ""Constant
typesuite_864L has constant value ""Constant
equalitydataagreement_891L is highly imbalanced (62.1%)Imbalance
equalityempfrom_62L is highly imbalanced (83.4%)Imbalance
isbidproduct_1095L is highly imbalanced (50.2%)Imbalance
opencred_647L is highly imbalanced (76.2%)Imbalance
actualdpdtolerance_344P has 121492 (23.2%) missing valuesMissing
amtinstpaidbefduel24m_4187115A has 132270 (25.3%) missing valuesMissing
avgdbddpdlast24m_3658932P has 187071 (35.8%) missing valuesMissing
avgdbddpdlast3m_4187120P has 290241 (55.5%) missing valuesMissing
avgdbdtollast24m_4525197P has 187102 (35.8%) missing valuesMissing
avgdpdtolclosure24_3658938P has 137903 (26.4%) missing valuesMissing
avginstallast24m_3658937A has 191154 (36.6%) missing valuesMissing
avglnamtstart24m_4525187A has 430200 (82.3%) missing valuesMissing
avgmaxdpdlast9m_3716943P has 241713 (46.2%) missing valuesMissing
avgoutstandbalancel6m_4187114A has 242187 (46.3%) missing valuesMissing
avgpmtlast12m_4525200A has 219677 (42.0%) missing valuesMissing
bankacctype_710L has 410562 (78.5%) missing valuesMissing
cardtype_51L has 456995 (87.4%) missing valuesMissing
clientscnt_136L has 522882 (> 99.9%) missing valuesMissing
cntincpaycont9m_3716944L has 133113 (25.5%) missing valuesMissing
cntpmts24_3658933L has 133089 (25.5%) missing valuesMissing
commnoinclast6m_3546845L has 83853 (16.0%) missing valuesMissing
datefirstoffer_1144D has 273022 (52.2%) missing valuesMissing
datelastinstal40dpd_247D has 460577 (88.1%) missing valuesMissing
datelastunpaid_3546854D has 280909 (53.7%) missing valuesMissing
daysoverduetolerancedd_3976961L has 132270 (25.3%) missing valuesMissing
dtlastpmtallstes_4499206D has 218215 (41.7%) missing valuesMissing
eir_270L has 65320 (12.5%) missing valuesMissing
equalitydataagreement_891L has 497187 (95.1%) missing valuesMissing
equalityempfrom_62L has 513495 (98.2%) missing valuesMissing
firstclxcampaign_1125D has 287559 (55.0%) missing valuesMissing
firstdatedue_489D has 142416 (27.2%) missing valuesMissing
inittransactionamount_650A has 456469 (87.3%) missing valuesMissing
interestrate_311L has 65320 (12.5%) missing valuesMissing
interestrategrace_34L has 518602 (99.2%) missing valuesMissing
isbidproductrequest_292L has 518920 (99.2%) missing valuesMissing
isdebitcard_729L has 456469 (87.3%) missing valuesMissing
lastactivateddate_801D has 132745 (25.4%) missing valuesMissing
lastapplicationdate_877D has 84377 (16.1%) missing valuesMissing
lastapprcredamount_781A has 128918 (24.7%) missing valuesMissing
lastapprdate_640D has 128918 (24.7%) missing valuesMissing
lastdelinqdate_224D has 309295 (59.1%) missing valuesMissing
lastdependentsnum_448L has 518443 (99.1%) missing valuesMissing
lastotherinc_902A has 522074 (99.8%) missing valuesMissing
lastotherlnsexpense_631A has 522015 (99.8%) missing valuesMissing
lastrejectcredamount_222A has 238147 (45.5%) missing valuesMissing
lastrejectdate_50D has 238147 (45.5%) missing valuesMissing
lastrepayingdate_696D has 522130 (99.9%) missing valuesMissing
lastst_736L has 84377 (16.1%) missing valuesMissing
maininc_215A has 170305 (32.6%) missing valuesMissing
mastercontrelectronic_519L has 83853 (16.0%) missing valuesMissing
mastercontrexist_109L has 83853 (16.0%) missing valuesMissing
maxannuity_159A has 83853 (16.0%) missing valuesMissing
maxannuity_4075009A has 497091 (95.1%) missing valuesMissing
maxdbddpdlast1m_3658939P has 318036 (60.8%) missing valuesMissing
maxdbddpdtollast12m_3658940P has 222332 (42.5%) missing valuesMissing
maxdbddpdtollast6m_4187119P has 259441 (49.6%) missing valuesMissing
maxdebt4_972A has 83853 (16.0%) missing valuesMissing
maxdpdfrom6mto36m_3546853P has 83853 (16.0%) missing valuesMissing
maxdpdinstldate_3546855D has 258192 (49.4%) missing valuesMissing
maxdpdinstlnum_3546846P has 260417 (49.8%) missing valuesMissing
maxdpdlast12m_727P has 83853 (16.0%) missing valuesMissing
maxdpdlast24m_143P has 83853 (16.0%) missing valuesMissing
maxdpdlast3m_392P has 83853 (16.0%) missing valuesMissing
maxdpdlast6m_474P has 83853 (16.0%) missing valuesMissing
maxdpdlast9m_1059P has 83853 (16.0%) missing valuesMissing
maxdpdtolerance_374P has 83853 (16.0%) missing valuesMissing
maxinstallast24m_3658928A has 191154 (36.6%) missing valuesMissing
maxlnamtstart6m_4525199A has 225395 (43.1%) missing valuesMissing
maxoutstandbalancel12m_4187113A has 211762 (40.5%) missing valuesMissing
maxpmtlast3m_4525190A has 283339 (54.2%) missing valuesMissing
mindbddpdlast24m_3658935P has 187071 (35.8%) missing valuesMissing
mindbdtollast24m_4525191P has 187102 (35.8%) missing valuesMissing
monthsannuity_845L has 132270 (25.3%) missing valuesMissing
numincomingpmts_3546848L has 133089 (25.5%) missing valuesMissing
numinstlallpaidearly3d_817L has 130280 (24.9%) missing valuesMissing
numinstlsallpaid_934L has 130280 (24.9%) missing valuesMissing
numinstlswithdpd10_728L has 133502 (25.5%) missing valuesMissing
numinstlswithdpd5_4187116L has 132270 (25.3%) missing valuesMissing
numinstlswithoutdpd_562L has 133502 (25.5%) missing valuesMissing
numinstmatpaidtearly2d_4499204L has 132270 (25.3%) missing valuesMissing
numinstpaid_4499208L has 132270 (25.3%) missing valuesMissing
numinstpaidearly3d_3546850L has 130280 (24.9%) missing valuesMissing
numinstpaidearly3dest_4493216L has 132270 (25.3%) missing valuesMissing
numinstpaidearly5d_1087L has 132270 (25.3%) missing valuesMissing
numinstpaidearly5dest_4493211L has 132270 (25.3%) missing valuesMissing
numinstpaidearly5dobd_4499205L has 132270 (25.3%) missing valuesMissing
numinstpaidearly_338L has 132270 (25.3%) missing valuesMissing
numinstpaidearlyest_4493214L has 132270 (25.3%) missing valuesMissing
numinstpaidlastcontr_4325080L has 132270 (25.3%) missing valuesMissing
numinstpaidlate1d_3546852L has 132270 (25.3%) missing valuesMissing
numinstregularpaid_973L has 132270 (25.3%) missing valuesMissing
numinstregularpaidest_4493210L has 132270 (25.3%) missing valuesMissing
numinsttopaygr_769L has 132270 (25.3%) missing valuesMissing
numinsttopaygrest_4493213L has 132270 (25.3%) missing valuesMissing
numinstunpaidmax_3546851L has 132270 (25.3%) missing valuesMissing
numinstunpaidmaxest_4493212L has 132270 (25.3%) missing valuesMissing
opencred_647L has 84377 (16.1%) missing valuesMissing
paytype1st_925L has 57514 (11.0%) missing valuesMissing
paytype_783L has 57514 (11.0%) missing valuesMissing
payvacationpostpone_4187118D has 515040 (98.5%) missing valuesMissing
pctinstlsallpaidearl3d_427L has 134695 (25.8%) missing valuesMissing
pctinstlsallpaidlat10d_839L has 135471 (25.9%) missing valuesMissing
pctinstlsallpaidlate1d_3546856L has 134695 (25.8%) missing valuesMissing
pctinstlsallpaidlate4d_3546849L has 135047 (25.8%) missing valuesMissing
pctinstlsallpaidlate6d_3546844L has 135113 (25.8%) missing valuesMissing
pmtnum_254L has 16300 (3.1%) missing valuesMissing
posfpd10lastmonth_333P has 59334 (11.3%) missing valuesMissing
posfpd30lastmonth_3976960P has 68094 (13.0%) missing valuesMissing
posfstqpd30lastmonth_3976962P has 88668 (17.0%) missing valuesMissing
price_1097A has 88868 (17.0%) missing valuesMissing
sumoutstandtotal_3546847A has 129501 (24.8%) missing valuesMissing
sumoutstandtotalest_4493215A has 132270 (25.3%) missing valuesMissing
totinstallast1m_4525188A has 309146 (59.1%) missing valuesMissing
typesuite_864L has 406268 (77.7%) missing valuesMissing
validfrom_1069D has 490534 (93.8%) missing valuesMissing
actualdpdtolerance_344P is highly skewed (γ1 = 209.6202818)Skewed
applicationcnt_361L is highly skewed (γ1 = 496.9574828)Skewed
applicationscnt_1086L is highly skewed (γ1 = 80.05509858)Skewed
applicationscnt_629L is highly skewed (γ1 = 20.58679236)Skewed
avgmaxdpdlast9m_3716943P is highly skewed (γ1 = 23.44456716)Skewed
clientscnt12m_3712952L is highly skewed (γ1 = 107.9349739)Skewed
clientscnt3m_3712950L is highly skewed (γ1 = 94.45459363)Skewed
clientscnt6m_3712949L is highly skewed (γ1 = 105.3349156)Skewed
clientscnt_100L is highly skewed (γ1 = 150.5313929)Skewed
clientscnt_1022L is highly skewed (γ1 = 96.31693227)Skewed
clientscnt_1130L is highly skewed (γ1 = 72.59626667)Skewed
clientscnt_157L is highly skewed (γ1 = 243.2863469)Skewed
clientscnt_257L is highly skewed (γ1 = 65.57370472)Skewed
clientscnt_304L is highly skewed (γ1 = 236.5501785)Skewed
clientscnt_360L is highly skewed (γ1 = 87.14419832)Skewed
clientscnt_493L is highly skewed (γ1 = 299.6010338)Skewed
clientscnt_887L is highly skewed (γ1 = 29.81122871)Skewed
clientscnt_946L is highly skewed (γ1 = 77.90736611)Skewed
maxannuity_159A is highly skewed (γ1 = 31.98568162)Skewed
maxdpdlast3m_392P is highly skewed (γ1 = 27.76463021)Skewed
maxdpdlast6m_474P is highly skewed (γ1 = 22.82609267)Skewed
totalsettled_863A is highly skewed (γ1 = 56.91368198)Skewed
case_id has unique valuesUnique
actualdpdtolerance_344P has 400118 (76.5%) zerosZeros
amtinstpaidbefduel24m_4187115A has 61212 (11.7%) zerosZeros
annuitynextmonth_57A has 353591 (67.6%) zerosZeros
applicationcnt_361L has 522899 (> 99.9%) zerosZeros
applications30d_658L has 462539 (88.5%) zerosZeros
applicationscnt_1086L has 498636 (95.4%) zerosZeros
applicationscnt_464L has 492508 (94.2%) zerosZeros
applicationscnt_629L has 505828 (96.7%) zerosZeros
applicationscnt_867L has 144078 (27.6%) zerosZeros
avgdbddpdlast24m_3658932P has 20822 (4.0%) zerosZeros
avgdbddpdlast3m_4187120P has 22535 (4.3%) zerosZeros
avgdbdtollast24m_4525197P has 20610 (3.9%) zerosZeros
avgdpdtolclosure24_3658938P has 276030 (52.8%) zerosZeros
avgmaxdpdlast9m_3716943P has 220369 (42.1%) zerosZeros
avgoutstandbalancel6m_4187114A has 13483 (2.6%) zerosZeros
clientscnt12m_3712952L has 507552 (97.1%) zerosZeros
clientscnt3m_3712950L has 516417 (98.8%) zerosZeros
clientscnt6m_3712949L has 513182 (98.1%) zerosZeros
clientscnt_100L has 505091 (96.6%) zerosZeros
clientscnt_1022L has 473023 (90.5%) zerosZeros
clientscnt_1071L has 508898 (97.3%) zerosZeros
clientscnt_1130L has 511118 (97.7%) zerosZeros
clientscnt_157L has 505473 (96.7%) zerosZeros
clientscnt_257L has 521669 (99.8%) zerosZeros
clientscnt_304L has 505164 (96.6%) zerosZeros
clientscnt_360L has 521847 (99.8%) zerosZeros
clientscnt_493L has 519697 (99.4%) zerosZeros
clientscnt_533L has 485750 (92.9%) zerosZeros
clientscnt_887L has 487039 (93.1%) zerosZeros
clientscnt_946L has 504941 (96.6%) zerosZeros
cntincpaycont9m_3716944L has 108215 (20.7%) zerosZeros
cntpmts24_3658933L has 52103 (10.0%) zerosZeros
commnoinclast6m_3546845L has 439049 (84.0%) zerosZeros
currdebt_22A has 338451 (64.7%) zerosZeros
currdebtcredtyperange_828A has 411006 (78.6%) zerosZeros
daysoverduetolerancedd_3976961L has 102160 (19.5%) zerosZeros
deferredmnthsnum_166L has 522902 (100.0%) zerosZeros
disbursedcredamount_1113A has 13442 (2.6%) zerosZeros
downpmt_116A has 493230 (94.3%) zerosZeros
eir_270L has 139593 (26.7%) zerosZeros
homephncnt_628L has 278639 (53.3%) zerosZeros
inittransactionamount_650A has 13220 (2.5%) zerosZeros
interestrate_311L has 139593 (26.7%) zerosZeros
lastapprcredamount_781A has 38116 (7.3%) zerosZeros
lastrejectcredamount_222A has 13362 (2.6%) zerosZeros
mastercontrelectronic_519L has 439049 (84.0%) zerosZeros
mastercontrexist_109L has 439049 (84.0%) zerosZeros
maxannuity_159A has 49177 (9.4%) zerosZeros
maxdbddpdlast1m_3658939P has 41836 (8.0%) zerosZeros
maxdbddpdtollast12m_3658940P has 107093 (20.5%) zerosZeros
maxdbddpdtollast6m_4187119P has 87359 (16.7%) zerosZeros
maxdebt4_972A has 87765 (16.8%) zerosZeros
maxdpdfrom6mto36m_3546853P has 285456 (54.6%) zerosZeros
maxdpdlast12m_727P has 322356 (61.6%) zerosZeros
maxdpdlast24m_143P has 282976 (54.1%) zerosZeros
maxdpdlast3m_392P has 382681 (73.2%) zerosZeros
maxdpdlast6m_474P has 355495 (68.0%) zerosZeros
maxdpdlast9m_1059P has 337025 (64.5%) zerosZeros
maxdpdtolerance_374P has 199059 (38.1%) zerosZeros
maxoutstandbalancel12m_4187113A has 10098 (1.9%) zerosZeros
mindbddpdlast24m_3658935P has 7767 (1.5%) zerosZeros
mindbdtollast24m_4525191P has 7471 (1.4%) zerosZeros
numactivecreds_622L has 317250 (60.7%) zerosZeros
numactivecredschannel_414L has 471119 (90.1%) zerosZeros
numactiverelcontr_750L has 380535 (72.8%) zerosZeros
numcontrs3months_479L has 422147 (80.7%) zerosZeros
numinstlallpaidearly3d_817L has 23723 (4.5%) zerosZeros
numinstls_657L has 346981 (66.4%) zerosZeros
numinstlsallpaid_934L has 8483 (1.6%) zerosZeros
numinstlswithdpd10_728L has 272706 (52.2%) zerosZeros
numinstlswithdpd5_4187116L has 233690 (44.7%) zerosZeros
numinstmatpaidtearly2d_4499204L has 16680 (3.2%) zerosZeros
numinstpaidearly3d_3546850L has 24488 (4.7%) zerosZeros
numinstpaidearly3dest_4493216L has 22623 (4.3%) zerosZeros
numinstpaidearly5d_1087L has 139915 (26.8%) zerosZeros
numinstpaidearly5dest_4493211L has 139915 (26.8%) zerosZeros
numinstpaidearly5dobd_4499205L has 43430 (8.3%) zerosZeros
numinstpaidearly_338L has 43430 (8.3%) zerosZeros
numinstpaidearlyest_4493214L has 43430 (8.3%) zerosZeros
numinstpaidlastcontr_4325080L has 34627 (6.6%) zerosZeros
numinstpaidlate1d_3546852L has 127635 (24.4%) zerosZeros
numinsttopaygr_769L has 213483 (40.8%) zerosZeros
numinsttopaygrest_4493213L has 213483 (40.8%) zerosZeros
numinstunpaidmax_3546851L has 212440 (40.6%) zerosZeros
numinstunpaidmaxest_4493212L has 212440 (40.6%) zerosZeros
numnotactivated_1143L has 509261 (97.4%) zerosZeros
numpmtchanneldd_318L has 506919 (96.9%) zerosZeros
numrejects9m_859L has 415273 (79.4%) zerosZeros
pctinstlsallpaidearl3d_427L has 20535 (3.9%) zerosZeros
pctinstlsallpaidlat10d_839L has 212692 (40.7%) zerosZeros
pctinstlsallpaidlate1d_3546856L has 124375 (23.8%) zerosZeros
pctinstlsallpaidlate4d_3546849L has 178458 (34.1%) zerosZeros
pctinstlsallpaidlate6d_3546844L has 194506 (37.2%) zerosZeros
posfpd10lastmonth_333P has 456007 (87.2%) zerosZeros
posfpd30lastmonth_3976960P has 450849 (86.2%) zerosZeros
posfstqpd30lastmonth_3976962P has 419528 (80.2%) zerosZeros
price_1097A has 54019 (10.3%) zerosZeros
sellerplacecnt_915L has 446049 (85.3%) zerosZeros
sellerplacescnt_216L has 171739 (32.8%) zerosZeros
sumoutstandtotal_3546847A has 207109 (39.6%) zerosZeros
sumoutstandtotalest_4493215A has 204415 (39.1%) zerosZeros
totaldebt_9A has 338444 (64.7%) zerosZeros
totalsettled_863A has 133695 (25.6%) zerosZeros

Reproduction

Analysis started2024-02-13 19:56:02.417810
Analysis finished2024-02-13 19:56:14.034891
Duration11.62 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

case_id
Real number (ℝ)

UNIQUE 

Distinct522902
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1418820.989
Minimum40626
Maximum2703454
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:14.177892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum40626
5-th percentile195367.05
Q1942363.25
median1738029.5
Q31868754.75
95-th percentile2677267.95
Maximum2703454
Range2662828
Interquartile range (IQR)926391.5

Descriptive statistics

Standard deviation742410.9563
Coefficient of variation (CV)0.5232590735
Kurtosis-0.7652641394
Mean1418820.989
Median Absolute Deviation (MAD)717315
Skewness-0.2746679979
Sum7.419043329 × 1011
Variance5.511740281 × 1011
MonotonicityStrictly increasing
2024-02-13T20:56:14.364891image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40626 1
 
< 0.1%
1825175 1
 
< 0.1%
1825188 1
 
< 0.1%
1825187 1
 
< 0.1%
1825186 1
 
< 0.1%
1825185 1
 
< 0.1%
1825184 1
 
< 0.1%
1825183 1
 
< 0.1%
1825182 1
 
< 0.1%
1825181 1
 
< 0.1%
Other values (522892) 522892
> 99.9%
ValueCountFrequency (%)
40626 1
< 0.1%
40704 1
< 0.1%
40734 1
< 0.1%
40737 1
< 0.1%
40766 1
< 0.1%
ValueCountFrequency (%)
2703454 1
< 0.1%
2703453 1
< 0.1%
2703452 1
< 0.1%
2703451 1
< 0.1%
2703450 1
< 0.1%

actualdpdtolerance_344P
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct136
Distinct (%)< 0.1%
Missing121492
Missing (%)23.2%
Infinite0
Infinite (%)0.0%
Mean0.1229939463
Minimum0
Maximum4206
Zeros400118
Zeros (%)76.5%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:14.523925image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4206
Range4206
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.56441695
Coefficient of variation (CV)118.4157219
Kurtosis49626.89238
Mean0.1229939463
Median Absolute Deviation (MAD)0
Skewness209.6202818
Sum49371
Variance212.1222411
MonotonicityNot monotonic
2024-02-13T20:56:14.668926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 400118
76.5%
1 525
 
0.1%
2 200
 
< 0.1%
3 135
 
< 0.1%
4 61
 
< 0.1%
5 52
 
< 0.1%
6 23
 
< 0.1%
7 20
 
< 0.1%
8 20
 
< 0.1%
9 16
 
< 0.1%
Other values (126) 240
 
< 0.1%
(Missing) 121492
 
23.2%
ValueCountFrequency (%)
0 400118
76.5%
1 525
 
0.1%
2 200
 
< 0.1%
3 135
 
< 0.1%
4 61
 
< 0.1%
ValueCountFrequency (%)
4206 1
< 0.1%
3980 1
< 0.1%
3623 1
< 0.1%
2617 1
< 0.1%
2505 1
< 0.1%

amtinstpaidbefduel24m_4187115A
Real number (ℝ)

MISSING  ZEROS 

Distinct288167
Distinct (%)73.8%
Missing132270
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean63708.11959
Minimum0
Maximum1408010.2
Zeros61212
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:14.829889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19042.55075
median34623.065
Q388321.96875
95-th percentile231845.7425
Maximum1408010.2
Range1408010.2
Interquartile range (IQR)79279.418

Descriptive statistics

Standard deviation79181.0955
Coefficient of variation (CV)1.242872902
Kurtosis6.888313954
Mean63708.11959
Median Absolute Deviation (MAD)31873.265
Skewness2.165161925
Sum2.488643017 × 1010
Variance6269645885
MonotonicityNot monotonic
2024-02-13T20:56:15.027135image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 61212
 
11.7%
800 46
 
< 0.1%
9998 46
 
< 0.1%
11998 42
 
< 0.1%
13998 39
 
< 0.1%
6000 38
 
< 0.1%
17998 37
 
< 0.1%
7998 36
 
< 0.1%
12000 34
 
< 0.1%
19998 28
 
< 0.1%
Other values (288157) 329074
62.9%
(Missing) 132270
25.3%
ValueCountFrequency (%)
0 61212
11.7%
0.266 1
 
< 0.1%
0.28 1
 
< 0.1%
0.28800002 1
 
< 0.1%
0.35 1
 
< 0.1%
ValueCountFrequency (%)
1408010.2 1
< 0.1%
1254777.9 1
< 0.1%
1198912.6 1
< 0.1%
1128892.8 1
< 0.1%
1052332.9 1
< 0.1%

annuity_780A
Real number (ℝ)

Distinct59226
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4353.270559
Minimum80.8
Maximum91601.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:15.188641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum80.8
5-th percentile1199.8
Q12122.85
median3478.2
Q35723.4
95-th percentile10496.39
Maximum91601.4
Range91520.6
Interquartile range (IQR)3600.55

Descriptive statistics

Standard deviation3142.026961
Coefficient of variation (CV)0.7217623896
Kurtosis11.69411482
Mean4353.270559
Median Absolute Deviation (MAD)1597.4
Skewness2.206458923
Sum2276333882
Variance9872333.426
MonotonicityNot monotonic
2024-02-13T20:56:15.343593image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6000 2160
 
0.4%
600 904
 
0.2%
9000 898
 
0.2%
2436 777
 
0.1%
1218 749
 
0.1%
1500 691
 
0.1%
12000 643
 
0.1%
1200 615
 
0.1%
1666.6 566
 
0.1%
2000 561
 
0.1%
Other values (59216) 514338
98.4%
ValueCountFrequency (%)
80.8 1
< 0.1%
120.6 1
< 0.1%
126.200005 1
< 0.1%
135 1
< 0.1%
138.2 1
< 0.1%
ValueCountFrequency (%)
91601.4 1
< 0.1%
75101.4 1
< 0.1%
75020.4 1
< 0.1%
56326 1
< 0.1%
55109 1
< 0.1%

annuitynextmonth_57A
Real number (ℝ)

ZEROS 

Distinct51866
Distinct (%)9.9%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1603.309984
Minimum0
Maximum87500
Zeros353591
Zeros (%)67.6%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:15.490163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32333.2
95-th percentile8090.4
Maximum87500
Range87500
Interquartile range (IQR)2333.2

Descriptive statistics

Standard deviation3016.446507
Coefficient of variation (CV)1.881386966
Kurtosis11.18548044
Mean1603.309984
Median Absolute Deviation (MAD)0
Skewness2.581147432
Sum838370790.8
Variance9098949.532
MonotonicityNot monotonic
2024-02-13T20:56:15.641674image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 353591
67.6%
2000 172
 
< 0.1%
1666.6 147
 
< 0.1%
1500 140
 
< 0.1%
2166.6 138
 
< 0.1%
1000 120
 
< 0.1%
2500 112
 
< 0.1%
1833.2001 109
 
< 0.1%
1998.2001 99
 
< 0.1%
1665 88
 
< 0.1%
Other values (51856) 168184
32.2%
ValueCountFrequency (%)
0 353591
67.6%
1.8260001 1
 
< 0.1%
36 2
 
< 0.1%
41.8 1
 
< 0.1%
44 1
 
< 0.1%
ValueCountFrequency (%)
87500 1
< 0.1%
71878.6 1
< 0.1%
65747.805 1
< 0.1%
61800 1
< 0.1%
55335.2 1
< 0.1%

applicationcnt_361L
Real number (ℝ)

SKEWED  ZEROS 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.147442542 × 10-5
Minimum0
Maximum3
Zeros522899
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:15.868991image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.005174319168
Coefficient of variation (CV)450.9436402
Kurtosis261448.6633
Mean1.147442542 × 10-5
Median Absolute Deviation (MAD)0
Skewness496.9574828
Sum6
Variance2.677357885 × 10-5
MonotonicityNot monotonic
2024-02-13T20:56:15.980028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
0 522899
> 99.9%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
0 522899
> 99.9%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
3 1
 
< 0.1%
2 1
 
< 0.1%
1 1
 
< 0.1%
0 522899
> 99.9%

applications30d_658L
Real number (ℝ)

ZEROS 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1611257941
Minimum0
Maximum28
Zeros462539
Zeros (%)88.5%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:16.099991image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum28
Range28
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5611154533
Coefficient of variation (CV)3.482468194
Kurtosis131.5132727
Mean0.1611257941
Median Absolute Deviation (MAD)0
Skewness7.658412415
Sum84253
Variance0.314850552
MonotonicityNot monotonic
2024-02-13T20:56:16.222131image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 462539
88.5%
1 45775
 
8.8%
2 9934
 
1.9%
3 2721
 
0.5%
4 987
 
0.2%
5 396
 
0.1%
6 199
 
< 0.1%
7 115
 
< 0.1%
8 76
 
< 0.1%
9 50
 
< 0.1%
Other values (15) 110
 
< 0.1%
ValueCountFrequency (%)
0 462539
88.5%
1 45775
 
8.8%
2 9934
 
1.9%
3 2721
 
0.5%
4 987
 
0.2%
ValueCountFrequency (%)
28 1
< 0.1%
25 1
< 0.1%
22 2
< 0.1%
21 1
< 0.1%
20 2
< 0.1%

applicationscnt_1086L
Real number (ℝ)

SKEWED  ZEROS 

Distinct110
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4331652967
Minimum0
Maximum728
Zeros498636
Zeros (%)95.4%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:16.364428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum728
Range728
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.83998824
Coefficient of variation (CV)8.864948945
Kurtosis13020.74217
Mean0.4331652967
Median Absolute Deviation (MAD)0
Skewness80.05509858
Sum226503
Variance14.74550969
MonotonicityNot monotonic
2024-02-13T20:56:16.521346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 498636
95.4%
1 2975
 
0.6%
2 2575
 
0.5%
3 2202
 
0.4%
4 1882
 
0.4%
5 1702
 
0.3%
6 1540
 
0.3%
7 1331
 
0.3%
8 1135
 
0.2%
9 981
 
0.2%
Other values (100) 7943
 
1.5%
ValueCountFrequency (%)
0 498636
95.4%
1 2975
 
0.6%
2 2575
 
0.5%
3 2202
 
0.4%
4 1882
 
0.4%
ValueCountFrequency (%)
728 1
< 0.1%
726 1
< 0.1%
717 1
< 0.1%
664 1
< 0.1%
634 1
< 0.1%

applicationscnt_464L
Real number (ℝ)

ZEROS 

Distinct246
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7562774669
Minimum0
Maximum246
Zeros492508
Zeros (%)94.2%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:16.680314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum246
Range246
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.434807212
Coefficient of variation (CV)11.153059
Kurtosis393.6232831
Mean0.7562774669
Median Absolute Deviation (MAD)0
Skewness18.46309949
Sum395459
Variance71.1459727
MonotonicityNot monotonic
2024-02-13T20:56:16.842786image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 492508
94.2%
1 11382
 
2.2%
2 4358
 
0.8%
3 2516
 
0.5%
4 1712
 
0.3%
5 1268
 
0.2%
6 967
 
0.2%
7 725
 
0.1%
8 539
 
0.1%
9 471
 
0.1%
Other values (236) 6456
 
1.2%
ValueCountFrequency (%)
0 492508
94.2%
1 11382
 
2.2%
2 4358
 
0.8%
3 2516
 
0.5%
4 1712
 
0.3%
ValueCountFrequency (%)
246 1
 
< 0.1%
245 2
 
< 0.1%
244 2
 
< 0.1%
243 4
< 0.1%
242 6
< 0.1%

applicationscnt_629L
Real number (ℝ)

SKEWED  ZEROS 

Distinct90
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1771804277
Minimum0
Maximum90
Zeros505828
Zeros (%)96.7%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:17.007787image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum90
Range90
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.06274327
Coefficient of variation (CV)11.64204928
Kurtosis525.9238267
Mean0.1771804277
Median Absolute Deviation (MAD)0
Skewness20.58679236
Sum92648
Variance4.254909797
MonotonicityNot monotonic
2024-02-13T20:56:17.178443image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 505828
96.7%
1 8241
 
1.6%
2 2660
 
0.5%
3 1303
 
0.2%
4 753
 
0.1%
5 543
 
0.1%
6 385
 
0.1%
7 336
 
0.1%
8 260
 
< 0.1%
9 194
 
< 0.1%
Other values (80) 2399
 
0.5%
ValueCountFrequency (%)
0 505828
96.7%
1 8241
 
1.6%
2 2660
 
0.5%
3 1303
 
0.2%
4 753
 
0.1%
ValueCountFrequency (%)
90 1
< 0.1%
89 1
< 0.1%
88 1
< 0.1%
87 2
< 0.1%
86 1
< 0.1%

applicationscnt_867L
Real number (ℝ)

ZEROS 

Distinct150
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.520504798
Minimum0
Maximum287
Zeros144078
Zeros (%)27.6%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:17.324330image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile13
Maximum287
Range287
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.259180062
Coefficient of variation (CV)1.493871011
Kurtosis275.1094261
Mean3.520504798
Median Absolute Deviation (MAD)2
Skewness8.728965492
Sum1840879
Variance27.65897493
MonotonicityNot monotonic
2024-02-13T20:56:17.474332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 144078
27.6%
1 89222
17.1%
2 64949
12.4%
3 49132
 
9.4%
4 37137
 
7.1%
5 28522
 
5.5%
6 22001
 
4.2%
7 17137
 
3.3%
8 13377
 
2.6%
9 10510
 
2.0%
Other values (140) 46837
 
9.0%
ValueCountFrequency (%)
0 144078
27.6%
1 89222
17.1%
2 64949
12.4%
3 49132
 
9.4%
4 37137
 
7.1%
ValueCountFrequency (%)
287 2
< 0.1%
283 1
< 0.1%
278 1
< 0.1%
275 1
< 0.1%
274 1
< 0.1%

avgdbddpdlast24m_3658932P
Real number (ℝ)

MISSING  ZEROS 

Distinct3472
Distinct (%)1.0%
Missing187071
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean30.92991415
Minimum-1220
Maximum4750
Zeros20822
Zeros (%)4.0%
Negative270171
Negative (%)51.7%
Memory size4.0 MiB
2024-02-13T20:56:17.621296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1220
5-th percentile-35
Q1-11
median-5
Q3-1
95-th percentile9
Maximum4750
Range5970
Interquartile range (IQR)10

Descriptive statistics

Standard deviation319.1294269
Coefficient of variation (CV)10.31782453
Kurtosis121.1697957
Mean30.92991415
Median Absolute Deviation (MAD)4
Skewness10.46197078
Sum10387224
Variance101843.5911
MonotonicityNot monotonic
2024-02-13T20:56:17.771505image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 26634
 
5.1%
-2 26142
 
5.0%
-3 23237
 
4.4%
0 20822
 
4.0%
-4 20727
 
4.0%
-5 18397
 
3.5%
-6 16212
 
3.1%
-7 14370
 
2.7%
-8 12689
 
2.4%
-9 11327
 
2.2%
Other values (3462) 145274
27.8%
(Missing) 187071
35.8%
ValueCountFrequency (%)
-1220 1
< 0.1%
-955 1
< 0.1%
-923 1
< 0.1%
-895 1
< 0.1%
-888 2
< 0.1%
ValueCountFrequency (%)
4750 2
< 0.1%
4719 1
< 0.1%
4709 1
< 0.1%
4680 1
< 0.1%
4669 1
< 0.1%

avgdbddpdlast3m_4187120P
Real number (ℝ)

MISSING  ZEROS 

Distinct2552
Distinct (%)1.1%
Missing290241
Missing (%)55.5%
Infinite0
Infinite (%)0.0%
Mean28.83077955
Minimum-519
Maximum4750
Zeros22535
Zeros (%)4.3%
Negative180294
Negative (%)34.5%
Memory size4.0 MiB
2024-02-13T20:56:17.934112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-519
5-th percentile-47
Q1-11
median-4
Q3-1
95-th percentile5
Maximum4750
Range5269
Interquartile range (IQR)10

Descriptive statistics

Standard deviation372.3848217
Coefficient of variation (CV)12.91622452
Kurtosis109.261787
Mean28.83077955
Median Absolute Deviation (MAD)4
Skewness10.28943704
Sum6707798
Variance138670.4554
MonotonicityNot monotonic
2024-02-13T20:56:18.313653image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 23304
 
4.5%
0 22535
 
4.3%
-2 19446
 
3.7%
-3 15407
 
2.9%
-4 12834
 
2.5%
-5 10643
 
2.0%
-6 9395
 
1.8%
-7 8239
 
1.6%
1 7705
 
1.5%
-8 7210
 
1.4%
Other values (2542) 95943
 
18.3%
(Missing) 290241
55.5%
ValueCountFrequency (%)
-519 1
< 0.1%
-490 1
< 0.1%
-488 1
< 0.1%
-479 1
< 0.1%
-474 1
< 0.1%
ValueCountFrequency (%)
4750 2
< 0.1%
4719 1
< 0.1%
4709 1
< 0.1%
4684 1
< 0.1%
4680 1
< 0.1%

avgdbdtollast24m_4525197P
Real number (ℝ)

MISSING  ZEROS 

Distinct3450
Distinct (%)1.0%
Missing187102
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean29.9730673
Minimum-1220
Maximum4750
Zeros20610
Zeros (%)3.9%
Negative280489
Negative (%)53.6%
Memory size4.0 MiB
2024-02-13T20:56:18.454595image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1220
5-th percentile-36
Q1-12
median-5
Q3-2
95-th percentile6
Maximum4750
Range5970
Interquartile range (IQR)10

Descriptive statistics

Standard deviation318.2518806
Coefficient of variation (CV)10.61792834
Kurtosis122.148519
Mean29.9730673
Median Absolute Deviation (MAD)4
Skewness10.50819608
Sum10064956
Variance101284.2595
MonotonicityNot monotonic
2024-02-13T20:56:18.605868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 27107
 
5.2%
-2 26544
 
5.1%
-3 23882
 
4.6%
-4 21229
 
4.1%
0 20610
 
3.9%
-5 18939
 
3.6%
-6 16764
 
3.2%
-7 15061
 
2.9%
-8 13267
 
2.5%
-9 12741
 
2.4%
Other values (3440) 139656
26.7%
(Missing) 187102
35.8%
ValueCountFrequency (%)
-1220 1
< 0.1%
-955 1
< 0.1%
-923 1
< 0.1%
-895 1
< 0.1%
-888 2
< 0.1%
ValueCountFrequency (%)
4750 2
< 0.1%
4719 1
< 0.1%
4709 1
< 0.1%
4680 1
< 0.1%
4669 1
< 0.1%

avgdpdtolclosure24_3658938P
Real number (ℝ)

MISSING  ZEROS 

Distinct3487
Distinct (%)0.9%
Missing137903
Missing (%)26.4%
Infinite0
Infinite (%)0.0%
Mean48.05180533
Minimum0
Maximum4750
Zeros276030
Zeros (%)52.8%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:18.759835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile31
Maximum4750
Range4750
Interquartile range (IQR)1

Descriptive statistics

Standard deviation323.6392771
Coefficient of variation (CV)6.735215771
Kurtosis103.4279367
Mean48.05180533
Median Absolute Deviation (MAD)0
Skewness9.510285184
Sum18499897
Variance104742.3817
MonotonicityNot monotonic
2024-02-13T20:56:18.911801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 276030
52.8%
1 47078
 
9.0%
2 14396
 
2.8%
3 7596
 
1.5%
4 4635
 
0.9%
5 3163
 
0.6%
6 2274
 
0.4%
7 1753
 
0.3%
8 1313
 
0.3%
9 1088
 
0.2%
Other values (3477) 25673
 
4.9%
(Missing) 137903
26.4%
ValueCountFrequency (%)
0 276030
52.8%
1 47078
 
9.0%
2 14396
 
2.8%
3 7596
 
1.5%
4 4635
 
0.9%
ValueCountFrequency (%)
4750 2
< 0.1%
4719 1
< 0.1%
4709 1
< 0.1%
4680 1
< 0.1%
4669 1
< 0.1%

avginstallast24m_3658937A
Real number (ℝ)

MISSING 

Distinct74715
Distinct (%)22.5%
Missing191154
Missing (%)36.6%
Infinite0
Infinite (%)0.0%
Mean5890.370765
Minimum0.2
Maximum496148.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:19.065385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1296.8
Q12707.6
median4411
Q37183.8003
95-th percentile13854.13
Maximum496148.8
Range496148.6
Interquartile range (IQR)4476.2003

Descriptive statistics

Standard deviation7124.068682
Coefficient of variation (CV)1.209443168
Kurtosis346.5483529
Mean5890.370765
Median Absolute Deviation (MAD)2011.4
Skewness13.0771879
Sum1954118721
Variance50752354.58
MonotonicityNot monotonic
2024-02-13T20:56:19.225468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 82
 
< 0.1%
2166.6 76
 
< 0.1%
1666.6 75
 
< 0.1%
800 67
 
< 0.1%
1500 65
 
< 0.1%
1998.2001 63
 
< 0.1%
3000 62
 
< 0.1%
1000 61
 
< 0.1%
1999.6 56
 
< 0.1%
1999.8 55
 
< 0.1%
Other values (74705) 331086
63.3%
(Missing) 191154
36.6%
ValueCountFrequency (%)
0.2 2
 
< 0.1%
0.4 4
< 0.1%
0.6 7
< 0.1%
0.8 5
< 0.1%
1 3
< 0.1%
ValueCountFrequency (%)
496148.8 1
< 0.1%
400000 1
< 0.1%
303980 1
< 0.1%
269402.8 1
< 0.1%
261869.61 1
< 0.1%

avglnamtstart24m_4525187A
Real number (ℝ)

MISSING 

Distinct38222
Distinct (%)41.2%
Missing430200
Missing (%)82.3%
Infinite0
Infinite (%)0.0%
Mean45484.28211
Minimum0
Maximum513520
Zeros19
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:19.401889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7410
Q115691.601
median29000
Q357938
95-th percentile140000
Maximum513520
Range513520
Interquartile range (IQR)42246.399

Descriptive statistics

Standard deviation45462.53714
Coefficient of variation (CV)0.9995219232
Kurtosis6.481144648
Mean45484.28211
Median Absolute Deviation (MAD)16448
Skewness2.230059927
Sum4216483921
Variance2066842283
MonotonicityNot monotonic
2024-02-13T20:56:19.564888image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 1101
 
0.2%
40000 599
 
0.1%
60000 593
 
0.1%
20000 592
 
0.1%
150000 523
 
0.1%
23682.8 514
 
0.1%
21841.4 500
 
0.1%
10920.8 407
 
0.1%
30000 400
 
0.1%
200000 386
 
0.1%
Other values (38212) 87087
 
16.7%
(Missing) 430200
82.3%
ValueCountFrequency (%)
0 19
< 0.1%
2000 12
< 0.1%
2004 1
 
< 0.1%
2014 1
 
< 0.1%
2034 1
 
< 0.1%
ValueCountFrequency (%)
513520 1
 
< 0.1%
496148.8 3
 
< 0.1%
404443.6 1
 
< 0.1%
402455.8 2
 
< 0.1%
400000 11
< 0.1%

avgmaxdpdlast9m_3716943P
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct192
Distinct (%)0.1%
Missing241713
Missing (%)46.2%
Infinite0
Infinite (%)0.0%
Mean0.787708623
Minimum0
Maximum240
Zeros220369
Zeros (%)42.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:19.714200image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum240
Range240
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.056770995
Coefficient of variation (CV)6.419595834
Kurtosis730.9050404
Mean0.787708623
Median Absolute Deviation (MAD)0
Skewness23.44456716
Sum221495
Variance25.57093289
MonotonicityNot monotonic
2024-02-13T20:56:19.871176image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 220369
42.1%
1 31613
 
6.0%
2 10671
 
2.0%
3 5711
 
1.1%
4 3222
 
0.6%
5 2172
 
0.4%
6 1523
 
0.3%
7 1045
 
0.2%
8 834
 
0.2%
9 590
 
0.1%
Other values (182) 3439
 
0.7%
(Missing) 241713
46.2%
ValueCountFrequency (%)
0 220369
42.1%
1 31613
 
6.0%
2 10671
 
2.0%
3 5711
 
1.1%
4 3222
 
0.6%
ValueCountFrequency (%)
240 1
< 0.1%
237 1
< 0.1%
234 1
< 0.1%
231 1
< 0.1%
228 1
< 0.1%

avgoutstandbalancel6m_4187114A
Real number (ℝ)

MISSING  ZEROS 

Distinct241148
Distinct (%)85.9%
Missing242187
Missing (%)46.3%
Infinite0
Infinite (%)0.0%
Mean48024.43097
Minimum-2000000
Maximum1080852.9
Zeros13483
Zeros (%)2.6%
Negative7069
Negative (%)1.4%
Memory size4.0 MiB
2024-02-13T20:56:20.024183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-2000000
5-th percentile0
Q18488.1
median22875.715
Q357910.52
95-th percentile187549.949
Maximum1080852.9
Range3080852.9
Interquartile range (IQR)49422.42

Descriptive statistics

Standard deviation67200.16233
Coefficient of variation (CV)1.399291172
Kurtosis15.65070073
Mean48024.43097
Median Absolute Deviation (MAD)17984.715
Skewness2.79482353
Sum1.348117814 × 1010
Variance4515861817
MonotonicityNot monotonic
2024-02-13T20:56:20.179090image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13483
 
2.6%
-2 280
 
0.1%
-4 139
 
< 0.1%
3000 125
 
< 0.1%
6000 110
 
< 0.1%
2000 98
 
< 0.1%
4000 96
 
< 0.1%
-6 78
 
< 0.1%
1500 78
 
< 0.1%
5000 75
 
< 0.1%
Other values (241138) 266153
50.9%
(Missing) 242187
46.3%
ValueCountFrequency (%)
-2000000 1
< 0.1%
-510000 1
< 0.1%
-320000 1
< 0.1%
-313250.5 1
< 0.1%
-251279.88 1
< 0.1%
ValueCountFrequency (%)
1080852.9 1
< 0.1%
1042131 1
< 0.1%
1036999.3 1
< 0.1%
1026832.44 1
< 0.1%
928688.8 1
< 0.1%

avgpmtlast12m_4525200A
Real number (ℝ)

MISSING 

Distinct80897
Distinct (%)26.7%
Missing219677
Missing (%)42.0%
Infinite0
Infinite (%)0.0%
Mean6707.100049
Minimum0
Maximum495910.4
Zeros491
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:20.336202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1196.84
Q12699.6
median4626.6
Q37870.4
95-th percentile16769.92
Maximum495910.4
Range495910.4
Interquartile range (IQR)5170.8

Descriptive statistics

Standard deviation9605.708913
Coefficient of variation (CV)1.432170214
Kurtosis213.9481571
Mean6707.100049
Median Absolute Deviation (MAD)2291
Skewness10.99904166
Sum2033760412
Variance92269643.72
MonotonicityNot monotonic
2024-02-13T20:56:20.497893image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 491
 
0.1%
2000 99
 
< 0.1%
2166.6 89
 
< 0.1%
1666.6 88
 
< 0.1%
1500 86
 
< 0.1%
1000 85
 
< 0.1%
600 80
 
< 0.1%
1833.2001 67
 
< 0.1%
2500 67
 
< 0.1%
1998.2001 65
 
< 0.1%
Other values (80887) 302008
57.8%
(Missing) 219677
42.0%
ValueCountFrequency (%)
0 491
0.1%
0.2 10
 
< 0.1%
0.4 22
 
< 0.1%
0.6 16
 
< 0.1%
0.8 17
 
< 0.1%
ValueCountFrequency (%)
495910.4 1
< 0.1%
400000 1
< 0.1%
383957.6 1
< 0.1%
379675.22 1
< 0.1%
378638.22 1
< 0.1%

bankacctype_710L
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing410562
Missing (%)78.5%
Memory size4.0 MiB
2024-02-13T20:56:20.585227image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters224680
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCA
2nd rowCA
3rd rowCA
4th rowCA
5th rowCA
ValueCountFrequency (%)
ca 112340
100.0%
2024-02-13T20:56:20.786142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 112340
50.0%
A 112340
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 224680
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 112340
50.0%
A 112340
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 224680
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 112340
50.0%
A 112340
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 224680
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 112340
50.0%
A 112340
50.0%

cardtype_51L
Text

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing456995
Missing (%)87.4%
Memory size4.0 MiB
2024-02-13T20:56:20.934953image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length7
Mean length7.029283688
Min length7

Characters and Unicode

Total characters463279
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINSTANT
2nd rowINSTANT
3rd rowINSTANT
4th rowINSTANT
5th rowINSTANT
ValueCountFrequency (%)
instant 65521
99.4%
personalized 386
 
0.6%
2024-02-13T20:56:21.215761image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 131428
28.4%
T 131042
28.3%
I 65907
14.2%
S 65907
14.2%
A 65907
14.2%
E 772
 
0.2%
P 386
 
0.1%
R 386
 
0.1%
O 386
 
0.1%
L 386
 
0.1%
Other values (2) 772
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 463279
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 131428
28.4%
T 131042
28.3%
I 65907
14.2%
S 65907
14.2%
A 65907
14.2%
E 772
 
0.2%
P 386
 
0.1%
R 386
 
0.1%
O 386
 
0.1%
L 386
 
0.1%
Other values (2) 772
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 463279
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 131428
28.4%
T 131042
28.3%
I 65907
14.2%
S 65907
14.2%
A 65907
14.2%
E 772
 
0.2%
P 386
 
0.1%
R 386
 
0.1%
O 386
 
0.1%
L 386
 
0.1%
Other values (2) 772
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 463279
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 131428
28.4%
T 131042
28.3%
I 65907
14.2%
S 65907
14.2%
A 65907
14.2%
E 772
 
0.2%
P 386
 
0.1%
R 386
 
0.1%
O 386
 
0.1%
L 386
 
0.1%
Other values (2) 772
 
0.2%

clientscnt12m_3712952L
Real number (ℝ)

SKEWED  ZEROS 

Distinct86
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04588240244
Minimum0
Maximum198
Zeros507552
Zeros (%)97.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:21.368759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum198
Range198
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.277743604
Coefficient of variation (CV)27.848228
Kurtosis13201.89317
Mean0.04588240244
Median Absolute Deviation (MAD)0
Skewness107.9349739
Sum23992
Variance1.632628718
MonotonicityNot monotonic
2024-02-13T20:56:21.545057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 507552
97.1%
1 14564
 
2.8%
2 491
 
0.1%
3 69
 
< 0.1%
4 23
 
< 0.1%
6 18
 
< 0.1%
7 16
 
< 0.1%
5 16
 
< 0.1%
8 10
 
< 0.1%
14 8
 
< 0.1%
Other values (76) 135
 
< 0.1%
ValueCountFrequency (%)
0 507552
97.1%
1 14564
 
2.8%
2 491
 
0.1%
3 69
 
< 0.1%
4 23
 
< 0.1%
ValueCountFrequency (%)
198 2
< 0.1%
194 1
< 0.1%
190 1
< 0.1%
187 1
< 0.1%
186 1
< 0.1%

clientscnt3m_3712950L
Real number (ℝ)

SKEWED  ZEROS 

Distinct74
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02347093719
Minimum0
Maximum112
Zeros516417
Zeros (%)98.8%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:21.714537image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum112
Range112
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8415285306
Coefficient of variation (CV)35.85406597
Kurtosis9965.706134
Mean0.02347093719
Median Absolute Deviation (MAD)0
Skewness94.45459363
Sum12273
Variance0.7081702678
MonotonicityNot monotonic
2024-02-13T20:56:21.870718image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 516417
98.8%
1 6107
 
1.2%
2 150
 
< 0.1%
3 39
 
< 0.1%
4 18
 
< 0.1%
5 16
 
< 0.1%
6 15
 
< 0.1%
7 10
 
< 0.1%
9 7
 
< 0.1%
17 7
 
< 0.1%
Other values (64) 116
 
< 0.1%
ValueCountFrequency (%)
0 516417
98.8%
1 6107
 
1.2%
2 150
 
< 0.1%
3 39
 
< 0.1%
4 18
 
< 0.1%
ValueCountFrequency (%)
112 1
< 0.1%
111 1
< 0.1%
110 1
< 0.1%
109 1
< 0.1%
108 1
< 0.1%

clientscnt6m_3712949L
Real number (ℝ)

SKEWED  ZEROS 

Distinct85
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03357416877
Minimum0
Maximum175
Zeros513182
Zeros (%)98.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:22.021407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum175
Range175
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.204905763
Coefficient of variation (CV)35.88788067
Kurtosis12222.2412
Mean0.03357416877
Median Absolute Deviation (MAD)0
Skewness105.3349156
Sum17556
Variance1.451797897
MonotonicityNot monotonic
2024-02-13T20:56:22.179373image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 513182
98.1%
1 9215
 
1.8%
2 248
 
< 0.1%
3 53
 
< 0.1%
4 21
 
< 0.1%
6 18
 
< 0.1%
5 15
 
< 0.1%
9 11
 
< 0.1%
7 11
 
< 0.1%
17 7
 
< 0.1%
Other values (75) 121
 
< 0.1%
ValueCountFrequency (%)
0 513182
98.1%
1 9215
 
1.8%
2 248
 
< 0.1%
3 53
 
< 0.1%
4 21
 
< 0.1%
ValueCountFrequency (%)
175 1
< 0.1%
169 1
< 0.1%
168 1
< 0.1%
162 1
< 0.1%
161 1
< 0.1%

clientscnt_100L
Real number (ℝ)

SKEWED  ZEROS 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04012797809
Minimum0
Maximum109
Zeros505091
Zeros (%)96.6%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:22.317202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum109
Range109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6214262654
Coefficient of variation (CV)15.48610957
Kurtosis25511.00726
Mean0.04012797809
Median Absolute Deviation (MAD)0
Skewness150.5313929
Sum20983
Variance0.3861706033
MonotonicityNot monotonic
2024-02-13T20:56:22.436238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 505091
96.6%
1 16497
 
3.2%
2 1171
 
0.2%
3 110
 
< 0.1%
4 12
 
< 0.1%
106 6
 
< 0.1%
109 4
 
< 0.1%
68 2
 
< 0.1%
5 2
 
< 0.1%
104 2
 
< 0.1%
Other values (4) 5
 
< 0.1%
ValueCountFrequency (%)
0 505091
96.6%
1 16497
 
3.2%
2 1171
 
0.2%
3 110
 
< 0.1%
4 12
 
< 0.1%
ValueCountFrequency (%)
109 4
< 0.1%
108 2
 
< 0.1%
106 6
< 0.1%
105 1
 
< 0.1%
104 2
 
< 0.1%

clientscnt_1022L
Real number (ℝ)

SKEWED  ZEROS 

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1279398434
Minimum0
Maximum266
Zeros473023
Zeros (%)90.5%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:22.578392image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum266
Range266
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.972572952
Coefficient of variation (CV)15.41797222
Kurtosis10288.6985
Mean0.1279398434
Median Absolute Deviation (MAD)0
Skewness96.31693227
Sum66900
Variance3.891044049
MonotonicityNot monotonic
2024-02-13T20:56:22.738456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 473023
90.5%
1 45658
 
8.7%
2 3561
 
0.7%
3 352
 
0.1%
4 77
 
< 0.1%
5 24
 
< 0.1%
7 22
 
< 0.1%
6 22
 
< 0.1%
9 10
 
< 0.1%
14 8
 
< 0.1%
Other values (90) 145
 
< 0.1%
ValueCountFrequency (%)
0 473023
90.5%
1 45658
 
8.7%
2 3561
 
0.7%
3 352
 
0.1%
4 77
 
< 0.1%
ValueCountFrequency (%)
266 2
< 0.1%
262 1
< 0.1%
258 1
< 0.1%
255 1
< 0.1%
254 1
< 0.1%

clientscnt_1071L
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02772029941
Minimum0
Maximum24
Zeros508898
Zeros (%)97.3%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:22.862424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1732391081
Coefficient of variation (CV)6.249539573
Kurtosis751.7012572
Mean0.02772029941
Median Absolute Deviation (MAD)0
Skewness11.29407502
Sum14495
Variance0.03001178858
MonotonicityNot monotonic
2024-02-13T20:56:22.978798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 508898
97.3%
1 13571
 
2.6%
2 405
 
0.1%
3 23
 
< 0.1%
4 2
 
< 0.1%
24 1
 
< 0.1%
8 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 508898
97.3%
1 13571
 
2.6%
2 405
 
0.1%
3 23
 
< 0.1%
4 2
 
< 0.1%
ValueCountFrequency (%)
24 1
 
< 0.1%
8 1
 
< 0.1%
5 1
 
< 0.1%
4 2
 
< 0.1%
3 23
< 0.1%

clientscnt_1130L
Real number (ℝ)

SKEWED  ZEROS 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02548087405
Minimum0
Maximum35
Zeros511118
Zeros (%)97.7%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:23.094798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum35
Range35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2619433391
Coefficient of variation (CV)10.27999819
Kurtosis8588.959351
Mean0.02548087405
Median Absolute Deviation (MAD)0
Skewness72.59626667
Sum13324
Variance0.06861431291
MonotonicityNot monotonic
2024-02-13T20:56:23.222804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 511118
97.7%
1 11103
 
2.1%
2 555
 
0.1%
3 54
 
< 0.1%
4 17
 
< 0.1%
5 10
 
< 0.1%
6 8
 
< 0.1%
33 6
 
< 0.1%
32 5
 
< 0.1%
7 5
 
< 0.1%
Other values (10) 21
 
< 0.1%
ValueCountFrequency (%)
0 511118
97.7%
1 11103
 
2.1%
2 555
 
0.1%
3 54
 
< 0.1%
4 17
 
< 0.1%
ValueCountFrequency (%)
35 1
 
< 0.1%
34 2
 
< 0.1%
33 6
< 0.1%
32 5
< 0.1%
31 3
< 0.1%

clientscnt_136L
Real number (ℝ)

MISSING 

Distinct3
Distinct (%)15.0%
Missing522882
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean0.15
Minimum0
Maximum2
Zeros18
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:23.338802image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.05
Maximum2
Range2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4893604849
Coefficient of variation (CV)3.262403233
Kurtosis11.88503804
Mean0.15
Median Absolute Deviation (MAD)0
Skewness3.435746721
Sum3
Variance0.2394736842
MonotonicityNot monotonic
2024-02-13T20:56:23.459304image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
0 18
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
(Missing) 522882
> 99.9%
ValueCountFrequency (%)
0 18
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
ValueCountFrequency (%)
2 1
 
< 0.1%
1 1
 
< 0.1%
0 18
< 0.1%

clientscnt_157L
Real number (ℝ)

SKEWED  ZEROS 

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0597683696
Minimum0
Maximum390
Zeros505473
Zeros (%)96.7%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:23.594304image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum390
Range390
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.025449733
Coefficient of variation (CV)17.15706385
Kurtosis83311.90558
Mean0.0597683696
Median Absolute Deviation (MAD)0
Skewness243.2863469
Sum31253
Variance1.051547155
MonotonicityNot monotonic
2024-02-13T20:56:23.753596image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 505473
96.7%
1 11139
 
2.1%
2 3466
 
0.7%
3 1670
 
0.3%
4 658
 
0.1%
5 240
 
< 0.1%
6 87
 
< 0.1%
7 53
 
< 0.1%
9 40
 
< 0.1%
8 27
 
< 0.1%
Other values (17) 49
 
< 0.1%
ValueCountFrequency (%)
0 505473
96.7%
1 11139
 
2.1%
2 3466
 
0.7%
3 1670
 
0.3%
4 658
 
0.1%
ValueCountFrequency (%)
390 2
 
< 0.1%
110 1
 
< 0.1%
108 2
 
< 0.1%
107 6
< 0.1%
105 3
< 0.1%

clientscnt_257L
Real number (ℝ)

SKEWED  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.002576008506
Minimum0
Maximum12
Zeros521669
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:23.885189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.06330488398
Coefficient of variation (CV)24.57479617
Kurtosis8904.292071
Mean0.002576008506
Median Absolute Deviation (MAD)0
Skewness65.57370472
Sum1347
Variance0.004007508336
MonotonicityNot monotonic
2024-02-13T20:56:24.005631image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 521669
99.8%
1 1188
 
0.2%
2 29
 
< 0.1%
4 5
 
< 0.1%
3 3
 
< 0.1%
9 2
 
< 0.1%
12 2
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
0 521669
99.8%
1 1188
 
0.2%
2 29
 
< 0.1%
3 3
 
< 0.1%
4 5
 
< 0.1%
ValueCountFrequency (%)
12 2
< 0.1%
10 1
< 0.1%
9 2
< 0.1%
8 1
< 0.1%
7 1
< 0.1%

clientscnt_304L
Real number (ℝ)

SKEWED  ZEROS 

Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07445754654
Minimum0
Maximum510
Zeros505164
Zeros (%)96.6%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:24.143252image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum510
Range510
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.87841436
Coefficient of variation (CV)25.22799162
Kurtosis59715.42376
Mean0.07445754654
Median Absolute Deviation (MAD)0
Skewness236.5501785
Sum38934
Variance3.528440508
MonotonicityNot monotonic
2024-02-13T20:56:24.288250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 505164
96.6%
1 8368
 
1.6%
2 4934
 
0.9%
3 2525
 
0.5%
4 1107
 
0.2%
5 445
 
0.1%
6 191
 
< 0.1%
7 72
 
< 0.1%
8 31
 
< 0.1%
9 15
 
< 0.1%
Other values (27) 50
 
< 0.1%
ValueCountFrequency (%)
0 505164
96.6%
1 8368
 
1.6%
2 4934
 
0.9%
3 2525
 
0.5%
4 1107
 
0.2%
ValueCountFrequency (%)
510 1
< 0.1%
508 1
< 0.1%
503 1
< 0.1%
482 1
< 0.1%
462 1
< 0.1%

clientscnt_360L
Real number (ℝ)

SKEWED  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.002172491213
Minimum0
Maximum15
Zeros521847
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:24.403272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.05800812858
Coefficient of variation (CV)26.70120286
Kurtosis16943.87861
Mean0.002172491213
Median Absolute Deviation (MAD)0
Skewness87.14419832
Sum1136
Variance0.003364942982
MonotonicityNot monotonic
2024-02-13T20:56:24.513379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 521847
99.8%
1 1017
 
0.2%
2 31
 
< 0.1%
3 3
 
< 0.1%
12 2
 
< 0.1%
9 1
 
< 0.1%
15 1
 
< 0.1%
ValueCountFrequency (%)
0 521847
99.8%
1 1017
 
0.2%
2 31
 
< 0.1%
3 3
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
12 2
 
< 0.1%
9 1
 
< 0.1%
3 3
 
< 0.1%
2 31
< 0.1%

clientscnt_493L
Real number (ℝ)

SKEWED  ZEROS 

Distinct67
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02300431056
Minimum0
Maximum922
Zeros519697
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:24.656063image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum922
Range922
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.014140215
Coefficient of variation (CV)87.55490452
Kurtosis112170.2443
Mean0.02300431056
Median Absolute Deviation (MAD)0
Skewness299.6010338
Sum12029
Variance4.056760804
MonotonicityNot monotonic
2024-02-13T20:56:24.805065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 519697
99.4%
1 2432
 
0.5%
2 394
 
0.1%
3 100
 
< 0.1%
5 38
 
< 0.1%
4 37
 
< 0.1%
8 16
 
< 0.1%
9 14
 
< 0.1%
7 13
 
< 0.1%
14 13
 
< 0.1%
Other values (57) 148
 
< 0.1%
ValueCountFrequency (%)
0 519697
99.4%
1 2432
 
0.5%
2 394
 
0.1%
3 100
 
< 0.1%
4 37
 
< 0.1%
ValueCountFrequency (%)
922 1
< 0.1%
607 1
< 0.1%
510 1
< 0.1%
354 1
< 0.1%
327 1
< 0.1%

clientscnt_533L
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0753429897
Minimum0
Maximum12
Zeros485750
Zeros (%)92.9%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:24.929563image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2810269711
Coefficient of variation (CV)3.729968404
Kurtosis23.20565046
Mean0.0753429897
Median Absolute Deviation (MAD)0
Skewness4.034236064
Sum39397
Variance0.07897615846
MonotonicityNot monotonic
2024-02-13T20:56:25.048450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 485750
92.9%
1 35022
 
6.7%
2 2043
 
0.4%
3 74
 
< 0.1%
4 8
 
< 0.1%
6 3
 
< 0.1%
12 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 485750
92.9%
1 35022
 
6.7%
2 2043
 
0.4%
3 74
 
< 0.1%
4 8
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
6 3
 
< 0.1%
5 1
 
< 0.1%
4 8
 
< 0.1%
3 74
< 0.1%

clientscnt_887L
Real number (ℝ)

SKEWED  ZEROS 

Distinct456
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.75930289
Minimum0
Maximum1242
Zeros487039
Zeros (%)93.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:25.188406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1242
Range1242
Interquartile range (IQR)0

Descriptive statistics

Standard deviation37.57924355
Coefficient of variation (CV)21.36030342
Kurtosis922.4859244
Mean1.75930289
Median Absolute Deviation (MAD)0
Skewness29.81122871
Sum919943
Variance1412.199546
MonotonicityNot monotonic
2024-02-13T20:56:25.345975image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 487039
93.1%
1 15662
 
3.0%
2 5624
 
1.1%
3 3178
 
0.6%
4 1990
 
0.4%
5 1364
 
0.3%
6 1029
 
0.2%
7 761
 
0.1%
8 610
 
0.1%
9 450
 
0.1%
Other values (446) 5195
 
1.0%
ValueCountFrequency (%)
0 487039
93.1%
1 15662
 
3.0%
2 5624
 
1.1%
3 3178
 
0.6%
4 1990
 
0.4%
ValueCountFrequency (%)
1242 1
 
< 0.1%
1241 1
 
< 0.1%
1240 3
< 0.1%
1239 4
< 0.1%
1238 6
< 0.1%

clientscnt_946L
Real number (ℝ)

SKEWED  ZEROS 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04151638357
Minimum0
Maximum72
Zeros504941
Zeros (%)96.6%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:25.474575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72
Range72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4384501481
Coefficient of variation (CV)10.56089453
Kurtosis9337.343057
Mean0.04151638357
Median Absolute Deviation (MAD)0
Skewness77.90736611
Sum21709
Variance0.1922385324
MonotonicityNot monotonic
2024-02-13T20:56:25.596089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 504941
96.6%
1 16521
 
3.2%
2 1134
 
0.2%
3 185
 
< 0.1%
4 33
 
< 0.1%
28 24
 
< 0.1%
32 15
 
< 0.1%
5 9
 
< 0.1%
29 7
 
< 0.1%
7 6
 
< 0.1%
Other values (9) 27
 
< 0.1%
ValueCountFrequency (%)
0 504941
96.6%
1 16521
 
3.2%
2 1134
 
0.2%
3 185
 
< 0.1%
4 33
 
< 0.1%
ValueCountFrequency (%)
72 5
 
< 0.1%
34 4
 
< 0.1%
32 15
< 0.1%
31 1
 
< 0.1%
30 6
 
< 0.1%

cntincpaycont9m_3716944L
Real number (ℝ)

MISSING  ZEROS 

Distinct101
Distinct (%)< 0.1%
Missing133113
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean6.449366196
Minimum0
Maximum575
Zeros108215
Zeros (%)20.7%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:25.737045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q310
95-th percentile19
Maximum575
Range575
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.586462182
Coefficient of variation (CV)1.021257281
Kurtosis211.7357796
Mean6.449366196
Median Absolute Deviation (MAD)5
Skewness4.286879228
Sum2513892
Variance43.38148408
MonotonicityNot monotonic
2024-02-13T20:56:25.914994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 108215
20.7%
9 31951
 
6.1%
8 24089
 
4.6%
6 21032
 
4.0%
3 20319
 
3.9%
7 19160
 
3.7%
4 19113
 
3.7%
5 18753
 
3.6%
10 16606
 
3.2%
2 15127
 
2.9%
Other values (91) 95424
18.2%
(Missing) 133113
25.5%
ValueCountFrequency (%)
0 108215
20.7%
1 13561
 
2.6%
2 15127
 
2.9%
3 20319
 
3.9%
4 19113
 
3.7%
ValueCountFrequency (%)
575 1
< 0.1%
392 1
< 0.1%
338 1
< 0.1%
293 1
< 0.1%
231 1
< 0.1%

cntpmts24_3658933L
Real number (ℝ)

MISSING  ZEROS 

Distinct26
Distinct (%)< 0.1%
Missing133089
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean11.27812567
Minimum0
Maximum25
Zeros52103
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:26.310095image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median11
Q318
95-th percentile24
Maximum25
Range25
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.098109367
Coefficient of variation (CV)0.7180368092
Kurtosis-1.223470188
Mean11.27812567
Median Absolute Deviation (MAD)7
Skewness0.1726862068
Sum4396360
Variance65.57937531
MonotonicityNot monotonic
2024-02-13T20:56:26.438256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 52103
 
10.0%
6 19206
 
3.7%
25 19190
 
3.7%
12 18926
 
3.6%
24 17967
 
3.4%
3 16690
 
3.2%
11 16545
 
3.2%
5 14570
 
2.8%
9 14291
 
2.7%
10 14043
 
2.7%
Other values (16) 186282
35.6%
(Missing) 133089
25.5%
ValueCountFrequency (%)
0 52103
10.0%
1 9288
 
1.8%
2 10681
 
2.0%
3 16690
 
3.2%
4 13529
 
2.6%
ValueCountFrequency (%)
25 19190
3.7%
24 17967
3.4%
23 12590
2.4%
22 11772
2.3%
21 11457
2.2%

commnoinclast6m_3546845L
Real number (ℝ)

CONSTANT  MISSING  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing83853
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros439049
Zeros (%)84.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:26.547224image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2024-02-13T20:56:26.644679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 439049
84.0%
(Missing) 83853
 
16.0%
ValueCountFrequency (%)
0 439049
84.0%
ValueCountFrequency (%)
0 439049
84.0%

credamount_770A
Real number (ℝ)

Distinct92052
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53669.21569
Minimum2000
Maximum950000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:26.773677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile10920
Q120000
median38802.201
Q370000
95-th percentile150000
Maximum950000
Range948000
Interquartile range (IQR)50000

Descriptive statistics

Standard deviation47532.50004
Coefficient of variation (CV)0.8856566922
Kurtosis7.993117895
Mean53669.21569
Median Absolute Deviation (MAD)21197.799
Skewness2.211822073
Sum2.806374022 × 1010
Variance2259338560
MonotonicityNot monotonic
2024-02-13T20:56:26.931613image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 21737
 
4.2%
40000 13392
 
2.6%
60000 12528
 
2.4%
20000 10856
 
2.1%
150000 9356
 
1.8%
30000 9233
 
1.8%
50000 6748
 
1.3%
80000 5765
 
1.1%
200000 5568
 
1.1%
70000 4346
 
0.8%
Other values (92042) 423373
81.0%
ValueCountFrequency (%)
2000 47
< 0.1%
2013 1
 
< 0.1%
2036 1
 
< 0.1%
2055 1
 
< 0.1%
2094 1
 
< 0.1%
ValueCountFrequency (%)
950000 1
 
< 0.1%
600000 4
< 0.1%
540000 1
 
< 0.1%
500000 2
< 0.1%
440000 2
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:27.061280image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1568706
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCAL
2nd rowCAL
3rd rowCAL
4th rowCAL
5th rowCAL
ValueCountFrequency (%)
col 326802
62.5%
cal 129667
 
24.8%
rel 66433
 
12.7%
2024-02-13T20:56:27.307208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 522902
33.3%
C 456469
29.1%
O 326802
20.8%
A 129667
 
8.3%
R 66433
 
4.2%
E 66433
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1568706
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 522902
33.3%
C 456469
29.1%
O 326802
20.8%
A 129667
 
8.3%
R 66433
 
4.2%
E 66433
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 1568706
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 522902
33.3%
C 456469
29.1%
O 326802
20.8%
A 129667
 
8.3%
R 66433
 
4.2%
E 66433
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1568706
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 522902
33.3%
C 456469
29.1%
O 326802
20.8%
A 129667
 
8.3%
R 66433
 
4.2%
E 66433
 
4.2%

currdebt_22A
Real number (ℝ)

ZEROS 

Distinct159953
Distinct (%)30.6%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean22559.74128
Minimum0
Maximum1029392.8
Zeros338451
Zeros (%)64.7%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:27.459182image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q316637.25075
95-th percentile132822.35
Maximum1029392.8
Range1029392.8
Interquartile range (IQR)16637.25075

Descriptive statistics

Standard deviation56094.57005
Coefficient of variation (CV)2.486489953
Kurtosis24.49756921
Mean22559.74128
Median Absolute Deviation (MAD)0
Skewness4.211972747
Sum1.179648872 × 1010
Variance3146600789
MonotonicityNot monotonic
2024-02-13T20:56:27.614183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 338451
64.7%
10 97
 
< 0.1%
9998 63
 
< 0.1%
11998 58
 
< 0.1%
17998 43
 
< 0.1%
5998 41
 
< 0.1%
7998 41
 
< 0.1%
15998 39
 
< 0.1%
3998 36
 
< 0.1%
14998 36
 
< 0.1%
Other values (159943) 183995
35.2%
ValueCountFrequency (%)
0 338451
64.7%
0.002 1
 
< 0.1%
0.004 1
 
< 0.1%
0.006 1
 
< 0.1%
0.008 1
 
< 0.1%
ValueCountFrequency (%)
1029392.8 1
< 0.1%
1022051.44 1
< 0.1%
987535 1
< 0.1%
984399 1
< 0.1%
980540.7 1
< 0.1%

currdebtcredtyperange_828A
Real number (ℝ)

ZEROS 

Distinct99219
Distinct (%)19.0%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean12277.84754
Minimum0
Maximum1029392.8
Zeros411006
Zeros (%)78.6%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:27.769084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile77646.256
Maximum1029392.8
Range1029392.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation41022.93622
Coefficient of variation (CV)3.34121564
Kurtosis44.61836291
Mean12277.84754
Median Absolute Deviation (MAD)0
Skewness5.665563542
Sum6420086478
Variance1682881296
MonotonicityNot monotonic
2024-02-13T20:56:27.937099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 411006
78.6%
9998 43
 
< 0.1%
11998 37
 
< 0.1%
7998 33
 
< 0.1%
5998 29
 
< 0.1%
17998 27
 
< 0.1%
2998 26
 
< 0.1%
19998 26
 
< 0.1%
8998 23
 
< 0.1%
14998 22
 
< 0.1%
Other values (99209) 111628
 
21.3%
ValueCountFrequency (%)
0 411006
78.6%
0.030000001 1
 
< 0.1%
0.092 1
 
< 0.1%
0.116 1
 
< 0.1%
0.168 1
 
< 0.1%
ValueCountFrequency (%)
1029392.8 1
< 0.1%
987535 1
< 0.1%
869874.7 1
< 0.1%
848340.3 1
< 0.1%
842234 1
< 0.1%

datefirstoffer_1144D
Text

MISSING 

Distinct1377
Distinct (%)0.6%
Missing273022
Missing (%)52.2%
Memory size4.0 MiB
2024-02-13T20:56:28.281007image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2498800
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)< 0.1%

Sample

1st row2014-12-25
2nd row2014-02-19
3rd row2019-07-16
4th row2007-07-15
5th row2008-04-03
ValueCountFrequency (%)
2008-04-03 50225
 
20.1%
2007-10-29 39908
 
16.0%
2014-02-19 6367
 
2.5%
2016-03-28 2677
 
1.1%
2017-07-25 2200
 
0.9%
2014-03-08 2161
 
0.9%
2017-07-21 2036
 
0.8%
2017-07-29 1990
 
0.8%
2017-03-31 1986
 
0.8%
2019-06-18 1921
 
0.8%
Other values (1367) 138409
55.4%
2024-02-13T20:56:28.741899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 679493
27.2%
- 499760
20.0%
2 388138
15.5%
1 330297
13.2%
7 110216
 
4.4%
8 104289
 
4.2%
4 98473
 
3.9%
9 95509
 
3.8%
3 92941
 
3.7%
6 54653
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1999040
80.0%
Dash Punctuation 499760
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 679493
34.0%
2 388138
19.4%
1 330297
16.5%
7 110216
 
5.5%
8 104289
 
5.2%
4 98473
 
4.9%
9 95509
 
4.8%
3 92941
 
4.6%
6 54653
 
2.7%
5 45031
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 499760
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2498800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 679493
27.2%
- 499760
20.0%
2 388138
15.5%
1 330297
13.2%
7 110216
 
4.4%
8 104289
 
4.2%
4 98473
 
3.9%
9 95509
 
3.8%
3 92941
 
3.7%
6 54653
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2498800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 679493
27.2%
- 499760
20.0%
2 388138
15.5%
1 330297
13.2%
7 110216
 
4.4%
8 104289
 
4.2%
4 98473
 
3.9%
9 95509
 
3.8%
3 92941
 
3.7%
6 54653
 
2.2%
Distinct5020
Distinct (%)8.1%
Missing460577
Missing (%)88.1%
Memory size4.0 MiB
2024-02-13T20:56:29.126630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters623250
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique316 ?
Unique (%)0.5%

Sample

1st row2017-09-06
2nd row2016-09-22
3rd row2019-03-16
4th row2015-02-04
5th row2019-03-10
ValueCountFrequency (%)
2020-06-15 355
 
0.6%
2020-06-11 288
 
0.5%
2020-06-27 232
 
0.4%
2020-06-25 231
 
0.4%
2020-06-29 229
 
0.4%
2020-06-28 222
 
0.4%
2020-06-24 207
 
0.3%
2020-05-31 193
 
0.3%
2020-06-02 188
 
0.3%
2020-06-06 184
 
0.3%
Other values (5010) 59996
96.3%
2024-02-13T20:56:29.617047image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 160681
25.8%
- 124650
20.0%
2 106045
17.0%
1 94024
15.1%
7 24224
 
3.9%
6 23054
 
3.7%
8 21544
 
3.5%
9 19523
 
3.1%
5 18899
 
3.0%
3 15760
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 498600
80.0%
Dash Punctuation 124650
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 160681
32.2%
2 106045
21.3%
1 94024
18.9%
7 24224
 
4.9%
6 23054
 
4.6%
8 21544
 
4.3%
9 19523
 
3.9%
5 18899
 
3.8%
3 15760
 
3.2%
4 14846
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 124650
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 623250
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 160681
25.8%
- 124650
20.0%
2 106045
17.0%
1 94024
15.1%
7 24224
 
3.9%
6 23054
 
3.7%
8 21544
 
3.5%
9 19523
 
3.1%
5 18899
 
3.0%
3 15760
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 623250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 160681
25.8%
- 124650
20.0%
2 106045
17.0%
1 94024
15.1%
7 24224
 
3.9%
6 23054
 
3.7%
8 21544
 
3.5%
9 19523
 
3.1%
5 18899
 
3.0%
3 15760
 
2.5%
Distinct5157
Distinct (%)2.1%
Missing280909
Missing (%)53.7%
Memory size4.0 MiB
2024-02-13T20:56:30.003074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2419930
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)< 0.1%

Sample

1st row2019-11-15
2nd row2019-11-28
3rd row2019-10-27
4th row2019-12-22
5th row2019-11-28
ValueCountFrequency (%)
2020-01-18 1078
 
0.4%
2019-12-15 860
 
0.4%
2019-12-08 817
 
0.3%
2020-01-15 737
 
0.3%
2019-09-15 686
 
0.3%
2020-02-11 666
 
0.3%
2020-01-19 642
 
0.3%
2020-01-28 633
 
0.3%
2020-03-15 606
 
0.3%
2019-08-11 597
 
0.2%
Other values (5147) 234671
97.0%
2024-02-13T20:56:30.534848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 611106
25.3%
- 483986
20.0%
2 448145
18.5%
1 378891
15.7%
9 116114
 
4.8%
8 91846
 
3.8%
7 74035
 
3.1%
5 64195
 
2.7%
6 55681
 
2.3%
3 52563
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1935944
80.0%
Dash Punctuation 483986
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 611106
31.6%
2 448145
23.1%
1 378891
19.6%
9 116114
 
6.0%
8 91846
 
4.7%
7 74035
 
3.8%
5 64195
 
3.3%
6 55681
 
2.9%
3 52563
 
2.7%
4 43368
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 483986
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2419930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 611106
25.3%
- 483986
20.0%
2 448145
18.5%
1 378891
15.7%
9 116114
 
4.8%
8 91846
 
3.8%
7 74035
 
3.1%
5 64195
 
2.7%
6 55681
 
2.3%
3 52563
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2419930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 611106
25.3%
- 483986
20.0%
2 448145
18.5%
1 378891
15.7%
9 116114
 
4.8%
8 91846
 
3.8%
7 74035
 
3.1%
5 64195
 
2.7%
6 55681
 
2.3%
3 52563
 
2.2%

daysoverduetolerancedd_3976961L
Real number (ℝ)

MISSING  ZEROS 

Distinct4322
Distinct (%)1.1%
Missing132270
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean111.6528165
Minimum0
Maximum5337
Zeros102160
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:30.697843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q316
95-th percentile505
Maximum5337
Range5337
Interquartile range (IQR)16

Descriptive statistics

Standard deviation518.430952
Coefficient of variation (CV)4.643241151
Kurtosis54.04790913
Mean111.6528165
Median Absolute Deviation (MAD)3
Skewness6.984491263
Sum43615163
Variance268770.652
MonotonicityNot monotonic
2024-02-13T20:56:30.856449image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 102160
19.5%
1 68208
13.0%
2 22511
 
4.3%
3 18580
 
3.6%
4 13877
 
2.7%
5 10810
 
2.1%
6 8682
 
1.7%
7 7731
 
1.5%
8 6629
 
1.3%
9 5795
 
1.1%
Other values (4312) 125649
24.0%
(Missing) 132270
25.3%
ValueCountFrequency (%)
0 102160
19.5%
1 68208
13.0%
2 22511
 
4.3%
3 18580
 
3.6%
4 13877
 
2.7%
ValueCountFrequency (%)
5337 1
< 0.1%
5322 1
< 0.1%
5301 1
< 0.1%
5280 1
< 0.1%
5277 1
< 0.1%

deferredmnthsnum_166L
Real number (ℝ)

CONSTANT  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros522902
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:30.978445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2024-02-13T20:56:31.083258image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 522902
100.0%
ValueCountFrequency (%)
0 522902
100.0%
ValueCountFrequency (%)
0 522902
100.0%

disbursedcredamount_1113A
Real number (ℝ)

ZEROS 

Distinct95505
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49721.60743
Minimum0
Maximum950000
Zeros13442
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:31.222220image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9000
Q119978
median34622.5
Q364000
95-th percentile143399.82
Maximum950000
Range950000
Interquartile range (IQR)44022

Descriptive statistics

Standard deviation45881.57726
Coefficient of variation (CV)0.9227693881
Kurtosis9.128326586
Mean49721.60743
Median Absolute Deviation (MAD)18679.8
Skewness2.356688291
Sum2.599952797 × 1010
Variance2105119132
MonotonicityNot monotonic
2024-02-13T20:56:31.376781image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 18922
 
3.6%
0 13442
 
2.6%
40000 12945
 
2.5%
60000 12110
 
2.3%
20000 10550
 
2.0%
30000 9028
 
1.7%
150000 7272
 
1.4%
50000 6554
 
1.3%
80000 5548
 
1.1%
200000 4679
 
0.9%
Other values (95495) 421852
80.7%
ValueCountFrequency (%)
0 13442
2.6%
1994.226 1
 
< 0.1%
2000 46
 
< 0.1%
2000.1439 1
 
< 0.1%
2000.1699 1
 
< 0.1%
ValueCountFrequency (%)
950000 1
 
< 0.1%
600000 4
< 0.1%
540000 1
 
< 0.1%
500000 2
< 0.1%
440000 2
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing248
Missing (%)< 0.1%
Memory size4.0 MiB
2024-02-13T20:56:31.506719image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.975205394
Min length2

Characters and Unicode

Total characters1555003
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGBA
2nd rowGBA
3rd rowGBA
4th rowGBA
5th rowGBA
ValueCountFrequency (%)
sba 380009
72.7%
gba 129686
 
24.8%
dd 12959
 
2.5%
2024-02-13T20:56:31.766809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 509695
32.8%
A 509695
32.8%
S 380009
24.4%
G 129686
 
8.3%
D 25918
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1555003
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 509695
32.8%
A 509695
32.8%
S 380009
24.4%
G 129686
 
8.3%
D 25918
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 1555003
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 509695
32.8%
A 509695
32.8%
S 380009
24.4%
G 129686
 
8.3%
D 25918
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1555003
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 509695
32.8%
A 509695
32.8%
S 380009
24.4%
G 129686
 
8.3%
D 25918
 
1.7%

downpmt_116A
Real number (ℝ)

ZEROS 

Distinct4867
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean597.2261464
Minimum0
Maximum380000
Zeros493230
Zeros (%)94.3%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:31.912827image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile990.980002
Maximum380000
Range380000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4335.031537
Coefficient of variation (CV)7.258609764
Kurtosis561.5661753
Mean597.2261464
Median Absolute Deviation (MAD)0
Skewness16.56191971
Sum312290746.4
Variance18792498.43
MonotonicityNot monotonic
2024-02-13T20:56:32.065790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 493230
94.3%
2000 2856
 
0.5%
4000 2214
 
0.4%
20000 2205
 
0.4%
10000 1956
 
0.4%
6000 1367
 
0.3%
1000 1357
 
0.3%
8000 857
 
0.2%
200 853
 
0.2%
400 760
 
0.1%
Other values (4857) 15247
 
2.9%
ValueCountFrequency (%)
0 493230
94.3%
0.2 33
 
< 0.1%
0.4 9
 
< 0.1%
0.6 2
 
< 0.1%
0.8 1
 
< 0.1%
ValueCountFrequency (%)
380000 1
 
< 0.1%
372000 1
 
< 0.1%
300000 1
 
< 0.1%
246998 1
 
< 0.1%
200000 5
< 0.1%
Distinct4387
Distinct (%)1.4%
Missing218215
Missing (%)41.7%
Memory size4.0 MiB
2024-02-13T20:56:32.435266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3046870
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)0.1%

Sample

1st row2019-12-28
2nd row2019-12-27
3rd row2019-12-28
4th row2019-12-26
5th row2019-12-05
ValueCountFrequency (%)
2019-09-16 4647
 
1.5%
2020-01-20 1959
 
0.6%
2019-12-27 1793
 
0.6%
2019-12-25 1761
 
0.6%
2020-01-01 1704
 
0.6%
2019-12-26 1654
 
0.5%
2020-01-24 1652
 
0.5%
2019-12-23 1611
 
0.5%
2019-12-24 1609
 
0.5%
2020-01-22 1551
 
0.5%
Other values (4377) 284746
93.5%
2024-02-13T20:56:32.884203image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 830499
27.3%
2 673921
22.1%
- 609374
20.0%
1 391732
12.9%
9 145764
 
4.8%
8 84263
 
2.8%
3 73758
 
2.4%
7 68017
 
2.2%
6 63802
 
2.1%
5 56491
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2437496
80.0%
Dash Punctuation 609374
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 830499
34.1%
2 673921
27.6%
1 391732
16.1%
9 145764
 
6.0%
8 84263
 
3.5%
3 73758
 
3.0%
7 68017
 
2.8%
6 63802
 
2.6%
5 56491
 
2.3%
4 49249
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 609374
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3046870
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 830499
27.3%
2 673921
22.1%
- 609374
20.0%
1 391732
12.9%
9 145764
 
4.8%
8 84263
 
2.8%
3 73758
 
2.4%
7 68017
 
2.2%
6 63802
 
2.1%
5 56491
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3046870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 830499
27.3%
2 673921
22.1%
- 609374
20.0%
1 391732
12.9%
9 145764
 
4.8%
8 84263
 
2.8%
3 73758
 
2.4%
7 68017
 
2.2%
6 63802
 
2.1%
5 56491
 
1.9%

eir_270L
Real number (ℝ)

MISSING  ZEROS 

Distinct101
Distinct (%)< 0.1%
Missing65320
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean0.2829478026
Minimum0
Maximum0.45
Zeros139593
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:33.052527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.4005
Q30.433
95-th percentile0.45
Maximum0.45
Range0.45
Interquartile range (IQR)0.433

Descriptive statistics

Standard deviation0.1935718332
Coefficient of variation (CV)0.6841255928
Kurtosis-1.396943227
Mean0.2829478026
Median Absolute Deviation (MAD)0.0495
Skewness-0.6939676767
Sum129471.8214
Variance0.0374700546
MonotonicityNot monotonic
2024-02-13T20:56:33.205236image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 139593
26.7%
0.45 109315
20.9%
0.42 48119
 
9.2%
0.39 32434
 
6.2%
0.4005 24865
 
4.8%
0.4175 20895
 
4.0%
0.433 10833
 
2.1%
0.4 7485
 
1.4%
0.4097 6679
 
1.3%
0.36 5768
 
1.1%
Other values (91) 51596
 
9.9%
(Missing) 65320
12.5%
ValueCountFrequency (%)
0 139593
26.7%
0.0012 269
 
0.1%
0.05 99
 
< 0.1%
0.1 36
 
< 0.1%
0.15 386
 
0.1%
ValueCountFrequency (%)
0.45 109315
20.9%
0.4472 50
 
< 0.1%
0.446 8
 
< 0.1%
0.4458 12
 
< 0.1%
0.4449 1
 
< 0.1%

equalitydataagreement_891L
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing497187
Missing (%)95.1%
Memory size4.0 MiB
True
 
23824
False
 
1891
(Missing)
497187 
ValueCountFrequency (%)
True 23824
 
4.6%
False 1891
 
0.4%
(Missing) 497187
95.1%
2024-02-13T20:56:33.328177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

equalityempfrom_62L
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing513495
Missing (%)98.2%
Memory size4.0 MiB
True
 
9176
False
 
231
(Missing)
513495 
ValueCountFrequency (%)
True 9176
 
1.8%
False 231
 
< 0.1%
(Missing) 513495
98.2%
2024-02-13T20:56:33.416176image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Distinct1236
Distinct (%)0.5%
Missing287559
Missing (%)55.0%
Memory size4.0 MiB
2024-02-13T20:56:33.733826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2353430
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row2016-02-01
2nd row2019-08-05
3rd row2019-11-30
4th row2016-01-31
5th row2016-07-05
ValueCountFrequency (%)
2016-01-31 79669
33.9%
2017-07-24 9917
 
4.2%
2016-03-27 5609
 
2.4%
2016-02-01 5551
 
2.4%
2017-04-18 3287
 
1.4%
2017-07-20 2842
 
1.2%
2016-03-23 2623
 
1.1%
2016-11-27 2575
 
1.1%
2017-09-06 2552
 
1.1%
2017-03-16 2468
 
1.0%
Other values (1226) 118250
50.2%
2024-02-13T20:56:34.579771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 526949
22.4%
1 488294
20.7%
- 470686
20.0%
2 342207
14.5%
6 163472
 
6.9%
3 130731
 
5.6%
7 95200
 
4.0%
9 43561
 
1.9%
8 40298
 
1.7%
4 31991
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1882744
80.0%
Dash Punctuation 470686
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 526949
28.0%
1 488294
25.9%
2 342207
18.2%
6 163472
 
8.7%
3 130731
 
6.9%
7 95200
 
5.1%
9 43561
 
2.3%
8 40298
 
2.1%
4 31991
 
1.7%
5 20041
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 470686
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2353430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 526949
22.4%
1 488294
20.7%
- 470686
20.0%
2 342207
14.5%
6 163472
 
6.9%
3 130731
 
5.6%
7 95200
 
4.0%
9 43561
 
1.9%
8 40298
 
1.7%
4 31991
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2353430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 526949
22.4%
1 488294
20.7%
- 470686
20.0%
2 342207
14.5%
6 163472
 
6.9%
3 130731
 
5.6%
7 95200
 
4.0%
9 43561
 
1.9%
8 40298
 
1.7%
4 31991
 
1.4%

firstdatedue_489D
Text

MISSING 

Distinct5116
Distinct (%)1.3%
Missing142416
Missing (%)27.2%
Memory size4.0 MiB
2024-02-13T20:56:34.953557image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3804860
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)< 0.1%

Sample

1st row2019-10-15
2nd row2019-12-31
3rd row2019-11-27
4th row2019-11-16
5th row2019-12-30
ValueCountFrequency (%)
2018-02-15 777
 
0.2%
2019-02-15 555
 
0.1%
2018-02-11 550
 
0.1%
2020-01-15 493
 
0.1%
2019-03-14 483
 
0.1%
2019-10-15 479
 
0.1%
2019-02-11 475
 
0.1%
2018-03-14 466
 
0.1%
2018-06-15 457
 
0.1%
2019-09-15 453
 
0.1%
Other values (5106) 375298
98.6%
2024-02-13T20:56:35.496011image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 926480
24.3%
- 760972
20.0%
1 671573
17.7%
2 651866
17.1%
8 140589
 
3.7%
9 128055
 
3.4%
7 122778
 
3.2%
5 108011
 
2.8%
3 106820
 
2.8%
6 105497
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3043888
80.0%
Dash Punctuation 760972
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 926480
30.4%
1 671573
22.1%
2 651866
21.4%
8 140589
 
4.6%
9 128055
 
4.2%
7 122778
 
4.0%
5 108011
 
3.5%
3 106820
 
3.5%
6 105497
 
3.5%
4 82219
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 760972
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3804860
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 926480
24.3%
- 760972
20.0%
1 671573
17.7%
2 651866
17.1%
8 140589
 
3.7%
9 128055
 
3.4%
7 122778
 
3.2%
5 108011
 
2.8%
3 106820
 
2.8%
6 105497
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3804860
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 926480
24.3%
- 760972
20.0%
1 671573
17.7%
2 651866
17.1%
8 140589
 
3.7%
9 128055
 
3.4%
7 122778
 
3.2%
5 108011
 
2.8%
3 106820
 
2.8%
6 105497
 
2.8%

homephncnt_628L
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6655415355
Minimum0
Maximum14
Zeros278639
Zeros (%)53.3%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:35.635101image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8709272728
Coefficient of variation (CV)1.308599428
Kurtosis3.554372703
Mean0.6655415355
Median Absolute Deviation (MAD)0
Skewness1.56133203
Sum348013
Variance0.7585143146
MonotonicityNot monotonic
2024-02-13T20:56:35.751308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 278639
53.3%
1 167683
32.1%
2 56142
 
10.7%
3 15399
 
2.9%
4 3812
 
0.7%
5 914
 
0.2%
6 223
 
< 0.1%
7 62
 
< 0.1%
8 16
 
< 0.1%
9 5
 
< 0.1%
Other values (4) 7
 
< 0.1%
ValueCountFrequency (%)
0 278639
53.3%
1 167683
32.1%
2 56142
 
10.7%
3 15399
 
2.9%
4 3812
 
0.7%
ValueCountFrequency (%)
14 1
 
< 0.1%
13 2
 
< 0.1%
12 3
< 0.1%
10 1
 
< 0.1%
9 5
< 0.1%

inittransactionamount_650A
Real number (ℝ)

MISSING  ZEROS 

Distinct22748
Distinct (%)34.2%
Missing456469
Missing (%)87.3%
Infinite0
Infinite (%)0.0%
Mean39121.68435
Minimum0
Maximum200000
Zeros13220
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:35.894571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112998
median29978
Q355998
95-th percentile113995
Maximum200000
Range200000
Interquartile range (IQR)43000

Descriptive statistics

Standard deviation37894.09253
Coefficient of variation (CV)0.9686211922
Kurtosis2.948726117
Mean39121.68435
Median Absolute Deviation (MAD)21000
Skewness1.541888211
Sum2598970856
Variance1435962249
MonotonicityNot monotonic
2024-02-13T20:56:36.060277image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13220
 
2.5%
17998 617
 
0.1%
21998 510
 
0.1%
19998 451
 
0.1%
25998 422
 
0.1%
23998 399
 
0.1%
29998 340
 
0.1%
35998 336
 
0.1%
11998 316
 
0.1%
15998 308
 
0.1%
Other values (22738) 49514
 
9.5%
(Missing) 456469
87.3%
ValueCountFrequency (%)
0 13220
2.5%
2000 1
 
< 0.1%
2060 1
 
< 0.1%
2098 2
 
< 0.1%
2100 1
 
< 0.1%
ValueCountFrequency (%)
200000 86
< 0.1%
199998 57
< 0.1%
199996.4 1
 
< 0.1%
199996 28
 
< 0.1%
199995.61 1
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:36.180751image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.24797572
Min length3

Characters and Unicode

Total characters1698373
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCASH
2nd rowCASH
3rd rowCASH
4th rowCASH
5th rowCASH
ValueCountFrequency (%)
pos 380015
72.7%
cash 129667
 
24.8%
ndf 13220
 
2.5%
2024-02-13T20:56:36.411448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 509682
30.0%
P 380015
22.4%
O 380015
22.4%
C 129667
 
7.6%
A 129667
 
7.6%
H 129667
 
7.6%
N 13220
 
0.8%
D 13220
 
0.8%
F 13220
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1698373
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 509682
30.0%
P 380015
22.4%
O 380015
22.4%
C 129667
 
7.6%
A 129667
 
7.6%
H 129667
 
7.6%
N 13220
 
0.8%
D 13220
 
0.8%
F 13220
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 1698373
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 509682
30.0%
P 380015
22.4%
O 380015
22.4%
C 129667
 
7.6%
A 129667
 
7.6%
H 129667
 
7.6%
N 13220
 
0.8%
D 13220
 
0.8%
F 13220
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1698373
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 509682
30.0%
P 380015
22.4%
O 380015
22.4%
C 129667
 
7.6%
A 129667
 
7.6%
H 129667
 
7.6%
N 13220
 
0.8%
D 13220
 
0.8%
F 13220
 
0.8%

interestrate_311L
Real number (ℝ)

MISSING  ZEROS 

Distinct101
Distinct (%)< 0.1%
Missing65320
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean0.2829478026
Minimum0
Maximum0.45
Zeros139593
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:36.586154image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.4005
Q30.433
95-th percentile0.45
Maximum0.45
Range0.45
Interquartile range (IQR)0.433

Descriptive statistics

Standard deviation0.1935718332
Coefficient of variation (CV)0.6841255928
Kurtosis-1.396943227
Mean0.2829478026
Median Absolute Deviation (MAD)0.0495
Skewness-0.6939676767
Sum129471.8214
Variance0.0374700546
MonotonicityNot monotonic
2024-02-13T20:56:36.743559image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 139593
26.7%
0.45 109315
20.9%
0.42 48119
 
9.2%
0.39 32434
 
6.2%
0.4005 24865
 
4.8%
0.4175 20895
 
4.0%
0.433 10833
 
2.1%
0.4 7485
 
1.4%
0.4097 6679
 
1.3%
0.36 5768
 
1.1%
Other values (91) 51596
 
9.9%
(Missing) 65320
12.5%
ValueCountFrequency (%)
0 139593
26.7%
0.0012 269
 
0.1%
0.05 99
 
< 0.1%
0.1 36
 
< 0.1%
0.15 386
 
0.1%
ValueCountFrequency (%)
0.45 109315
20.9%
0.4472 50
 
< 0.1%
0.446 8
 
< 0.1%
0.4458 12
 
< 0.1%
0.4449 1
 
< 0.1%

interestrategrace_34L
Real number (ℝ)

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing518602
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros4300
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:36.863117image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2024-02-13T20:56:36.959976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 4300
 
0.8%
(Missing) 518602
99.2%
ValueCountFrequency (%)
0 4300
0.8%
ValueCountFrequency (%)
0 4300
0.8%

isbidproduct_1095L
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size510.8 KiB
False
465716 
True
57186 
ValueCountFrequency (%)
False 465716
89.1%
True 57186
 
10.9%
2024-02-13T20:56:37.060947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

isbidproductrequest_292L
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing518920
Missing (%)99.2%
Memory size4.0 MiB
False
 
3982
(Missing)
518920 
ValueCountFrequency (%)
False 3982
 
0.8%
(Missing) 518920
99.2%
2024-02-13T20:56:37.153979image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

isdebitcard_729L
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing456469
Missing (%)87.3%
Memory size4.0 MiB
False
66433 
(Missing)
456469 
ValueCountFrequency (%)
False 66433
 
12.7%
(Missing) 456469
87.3%
2024-02-13T20:56:37.243111image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Distinct4082
Distinct (%)1.0%
Missing132745
Missing (%)25.4%
Memory size4.0 MiB
2024-02-13T20:56:37.628912image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3901570
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique160 ?
Unique (%)< 0.1%

Sample

1st row2019-11-06
2nd row2019-10-21
3rd row2019-12-06
4th row2019-11-05
5th row2019-11-01
ValueCountFrequency (%)
2020-01-08 1461
 
0.4%
2020-01-03 1418
 
0.4%
2020-01-02 1381
 
0.4%
2019-10-23 1374
 
0.4%
2019-12-11 1337
 
0.3%
2019-10-29 1321
 
0.3%
2020-01-09 1305
 
0.3%
2019-10-28 1295
 
0.3%
2020-01-06 1292
 
0.3%
2019-11-01 1280
 
0.3%
Other values (4072) 376693
96.5%
2024-02-13T20:56:38.173908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 943074
24.2%
- 780314
20.0%
2 699245
17.9%
1 683053
17.5%
9 249762
 
6.4%
8 142837
 
3.7%
7 96705
 
2.5%
3 92879
 
2.4%
6 79743
 
2.0%
4 67995
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3121256
80.0%
Dash Punctuation 780314
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 943074
30.2%
2 699245
22.4%
1 683053
21.9%
9 249762
 
8.0%
8 142837
 
4.6%
7 96705
 
3.1%
3 92879
 
3.0%
6 79743
 
2.6%
4 67995
 
2.2%
5 65963
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 780314
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3901570
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 943074
24.2%
- 780314
20.0%
2 699245
17.9%
1 683053
17.5%
9 249762
 
6.4%
8 142837
 
3.7%
7 96705
 
2.5%
3 92879
 
2.4%
6 79743
 
2.0%
4 67995
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3901570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 943074
24.2%
- 780314
20.0%
2 699245
17.9%
1 683053
17.5%
9 249762
 
6.4%
8 142837
 
3.7%
7 96705
 
2.5%
3 92879
 
2.4%
6 79743
 
2.0%
4 67995
 
1.7%
Distinct5207
Distinct (%)1.2%
Missing84377
Missing (%)16.1%
Memory size4.0 MiB
2024-02-13T20:56:38.590947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters4385250
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique312 ?
Unique (%)0.1%

Sample

1st row2018-11-20
2nd row2019-12-26
3rd row2014-07-17
4th row2017-08-21
5th row2018-01-26
ValueCountFrequency (%)
2019-12-13 1472
 
0.3%
2020-01-13 1437
 
0.3%
2020-01-11 1415
 
0.3%
2019-12-14 1403
 
0.3%
2020-01-10 1392
 
0.3%
2019-12-27 1375
 
0.3%
2020-01-01 1313
 
0.3%
2020-01-12 1308
 
0.3%
2019-12-28 1219
 
0.3%
2020-01-03 1208
 
0.3%
Other values (5197) 424983
96.9%
2024-02-13T20:56:39.161008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1099650
25.1%
- 877050
20.0%
2 837411
19.1%
1 708978
16.2%
9 279716
 
6.4%
8 140327
 
3.2%
3 106586
 
2.4%
7 101389
 
2.3%
6 86971
 
2.0%
4 74844
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3508200
80.0%
Dash Punctuation 877050
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1099650
31.3%
2 837411
23.9%
1 708978
20.2%
9 279716
 
8.0%
8 140327
 
4.0%
3 106586
 
3.0%
7 101389
 
2.9%
6 86971
 
2.5%
4 74844
 
2.1%
5 72328
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 877050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4385250
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1099650
25.1%
- 877050
20.0%
2 837411
19.1%
1 708978
16.2%
9 279716
 
6.4%
8 140327
 
3.2%
3 106586
 
2.4%
7 101389
 
2.3%
6 86971
 
2.0%
4 74844
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4385250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1099650
25.1%
- 877050
20.0%
2 837411
19.1%
1 708978
16.2%
9 279716
 
6.4%
8 140327
 
3.2%
3 106586
 
2.4%
7 101389
 
2.3%
6 86971
 
2.0%
4 74844
 
1.7%
Distinct42
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:39.369391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.815864158
Min length8

Characters and Unicode

Total characters4609833
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowa55475b1
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 334792
64.0%
p159_130_59 72133
 
13.8%
p12_6_178 43656
 
8.3%
p148_110_5 17001
 
3.3%
p109_133_183 16376
 
3.1%
p53_45_92 8680
 
1.7%
p52_56_90 6266
 
1.2%
p33_29_177 6199
 
1.2%
p100_96_175 4539
 
0.9%
p21_79_33 4235
 
0.8%
Other values (32) 9025
 
1.7%
2024-02-13T20:56:39.700552image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 1201438
26.1%
1 702534
15.2%
7 406421
 
8.8%
_ 376220
 
8.2%
4 368741
 
8.0%
a 334792
 
7.3%
b 334792
 
7.3%
9 197135
 
4.3%
P 188110
 
4.1%
3 156400
 
3.4%
Other values (4) 343250
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3375919
73.2%
Lowercase Letter 669584
 
14.5%
Connector Punctuation 376220
 
8.2%
Uppercase Letter 188110
 
4.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1201438
35.6%
1 702534
20.8%
7 406421
 
12.0%
4 368741
 
10.9%
9 197135
 
5.8%
3 156400
 
4.6%
0 123367
 
3.7%
8 85272
 
2.5%
2 74907
 
2.2%
6 59704
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
a 334792
50.0%
b 334792
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 376220
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 188110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3752139
81.4%
Latin 857694
 
18.6%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1201438
32.0%
1 702534
18.7%
7 406421
 
10.8%
_ 376220
 
10.0%
4 368741
 
9.8%
9 197135
 
5.3%
3 156400
 
4.2%
0 123367
 
3.3%
8 85272
 
2.3%
2 74907
 
2.0%
Latin
ValueCountFrequency (%)
a 334792
39.0%
b 334792
39.0%
P 188110
21.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4609833
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 1201438
26.1%
1 702534
15.2%
7 406421
 
8.8%
_ 376220
 
8.2%
4 368741
 
8.0%
a 334792
 
7.3%
b 334792
 
7.3%
9 197135
 
4.3%
P 188110
 
4.1%
3 156400
 
3.4%
Other values (4) 343250
 
7.4%
Distinct214
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:40.095183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.098022192
Min length8

Characters and Unicode

Total characters4234472
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)< 0.1%

Sample

1st rowa55475b1
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 499900
95.6%
p75_90_70 7154
 
1.4%
p111_89_135 2081
 
0.4%
p142_50_170 1366
 
0.3%
p174_113_42 1025
 
0.2%
p172_87_177 1017
 
0.2%
p79_131_167 945
 
0.2%
p42_60_161 647
 
0.1%
p200_75_140 641
 
0.1%
p95_36_171 640
 
0.1%
Other values (204) 7486
 
1.4%
2024-02-13T20:56:40.657220image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 1517765
35.8%
1 539921
 
12.8%
7 527455
 
12.5%
4 508834
 
12.0%
a 499900
 
11.8%
b 499900
 
11.8%
_ 46004
 
1.1%
P 23002
 
0.5%
0 22485
 
0.5%
9 14610
 
0.3%
Other values (4) 34596
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3165666
74.8%
Lowercase Letter 999800
 
23.6%
Connector Punctuation 46004
 
1.1%
Uppercase Letter 23002
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1517765
47.9%
1 539921
 
17.1%
7 527455
 
16.7%
4 508834
 
16.1%
0 22485
 
0.7%
9 14610
 
0.5%
2 9929
 
0.3%
8 8374
 
0.3%
6 8211
 
0.3%
3 8082
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
a 499900
50.0%
b 499900
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 46004
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 23002
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3211670
75.8%
Latin 1022802
 
24.2%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1517765
47.3%
1 539921
 
16.8%
7 527455
 
16.4%
4 508834
 
15.8%
_ 46004
 
1.4%
0 22485
 
0.7%
9 14610
 
0.5%
2 9929
 
0.3%
8 8374
 
0.3%
6 8211
 
0.3%
Latin
ValueCountFrequency (%)
a 499900
48.9%
b 499900
48.9%
P 23002
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4234472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 1517765
35.8%
1 539921
 
12.8%
7 527455
 
12.5%
4 508834
 
12.0%
a 499900
 
11.8%
b 499900
 
11.8%
_ 46004
 
1.1%
P 23002
 
0.5%
0 22485
 
0.5%
9 14610
 
0.3%
Other values (4) 34596
 
0.8%

lastapprcredamount_781A
Real number (ℝ)

MISSING  ZEROS 

Distinct68115
Distinct (%)17.3%
Missing128918
Missing (%)24.7%
Infinite0
Infinite (%)0.0%
Mean36879.259
Minimum0
Maximum400000
Zeros38116
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:40.841556image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111996
median24000
Q348396
95-th percentile110923.98
Maximum400000
Range400000
Interquartile range (IQR)36400

Descriptive statistics

Standard deviation38957.17242
Coefficient of variation (CV)1.056343687
Kurtosis7.749196151
Mean36879.259
Median Absolute Deviation (MAD)15414.701
Skewness2.267406445
Sum1.452983798 × 1010
Variance1517661283
MonotonicityNot monotonic
2024-02-13T20:56:41.010770image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38116
 
7.3%
100000 13332
 
2.5%
40000 8531
 
1.6%
20000 8141
 
1.6%
60000 7474
 
1.4%
30000 6656
 
1.3%
150000 4425
 
0.8%
50000 3622
 
0.7%
24000 2698
 
0.5%
10000 2655
 
0.5%
Other values (68105) 298334
57.1%
(Missing) 128918
24.7%
ValueCountFrequency (%)
0 38116
7.3%
0.2 50
 
< 0.1%
2000 105
 
< 0.1%
2001.6 1
 
< 0.1%
2002 1
 
< 0.1%
ValueCountFrequency (%)
400000 106
< 0.1%
391400 1
 
< 0.1%
390000 1
 
< 0.1%
382000 1
 
< 0.1%
380000 1
 
< 0.1%

lastapprdate_640D
Text

MISSING 

Distinct5300
Distinct (%)1.3%
Missing128918
Missing (%)24.7%
Memory size4.0 MiB
2024-02-13T20:56:41.417380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3939840
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique231 ?
Unique (%)0.1%

Sample

1st row2019-10-28
2nd row2019-10-09
3rd row2019-12-01
4th row2019-10-27
5th row2018-11-24
ValueCountFrequency (%)
2019-12-14 1336
 
0.3%
2019-12-13 1286
 
0.3%
2019-12-28 1057
 
0.3%
2019-12-01 1056
 
0.3%
2020-01-11 1049
 
0.3%
2019-12-15 1048
 
0.3%
2019-11-30 1047
 
0.3%
2019-12-27 1018
 
0.3%
2020-01-13 1016
 
0.3%
2020-01-12 972
 
0.2%
Other values (5290) 383099
97.2%
2024-02-13T20:56:41.940230image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 943783
24.0%
- 787968
20.0%
2 711942
18.1%
1 682859
17.3%
9 262870
 
6.7%
8 141320
 
3.6%
7 97549
 
2.5%
3 96750
 
2.5%
6 79977
 
2.0%
4 68282
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3151872
80.0%
Dash Punctuation 787968
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 943783
29.9%
2 711942
22.6%
1 682859
21.7%
9 262870
 
8.3%
8 141320
 
4.5%
7 97549
 
3.1%
3 96750
 
3.1%
6 79977
 
2.5%
4 68282
 
2.2%
5 66540
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 787968
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3939840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 943783
24.0%
- 787968
20.0%
2 711942
18.1%
1 682859
17.3%
9 262870
 
6.7%
8 141320
 
3.6%
7 97549
 
2.5%
3 96750
 
2.5%
6 79977
 
2.0%
4 68282
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3939840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 943783
24.0%
- 787968
20.0%
2 711942
18.1%
1 682859
17.3%
9 262870
 
6.7%
8 141320
 
3.6%
7 97549
 
2.5%
3 96750
 
2.5%
6 79977
 
2.0%
4 68282
 
1.7%
Distinct67
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:42.153308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.893209435
Min length8

Characters and Unicode

Total characters4650277
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowa55475b1
2nd rowP94_109_143
3rd rowP94_109_143
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 352222
67.4%
p94_109_143 112017
 
21.4%
p85_114_140 8316
 
1.6%
p30_86_84 8001
 
1.5%
p198_89_166 6655
 
1.3%
p180_60_137 5405
 
1.0%
p24_27_36 4808
 
0.9%
p73_130_169 3649
 
0.7%
p52_67_90 3618
 
0.7%
p19_105_83 2611
 
0.5%
Other values (57) 15600
 
3.0%
2024-02-13T20:56:42.769035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 1078003
23.2%
1 675313
14.5%
4 610476
13.1%
7 375749
 
8.1%
a 352222
 
7.6%
b 352222
 
7.6%
_ 341360
 
7.3%
9 255392
 
5.5%
P 170680
 
3.7%
0 157606
 
3.4%
Other values (4) 281254
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3433793
73.8%
Lowercase Letter 704444
 
15.1%
Connector Punctuation 341360
 
7.3%
Uppercase Letter 170680
 
3.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1078003
31.4%
1 675313
19.7%
4 610476
17.8%
7 375749
 
10.9%
9 255392
 
7.4%
0 157606
 
4.6%
3 148124
 
4.3%
6 56973
 
1.7%
8 49950
 
1.5%
2 26207
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
a 352222
50.0%
b 352222
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 341360
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 170680
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3775153
81.2%
Latin 875124
 
18.8%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1078003
28.6%
1 675313
17.9%
4 610476
16.2%
7 375749
 
10.0%
_ 341360
 
9.0%
9 255392
 
6.8%
0 157606
 
4.2%
3 148124
 
3.9%
6 56973
 
1.5%
8 49950
 
1.3%
Latin
ValueCountFrequency (%)
a 352222
40.2%
b 352222
40.2%
P 170680
19.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4650277
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 1078003
23.2%
1 675313
14.5%
4 610476
13.1%
7 375749
 
8.1%
a 352222
 
7.6%
b 352222
 
7.6%
_ 341360
 
7.3%
9 255392
 
5.5%
P 170680
 
3.7%
0 157606
 
3.4%
Other values (4) 281254
 
6.0%

lastdelinqdate_224D
Text

MISSING 

Distinct3959
Distinct (%)1.9%
Missing309295
Missing (%)59.1%
Memory size4.0 MiB
2024-02-13T20:56:43.194507image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2136070
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique581 ?
Unique (%)0.3%

Sample

1st row2019-11-15
2nd row2019-10-18
3rd row2019-11-28
4th row2019-10-27
5th row2019-11-11
ValueCountFrequency (%)
2020-01-18 1052
 
0.5%
2019-12-15 864
 
0.4%
2019-12-08 773
 
0.4%
2020-01-15 727
 
0.3%
2019-09-15 700
 
0.3%
2020-02-11 666
 
0.3%
2020-01-19 658
 
0.3%
2020-01-28 641
 
0.3%
2020-03-15 596
 
0.3%
2019-08-11 596
 
0.3%
Other values (3949) 206334
96.6%
2024-02-13T20:56:43.744141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 532592
24.9%
- 427214
20.0%
2 402092
18.8%
1 337193
15.8%
9 109425
 
5.1%
8 79637
 
3.7%
7 62616
 
2.9%
5 58527
 
2.7%
6 48741
 
2.3%
3 41828
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1708856
80.0%
Dash Punctuation 427214
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 532592
31.2%
2 402092
23.5%
1 337193
19.7%
9 109425
 
6.4%
8 79637
 
4.7%
7 62616
 
3.7%
5 58527
 
3.4%
6 48741
 
2.9%
3 41828
 
2.4%
4 36205
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 427214
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2136070
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 532592
24.9%
- 427214
20.0%
2 402092
18.8%
1 337193
15.8%
9 109425
 
5.1%
8 79637
 
3.7%
7 62616
 
2.9%
5 58527
 
2.7%
6 48741
 
2.3%
3 41828
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2136070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 532592
24.9%
- 427214
20.0%
2 402092
18.8%
1 337193
15.8%
9 109425
 
5.1%
8 79637
 
3.7%
7 62616
 
2.9%
5 58527
 
2.7%
6 48741
 
2.3%
3 41828
 
2.0%

lastdependentsnum_448L
Real number (ℝ)

MISSING 

Distinct9
Distinct (%)0.2%
Missing518443
Missing (%)99.1%
Infinite0
Infinite (%)0.0%
Mean0.5913882036
Minimum0
Maximum8
Zeros3050
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:43.884140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.034781974
Coefficient of variation (CV)1.749750786
Kurtosis3.921205276
Mean0.5913882036
Median Absolute Deviation (MAD)0
Skewness1.943510438
Sum2637
Variance1.070773734
MonotonicityNot monotonic
2024-02-13T20:56:44.009145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 3050
 
0.6%
1 616
 
0.1%
2 490
 
0.1%
3 211
 
< 0.1%
4 60
 
< 0.1%
5 28
 
< 0.1%
7 2
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
(Missing) 518443
99.1%
ValueCountFrequency (%)
0 3050
0.6%
1 616
 
0.1%
2 490
 
0.1%
3 211
 
< 0.1%
4 60
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 2
 
< 0.1%
6 1
 
< 0.1%
5 28
< 0.1%
4 60
< 0.1%

lastotherinc_902A
Real number (ℝ)

MISSING 

Distinct19
Distinct (%)2.3%
Missing522074
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean487.8455314
Minimum0
Maximum25228.3
Zeros91
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:44.137142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2
median0.2
Q30.2
95-th percentile0.2
Maximum25228.3
Range25228.3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2778.497697
Coefficient of variation (CV)5.695445624
Kurtosis53.39166726
Mean487.8455314
Median Absolute Deviation (MAD)0
Skewness7.063635714
Sum403936.1
Variance7720049.453
MonotonicityNot monotonic
2024-02-13T20:56:44.262648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.2 697
 
0.1%
0 91
 
< 0.1%
3000 7
 
< 0.1%
25228.3 5
 
< 0.1%
5000 4
 
< 0.1%
6000 4
 
< 0.1%
4000 3
 
< 0.1%
10000 3
 
< 0.1%
20000 2
 
< 0.1%
16000 2
 
< 0.1%
Other values (9) 10
 
< 0.1%
(Missing) 522074
99.8%
ValueCountFrequency (%)
0 91
 
< 0.1%
0.2 697
0.1%
2000 1
 
< 0.1%
2400 2
 
< 0.1%
3000 7
 
< 0.1%
ValueCountFrequency (%)
25228.3 5
< 0.1%
24000 1
 
< 0.1%
20000 2
 
< 0.1%
18000 1
 
< 0.1%
16000 2
 
< 0.1%

lastotherlnsexpense_631A
Real number (ℝ)

MISSING 

Distinct125
Distinct (%)14.1%
Missing522015
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean10475.93935
Minimum0
Maximum110000
Zeros542
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:44.401955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q314000
95-th percentile53000
Maximum110000
Range110000
Interquartile range (IQR)14000

Descriptive statistics

Standard deviation19561.39044
Coefficient of variation (CV)1.867268394
Kurtosis4.167426723
Mean10475.93935
Median Absolute Deviation (MAD)0
Skewness2.070327187
Sum9292158.2
Variance382647995.9
MonotonicityNot monotonic
2024-02-13T20:56:44.552835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 542
 
0.1%
20000 17
 
< 0.1%
30000 14
 
< 0.1%
40000 13
 
< 0.1%
200 13
 
< 0.1%
38000 10
 
< 0.1%
42000 8
 
< 0.1%
10000 8
 
< 0.1%
22000 8
 
< 0.1%
50000 8
 
< 0.1%
Other values (115) 246
 
< 0.1%
(Missing) 522015
99.8%
ValueCountFrequency (%)
0 542
0.1%
0.2 5
 
< 0.1%
20 1
 
< 0.1%
40 2
 
< 0.1%
200 13
 
< 0.1%
ValueCountFrequency (%)
110000 3
< 0.1%
100000 1
 
< 0.1%
84000 2
< 0.1%
81000 2
< 0.1%
80000 1
 
< 0.1%
Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:44.733821image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.411082765
Min length8

Characters and Unicode

Total characters4398172
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowa55475b1
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 426651
81.6%
p159_130_59 32358
 
6.2%
p12_6_178 19478
 
3.7%
p148_110_5 12290
 
2.4%
p109_133_183 8909
 
1.7%
p53_45_92 5789
 
1.1%
p52_56_90 4986
 
1.0%
p33_29_177 3055
 
0.6%
p100_96_175 2899
 
0.6%
p21_79_33 2028
 
0.4%
Other values (30) 4459
 
0.9%
2024-02-13T20:56:45.056221image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 1382279
31.4%
1 613015
13.9%
7 460828
 
10.5%
4 448747
 
10.2%
a 426651
 
9.7%
b 426651
 
9.7%
_ 192502
 
4.4%
P 96251
 
2.2%
9 95400
 
2.2%
3 77566
 
1.8%
Other values (4) 178282
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3256117
74.0%
Lowercase Letter 853302
 
19.4%
Connector Punctuation 192502
 
4.4%
Uppercase Letter 96251
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1382279
42.5%
1 613015
18.8%
7 460828
 
14.2%
4 448747
 
13.8%
9 95400
 
2.9%
3 77566
 
2.4%
0 65589
 
2.0%
8 44751
 
1.4%
2 38116
 
1.2%
6 29826
 
0.9%
Lowercase Letter
ValueCountFrequency (%)
a 426651
50.0%
b 426651
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 192502
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 96251
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3448619
78.4%
Latin 949553
 
21.6%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1382279
40.1%
1 613015
17.8%
7 460828
 
13.4%
4 448747
 
13.0%
_ 192502
 
5.6%
9 95400
 
2.8%
3 77566
 
2.2%
0 65589
 
1.9%
8 44751
 
1.3%
2 38116
 
1.1%
Latin
ValueCountFrequency (%)
a 426651
44.9%
b 426651
44.9%
P 96251
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4398172
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 1382279
31.4%
1 613015
13.9%
7 460828
 
10.5%
4 448747
 
10.2%
a 426651
 
9.7%
b 426651
 
9.7%
_ 192502
 
4.4%
P 96251
 
2.2%
9 95400
 
2.2%
3 77566
 
1.8%
Other values (4) 178282
 
4.1%
Distinct187
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:45.403823image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.05680988
Min length8

Characters and Unicode

Total characters4212922
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)< 0.1%

Sample

1st rowa55475b1
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 510090
97.5%
p75_90_70 3429
 
0.7%
p111_89_135 940
 
0.2%
p142_50_170 796
 
0.2%
p79_131_167 796
 
0.2%
p108_95_156 627
 
0.1%
p95_36_171 535
 
0.1%
p174_113_42 530
 
0.1%
p172_87_177 523
 
0.1%
p200_75_140 401
 
0.1%
Other values (177) 4235
 
0.8%
2024-02-13T20:56:45.880626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 1540602
36.6%
1 533164
 
12.7%
7 525175
 
12.5%
4 515059
 
12.2%
a 510090
 
12.1%
b 510090
 
12.1%
_ 25624
 
0.6%
P 12812
 
0.3%
0 11872
 
0.3%
9 8285
 
0.2%
Other values (4) 20149
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3154306
74.9%
Lowercase Letter 1020180
 
24.2%
Connector Punctuation 25624
 
0.6%
Uppercase Letter 12812
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1540602
48.8%
1 533164
 
16.9%
7 525175
 
16.6%
4 515059
 
16.3%
0 11872
 
0.4%
9 8285
 
0.3%
6 5453
 
0.2%
2 5296
 
0.2%
3 4753
 
0.2%
8 4647
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
a 510090
50.0%
b 510090
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 25624
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 12812
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3179930
75.5%
Latin 1032992
 
24.5%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1540602
48.4%
1 533164
 
16.8%
7 525175
 
16.5%
4 515059
 
16.2%
_ 25624
 
0.8%
0 11872
 
0.4%
9 8285
 
0.3%
6 5453
 
0.2%
2 5296
 
0.2%
3 4753
 
0.1%
Latin
ValueCountFrequency (%)
a 510090
49.4%
b 510090
49.4%
P 12812
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4212922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 1540602
36.6%
1 533164
 
12.7%
7 525175
 
12.5%
4 515059
 
12.2%
a 510090
 
12.1%
b 510090
 
12.1%
_ 25624
 
0.6%
P 12812
 
0.3%
0 11872
 
0.3%
9 8285
 
0.2%
Other values (4) 20149
 
0.5%

lastrejectcredamount_222A
Real number (ℝ)

MISSING  ZEROS 

Distinct40867
Distinct (%)14.4%
Missing238147
Missing (%)45.5%
Infinite0
Infinite (%)0.0%
Mean55908.07535
Minimum0
Maximum1000000
Zeros13362
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:46.046210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2759.4
Q117998
median38582
Q374000
95-th percentile186000
Maximum1000000
Range1000000
Interquartile range (IQR)56002

Descriptive statistics

Standard deviation60306.14058
Coefficient of variation (CV)1.07866601
Kurtosis10.20441193
Mean55908.07535
Median Absolute Deviation (MAD)23582
Skewness2.611628263
Sum1.5920104 × 1010
Variance3636830591
MonotonicityNot monotonic
2024-02-13T20:56:46.211490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 22681
 
4.3%
60000 16888
 
3.2%
40000 16200
 
3.1%
20000 14649
 
2.8%
0 13362
 
2.6%
30000 10926
 
2.1%
200000 8438
 
1.6%
50000 5865
 
1.1%
80000 5620
 
1.1%
150000 5029
 
1.0%
Other values (40857) 165097
31.6%
(Missing) 238147
45.5%
ValueCountFrequency (%)
0 13362
2.6%
0.2 2
 
< 0.1%
2000 220
 
< 0.1%
2001 1
 
< 0.1%
2008 1
 
< 0.1%
ValueCountFrequency (%)
1000000 4
< 0.1%
950000 1
 
< 0.1%
600000 5
< 0.1%
479614 1
 
< 0.1%
409685.4 1
 
< 0.1%

lastrejectdate_50D
Text

MISSING 

Distinct5320
Distinct (%)1.9%
Missing238147
Missing (%)45.5%
Memory size4.0 MiB
2024-02-13T20:56:46.613466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2847550
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique116 ?
Unique (%)< 0.1%

Sample

1st row2018-11-20
2nd row2019-12-26
3rd row2014-07-17
4th row2017-08-21
5th row2018-01-26
ValueCountFrequency (%)
2020-01-01 632
 
0.2%
2019-12-27 570
 
0.2%
2020-01-10 548
 
0.2%
2020-01-06 544
 
0.2%
2020-01-03 526
 
0.2%
2020-01-13 512
 
0.2%
2020-01-02 509
 
0.2%
2020-01-08 500
 
0.2%
2020-01-24 493
 
0.2%
2019-12-02 482
 
0.2%
Other values (5310) 279439
98.1%
2024-02-13T20:56:47.131049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 701582
24.6%
- 569510
20.0%
2 519559
18.2%
1 470720
16.5%
9 146636
 
5.1%
8 102073
 
3.6%
7 78401
 
2.8%
3 73762
 
2.6%
6 67124
 
2.4%
4 59152
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2278040
80.0%
Dash Punctuation 569510
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 701582
30.8%
2 519559
22.8%
1 470720
20.7%
9 146636
 
6.4%
8 102073
 
4.5%
7 78401
 
3.4%
3 73762
 
3.2%
6 67124
 
2.9%
4 59152
 
2.6%
5 59031
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 569510
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2847550
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 701582
24.6%
- 569510
20.0%
2 519559
18.2%
1 470720
16.5%
9 146636
 
5.1%
8 102073
 
3.6%
7 78401
 
2.8%
3 73762
 
2.6%
6 67124
 
2.4%
4 59152
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2847550
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 701582
24.6%
- 569510
20.0%
2 519559
18.2%
1 470720
16.5%
9 146636
 
5.1%
8 102073
 
3.6%
7 78401
 
2.8%
3 73762
 
2.6%
6 67124
 
2.4%
4 59152
 
2.1%
Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:47.335270image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length8.914316641
Min length8

Characters and Unicode

Total characters4661314
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowa55475b1
2nd rowP198_131_9
3rd rowP45_84_106
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 301655
57.7%
p99_56_166 114045
 
21.8%
p94_109_143 36907
 
7.1%
p198_131_9 34064
 
6.5%
p45_84_106 32560
 
6.2%
p48_22_32 2008
 
0.4%
p121_60_164 684
 
0.1%
p196_88_176 434
 
0.1%
p30_86_84 256
 
< 0.1%
p52_67_90 139
 
< 0.1%
Other values (7) 150
 
< 0.1%
2024-02-13T20:56:47.643503image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 1051746
22.6%
1 627483
13.5%
4 443653
9.5%
_ 442494
9.5%
6 377494
 
8.1%
9 370688
 
8.0%
7 302283
 
6.5%
a 301655
 
6.5%
b 301655
 
6.5%
P 221247
 
4.7%
Other values (4) 220916
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3394263
72.8%
Lowercase Letter 603310
 
12.9%
Connector Punctuation 442494
 
9.5%
Uppercase Letter 221247
 
4.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1051746
31.0%
1 627483
18.5%
4 443653
13.1%
6 377494
 
11.1%
9 370688
 
10.9%
7 302283
 
8.9%
3 73265
 
2.2%
0 70585
 
2.1%
8 70091
 
2.1%
2 6975
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
a 301655
50.0%
b 301655
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 442494
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 221247
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3836757
82.3%
Latin 824557
 
17.7%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1051746
27.4%
1 627483
16.4%
4 443653
11.6%
_ 442494
11.5%
6 377494
 
9.8%
9 370688
 
9.7%
7 302283
 
7.9%
3 73265
 
1.9%
0 70585
 
1.8%
8 70091
 
1.8%
Latin
ValueCountFrequency (%)
a 301655
36.6%
b 301655
36.6%
P 221247
26.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4661314
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 1051746
22.6%
1 627483
13.5%
4 443653
9.5%
_ 442494
9.5%
6 377494
 
8.1%
9 370688
 
8.0%
7 302283
 
6.5%
a 301655
 
6.5%
b 301655
 
6.5%
P 221247
 
4.7%
Other values (4) 220916
 
4.7%
Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:47.821831image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length9.234642438
Min length8

Characters and Unicode

Total characters4828813
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowa55475b1
2nd rowP94_109_143
3rd rowP94_109_143
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 289832
55.4%
p94_109_143 199865
38.2%
p30_86_84 16151
 
3.1%
p52_67_90 6101
 
1.2%
p69_72_116 4678
 
0.9%
p129_162_80 3037
 
0.6%
p84_14_61 1531
 
0.3%
p64_121_167 846
 
0.2%
p19_25_34 449
 
0.1%
p5_143_178 341
 
0.1%
Other values (4) 71
 
< 0.1%
2024-02-13T20:56:48.140311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 876523
18.2%
1 711865
14.7%
4 710415
14.7%
_ 466140
9.7%
9 414001
8.6%
7 301798
 
6.2%
a 289832
 
6.0%
b 289832
 
6.0%
P 233070
 
4.8%
0 225223
 
4.7%
Other values (4) 310114
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3549939
73.5%
Lowercase Letter 579664
 
12.0%
Connector Punctuation 466140
 
9.7%
Uppercase Letter 233070
 
4.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 876523
24.7%
1 711865
20.1%
4 710415
20.0%
9 414001
11.7%
7 301798
 
8.5%
0 225223
 
6.3%
3 216875
 
6.1%
6 37870
 
1.1%
8 37213
 
1.0%
2 18156
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
a 289832
50.0%
b 289832
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 466140
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 233070
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4016079
83.2%
Latin 812734
 
16.8%

Most frequent character per script

Common
ValueCountFrequency (%)
5 876523
21.8%
1 711865
17.7%
4 710415
17.7%
_ 466140
11.6%
9 414001
10.3%
7 301798
 
7.5%
0 225223
 
5.6%
3 216875
 
5.4%
6 37870
 
0.9%
8 37213
 
0.9%
Latin
ValueCountFrequency (%)
a 289832
35.7%
b 289832
35.7%
P 233070
28.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4828813
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 876523
18.2%
1 711865
14.7%
4 710415
14.7%
_ 466140
9.7%
9 414001
8.6%
7 301798
 
6.2%
a 289832
 
6.0%
b 289832
 
6.0%
P 233070
 
4.8%
0 225223
 
4.7%
Other values (4) 310114
 
6.4%

lastrepayingdate_696D
Text

MISSING 

Distinct297
Distinct (%)38.5%
Missing522130
Missing (%)99.9%
Memory size4.0 MiB
2024-02-13T20:56:48.514289image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters7720
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique149 ?
Unique (%)19.3%

Sample

1st row2020-01-21
2nd row2020-02-16
3rd row2020-04-16
4th row2019-12-28
5th row2019-12-02
ValueCountFrequency (%)
2020-01-18 13
 
1.7%
2020-01-15 13
 
1.7%
2020-01-28 12
 
1.6%
2020-02-15 11
 
1.4%
2020-01-11 11
 
1.4%
2020-03-28 10
 
1.3%
2020-01-24 9
 
1.2%
2020-01-07 9
 
1.2%
2020-03-15 9
 
1.2%
2020-03-20 8
 
1.0%
Other values (287) 667
86.4%
2024-02-13T20:56:49.003165image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2342
30.3%
2 1859
24.1%
- 1544
20.0%
1 858
 
11.1%
9 304
 
3.9%
3 207
 
2.7%
8 159
 
2.1%
7 119
 
1.5%
6 115
 
1.5%
4 112
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6176
80.0%
Dash Punctuation 1544
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2342
37.9%
2 1859
30.1%
1 858
 
13.9%
9 304
 
4.9%
3 207
 
3.4%
8 159
 
2.6%
7 119
 
1.9%
6 115
 
1.9%
4 112
 
1.8%
5 101
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 1544
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7720
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2342
30.3%
2 1859
24.1%
- 1544
20.0%
1 858
 
11.1%
9 304
 
3.9%
3 207
 
2.7%
8 159
 
2.1%
7 119
 
1.5%
6 115
 
1.5%
4 112
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2342
30.3%
2 1859
24.1%
- 1544
20.0%
1 858
 
11.1%
9 304
 
3.9%
3 207
 
2.7%
8 159
 
2.1%
7 119
 
1.5%
6 115
 
1.5%
4 112
 
1.5%

lastst_736L
Text

MISSING 

Distinct11
Distinct (%)< 0.1%
Missing84377
Missing (%)16.1%
Memory size4.0 MiB
2024-02-13T20:56:49.142749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters438525
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowD
3rd rowD
4th rowD
5th rowD
ValueCountFrequency (%)
d 142582
32.5%
a 130138
29.7%
k 113037
25.8%
t 41350
 
9.4%
n 10655
 
2.4%
q 300
 
0.1%
s 244
 
0.1%
l 155
 
< 0.1%
h 49
 
< 0.1%
r 9
 
< 0.1%
2024-02-13T20:56:49.395693image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 142582
32.5%
A 130138
29.7%
K 113037
25.8%
T 41350
 
9.4%
N 10655
 
2.4%
Q 300
 
0.1%
S 244
 
0.1%
L 155
 
< 0.1%
H 49
 
< 0.1%
R 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 438525
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 142582
32.5%
A 130138
29.7%
K 113037
25.8%
T 41350
 
9.4%
N 10655
 
2.4%
Q 300
 
0.1%
S 244
 
0.1%
L 155
 
< 0.1%
H 49
 
< 0.1%
R 9
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 438525
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 142582
32.5%
A 130138
29.7%
K 113037
25.8%
T 41350
 
9.4%
N 10655
 
2.4%
Q 300
 
0.1%
S 244
 
0.1%
L 155
 
< 0.1%
H 49
 
< 0.1%
R 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 438525
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 142582
32.5%
A 130138
29.7%
K 113037
25.8%
T 41350
 
9.4%
N 10655
 
2.4%
Q 300
 
0.1%
S 244
 
0.1%
L 155
 
< 0.1%
H 49
 
< 0.1%
R 9
 
< 0.1%

maininc_215A
Real number (ℝ)

MISSING 

Distinct4068
Distinct (%)1.2%
Missing170305
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean51371.29595
Minimum0
Maximum200000
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:49.565158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12000
Q130000
median44000
Q364000
95-th percentile110000
Maximum200000
Range200000
Interquartile range (IQR)34000

Descriptive statistics

Standard deviation32769.77093
Coefficient of variation (CV)0.6379004136
Kurtosis4.226745359
Mean51371.29595
Median Absolute Deviation (MAD)16000
Skewness1.683083489
Sum1.811336484 × 1010
Variance1073857887
MonotonicityNot monotonic
2024-02-13T20:56:49.726238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40000 32914
 
6.3%
50000 29537
 
5.6%
30000 27754
 
5.3%
60000 26040
 
5.0%
70000 16982
 
3.2%
20000 11485
 
2.2%
36000 11390
 
2.2%
24000 10338
 
2.0%
80000 9918
 
1.9%
100000 9838
 
1.9%
Other values (4058) 166401
31.8%
(Missing) 170305
32.6%
ValueCountFrequency (%)
0 3
 
< 0.1%
0.2 9
< 0.1%
1 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
200000 2264
0.4%
199980 6
 
< 0.1%
199971.4 2
 
< 0.1%
199800 2
 
< 0.1%
199600 42
 
< 0.1%

mastercontrelectronic_519L
Real number (ℝ)

CONSTANT  MISSING  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing83853
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros439049
Zeros (%)84.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:49.854482image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2024-02-13T20:56:49.951316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 439049
84.0%
(Missing) 83853
 
16.0%
ValueCountFrequency (%)
0 439049
84.0%
ValueCountFrequency (%)
0 439049
84.0%

mastercontrexist_109L
Real number (ℝ)

CONSTANT  MISSING  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing83853
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros439049
Zeros (%)84.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:50.047353image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2024-02-13T20:56:50.145741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 439049
84.0%
(Missing) 83853
 
16.0%
ValueCountFrequency (%)
0 439049
84.0%
ValueCountFrequency (%)
0 439049
84.0%

maxannuity_159A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct175920
Distinct (%)40.1%
Missing83853
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean44642.78646
Minimum0
Maximum20923088
Zeros49177
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:50.278512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14720
median13887.241
Q343356
95-th percentile182385.544
Maximum20923088
Range20923088
Interquartile range (IQR)38636

Descriptive statistics

Standard deviation115327.2868
Coefficient of variation (CV)2.583335315
Kurtosis3457.497594
Mean44642.78646
Median Absolute Deviation (MAD)11727.241
Skewness31.98568162
Sum1.960037075 × 1010
Variance1.330038309 × 1010
MonotonicityNot monotonic
2024-02-13T20:56:50.435476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49177
 
9.4%
4000 3310
 
0.6%
6000 2535
 
0.5%
2000 2496
 
0.5%
3000 2248
 
0.4%
8000 2075
 
0.4%
5000 1809
 
0.3%
10000 1767
 
0.3%
20000 1494
 
0.3%
2400 1440
 
0.3%
Other values (175910) 370698
70.9%
(Missing) 83853
 
16.0%
ValueCountFrequency (%)
0 49177
9.4%
0.6 2
 
< 0.1%
1.2 1
 
< 0.1%
3.6000001 1
 
< 0.1%
7.37 1
 
< 0.1%
ValueCountFrequency (%)
20923088 1
< 0.1%
12229000 1
< 0.1%
11919068 1
< 0.1%
7540940 1
< 0.1%
7324640 1
< 0.1%

maxannuity_4075009A
Real number (ℝ)

MISSING 

Distinct3551
Distinct (%)13.8%
Missing497091
Missing (%)95.1%
Infinite0
Infinite (%)0.0%
Mean21109.9814
Minimum600
Maximum601320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:50.582005image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum600
5-th percentile4520
Q19680
median15000
Q322900
95-th percentile63470
Maximum601320
Range600720
Interquartile range (IQR)13220

Descriptive statistics

Standard deviation23063.05699
Coefficient of variation (CV)1.092519058
Kurtosis42.63392138
Mean21109.9814
Median Absolute Deviation (MAD)6140
Skewness4.675660194
Sum544869730
Variance531904597.8
MonotonicityNot monotonic
2024-02-13T20:56:50.737042image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15000 229
 
< 0.1%
10000 194
 
< 0.1%
18060 173
 
< 0.1%
20000 167
 
< 0.1%
12000 165
 
< 0.1%
16060 120
 
< 0.1%
19220 96
 
< 0.1%
16000 91
 
< 0.1%
18780 90
 
< 0.1%
25000 87
 
< 0.1%
Other values (3541) 24399
 
4.7%
(Missing) 497091
95.1%
ValueCountFrequency (%)
600 1
 
< 0.1%
620 1
 
< 0.1%
640 1
 
< 0.1%
660 1
 
< 0.1%
680 3
< 0.1%
ValueCountFrequency (%)
601320 1
< 0.1%
400000 2
< 0.1%
369460 1
< 0.1%
334920 1
< 0.1%
319440 1
< 0.1%

maxdbddpdlast1m_3658939P
Real number (ℝ)

MISSING  ZEROS 

Distinct1530
Distinct (%)0.7%
Missing318036
Missing (%)60.8%
Infinite0
Infinite (%)0.0%
Mean47.2198315
Minimum-547
Maximum4804
Zeros41836
Zeros (%)8.0%
Negative130027
Negative (%)24.9%
Memory size4.0 MiB
2024-02-13T20:56:50.891379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-547
5-th percentile-42
Q1-9
median-2
Q30
95-th percentile9
Maximum4804
Range5351
Interquartile range (IQR)9

Descriptive statistics

Standard deviation484.8058304
Coefficient of variation (CV)10.2669962
Kurtosis74.15186011
Mean47.2198315
Median Absolute Deviation (MAD)3
Skewness8.695344622
Sum9673738
Variance235036.6932
MonotonicityNot monotonic
2024-02-13T20:56:51.049826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 41836
 
8.0%
-1 21546
 
4.1%
-2 14239
 
2.7%
-3 11654
 
2.2%
-4 9410
 
1.8%
1 8412
 
1.6%
-5 6884
 
1.3%
-6 5557
 
1.1%
-7 4975
 
1.0%
2 4881
 
0.9%
Other values (1520) 75472
 
14.4%
(Missing) 318036
60.8%
ValueCountFrequency (%)
-547 1
< 0.1%
-544 1
< 0.1%
-516 1
< 0.1%
-506 1
< 0.1%
-501 1
< 0.1%
ValueCountFrequency (%)
4804 1
< 0.1%
4790 1
< 0.1%
4771 1
< 0.1%
4752 1
< 0.1%
4749 1
< 0.1%

maxdbddpdtollast12m_3658940P
Real number (ℝ)

MISSING  ZEROS 

Distinct3047
Distinct (%)1.0%
Missing222332
Missing (%)42.5%
Infinite0
Infinite (%)0.0%
Mean55.25346176
Minimum-1083
Maximum4804
Zeros107093
Zeros (%)20.5%
Negative78720
Negative (%)15.1%
Memory size4.0 MiB
2024-02-13T20:56:51.195324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1083
5-th percentile-11
Q1-1
median0
Q32
95-th percentile22
Maximum4804
Range5887
Interquartile range (IQR)3

Descriptive statistics

Standard deviation440.9617796
Coefficient of variation (CV)7.98070864
Kurtosis81.22908356
Mean55.25346176
Median Absolute Deviation (MAD)1
Skewness8.942463251
Sum16607533
Variance194447.2911
MonotonicityNot monotonic
2024-02-13T20:56:51.344817image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 107093
20.5%
1 26565
 
5.1%
-1 24388
 
4.7%
2 17607
 
3.4%
3 12745
 
2.4%
-2 10417
 
2.0%
4 8643
 
1.7%
-3 7229
 
1.4%
5 6472
 
1.2%
-4 5262
 
1.0%
Other values (3037) 74149
 
14.2%
(Missing) 222332
42.5%
ValueCountFrequency (%)
-1083 1
< 0.1%
-437 1
< 0.1%
-429 1
< 0.1%
-396 1
< 0.1%
-370 1
< 0.1%
ValueCountFrequency (%)
4804 1
< 0.1%
4790 1
< 0.1%
4771 1
< 0.1%
4752 1
< 0.1%
4749 1
< 0.1%

maxdbddpdtollast6m_4187119P
Real number (ℝ)

MISSING  ZEROS 

Distinct2301
Distinct (%)0.9%
Missing259441
Missing (%)49.6%
Infinite0
Infinite (%)0.0%
Mean48.47102607
Minimum-1225
Maximum4804
Zeros87359
Zeros (%)16.7%
Negative94127
Negative (%)18.0%
Memory size4.0 MiB
2024-02-13T20:56:51.495151image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1225
5-th percentile-18
Q1-2
median0
Q31
95-th percentile13
Maximum4804
Range6029
Interquartile range (IQR)3

Descriptive statistics

Standard deviation443.4438891
Coefficient of variation (CV)9.148638373
Kurtosis85.79129959
Mean48.47102607
Median Absolute Deviation (MAD)1
Skewness9.275868605
Sum12770225
Variance196642.4828
MonotonicityNot monotonic
2024-02-13T20:56:51.668989image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 87359
 
16.7%
-1 25982
 
5.0%
1 20374
 
3.9%
2 13195
 
2.5%
-2 12056
 
2.3%
3 9518
 
1.8%
-3 8632
 
1.7%
4 6349
 
1.2%
-4 6300
 
1.2%
5 4902
 
0.9%
Other values (2291) 68794
 
13.2%
(Missing) 259441
49.6%
ValueCountFrequency (%)
-1225 1
< 0.1%
-469 1
< 0.1%
-410 1
< 0.1%
-405 1
< 0.1%
-393 1
< 0.1%
ValueCountFrequency (%)
4804 1
< 0.1%
4790 1
< 0.1%
4771 1
< 0.1%
4752 1
< 0.1%
4749 1
< 0.1%

maxdebt4_972A
Real number (ℝ)

MISSING  ZEROS 

Distinct207890
Distinct (%)47.4%
Missing83853
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean55699.33266
Minimum0
Maximum1740000
Zeros87765
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:51.824908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111998
median38879.2
Q380100
95-th percentile174980.24
Maximum1740000
Range1740000
Interquartile range (IQR)68102

Descriptive statistics

Standard deviation58842.84699
Coefficient of variation (CV)1.056437199
Kurtosis8.142939376
Mean55699.33266
Median Absolute Deviation (MAD)33116.8
Skewness1.818851843
Sum2.44547363 × 1010
Variance3462480642
MonotonicityNot monotonic
2024-02-13T20:56:51.981623image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 87765
 
16.8%
100000 1978
 
0.4%
150000 1388
 
0.3%
103980 1355
 
0.3%
40000 1093
 
0.2%
60000 902
 
0.2%
200000 845
 
0.2%
20000 687
 
0.1%
30000 684
 
0.1%
72014 664
 
0.1%
Other values (207880) 341688
65.3%
(Missing) 83853
 
16.0%
ValueCountFrequency (%)
0 87765
16.8%
0.002 3
 
< 0.1%
0.004 2
 
< 0.1%
0.008 1
 
< 0.1%
0.010000001 1
 
< 0.1%
ValueCountFrequency (%)
1740000 2
< 0.1%
1316787.9 1
< 0.1%
1299800.1 1
< 0.1%
1083073.6 1
< 0.1%
778712.4 1
< 0.1%

maxdpdfrom6mto36m_3546853P
Real number (ℝ)

MISSING  ZEROS 

Distinct3279
Distinct (%)0.7%
Missing83853
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean38.40033345
Minimum0
Maximum4465
Zeros285456
Zeros (%)54.6%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:52.140678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile32
Maximum4465
Range4465
Interquartile range (IQR)2

Descriptive statistics

Standard deviation275.1289995
Coefficient of variation (CV)7.164755479
Kurtosis122.179228
Mean38.40033345
Median Absolute Deviation (MAD)0
Skewness10.36406095
Sum16859628
Variance75695.96635
MonotonicityNot monotonic
2024-02-13T20:56:52.286463image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 285456
54.6%
1 31301
 
6.0%
2 20750
 
4.0%
3 14987
 
2.9%
4 10495
 
2.0%
5 7661
 
1.5%
6 6147
 
1.2%
7 5008
 
1.0%
8 4191
 
0.8%
9 3805
 
0.7%
Other values (3269) 49248
 
9.4%
(Missing) 83853
 
16.0%
ValueCountFrequency (%)
0 285456
54.6%
1 31301
 
6.0%
2 20750
 
4.0%
3 14987
 
2.9%
4 10495
 
2.0%
ValueCountFrequency (%)
4465 1
< 0.1%
4464 1
< 0.1%
4459 2
< 0.1%
4453 1
< 0.1%
4440 1
< 0.1%
Distinct5195
Distinct (%)2.0%
Missing258192
Missing (%)49.4%
Memory size4.0 MiB
2024-02-13T20:56:52.707434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2647100
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)< 0.1%

Sample

1st row2019-11-15
2nd row2019-11-29
3rd row2019-10-18
4th row2019-11-28
5th row2019-10-27
ValueCountFrequency (%)
2020-01-18 895
 
0.3%
2020-01-15 877
 
0.3%
2019-12-15 869
 
0.3%
2019-12-08 689
 
0.3%
2019-09-15 644
 
0.2%
2019-08-11 607
 
0.2%
2019-12-18 583
 
0.2%
2019-11-11 582
 
0.2%
2019-12-28 572
 
0.2%
2020-02-11 564
 
0.2%
Other values (5185) 257828
97.4%
2024-02-13T20:56:53.260911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 653302
24.7%
- 529420
20.0%
2 475957
18.0%
1 434191
16.4%
9 133858
 
5.1%
8 102146
 
3.9%
7 78071
 
2.9%
5 72156
 
2.7%
6 59276
 
2.2%
3 57756
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2117680
80.0%
Dash Punctuation 529420
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 653302
30.8%
2 475957
22.5%
1 434191
20.5%
9 133858
 
6.3%
8 102146
 
4.8%
7 78071
 
3.7%
5 72156
 
3.4%
6 59276
 
2.8%
3 57756
 
2.7%
4 50967
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 529420
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2647100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 653302
24.7%
- 529420
20.0%
2 475957
18.0%
1 434191
16.4%
9 133858
 
5.1%
8 102146
 
3.9%
7 78071
 
2.9%
5 72156
 
2.7%
6 59276
 
2.2%
3 57756
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2647100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 653302
24.7%
- 529420
20.0%
2 475957
18.0%
1 434191
16.4%
9 133858
 
5.1%
8 102146
 
3.9%
7 78071
 
2.9%
5 72156
 
2.7%
6 59276
 
2.2%
3 57756
 
2.2%

maxdpdinstlnum_3546846P
Real number (ℝ)

MISSING 

Distinct62
Distinct (%)< 0.1%
Missing260417
Missing (%)49.8%
Infinite0
Infinite (%)0.0%
Mean8.560085338
Minimum0
Maximum72
Zeros1240
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:53.421418image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median7
Q311
95-th percentile20
Maximum72
Range72
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.107158757
Coefficient of variation (CV)0.7134460132
Kurtosis2.806521106
Mean8.560085338
Median Absolute Deviation (MAD)3
Skewness1.38849659
Sum2246894
Variance37.29738808
MonotonicityNot monotonic
2024-02-13T20:56:53.576455image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 24246
 
4.6%
4 22100
 
4.2%
6 20153
 
3.9%
3 19817
 
3.8%
9 18642
 
3.6%
8 17405
 
3.3%
7 17282
 
3.3%
10 16750
 
3.2%
2 16510
 
3.2%
1 14160
 
2.7%
Other values (52) 75420
 
14.4%
(Missing) 260417
49.8%
ValueCountFrequency (%)
0 1240
 
0.2%
1 14160
2.7%
2 16510
3.2%
3 19817
3.8%
4 22100
4.2%
ValueCountFrequency (%)
72 1
 
< 0.1%
66 1
 
< 0.1%
64 1
 
< 0.1%
63 3
< 0.1%
58 2
< 0.1%

maxdpdlast12m_727P
Real number (ℝ)

MISSING  ZEROS 

Distinct2427
Distinct (%)0.6%
Missing83853
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean18.00469196
Minimum0
Maximum4629
Zeros322356
Zeros (%)61.6%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:53.758420image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile12
Maximum4629
Range4629
Interquartile range (IQR)1

Descriptive statistics

Standard deviation204.1241045
Coefficient of variation (CV)11.3372728
Kurtosis294.903501
Mean18.00469196
Median Absolute Deviation (MAD)0
Skewness16.32346882
Sum7904942
Variance41666.65003
MonotonicityNot monotonic
2024-02-13T20:56:53.911669image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 322356
61.6%
1 27083
 
5.2%
2 18488
 
3.5%
3 13525
 
2.6%
4 8965
 
1.7%
5 6568
 
1.3%
6 4972
 
1.0%
7 3780
 
0.7%
8 3048
 
0.6%
9 2779
 
0.5%
Other values (2417) 27485
 
5.3%
(Missing) 83853
 
16.0%
ValueCountFrequency (%)
0 322356
61.6%
1 27083
 
5.2%
2 18488
 
3.5%
3 13525
 
2.6%
4 8965
 
1.7%
ValueCountFrequency (%)
4629 1
< 0.1%
4628 1
< 0.1%
4624 1
< 0.1%
4623 1
< 0.1%
4619 1
< 0.1%

maxdpdlast24m_143P
Real number (ℝ)

MISSING  ZEROS 

Distinct3128
Distinct (%)0.7%
Missing83853
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean31.65614772
Minimum0
Maximum4629
Zeros282976
Zeros (%)54.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:54.056669image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile24
Maximum4629
Range4629
Interquartile range (IQR)2

Descriptive statistics

Standard deviation257.880832
Coefficient of variation (CV)8.146311241
Kurtosis155.7365329
Mean31.65614772
Median Absolute Deviation (MAD)0
Skewness11.74628035
Sum13898600
Variance66502.52353
MonotonicityNot monotonic
2024-02-13T20:56:54.202296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 282976
54.1%
1 32636
 
6.2%
2 22795
 
4.4%
3 16805
 
3.2%
4 11306
 
2.2%
5 8385
 
1.6%
6 6532
 
1.2%
7 5166
 
1.0%
8 4176
 
0.8%
9 3918
 
0.7%
Other values (3118) 44354
 
8.5%
(Missing) 83853
 
16.0%
ValueCountFrequency (%)
0 282976
54.1%
1 32636
 
6.2%
2 22795
 
4.4%
3 16805
 
3.2%
4 11306
 
2.2%
ValueCountFrequency (%)
4629 1
< 0.1%
4628 1
< 0.1%
4624 1
< 0.1%
4623 1
< 0.1%
4619 1
< 0.1%

maxdpdlast3m_392P
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct1126
Distinct (%)0.3%
Missing83853
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean6.635191061
Minimum0
Maximum4629
Zeros382681
Zeros (%)73.2%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:54.343239image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum4629
Range4629
Interquartile range (IQR)0

Descriptive statistics

Standard deviation141.5338879
Coefficient of variation (CV)21.33079313
Kurtosis805.6893098
Mean6.635191061
Median Absolute Deviation (MAD)0
Skewness27.76463021
Sum2913174
Variance20031.84142
MonotonicityNot monotonic
2024-02-13T20:56:54.493368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 382681
73.2%
1 15793
 
3.0%
2 10319
 
2.0%
3 7208
 
1.4%
4 4607
 
0.9%
5 3298
 
0.6%
6 2304
 
0.4%
7 1568
 
0.3%
8 1285
 
0.2%
9 1163
 
0.2%
Other values (1116) 8823
 
1.7%
(Missing) 83853
 
16.0%
ValueCountFrequency (%)
0 382681
73.2%
1 15793
 
3.0%
2 10319
 
2.0%
3 7208
 
1.4%
4 4607
 
0.9%
ValueCountFrequency (%)
4629 1
< 0.1%
4628 1
< 0.1%
4624 1
< 0.1%
4623 1
< 0.1%
4619 1
< 0.1%

maxdpdlast6m_474P
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct1628
Distinct (%)0.4%
Missing83853
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean9.951137572
Minimum0
Maximum4629
Zeros355495
Zeros (%)68.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:54.637368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7
Maximum4629
Range4629
Interquartile range (IQR)0

Descriptive statistics

Standard deviation161.3062625
Coefficient of variation (CV)16.20983143
Kurtosis559.4839076
Mean9.951137572
Median Absolute Deviation (MAD)0
Skewness22.82609267
Sum4369037
Variance26019.71033
MonotonicityNot monotonic
2024-02-13T20:56:55.054940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 355495
68.0%
1 20964
 
4.0%
2 14094
 
2.7%
3 10280
 
2.0%
4 6654
 
1.3%
5 4965
 
0.9%
6 3634
 
0.7%
7 2698
 
0.5%
8 2100
 
0.4%
9 1872
 
0.4%
Other values (1618) 16293
 
3.1%
(Missing) 83853
 
16.0%
ValueCountFrequency (%)
0 355495
68.0%
1 20964
 
4.0%
2 14094
 
2.7%
3 10280
 
2.0%
4 6654
 
1.3%
ValueCountFrequency (%)
4629 1
< 0.1%
4628 1
< 0.1%
4624 1
< 0.1%
4623 1
< 0.1%
4619 1
< 0.1%

maxdpdlast9m_1059P
Real number (ℝ)

MISSING  ZEROS 

Distinct2051
Distinct (%)0.5%
Missing83853
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean13.88155081
Minimum0
Maximum4629
Zeros337025
Zeros (%)64.5%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:55.197678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10
Maximum4629
Range4629
Interquartile range (IQR)0

Descriptive statistics

Standard deviation183.7028851
Coefficient of variation (CV)13.2335996
Kurtosis393.7455787
Mean13.88155081
Median Absolute Deviation (MAD)0
Skewness18.98131001
Sum6094681
Variance33746.75001
MonotonicityNot monotonic
2024-02-13T20:56:55.343664image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 337025
64.5%
1 24544
 
4.7%
2 16732
 
3.2%
3 12148
 
2.3%
4 7915
 
1.5%
5 5854
 
1.1%
6 4437
 
0.8%
7 3277
 
0.6%
8 2641
 
0.5%
9 2377
 
0.5%
Other values (2041) 22099
 
4.2%
(Missing) 83853
 
16.0%
ValueCountFrequency (%)
0 337025
64.5%
1 24544
 
4.7%
2 16732
 
3.2%
3 12148
 
2.3%
4 7915
 
1.5%
ValueCountFrequency (%)
4629 1
< 0.1%
4628 1
< 0.1%
4624 1
< 0.1%
4623 1
< 0.1%
4619 1
< 0.1%

maxdpdtolerance_374P
Real number (ℝ)

MISSING  ZEROS 

Distinct3583
Distinct (%)0.8%
Missing83853
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean65.62178026
Minimum0
Maximum4629
Zeros199059
Zeros (%)38.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:55.491700image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q310
95-th percentile236
Maximum4629
Range4629
Interquartile range (IQR)10

Descriptive statistics

Standard deviation321.3444119
Coefficient of variation (CV)4.896917008
Kurtosis74.46468435
Mean65.62178026
Median Absolute Deviation (MAD)1
Skewness7.889748844
Sum28811177
Variance103262.231
MonotonicityNot monotonic
2024-02-13T20:56:55.639104image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 199059
38.1%
1 31653
 
6.1%
2 24013
 
4.6%
3 18876
 
3.6%
4 13682
 
2.6%
5 10697
 
2.0%
6 8889
 
1.7%
7 7622
 
1.5%
8 6524
 
1.2%
9 6332
 
1.2%
Other values (3573) 111702
21.4%
(Missing) 83853
16.0%
ValueCountFrequency (%)
0 199059
38.1%
1 31653
 
6.1%
2 24013
 
4.6%
3 18876
 
3.6%
4 13682
 
2.6%
ValueCountFrequency (%)
4629 1
< 0.1%
4628 1
< 0.1%
4624 1
< 0.1%
4623 1
< 0.1%
4619 1
< 0.1%

maxinstallast24m_3658928A
Real number (ℝ)

MISSING 

Distinct142612
Distinct (%)43.0%
Missing191154
Missing (%)36.6%
Infinite0
Infinite (%)0.0%
Mean18495.67792
Minimum0.2
Maximum1009600.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:55.788708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1466.270065
Q13708.6
median7940.90015
Q319527.85
95-th percentile73314.83
Maximum1009600.8
Range1009600.6
Interquartile range (IQR)15819.25

Descriptive statistics

Standard deviation29054.39041
Coefficient of variation (CV)1.570874587
Kurtosis28.95018775
Mean18495.67792
Median Absolute Deviation (MAD)5316.70015
Skewness4.103386029
Sum6135904159
Variance844157602.2
MonotonicityNot monotonic
2024-02-13T20:56:55.972911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
800 337
 
0.1%
600 240
 
< 0.1%
1000 120
 
< 0.1%
2000 119
 
< 0.1%
2166.6 80
 
< 0.1%
1500 77
 
< 0.1%
3000 75
 
< 0.1%
1666.6 72
 
< 0.1%
2500 72
 
< 0.1%
1998.2001 67
 
< 0.1%
Other values (142602) 330489
63.2%
(Missing) 191154
36.6%
ValueCountFrequency (%)
0.2 2
 
< 0.1%
0.4 4
< 0.1%
0.6 7
< 0.1%
0.8 5
< 0.1%
1 3
< 0.1%
ValueCountFrequency (%)
1009600.8 1
< 0.1%
541288.8 1
< 0.1%
538113 1
< 0.1%
530457.8 1
< 0.1%
517657.6 2
< 0.1%

maxlnamtstart6m_4525199A
Real number (ℝ)

MISSING 

Distinct163873
Distinct (%)55.1%
Missing225395
Missing (%)43.1%
Infinite0
Infinite (%)0.0%
Mean46629.90898
Minimum0
Maximum518629.6
Zeros1778
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:56.127910image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9229
Q119976.7
median34094
Q360586.9
95-th percentile124640.26
Maximum518629.6
Range518629.6
Interquartile range (IQR)40610.2

Descriptive statistics

Standard deviation39512.6489
Coefficient of variation (CV)0.8473670605
Kurtosis7.326112761
Mean46629.90898
Median Absolute Deviation (MAD)17486
Skewness2.11815673
Sum1.387272433 × 1010
Variance1561249423
MonotonicityNot monotonic
2024-02-13T20:56:56.284914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 2432
 
0.5%
0 1778
 
0.3%
150000 912
 
0.2%
40000 878
 
0.2%
60000 855
 
0.2%
20000 776
 
0.1%
30000 669
 
0.1%
103980 659
 
0.1%
23682.8 586
 
0.1%
200000 493
 
0.1%
Other values (163863) 287469
55.0%
(Missing) 225395
43.1%
ValueCountFrequency (%)
0 1778
0.3%
0.8 1
 
< 0.1%
1.2 1
 
< 0.1%
3 1
 
< 0.1%
15 1
 
< 0.1%
ValueCountFrequency (%)
518629.6 2
< 0.1%
513520 3
< 0.1%
506233.6 1
 
< 0.1%
501833.6 1
 
< 0.1%
500343.8 1
 
< 0.1%

maxoutstandbalancel12m_4187113A
Real number (ℝ)

MISSING  ZEROS 

Distinct233545
Distinct (%)75.1%
Missing211762
Missing (%)40.5%
Infinite0
Infinite (%)0.0%
Mean76505.65366
Minimum-2000000
Maximum1235312.9
Zeros10098
Zeros (%)1.9%
Negative2014
Negative (%)0.4%
Memory size4.0 MiB
2024-02-13T20:56:56.436876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-2000000
5-th percentile3741.235
Q121197.89
median45504.219
Q397815.97875
95-th percentile258386.3
Maximum1235312.9
Range3235312.9
Interquartile range (IQR)76618.08875

Descriptive statistics

Standard deviation86814.53705
Coefficient of variation (CV)1.134746687
Kurtosis9.497989448
Mean76505.65366
Median Absolute Deviation (MAD)30092.119
Skewness2.358554375
Sum2.380396908 × 1010
Variance7536763843
MonotonicityNot monotonic
2024-02-13T20:56:56.593803image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10098
 
1.9%
12000 218
 
< 0.1%
20000 155
 
< 0.1%
17998 148
 
< 0.1%
10000 146
 
< 0.1%
11998 146
 
< 0.1%
-2 145
 
< 0.1%
21998 142
 
< 0.1%
8000 140
 
< 0.1%
19998 139
 
< 0.1%
Other values (233535) 299663
57.3%
(Missing) 211762
40.5%
ValueCountFrequency (%)
-2000000 1
< 0.1%
-320000 1
< 0.1%
-259000 1
< 0.1%
-141000 1
< 0.1%
-60000 1
< 0.1%
ValueCountFrequency (%)
1235312.9 1
< 0.1%
1227672.2 1
< 0.1%
1210194.2 1
< 0.1%
1136727.2 1
< 0.1%
1106759 1
< 0.1%

maxpmtlast3m_4525190A
Real number (ℝ)

MISSING 

Distinct98638
Distinct (%)41.2%
Missing283339
Missing (%)54.2%
Infinite0
Infinite (%)0.0%
Mean9526.372104
Minimum0
Maximum538113
Zeros623
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:56.746118image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1107.8
Q12599.8
median4909.2
Q38982.8005
95-th percentile31766.5201
Maximum538113
Range538113
Interquartile range (IQR)6383.0005

Descriptive statistics

Standard deviation18553.72544
Coefficient of variation (CV)1.947617124
Kurtosis82.25682775
Mean9526.372104
Median Absolute Deviation (MAD)2710.6
Skewness7.401914459
Sum2282166280
Variance344240727.5
MonotonicityNot monotonic
2024-02-13T20:56:56.901147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 752
 
0.1%
0 623
 
0.1%
2000 163
 
< 0.1%
1000 129
 
< 0.1%
1500 126
 
< 0.1%
1666.6 124
 
< 0.1%
800 114
 
< 0.1%
2166.6 111
 
< 0.1%
2500 103
 
< 0.1%
1998.2001 96
 
< 0.1%
Other values (98628) 237222
45.4%
(Missing) 283339
54.2%
ValueCountFrequency (%)
0 623
0.1%
0.008 1
 
< 0.1%
0.060000002 1
 
< 0.1%
0.134 1
 
< 0.1%
0.2 2
 
< 0.1%
ValueCountFrequency (%)
538113 1
< 0.1%
513520 1
< 0.1%
508759.4 1
< 0.1%
501171.22 1
< 0.1%
477676.06 1
< 0.1%

mindbddpdlast24m_3658935P
Real number (ℝ)

MISSING  ZEROS 

Distinct3356
Distinct (%)1.0%
Missing187071
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean-2.818328266
Minimum-1280
Maximum4709
Zeros7767
Zeros (%)1.5%
Negative319807
Negative (%)61.2%
Memory size4.0 MiB
2024-02-13T20:56:57.047123image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1280
5-th percentile-117
Q1-38
median-17
Q3-7
95-th percentile-1
Maximum4709
Range5989
Interquartile range (IQR)31

Descriptive statistics

Standard deviation301.5312592
Coefficient of variation (CV)-106.9894032
Kurtosis145.9215432
Mean-2.818328266
Median Absolute Deviation (MAD)12
Skewness11.51537273
Sum-946482
Variance90921.10026
MonotonicityNot monotonic
2024-02-13T20:56:57.202125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3 11556
 
2.2%
-4 11555
 
2.2%
-2 10391
 
2.0%
-5 10251
 
2.0%
-9 10215
 
2.0%
-1 10131
 
1.9%
-6 9306
 
1.8%
-10 8951
 
1.7%
-7 8918
 
1.7%
-11 8814
 
1.7%
Other values (3346) 235743
45.1%
(Missing) 187071
35.8%
ValueCountFrequency (%)
-1280 1
< 0.1%
-1263 1
< 0.1%
-1234 1
< 0.1%
-1229 1
< 0.1%
-1133 1
< 0.1%
ValueCountFrequency (%)
4709 1
< 0.1%
4696 1
< 0.1%
4692 1
< 0.1%
4668 1
< 0.1%
4639 1
< 0.1%

mindbdtollast24m_4525191P
Real number (ℝ)

MISSING  ZEROS 

Distinct3349
Distinct (%)1.0%
Missing187102
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean-3.511947588
Minimum-1280
Maximum4709
Zeros7471
Zeros (%)1.4%
Negative320399
Negative (%)61.3%
Memory size4.0 MiB
2024-02-13T20:56:57.353332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1280
5-th percentile-118
Q1-39
median-18
Q3-7
95-th percentile-1
Maximum4709
Range5989
Interquartile range (IQR)32

Descriptive statistics

Standard deviation301.025905
Coefficient of variation (CV)-85.71480567
Kurtosis146.6038227
Mean-3.511947588
Median Absolute Deviation (MAD)13
Skewness11.54338363
Sum-1179312
Variance90616.59549
MonotonicityNot monotonic
2024-02-13T20:56:57.506335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4 11418
 
2.2%
-3 11408
 
2.2%
-2 10191
 
1.9%
-9 10129
 
1.9%
-5 10110
 
1.9%
-1 9850
 
1.9%
-6 9234
 
1.8%
-10 8822
 
1.7%
-7 8803
 
1.7%
-11 8690
 
1.7%
Other values (3339) 237145
45.4%
(Missing) 187102
35.8%
ValueCountFrequency (%)
-1280 1
< 0.1%
-1263 1
< 0.1%
-1234 1
< 0.1%
-1229 1
< 0.1%
-1133 1
< 0.1%
ValueCountFrequency (%)
4709 1
< 0.1%
4696 1
< 0.1%
4692 1
< 0.1%
4668 1
< 0.1%
4639 1
< 0.1%

mobilephncnt_593L
Real number (ℝ)

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.860826694
Minimum0
Maximum22
Zeros140
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:57.642324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum22
Range22
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.208000947
Coefficient of variation (CV)0.6491743435
Kurtosis8.199958721
Mean1.860826694
Median Absolute Deviation (MAD)0
Skewness2.192645184
Sum973030
Variance1.459266289
MonotonicityNot monotonic
2024-02-13T20:56:57.761314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 267648
51.2%
2 144224
27.6%
3 63724
 
12.2%
4 26905
 
5.1%
5 11472
 
2.2%
6 4741
 
0.9%
7 2104
 
0.4%
8 954
 
0.2%
9 523
 
0.1%
10 227
 
< 0.1%
Other values (12) 380
 
0.1%
ValueCountFrequency (%)
0 140
 
< 0.1%
1 267648
51.2%
2 144224
27.6%
3 63724
 
12.2%
4 26905
 
5.1%
ValueCountFrequency (%)
22 1
 
< 0.1%
21 4
< 0.1%
19 2
< 0.1%
18 1
 
< 0.1%
17 4
< 0.1%

monthsannuity_845L
Real number (ℝ)

MISSING 

Distinct167
Distinct (%)< 0.1%
Missing132270
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean28.19029163
Minimum0
Maximum167
Zeros3858
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:57.894703image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q111
median21
Q339
95-th percentile80
Maximum167
Range167
Interquartile range (IQR)28

Descriptive statistics

Standard deviation24.42491493
Coefficient of variation (CV)0.8664300197
Kurtosis2.261020409
Mean28.19029163
Median Absolute Deviation (MAD)12
Skewness1.485617026
Sum11012030
Variance596.5764695
MonotonicityNot monotonic
2024-02-13T20:56:58.062318image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 21906
 
4.2%
6 19785
 
3.8%
18 11974
 
2.3%
3 11330
 
2.2%
11 10554
 
2.0%
9 10189
 
1.9%
24 9964
 
1.9%
10 9323
 
1.8%
4 8629
 
1.7%
15 8394
 
1.6%
Other values (157) 268584
51.4%
(Missing) 132270
25.3%
ValueCountFrequency (%)
0 3858
 
0.7%
1 4387
 
0.8%
2 4423
 
0.8%
3 11330
2.2%
4 8629
1.7%
ValueCountFrequency (%)
167 1
 
< 0.1%
165 1
 
< 0.1%
164 4
< 0.1%
163 6
< 0.1%
162 4
< 0.1%

numactivecreds_622L
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5472593335
Minimum0
Maximum7
Zeros317250
Zeros (%)60.7%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:58.191476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.787824501
Coefficient of variation (CV)1.439581662
Kurtosis1.985733502
Mean0.5472593335
Median Absolute Deviation (MAD)0
Skewness1.455405002
Sum286163
Variance0.6206674444
MonotonicityNot monotonic
2024-02-13T20:56:58.308179image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 317250
60.7%
1 139573
26.7%
2 53850
 
10.3%
3 10340
 
2.0%
4 1608
 
0.3%
5 250
 
< 0.1%
6 29
 
< 0.1%
7 2
 
< 0.1%
ValueCountFrequency (%)
0 317250
60.7%
1 139573
26.7%
2 53850
 
10.3%
3 10340
 
2.0%
4 1608
 
0.3%
ValueCountFrequency (%)
7 2
 
< 0.1%
6 29
 
< 0.1%
5 250
 
< 0.1%
4 1608
 
0.3%
3 10340
2.0%

numactivecredschannel_414L
Real number (ℝ)

ZEROS 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1066356602
Minimum0
Maximum4
Zeros471119
Zeros (%)90.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:58.423143image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3324195308
Coefficient of variation (CV)3.117339267
Kurtosis9.841426305
Mean0.1066356602
Median Absolute Deviation (MAD)0
Skewness3.154609609
Sum55760
Variance0.1105027445
MonotonicityNot monotonic
2024-02-13T20:56:58.543647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
0 471119
90.1%
1 47812
 
9.1%
2 3966
 
0.8%
3 4
 
< 0.1%
4 1
 
< 0.1%
ValueCountFrequency (%)
0 471119
90.1%
1 47812
 
9.1%
2 3966
 
0.8%
3 4
 
< 0.1%
4 1
 
< 0.1%
ValueCountFrequency (%)
4 1
 
< 0.1%
3 4
 
< 0.1%
2 3966
 
0.8%
1 47812
 
9.1%
0 471119
90.1%

numactiverelcontr_750L
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30643983
Minimum0
Maximum6
Zeros380535
Zeros (%)72.8%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:58.654293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.540075717
Coefficient of variation (CV)1.762420104
Kurtosis3.859057101
Mean0.30643983
Median Absolute Deviation (MAD)0
Skewness1.804263675
Sum160238
Variance0.2916817801
MonotonicityNot monotonic
2024-02-13T20:56:58.767561image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 380535
72.8%
1 126923
 
24.3%
2 13382
 
2.6%
3 1726
 
0.3%
4 308
 
0.1%
5 27
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 380535
72.8%
1 126923
 
24.3%
2 13382
 
2.6%
3 1726
 
0.3%
4 308
 
0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 27
 
< 0.1%
4 308
 
0.1%
3 1726
 
0.3%
2 13382
2.6%

numcontrs3months_479L
Real number (ℝ)

ZEROS 

Distinct42
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3166616307
Minimum0
Maximum58
Zeros422147
Zeros (%)80.7%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:58.897077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum58
Range58
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.944722257
Coefficient of variation (CV)2.983380888
Kurtosis222.4016032
Mean0.3166616307
Median Absolute Deviation (MAD)0
Skewness9.460694248
Sum165583
Variance0.8925001429
MonotonicityNot monotonic
2024-02-13T20:56:59.042114image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 422147
80.7%
1 67972
 
13.0%
2 19885
 
3.8%
3 6511
 
1.2%
4 2737
 
0.5%
5 1369
 
0.3%
6 756
 
0.1%
7 431
 
0.1%
8 293
 
0.1%
9 193
 
< 0.1%
Other values (32) 608
 
0.1%
ValueCountFrequency (%)
0 422147
80.7%
1 67972
 
13.0%
2 19885
 
3.8%
3 6511
 
1.2%
4 2737
 
0.5%
ValueCountFrequency (%)
58 1
< 0.1%
53 1
< 0.1%
50 2
< 0.1%
49 1
< 0.1%
39 1
< 0.1%

numincomingpmts_3546848L
Real number (ℝ)

MISSING 

Distinct332
Distinct (%)0.1%
Missing133089
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean36.10983472
Minimum0
Maximum809
Zeros539
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:59.198114image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q112
median24
Q348
95-th percentile110
Maximum809
Range809
Interquartile range (IQR)36

Descriptive statistics

Standard deviation35.82571849
Coefficient of variation (CV)0.9921318879
Kurtosis7.204259329
Mean36.10983472
Median Absolute Deviation (MAD)15
Skewness2.117784971
Sum14076083
Variance1283.482105
MonotonicityNot monotonic
2024-02-13T20:56:59.363312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 15774
 
3.0%
12 14692
 
2.8%
3 10715
 
2.0%
11 9866
 
1.9%
9 9121
 
1.7%
10 8777
 
1.7%
13 8571
 
1.6%
18 8459
 
1.6%
7 8458
 
1.6%
4 8306
 
1.6%
Other values (322) 287074
54.9%
(Missing) 133089
25.5%
ValueCountFrequency (%)
0 539
 
0.1%
1 3508
 
0.7%
2 4730
0.9%
3 10715
2.0%
4 8306
1.6%
ValueCountFrequency (%)
809 1
< 0.1%
789 1
< 0.1%
674 1
< 0.1%
668 1
< 0.1%
581 1
< 0.1%

numinstlallpaidearly3d_817L
Real number (ℝ)

MISSING  ZEROS 

Distinct286
Distinct (%)0.1%
Missing130280
Missing (%)24.9%
Infinite0
Infinite (%)0.0%
Mean22.68714693
Minimum0
Maximum331
Zeros23723
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:59.526807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median13
Q329
95-th percentile79
Maximum331
Range331
Interquartile range (IQR)24

Descriptive statistics

Standard deviation27.76923842
Coefficient of variation (CV)1.224007519
Kurtosis9.073109149
Mean22.68714693
Median Absolute Deviation (MAD)10
Skewness2.565904281
Sum8907473
Variance771.1306026
MonotonicityNot monotonic
2024-02-13T20:56:59.681056image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23723
 
4.5%
1 17717
 
3.4%
3 17501
 
3.3%
6 17140
 
3.3%
2 16611
 
3.2%
4 15206
 
2.9%
5 14721
 
2.8%
12 12874
 
2.5%
7 11954
 
2.3%
8 11689
 
2.2%
Other values (276) 233486
44.7%
(Missing) 130280
24.9%
ValueCountFrequency (%)
0 23723
4.5%
1 17717
3.4%
2 16611
3.2%
3 17501
3.3%
4 15206
2.9%
ValueCountFrequency (%)
331 1
< 0.1%
315 1
< 0.1%
314 1
< 0.1%
304 1
< 0.1%
302 1
< 0.1%

numinstls_657L
Real number (ℝ)

ZEROS 

Distinct289
Distinct (%)0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean8.700732454
Minimum0
Maximum448
Zeros346981
Zeros (%)66.4%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:56:59.833829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile44
Maximum448
Range448
Interquartile range (IQR)12

Descriptive statistics

Standard deviation17.68747334
Coefficient of variation (CV)2.032871765
Kurtosis29.0248832
Mean8.700732454
Median Absolute Deviation (MAD)0
Skewness3.92240446
Sum4549613
Variance312.8467133
MonotonicityNot monotonic
2024-02-13T20:56:59.983815image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 346981
66.4%
12 44194
 
8.5%
24 28448
 
5.4%
36 14038
 
2.7%
18 11692
 
2.2%
6 11217
 
2.1%
48 9455
 
1.8%
30 6686
 
1.3%
16 6082
 
1.2%
3 2995
 
0.6%
Other values (279) 41112
 
7.9%
ValueCountFrequency (%)
0 346981
66.4%
3 2995
 
0.6%
4 1688
 
0.3%
5 416
 
0.1%
6 11217
 
2.1%
ValueCountFrequency (%)
448 1
< 0.1%
406 1
< 0.1%
398 1
< 0.1%
391 1
< 0.1%
380 1
< 0.1%

numinstlsallpaid_934L
Real number (ℝ)

MISSING  ZEROS 

Distinct301
Distinct (%)0.1%
Missing130280
Missing (%)24.9%
Infinite0
Infinite (%)0.0%
Mean30.35637585
Minimum0
Maximum385
Zeros8483
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:00.145557image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q19
median20
Q340
95-th percentile97
Maximum385
Range385
Interquartile range (IQR)31

Descriptive statistics

Standard deviation32.43920558
Coefficient of variation (CV)1.068612595
Kurtosis6.602041609
Mean30.35637585
Median Absolute Deviation (MAD)14
Skewness2.218055659
Sum11918581
Variance1052.302059
MonotonicityNot monotonic
2024-02-13T20:57:00.325465image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 16733
 
3.2%
12 14200
 
2.7%
3 13829
 
2.6%
5 11428
 
2.2%
4 11024
 
2.1%
13 10414
 
2.0%
9 10323
 
2.0%
10 10116
 
1.9%
11 9989
 
1.9%
8 9058
 
1.7%
Other values (291) 275508
52.7%
(Missing) 130280
24.9%
ValueCountFrequency (%)
0 8483
1.6%
1 7533
1.4%
2 8628
1.7%
3 13829
2.6%
4 11024
2.1%
ValueCountFrequency (%)
385 1
< 0.1%
341 1
< 0.1%
326 1
< 0.1%
314 1
< 0.1%
313 1
< 0.1%

numinstlswithdpd10_728L
Real number (ℝ)

MISSING  ZEROS 

Distinct142
Distinct (%)< 0.1%
Missing133502
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean3.346019517
Minimum0
Maximum187
Zeros272706
Zeros (%)52.2%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:00.481467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile21
Maximum187
Range187
Interquartile range (IQR)1

Descriptive statistics

Standard deviation9.194883668
Coefficient of variation (CV)2.748006585
Kurtosis28.96321715
Mean3.346019517
Median Absolute Deviation (MAD)0
Skewness4.581287119
Sum1302940
Variance84.54588566
MonotonicityNot monotonic
2024-02-13T20:57:00.641528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 272706
52.2%
1 22772
 
4.4%
2 11450
 
2.2%
4 8735
 
1.7%
3 8498
 
1.6%
5 6713
 
1.3%
6 5553
 
1.1%
7 4437
 
0.8%
8 3537
 
0.7%
9 2979
 
0.6%
Other values (132) 42020
 
8.0%
(Missing) 133502
25.5%
ValueCountFrequency (%)
0 272706
52.2%
1 22772
 
4.4%
2 11450
 
2.2%
3 8498
 
1.6%
4 8735
 
1.7%
ValueCountFrequency (%)
187 1
< 0.1%
160 1
< 0.1%
157 1
< 0.1%
149 1
< 0.1%
147 1
< 0.1%

numinstlswithdpd5_4187116L
Real number (ℝ)

MISSING  ZEROS 

Distinct78
Distinct (%)< 0.1%
Missing132270
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean2.451010158
Minimum0
Maximum85
Zeros233690
Zeros (%)44.7%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:00.801529image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile13
Maximum85
Range85
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.067194802
Coefficient of variation (CV)2.067390372
Kurtosis17.59401282
Mean2.451010158
Median Absolute Deviation (MAD)0
Skewness3.499932765
Sum957443
Variance25.67646316
MonotonicityNot monotonic
2024-02-13T20:57:00.949841image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 233690
44.7%
1 37280
 
7.1%
2 20040
 
3.8%
3 13987
 
2.7%
4 13971
 
2.7%
5 11540
 
2.2%
6 9383
 
1.8%
7 7463
 
1.4%
8 6389
 
1.2%
9 5289
 
1.0%
Other values (68) 31600
 
6.0%
(Missing) 132270
25.3%
ValueCountFrequency (%)
0 233690
44.7%
1 37280
 
7.1%
2 20040
 
3.8%
3 13987
 
2.7%
4 13971
 
2.7%
ValueCountFrequency (%)
85 1
< 0.1%
84 1
< 0.1%
80 1
< 0.1%
79 1
< 0.1%
77 1
< 0.1%

numinstlswithoutdpd_562L
Real number (ℝ)

MISSING 

Distinct367
Distinct (%)0.1%
Missing133502
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean35.57582178
Minimum0
Maximum861
Zeros1675
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:01.097880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q111
median24
Q348
95-th percentile110
Maximum861
Range861
Interquartile range (IQR)37

Descriptive statistics

Standard deviation36.26741081
Coefficient of variation (CV)1.019439861
Kurtosis8.904607307
Mean35.57582178
Median Absolute Deviation (MAD)16
Skewness2.255182659
Sum13853225
Variance1315.325087
MonotonicityNot monotonic
2024-02-13T20:57:01.251847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 12836
 
2.5%
4 12246
 
2.3%
13 11819
 
2.3%
6 10969
 
2.1%
12 9609
 
1.8%
5 9581
 
1.8%
14 9506
 
1.8%
11 9469
 
1.8%
10 8739
 
1.7%
8 8407
 
1.6%
Other values (357) 286219
54.7%
(Missing) 133502
25.5%
ValueCountFrequency (%)
0 1675
 
0.3%
1 5538
1.1%
2 6756
1.3%
3 8141
1.6%
4 12246
2.3%
ValueCountFrequency (%)
861 1
< 0.1%
733 1
< 0.1%
673 1
< 0.1%
616 1
< 0.1%
615 1
< 0.1%

numinstmatpaidtearly2d_4499204L
Real number (ℝ)

MISSING  ZEROS 

Distinct287
Distinct (%)0.1%
Missing132270
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean24.38533197
Minimum0
Maximum347
Zeros16680
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:01.400635image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median14
Q332
95-th percentile83
Maximum347
Range347
Interquartile range (IQR)26

Descriptive statistics

Standard deviation28.87743431
Coefficient of variation (CV)1.184213294
Kurtosis8.440217972
Mean24.38533197
Median Absolute Deviation (MAD)10
Skewness2.486183595
Sum9525691
Variance833.9062124
MonotonicityNot monotonic
2024-02-13T20:57:01.557773image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 16842
 
3.2%
3 16837
 
3.2%
0 16680
 
3.2%
2 15155
 
2.9%
1 15138
 
2.9%
5 14561
 
2.8%
4 14552
 
2.8%
12 13159
 
2.5%
8 11610
 
2.2%
9 11508
 
2.2%
Other values (277) 244590
46.8%
(Missing) 132270
25.3%
ValueCountFrequency (%)
0 16680
3.2%
1 15138
2.9%
2 15155
2.9%
3 16837
3.2%
4 14552
2.8%
ValueCountFrequency (%)
347 1
< 0.1%
318 1
< 0.1%
316 1
< 0.1%
308 1
< 0.1%
304 1
< 0.1%

numinstpaid_4499208L
Real number (ℝ)

MISSING 

Distinct318
Distinct (%)0.1%
Missing132270
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean35.82541881
Minimum0
Maximum393
Zeros2135
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:01.966781image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q112
median24
Q348
95-th percentile110
Maximum393
Range393
Interquartile range (IQR)36

Descriptive statistics

Standard deviation35.74974675
Coefficient of variation (CV)0.997887755
Kurtosis5.687115639
Mean35.82541881
Median Absolute Deviation (MAD)15
Skewness2.075205261
Sum13994555
Variance1278.044392
MonotonicityNot monotonic
2024-02-13T20:57:02.150261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 20550
 
3.9%
6 19976
 
3.8%
3 11164
 
2.1%
18 10836
 
2.1%
24 9745
 
1.9%
9 9109
 
1.7%
13 8978
 
1.7%
10 8465
 
1.6%
4 7814
 
1.5%
15 7363
 
1.4%
Other values (308) 276632
52.9%
(Missing) 132270
25.3%
ValueCountFrequency (%)
0 2135
 
0.4%
1 3374
 
0.6%
2 3732
 
0.7%
3 11164
2.1%
4 7814
1.5%
ValueCountFrequency (%)
393 1
< 0.1%
369 1
< 0.1%
350 1
< 0.1%
347 1
< 0.1%
344 1
< 0.1%

numinstpaidearly3d_3546850L
Real number (ℝ)

MISSING  ZEROS 

Distinct279
Distinct (%)0.1%
Missing130280
Missing (%)24.9%
Infinite0
Infinite (%)0.0%
Mean21.8772407
Minimum0
Maximum322
Zeros24488
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:02.316240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median12
Q328
95-th percentile76
Maximum322
Range322
Interquartile range (IQR)23

Descriptive statistics

Standard deviation26.76484871
Coefficient of variation (CV)1.223410624
Kurtosis9.217510924
Mean21.8772407
Median Absolute Deviation (MAD)9
Skewness2.574281278
Sum8589486
Variance716.3571262
MonotonicityNot monotonic
2024-02-13T20:57:02.480556image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24488
 
4.7%
1 18278
 
3.5%
3 17960
 
3.4%
2 17303
 
3.3%
6 16832
 
3.2%
4 15592
 
3.0%
5 15336
 
2.9%
12 12403
 
2.4%
7 12265
 
2.3%
8 11925
 
2.3%
Other values (269) 230240
44.0%
(Missing) 130280
24.9%
ValueCountFrequency (%)
0 24488
4.7%
1 18278
3.5%
2 17303
3.3%
3 17960
3.4%
4 15592
3.0%
ValueCountFrequency (%)
322 1
< 0.1%
314 1
< 0.1%
313 1
< 0.1%
300 1
< 0.1%
299 1
< 0.1%

numinstpaidearly3dest_4493216L
Real number (ℝ)

MISSING  ZEROS 

Distinct279
Distinct (%)0.1%
Missing132270
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean21.94611809
Minimum0
Maximum322
Zeros22623
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:02.638057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median13
Q328
95-th percentile76
Maximum322
Range322
Interquartile range (IQR)23

Descriptive statistics

Standard deviation26.75237501
Coefficient of variation (CV)1.2190026
Kurtosis9.213078913
Mean21.94611809
Median Absolute Deviation (MAD)10
Skewness2.573498992
Sum8572856
Variance715.6895687
MonotonicityNot monotonic
2024-02-13T20:57:02.792018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22623
 
4.3%
1 18482
 
3.5%
3 17980
 
3.4%
2 17220
 
3.3%
6 16874
 
3.2%
4 15620
 
3.0%
5 15248
 
2.9%
12 12423
 
2.4%
7 12274
 
2.3%
8 11935
 
2.3%
Other values (269) 229953
44.0%
(Missing) 132270
25.3%
ValueCountFrequency (%)
0 22623
4.3%
1 18482
3.5%
2 17220
3.3%
3 17980
3.4%
4 15620
3.0%
ValueCountFrequency (%)
322 1
< 0.1%
314 1
< 0.1%
313 1
< 0.1%
300 1
< 0.1%
299 1
< 0.1%

numinstpaidearly5d_1087L
Real number (ℝ)

MISSING  ZEROS 

Distinct50
Distinct (%)< 0.1%
Missing132270
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean3.265369965
Minimum0
Maximum59
Zeros139915
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:02.942966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile12
Maximum59
Range59
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.493627862
Coefficient of variation (CV)1.376146627
Kurtosis8.849969062
Mean3.265369965
Median Absolute Deviation (MAD)2
Skewness2.406796283
Sum1275558
Variance20.19269136
MonotonicityNot monotonic
2024-02-13T20:57:03.091100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 139915
26.8%
1 52924
 
10.1%
2 37673
 
7.2%
3 32299
 
6.2%
4 23590
 
4.5%
5 19867
 
3.8%
6 18994
 
3.6%
7 10593
 
2.0%
8 9280
 
1.8%
9 8495
 
1.6%
Other values (40) 37002
 
7.1%
(Missing) 132270
25.3%
ValueCountFrequency (%)
0 139915
26.8%
1 52924
 
10.1%
2 37673
 
7.2%
3 32299
 
6.2%
4 23590
 
4.5%
ValueCountFrequency (%)
59 1
 
< 0.1%
48 16
< 0.1%
47 18
< 0.1%
46 10
< 0.1%
45 4
 
< 0.1%

numinstpaidearly5dest_4493211L
Real number (ℝ)

MISSING  ZEROS 

Distinct50
Distinct (%)< 0.1%
Missing132270
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean3.265369965
Minimum0
Maximum59
Zeros139915
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:03.239618image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile12
Maximum59
Range59
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.493627862
Coefficient of variation (CV)1.376146627
Kurtosis8.849969062
Mean3.265369965
Median Absolute Deviation (MAD)2
Skewness2.406796283
Sum1275558
Variance20.19269136
MonotonicityNot monotonic
2024-02-13T20:57:03.390702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 139915
26.8%
1 52924
 
10.1%
2 37673
 
7.2%
3 32299
 
6.2%
4 23590
 
4.5%
5 19867
 
3.8%
6 18994
 
3.6%
7 10593
 
2.0%
8 9280
 
1.8%
9 8495
 
1.6%
Other values (40) 37002
 
7.1%
(Missing) 132270
25.3%
ValueCountFrequency (%)
0 139915
26.8%
1 52924
 
10.1%
2 37673
 
7.2%
3 32299
 
6.2%
4 23590
 
4.5%
ValueCountFrequency (%)
59 1
 
< 0.1%
48 16
< 0.1%
47 18
< 0.1%
46 10
< 0.1%
45 4
 
< 0.1%

numinstpaidearly5dobd_4499205L
Real number (ℝ)

MISSING  ZEROS 

Distinct263
Distinct (%)0.1%
Missing132270
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean17.67732034
Minimum0
Maximum306
Zeros43430
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:03.544702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median9
Q323
95-th percentile65
Maximum306
Range306
Interquartile range (IQR)20

Descriptive statistics

Standard deviation23.59842079
Coefficient of variation (CV)1.334954638
Kurtosis11.13091915
Mean17.67732034
Median Absolute Deviation (MAD)8
Skewness2.802783755
Sum6905327
Variance556.8854637
MonotonicityNot monotonic
2024-02-13T20:57:03.701707image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 43430
 
8.3%
1 25172
 
4.8%
2 20839
 
4.0%
3 19454
 
3.7%
4 17110
 
3.3%
6 16753
 
3.2%
5 16208
 
3.1%
7 12704
 
2.4%
8 12099
 
2.3%
9 11690
 
2.2%
Other values (253) 195173
37.3%
(Missing) 132270
25.3%
ValueCountFrequency (%)
0 43430
8.3%
1 25172
4.8%
2 20839
4.0%
3 19454
3.7%
4 17110
 
3.3%
ValueCountFrequency (%)
306 1
< 0.1%
300 1
< 0.1%
298 1
< 0.1%
289 1
< 0.1%
272 1
< 0.1%

numinstpaidearly_338L
Real number (ℝ)

MISSING  ZEROS 

Distinct263
Distinct (%)0.1%
Missing132270
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean17.67732034
Minimum0
Maximum306
Zeros43430
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:03.851371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median9
Q323
95-th percentile65
Maximum306
Range306
Interquartile range (IQR)20

Descriptive statistics

Standard deviation23.59842079
Coefficient of variation (CV)1.334954638
Kurtosis11.13091915
Mean17.67732034
Median Absolute Deviation (MAD)8
Skewness2.802783755
Sum6905327
Variance556.8854637
MonotonicityNot monotonic
2024-02-13T20:57:04.006294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 43430
 
8.3%
1 25172
 
4.8%
2 20839
 
4.0%
3 19454
 
3.7%
4 17110
 
3.3%
6 16753
 
3.2%
5 16208
 
3.1%
7 12704
 
2.4%
8 12099
 
2.3%
9 11690
 
2.2%
Other values (253) 195173
37.3%
(Missing) 132270
25.3%
ValueCountFrequency (%)
0 43430
8.3%
1 25172
4.8%
2 20839
4.0%
3 19454
3.7%
4 17110
 
3.3%
ValueCountFrequency (%)
306 1
< 0.1%
300 1
< 0.1%
298 1
< 0.1%
289 1
< 0.1%
272 1
< 0.1%

numinstpaidearlyest_4493214L
Real number (ℝ)

MISSING  ZEROS 

Distinct263
Distinct (%)0.1%
Missing132270
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean17.67732034
Minimum0
Maximum306
Zeros43430
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:04.155272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median9
Q323
95-th percentile65
Maximum306
Range306
Interquartile range (IQR)20

Descriptive statistics

Standard deviation23.59842079
Coefficient of variation (CV)1.334954638
Kurtosis11.13091915
Mean17.67732034
Median Absolute Deviation (MAD)8
Skewness2.802783755
Sum6905327
Variance556.8854637
MonotonicityNot monotonic
2024-02-13T20:57:04.310258image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 43430
 
8.3%
1 25172
 
4.8%
2 20839
 
4.0%
3 19454
 
3.7%
4 17110
 
3.3%
6 16753
 
3.2%
5 16208
 
3.1%
7 12704
 
2.4%
8 12099
 
2.3%
9 11690
 
2.2%
Other values (253) 195173
37.3%
(Missing) 132270
25.3%
ValueCountFrequency (%)
0 43430
8.3%
1 25172
4.8%
2 20839
4.0%
3 19454
3.7%
4 17110
 
3.3%
ValueCountFrequency (%)
306 1
< 0.1%
300 1
< 0.1%
298 1
< 0.1%
289 1
< 0.1%
272 1
< 0.1%

numinstpaidlastcontr_4325080L
Real number (ℝ)

MISSING  ZEROS 

Distinct64
Distinct (%)< 0.1%
Missing132270
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean7.729476848
Minimum0
Maximum77
Zeros34627
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:04.482666image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q312
95-th percentile20
Maximum77
Range77
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.287737858
Coefficient of variation (CV)0.8134752172
Kurtosis3.990644777
Mean7.729476848
Median Absolute Deviation (MAD)4
Skewness1.527741591
Sum3019381
Variance39.53564737
MonotonicityNot monotonic
2024-02-13T20:57:04.655794image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 54297
10.4%
3 42114
 
8.1%
12 39249
 
7.5%
0 34627
 
6.6%
4 27170
 
5.2%
5 22807
 
4.4%
2 16369
 
3.1%
9 16333
 
3.1%
8 16228
 
3.1%
7 16024
 
3.1%
Other values (54) 105414
20.2%
(Missing) 132270
25.3%
ValueCountFrequency (%)
0 34627
6.6%
1 15918
 
3.0%
2 16369
 
3.1%
3 42114
8.1%
4 27170
5.2%
ValueCountFrequency (%)
77 1
< 0.1%
74 1
< 0.1%
72 1
< 0.1%
69 1
< 0.1%
67 1
< 0.1%

numinstpaidlate1d_3546852L
Real number (ℝ)

MISSING  ZEROS 

Distinct114
Distinct (%)< 0.1%
Missing132270
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean5.990080178
Minimum0
Maximum137
Zeros127635
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:04.809477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q38
95-th percentile24
Maximum137
Range137
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.171215338
Coefficient of variation (CV)1.531067209
Kurtosis11.27082423
Mean5.990080178
Median Absolute Deviation (MAD)2
Skewness2.786049411
Sum2339917
Variance84.11119077
MonotonicityNot monotonic
2024-02-13T20:57:04.957490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 127635
24.4%
1 46413
 
8.9%
2 28924
 
5.5%
3 21823
 
4.2%
4 19172
 
3.7%
5 16492
 
3.2%
6 14294
 
2.7%
7 11881
 
2.3%
8 10544
 
2.0%
9 9349
 
1.8%
Other values (104) 84105
16.1%
(Missing) 132270
25.3%
ValueCountFrequency (%)
0 127635
24.4%
1 46413
 
8.9%
2 28924
 
5.5%
3 21823
 
4.2%
4 19172
 
3.7%
ValueCountFrequency (%)
137 1
< 0.1%
131 1
< 0.1%
125 1
< 0.1%
119 1
< 0.1%
112 1
< 0.1%

numinstregularpaid_973L
Real number (ℝ)

MISSING 

Distinct317
Distinct (%)0.1%
Missing132270
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean35.78720125
Minimum0
Maximum393
Zeros2135
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:05.112614image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q112
median24
Q348
95-th percentile110
Maximum393
Range393
Interquartile range (IQR)36

Descriptive statistics

Standard deviation35.71811325
Coefficient of variation (CV)0.9980694772
Kurtosis5.691495903
Mean35.78720125
Median Absolute Deviation (MAD)15
Skewness2.075654674
Sum13979626
Variance1275.783614
MonotonicityNot monotonic
2024-02-13T20:57:05.279650image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 20559
 
3.9%
6 20006
 
3.8%
3 11158
 
2.1%
18 10832
 
2.1%
24 9713
 
1.9%
9 9107
 
1.7%
13 9074
 
1.7%
10 8479
 
1.6%
4 7845
 
1.5%
15 7347
 
1.4%
Other values (307) 276512
52.9%
(Missing) 132270
25.3%
ValueCountFrequency (%)
0 2135
 
0.4%
1 3618
 
0.7%
2 3615
 
0.7%
3 11158
2.1%
4 7845
1.5%
ValueCountFrequency (%)
393 1
< 0.1%
369 1
< 0.1%
350 1
< 0.1%
347 1
< 0.1%
344 1
< 0.1%

numinstregularpaidest_4493210L
Real number (ℝ)

MISSING 

Distinct318
Distinct (%)0.1%
Missing132270
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean35.82541881
Minimum0
Maximum393
Zeros2135
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:05.441428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q112
median24
Q348
95-th percentile110
Maximum393
Range393
Interquartile range (IQR)36

Descriptive statistics

Standard deviation35.74974675
Coefficient of variation (CV)0.997887755
Kurtosis5.687115639
Mean35.82541881
Median Absolute Deviation (MAD)15
Skewness2.075205261
Sum13994555
Variance1278.044392
MonotonicityNot monotonic
2024-02-13T20:57:05.614408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 20550
 
3.9%
6 19976
 
3.8%
3 11164
 
2.1%
18 10836
 
2.1%
24 9745
 
1.9%
9 9109
 
1.7%
13 8978
 
1.7%
10 8465
 
1.6%
4 7814
 
1.5%
15 7363
 
1.4%
Other values (308) 276632
52.9%
(Missing) 132270
25.3%
ValueCountFrequency (%)
0 2135
 
0.4%
1 3374
 
0.6%
2 3732
 
0.7%
3 11164
2.1%
4 7814
1.5%
ValueCountFrequency (%)
393 1
< 0.1%
369 1
< 0.1%
350 1
< 0.1%
347 1
< 0.1%
344 1
< 0.1%

numinsttopaygr_769L
Real number (ℝ)

MISSING  ZEROS 

Distinct104
Distinct (%)< 0.1%
Missing132270
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean6.005957013
Minimum0
Maximum152
Zeros213483
Zeros (%)40.8%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:05.778334image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39
95-th percentile29
Maximum152
Range152
Interquartile range (IQR)9

Descriptive statistics

Standard deviation10.42265709
Coefficient of variation (CV)1.735386561
Kurtosis7.159355727
Mean6.005957013
Median Absolute Deviation (MAD)0
Skewness2.423007968
Sum2346119
Variance108.6317807
MonotonicityNot monotonic
2024-02-13T20:57:05.932336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 213483
40.8%
1 15766
 
3.0%
6 9717
 
1.9%
2 9655
 
1.8%
4 9613
 
1.8%
3 9323
 
1.8%
5 9033
 
1.7%
9 8469
 
1.6%
7 8344
 
1.6%
8 7991
 
1.5%
Other values (94) 89238
17.1%
(Missing) 132270
25.3%
ValueCountFrequency (%)
0 213483
40.8%
1 15766
 
3.0%
2 9655
 
1.8%
3 9323
 
1.8%
4 9613
 
1.8%
ValueCountFrequency (%)
152 1
< 0.1%
122 1
< 0.1%
117 1
< 0.1%
116 2
< 0.1%
113 1
< 0.1%

numinsttopaygrest_4493213L
Real number (ℝ)

MISSING  ZEROS 

Distinct104
Distinct (%)< 0.1%
Missing132270
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean6.005957013
Minimum0
Maximum152
Zeros213483
Zeros (%)40.8%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:06.084498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39
95-th percentile29
Maximum152
Range152
Interquartile range (IQR)9

Descriptive statistics

Standard deviation10.42265709
Coefficient of variation (CV)1.735386561
Kurtosis7.159355727
Mean6.005957013
Median Absolute Deviation (MAD)0
Skewness2.423007968
Sum2346119
Variance108.6317807
MonotonicityNot monotonic
2024-02-13T20:57:06.239201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 213483
40.8%
1 15766
 
3.0%
6 9717
 
1.9%
2 9655
 
1.8%
4 9613
 
1.8%
3 9323
 
1.8%
5 9033
 
1.7%
9 8469
 
1.6%
7 8344
 
1.6%
8 7991
 
1.5%
Other values (94) 89238
17.1%
(Missing) 132270
25.3%
ValueCountFrequency (%)
0 213483
40.8%
1 15766
 
3.0%
2 9655
 
1.8%
3 9323
 
1.8%
4 9613
 
1.8%
ValueCountFrequency (%)
152 1
< 0.1%
122 1
< 0.1%
117 1
< 0.1%
116 2
< 0.1%
113 1
< 0.1%

numinstunpaidmax_3546851L
Real number (ℝ)

MISSING  ZEROS 

Distinct63
Distinct (%)< 0.1%
Missing132270
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean5.439559483
Minimum0
Maximum72
Zeros212440
Zeros (%)40.6%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:06.394243image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile25
Maximum72
Range72
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.044310505
Coefficient of variation (CV)1.662691719
Kurtosis4.540367978
Mean5.439559483
Median Absolute Deviation (MAD)0
Skewness2.107537211
Sum2124866
Variance81.79955252
MonotonicityNot monotonic
2024-02-13T20:57:06.544902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 212440
40.6%
1 16578
 
3.2%
6 10390
 
2.0%
5 9874
 
1.9%
2 9841
 
1.9%
3 9827
 
1.9%
4 9740
 
1.9%
9 9219
 
1.8%
7 8990
 
1.7%
8 8469
 
1.6%
Other values (53) 85264
16.3%
(Missing) 132270
25.3%
ValueCountFrequency (%)
0 212440
40.6%
1 16578
 
3.2%
2 9841
 
1.9%
3 9827
 
1.9%
4 9740
 
1.9%
ValueCountFrequency (%)
72 1
 
< 0.1%
61 1
 
< 0.1%
60 12
< 0.1%
59 20
< 0.1%
58 14
< 0.1%

numinstunpaidmaxest_4493212L
Real number (ℝ)

MISSING  ZEROS 

Distinct63
Distinct (%)< 0.1%
Missing132270
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean5.439559483
Minimum0
Maximum72
Zeros212440
Zeros (%)40.6%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:06.719730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile25
Maximum72
Range72
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.044310505
Coefficient of variation (CV)1.662691719
Kurtosis4.540367978
Mean5.439559483
Median Absolute Deviation (MAD)0
Skewness2.107537211
Sum2124866
Variance81.79955252
MonotonicityNot monotonic
2024-02-13T20:57:06.876197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 212440
40.6%
1 16578
 
3.2%
6 10390
 
2.0%
5 9874
 
1.9%
2 9841
 
1.9%
3 9827
 
1.9%
4 9740
 
1.9%
9 9219
 
1.8%
7 8990
 
1.7%
8 8469
 
1.6%
Other values (53) 85264
16.3%
(Missing) 132270
25.3%
ValueCountFrequency (%)
0 212440
40.6%
1 16578
 
3.2%
2 9841
 
1.9%
3 9827
 
1.9%
4 9740
 
1.9%
ValueCountFrequency (%)
72 1
 
< 0.1%
61 1
 
< 0.1%
60 12
< 0.1%
59 20
< 0.1%
58 14
< 0.1%

numnotactivated_1143L
Real number (ℝ)

ZEROS 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02893276369
Minimum0
Maximum4
Zeros509261
Zeros (%)97.4%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:07.001196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1856964054
Coefficient of variation (CV)6.418204889
Kurtosis66.64310748
Mean0.02893276369
Median Absolute Deviation (MAD)0
Skewness7.401674486
Sum15129
Variance0.03448315497
MonotonicityNot monotonic
2024-02-13T20:57:07.116201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
0 509261
97.4%
1 12315
 
2.4%
2 1184
 
0.2%
3 122
 
< 0.1%
4 20
 
< 0.1%
ValueCountFrequency (%)
0 509261
97.4%
1 12315
 
2.4%
2 1184
 
0.2%
3 122
 
< 0.1%
4 20
 
< 0.1%
ValueCountFrequency (%)
4 20
 
< 0.1%
3 122
 
< 0.1%
2 1184
 
0.2%
1 12315
 
2.4%
0 509261
97.4%

numpmtchanneldd_318L
Real number (ℝ)

ZEROS 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03221636177
Minimum0
Maximum4
Zeros506919
Zeros (%)96.9%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:07.233551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1865697661
Coefficient of variation (CV)5.791149461
Kurtosis46.06119592
Mean0.03221636177
Median Absolute Deviation (MAD)0
Skewness6.287591273
Sum16846
Variance0.03480827761
MonotonicityNot monotonic
2024-02-13T20:57:07.351553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
0 506919
96.9%
1 15194
 
2.9%
2 727
 
0.1%
3 50
 
< 0.1%
4 12
 
< 0.1%
ValueCountFrequency (%)
0 506919
96.9%
1 15194
 
2.9%
2 727
 
0.1%
3 50
 
< 0.1%
4 12
 
< 0.1%
ValueCountFrequency (%)
4 12
 
< 0.1%
3 50
 
< 0.1%
2 727
 
0.1%
1 15194
 
2.9%
0 506919
96.9%

numrejects9m_859L
Real number (ℝ)

ZEROS 

Distinct62
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4594991031
Minimum0
Maximum153
Zeros415273
Zeros (%)79.4%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:07.493889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum153
Range153
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.579766206
Coefficient of variation (CV)3.438018041
Kurtosis723.03156
Mean0.4594991031
Median Absolute Deviation (MAD)0
Skewness14.86047304
Sum240273
Variance2.495661267
MonotonicityNot monotonic
2024-02-13T20:57:07.658051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 415273
79.4%
1 61336
 
11.7%
2 21182
 
4.1%
3 9829
 
1.9%
4 5054
 
1.0%
5 3039
 
0.6%
6 1868
 
0.4%
7 1322
 
0.3%
8 872
 
0.2%
9 655
 
0.1%
Other values (52) 2472
 
0.5%
ValueCountFrequency (%)
0 415273
79.4%
1 61336
 
11.7%
2 21182
 
4.1%
3 9829
 
1.9%
4 5054
 
1.0%
ValueCountFrequency (%)
153 1
< 0.1%
150 1
< 0.1%
147 1
< 0.1%
125 1
< 0.1%
103 1
< 0.1%

opencred_647L
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing84377
Missing (%)16.1%
Memory size4.0 MiB
False
421390 
True
 
17135
(Missing)
84377 
ValueCountFrequency (%)
False 421390
80.6%
True 17135
 
3.3%
(Missing) 84377
 
16.1%
2024-02-13T20:57:07.797015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

paytype1st_925L
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing57514
Missing (%)11.0%
Memory size4.0 MiB
2024-02-13T20:57:07.909051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters2326940
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOTHER
2nd rowOTHER
3rd rowOTHER
4th rowOTHER
5th rowOTHER
ValueCountFrequency (%)
other 465388
100.0%
2024-02-13T20:57:08.158733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 465388
20.0%
T 465388
20.0%
H 465388
20.0%
E 465388
20.0%
R 465388
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2326940
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 465388
20.0%
T 465388
20.0%
H 465388
20.0%
E 465388
20.0%
R 465388
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2326940
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 465388
20.0%
T 465388
20.0%
H 465388
20.0%
E 465388
20.0%
R 465388
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2326940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 465388
20.0%
T 465388
20.0%
H 465388
20.0%
E 465388
20.0%
R 465388
20.0%

paytype_783L
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing57514
Missing (%)11.0%
Memory size4.0 MiB
2024-02-13T20:57:08.290813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters2326940
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOTHER
2nd rowOTHER
3rd rowOTHER
4th rowOTHER
5th rowOTHER
ValueCountFrequency (%)
other 465388
100.0%
2024-02-13T20:57:08.531936image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 465388
20.0%
T 465388
20.0%
H 465388
20.0%
E 465388
20.0%
R 465388
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2326940
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 465388
20.0%
T 465388
20.0%
H 465388
20.0%
E 465388
20.0%
R 465388
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2326940
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 465388
20.0%
T 465388
20.0%
H 465388
20.0%
E 465388
20.0%
R 465388
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2326940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 465388
20.0%
T 465388
20.0%
H 465388
20.0%
E 465388
20.0%
R 465388
20.0%
Distinct569
Distinct (%)7.2%
Missing515040
Missing (%)98.5%
Memory size4.0 MiB
2024-02-13T20:57:08.785024image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters78620
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)0.9%

Sample

1st row2019-03-05
2nd row2019-03-17
3rd row2019-08-24
4th row2019-06-15
5th row2019-12-03
ValueCountFrequency (%)
2020-06-10 353
 
4.5%
2020-06-06 285
 
3.6%
2020-06-22 233
 
3.0%
2020-06-20 232
 
3.0%
2020-06-24 230
 
2.9%
2020-06-23 223
 
2.8%
2020-06-19 203
 
2.6%
2020-05-26 192
 
2.4%
2020-05-28 184
 
2.3%
2020-06-21 183
 
2.3%
Other values (559) 5544
70.5%
2024-02-13T20:57:09.155000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 24330
30.9%
2 18246
23.2%
- 15724
20.0%
1 6259
 
8.0%
6 5045
 
6.4%
9 2574
 
3.3%
5 2144
 
2.7%
3 1418
 
1.8%
8 1031
 
1.3%
4 1006
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62896
80.0%
Dash Punctuation 15724
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24330
38.7%
2 18246
29.0%
1 6259
 
10.0%
6 5045
 
8.0%
9 2574
 
4.1%
5 2144
 
3.4%
3 1418
 
2.3%
8 1031
 
1.6%
4 1006
 
1.6%
7 843
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 15724
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 78620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24330
30.9%
2 18246
23.2%
- 15724
20.0%
1 6259
 
8.0%
6 5045
 
6.4%
9 2574
 
3.3%
5 2144
 
2.7%
3 1418
 
1.8%
8 1031
 
1.3%
4 1006
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24330
30.9%
2 18246
23.2%
- 15724
20.0%
1 6259
 
8.0%
6 5045
 
6.4%
9 2574
 
3.3%
5 2144
 
2.7%
3 1418
 
1.8%
8 1031
 
1.3%
4 1006
 
1.3%

pctinstlsallpaidearl3d_427L
Real number (ℝ)

MISSING  ZEROS 

Distinct8862
Distinct (%)2.3%
Missing134695
Missing (%)25.8%
Infinite0
Infinite (%)0.0%
Mean0.5901359182
Minimum0
Maximum11
Zeros20535
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:09.315008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.34884
median0.6092
Q30.83333
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0.48449

Descriptive statistics

Standard deviation0.331022332
Coefficient of variation (CV)0.5609255796
Kurtosis19.87594655
Mean0.5901359182
Median Absolute Deviation (MAD)0.23695
Skewness1.418755146
Sum229094.8944
Variance0.1095757843
MonotonicityNot monotonic
2024-02-13T20:57:09.465976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 28679
 
5.5%
0 20535
 
3.9%
0.5 11712
 
2.2%
0.66667 10028
 
1.9%
0.33333 9494
 
1.8%
0.83333 5786
 
1.1%
0.75 5600
 
1.1%
0.16667 5265
 
1.0%
0.25 4891
 
0.9%
0.8 3710
 
0.7%
Other values (8852) 282507
54.0%
(Missing) 134695
25.8%
ValueCountFrequency (%)
0 20535
3.9%
0.00943 1
 
< 0.1%
0.01124 1
 
< 0.1%
0.01163 1
 
< 0.1%
0.01205 2
 
< 0.1%
ValueCountFrequency (%)
11 1
< 0.1%
8 2
< 0.1%
7.66667 1
< 0.1%
7 2
< 0.1%
6.33333 1
< 0.1%

pctinstlsallpaidlat10d_839L
Real number (ℝ)

MISSING  ZEROS 

Distinct4178
Distinct (%)1.1%
Missing135471
Missing (%)25.9%
Infinite0
Infinite (%)0.0%
Mean0.08131768103
Minimum0
Maximum1
Zeros212692
Zeros (%)40.7%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:09.618964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.08696
95-th percentile0.41935
Maximum1
Range1
Interquartile range (IQR)0.08696

Descriptive statistics

Standard deviation0.1613216776
Coefficient of variation (CV)1.983845032
Kurtosis10.19424252
Mean0.08131768103
Median Absolute Deviation (MAD)0
Skewness2.992520455
Sum31504.99048
Variance0.02602468365
MonotonicityNot monotonic
2024-02-13T20:57:09.786191image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 212692
40.7%
0.16667 4570
 
0.9%
0.33333 3656
 
0.7%
0.08333 3564
 
0.7%
0.25 2922
 
0.6%
0.5 2701
 
0.5%
0.11111 2431
 
0.5%
0.125 2115
 
0.4%
1 2112
 
0.4%
0.2 1974
 
0.4%
Other values (4168) 148694
28.4%
(Missing) 135471
25.9%
ValueCountFrequency (%)
0 212692
40.7%
0.00291 1
 
< 0.1%
0.00292 1
 
< 0.1%
0.00304 1
 
< 0.1%
0.00322 1
 
< 0.1%
ValueCountFrequency (%)
1 2112
0.4%
0.96552 1
 
< 0.1%
0.96154 1
 
< 0.1%
0.95833 8
 
< 0.1%
0.95455 2
 
< 0.1%

pctinstlsallpaidlate1d_3546856L
Real number (ℝ)

MISSING  ZEROS 

Distinct6327
Distinct (%)1.6%
Missing134695
Missing (%)25.8%
Infinite0
Infinite (%)0.0%
Mean0.1793121614
Minimum0
Maximum1
Zeros124375
Zeros (%)23.8%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:10.207223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.08824
Q30.27778
95-th percentile0.66667
Maximum1
Range1
Interquartile range (IQR)0.27778

Descriptive statistics

Standard deviation0.2256419754
Coefficient of variation (CV)1.258375191
Kurtosis1.993233806
Mean0.1793121614
Median Absolute Deviation (MAD)0.08824
Skewness1.551834382
Sum69610.23623
Variance0.05091430104
MonotonicityNot monotonic
2024-02-13T20:57:10.365978image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 124375
23.8%
0.33333 7770
 
1.5%
0.16667 7232
 
1.4%
0.5 6170
 
1.2%
0.25 5219
 
1.0%
1 4459
 
0.9%
0.08333 4448
 
0.9%
0.2 3780
 
0.7%
0.66667 3741
 
0.7%
0.11111 3413
 
0.7%
Other values (6317) 217600
41.6%
(Missing) 134695
25.8%
ValueCountFrequency (%)
0 124375
23.8%
0.00326 1
 
< 0.1%
0.00345 1
 
< 0.1%
0.0037 1
 
< 0.1%
0.00383 1
 
< 0.1%
ValueCountFrequency (%)
1 4459
0.9%
0.97619 1
 
< 0.1%
0.97297 1
 
< 0.1%
0.97222 1
 
< 0.1%
0.97059 2
 
< 0.1%

pctinstlsallpaidlate4d_3546849L
Real number (ℝ)

MISSING  ZEROS 

Distinct5202
Distinct (%)1.3%
Missing135047
Missing (%)25.8%
Infinite0
Infinite (%)0.0%
Mean0.1157504808
Minimum0
Maximum1.33333
Zeros178458
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:10.520916image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.02381
Q30.15909
95-th percentile0.53333
Maximum1.33333
Range1.33333
Interquartile range (IQR)0.15909

Descriptive statistics

Standard deviation0.1895665398
Coefficient of variation (CV)1.637717083
Kurtosis5.465345366
Mean0.1157504808
Median Absolute Deviation (MAD)0.02381
Skewness2.278784179
Sum44894.40273
Variance0.03593547302
MonotonicityNot monotonic
2024-02-13T20:57:10.678950image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 178458
34.1%
0.16667 5749
 
1.1%
0.33333 5059
 
1.0%
0.5 3865
 
0.7%
0.08333 3843
 
0.7%
0.25 3763
 
0.7%
0.11111 2865
 
0.5%
1 2850
 
0.5%
0.2 2645
 
0.5%
0.125 2484
 
0.5%
Other values (5192) 176274
33.7%
(Missing) 135047
25.8%
ValueCountFrequency (%)
0 178458
34.1%
0.00291 1
 
< 0.1%
0.00319 1
 
< 0.1%
0.0034 1
 
< 0.1%
0.0036 1
 
< 0.1%
ValueCountFrequency (%)
1.33333 1
 
< 0.1%
1 2850
0.5%
0.97222 1
 
< 0.1%
0.96552 1
 
< 0.1%
0.96429 1
 
< 0.1%

pctinstlsallpaidlate6d_3546844L
Real number (ℝ)

MISSING  ZEROS 

Distinct4782
Distinct (%)1.2%
Missing135113
Missing (%)25.8%
Infinite0
Infinite (%)0.0%
Mean0.09910920758
Minimum0
Maximum1
Zeros194506
Zeros (%)37.2%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:10.835058image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.12281
95-th percentile0.5
Maximum1
Range1
Interquartile range (IQR)0.12281

Descriptive statistics

Standard deviation0.1768950767
Coefficient of variation (CV)1.784850076
Kurtosis7.284517719
Mean0.09910920758
Median Absolute Deviation (MAD)0
Skewness2.57723678
Sum38433.4605
Variance0.03129186816
MonotonicityNot monotonic
2024-02-13T20:57:11.017000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 194506
37.2%
0.16667 5106
 
1.0%
0.33333 4312
 
0.8%
0.08333 3727
 
0.7%
0.25 3395
 
0.6%
0.5 3299
 
0.6%
0.11111 2641
 
0.5%
1 2496
 
0.5%
0.125 2312
 
0.4%
0.2 2300
 
0.4%
Other values (4772) 163695
31.3%
(Missing) 135113
25.8%
ValueCountFrequency (%)
0 194506
37.2%
0.00291 1
 
< 0.1%
0.00322 1
 
< 0.1%
0.00342 1
 
< 0.1%
0.00382 1
 
< 0.1%
ValueCountFrequency (%)
1 2496
0.5%
0.97222 1
 
< 0.1%
0.96552 1
 
< 0.1%
0.96296 1
 
< 0.1%
0.96154 2
 
< 0.1%

pmtnum_254L
Real number (ℝ)

MISSING 

Distinct39
Distinct (%)< 0.1%
Missing16300
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean17.09321716
Minimum3
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:11.157902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q112
median12
Q324
95-th percentile36
Maximum60
Range57
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.15574617
Coefficient of variation (CV)0.5941389546
Kurtosis2.151092143
Mean17.09321716
Median Absolute Deviation (MAD)6
Skewness1.404794578
Sum8659458
Variance103.1391804
MonotonicityNot monotonic
2024-02-13T20:57:11.299530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
12 187054
35.8%
24 106002
20.3%
6 77013
14.7%
36 34301
 
6.6%
18 32987
 
6.3%
16 13278
 
2.5%
9 12583
 
2.4%
48 11083
 
2.1%
30 6147
 
1.2%
10 6135
 
1.2%
Other values (29) 20019
 
3.8%
(Missing) 16300
 
3.1%
ValueCountFrequency (%)
3 375
 
0.1%
4 186
 
< 0.1%
5 5
 
< 0.1%
6 77013
14.7%
7 414
 
0.1%
ValueCountFrequency (%)
60 2978
0.6%
58 28
 
< 0.1%
56 11
 
< 0.1%
54 304
 
0.1%
52 4
 
< 0.1%

posfpd10lastmonth_333P
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing59334
Missing (%)11.3%
Infinite0
Infinite (%)0.0%
Mean0.01631044421
Minimum0
Maximum1
Zeros456007
Zeros (%)87.2%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:11.425116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1266666816
Coefficient of variation (CV)7.765986014
Kurtosis56.32761005
Mean0.01631044421
Median Absolute Deviation (MAD)0
Skewness7.637235562
Sum7561
Variance0.01604444823
MonotonicityNot monotonic
2024-02-13T20:57:11.530967image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 456007
87.2%
1 7561
 
1.4%
(Missing) 59334
 
11.3%
ValueCountFrequency (%)
0 456007
87.2%
1 7561
 
1.4%
ValueCountFrequency (%)
1 7561
 
1.4%
0 456007
87.2%

posfpd30lastmonth_3976960P
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing68094
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean0.008704772124
Minimum0
Maximum1
Zeros450849
Zeros (%)86.2%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:11.636369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.09289250798
Coefficient of variation (CV)10.67144627
Kurtosis109.8895175
Mean0.008704772124
Median Absolute Deviation (MAD)0
Skewness10.57776131
Sum3959
Variance0.008629018039
MonotonicityNot monotonic
2024-02-13T20:57:11.744370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 450849
86.2%
1 3959
 
0.8%
(Missing) 68094
 
13.0%
ValueCountFrequency (%)
0 450849
86.2%
1 3959
 
0.8%
ValueCountFrequency (%)
1 3959
 
0.8%
0 450849
86.2%

posfstqpd30lastmonth_3976962P
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing88668
Missing (%)17.0%
Infinite0
Infinite (%)0.0%
Mean0.03386653279
Minimum0
Maximum1
Zeros419528
Zeros (%)80.2%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:11.848391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1808857819
Coefficient of variation (CV)5.341136721
Kurtosis24.56302611
Mean0.03386653279
Median Absolute Deviation (MAD)0
Skewness5.153922096
Sum14706
Variance0.0327196661
MonotonicityNot monotonic
2024-02-13T20:57:11.955349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 419528
80.2%
1 14706
 
2.8%
(Missing) 88668
 
17.0%
ValueCountFrequency (%)
0 419528
80.2%
1 14706
 
2.8%
ValueCountFrequency (%)
1 14706
 
2.8%
0 419528
80.2%
Distinct223
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:12.290924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length9.961335011
Min length8

Characters and Unicode

Total characters5208802
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowa55475b1
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 144032
27.5%
p131_33_167 19164
 
3.7%
p197_47_166 15561
 
3.0%
p123_6_84 14420
 
2.8%
p98_137_111 10373
 
2.0%
p204_99_158 9376
 
1.8%
p159_143_123 8478
 
1.6%
p62_144_102 8265
 
1.6%
p111_135_181 7768
 
1.5%
p147_21_170 7546
 
1.4%
Other values (213) 277919
53.1%
2024-02-13T20:57:12.786345image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1073313
20.6%
_ 757740
14.5%
5 637214
12.2%
7 442119
8.5%
P 378870
 
7.3%
4 376176
 
7.2%
6 246586
 
4.7%
3 234637
 
4.5%
2 219584
 
4.2%
8 204555
 
3.9%
Other values (4) 638008
12.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3784128
72.6%
Connector Punctuation 757740
 
14.5%
Uppercase Letter 378870
 
7.3%
Lowercase Letter 288064
 
5.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1073313
28.4%
5 637214
16.8%
7 442119
11.7%
4 376176
 
9.9%
6 246586
 
6.5%
3 234637
 
6.2%
2 219584
 
5.8%
8 204555
 
5.4%
9 203297
 
5.4%
0 146647
 
3.9%
Lowercase Letter
ValueCountFrequency (%)
a 144032
50.0%
b 144032
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 757740
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 378870
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4541868
87.2%
Latin 666934
 
12.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1073313
23.6%
_ 757740
16.7%
5 637214
14.0%
7 442119
9.7%
4 376176
 
8.3%
6 246586
 
5.4%
3 234637
 
5.2%
2 219584
 
4.8%
8 204555
 
4.5%
9 203297
 
4.5%
Latin
ValueCountFrequency (%)
P 378870
56.8%
a 144032
 
21.6%
b 144032
 
21.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5208802
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1073313
20.6%
_ 757740
14.5%
5 637214
12.2%
7 442119
8.5%
P 378870
 
7.3%
4 376176
 
7.2%
6 246586
 
4.7%
3 234637
 
4.5%
2 219584
 
4.2%
8 204555
 
3.9%
Other values (4) 638008
12.2%

price_1097A
Real number (ℝ)

MISSING  ZEROS 

Distinct86627
Distinct (%)20.0%
Missing88868
Missing (%)17.0%
Infinite0
Infinite (%)0.0%
Mean36718.44762
Minimum0
Maximum680000
Zeros54019
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:12.946235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114212
median26378
Q348877.8
95-th percentile104213.14
Maximum680000
Range680000
Interquartile range (IQR)34665.8

Descriptive statistics

Standard deviation36805.24961
Coefficient of variation (CV)1.002363989
Kurtosis10.54325274
Mean36718.44762
Median Absolute Deviation (MAD)14628.6995
Skewness2.544843945
Sum1.593705469 × 1010
Variance1354626399
MonotonicityNot monotonic
2024-02-13T20:57:13.125055image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 54019
 
10.3%
17998 2877
 
0.6%
21998 2087
 
0.4%
19998 1967
 
0.4%
25998 1864
 
0.4%
13998 1792
 
0.3%
11998 1792
 
0.3%
23998 1678
 
0.3%
15998 1500
 
0.3%
29998 1274
 
0.2%
Other values (86617) 363184
69.5%
(Missing) 88868
 
17.0%
ValueCountFrequency (%)
0 54019
10.3%
2000 1
 
< 0.1%
2013 1
 
< 0.1%
2036 1
 
< 0.1%
2055 1
 
< 0.1%
ValueCountFrequency (%)
680000 1
< 0.1%
600000 1
< 0.1%
471000 1
< 0.1%
455542 1
< 0.1%
439998 1
< 0.1%

sellerplacecnt_915L
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1802919094
Minimum0
Maximum8
Zeros446049
Zeros (%)85.3%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:13.270462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4798079037
Coefficient of variation (CV)2.661283611
Kurtosis13.48823273
Mean0.1802919094
Median Absolute Deviation (MAD)0
Skewness3.229831193
Sum94275
Variance0.2302156245
MonotonicityNot monotonic
2024-02-13T20:57:13.390501image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 446049
85.3%
1 62735
 
12.0%
2 11453
 
2.2%
3 2170
 
0.4%
4 385
 
0.1%
5 83
 
< 0.1%
6 21
 
< 0.1%
7 5
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 446049
85.3%
1 62735
 
12.0%
2 11453
 
2.2%
3 2170
 
0.4%
4 385
 
0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 5
 
< 0.1%
6 21
 
< 0.1%
5 83
 
< 0.1%
4 385
0.1%

sellerplacescnt_216L
Real number (ℝ)

ZEROS 

Distinct65
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.933916872
Minimum0
Maximum102
Zeros171739
Zeros (%)32.8%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:13.541949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum102
Range102
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.368192154
Coefficient of variation (CV)1.224557368
Kurtosis128.4941372
Mean1.933916872
Median Absolute Deviation (MAD)1
Skewness5.136130811
Sum1011249
Variance5.608334078
MonotonicityNot monotonic
2024-02-13T20:57:13.703175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 171739
32.8%
1 114609
21.9%
2 81059
15.5%
3 56054
 
10.7%
4 36720
 
7.0%
5 23699
 
4.5%
6 14929
 
2.9%
7 9402
 
1.8%
8 5563
 
1.1%
9 3489
 
0.7%
Other values (55) 5639
 
1.1%
ValueCountFrequency (%)
0 171739
32.8%
1 114609
21.9%
2 81059
15.5%
3 56054
 
10.7%
4 36720
 
7.0%
ValueCountFrequency (%)
102 4
< 0.1%
101 2
< 0.1%
99 1
 
< 0.1%
98 2
< 0.1%
96 1
 
< 0.1%

sumoutstandtotal_3546847A
Real number (ℝ)

MISSING  ZEROS 

Distinct159894
Distinct (%)40.6%
Missing129501
Missing (%)24.8%
Infinite0
Infinite (%)0.0%
Mean29807.99416
Minimum-25044
Maximum1029392.8
Zeros207109
Zeros (%)39.6%
Negative1
Negative (%)< 0.1%
Memory size4.0 MiB
2024-02-13T20:57:13.863210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-25044
5-th percentile0
Q10
median0
Q329654
95-th percentile158407.17
Maximum1029392.8
Range1054436.8
Interquartile range (IQR)29654

Descriptive statistics

Standard deviation62766.63569
Coefficient of variation (CV)2.105698068
Kurtosis18.489942
Mean29807.99416
Median Absolute Deviation (MAD)0
Skewness3.644702338
Sum1.172649471 × 1010
Variance3939650556
MonotonicityNot monotonic
2024-02-13T20:57:14.021131image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 207109
39.6%
10 88
 
< 0.1%
11998 83
 
< 0.1%
9998 76
 
< 0.1%
7998 62
 
< 0.1%
5998 60
 
< 0.1%
3998 51
 
< 0.1%
14998 46
 
< 0.1%
8998 45
 
< 0.1%
17998 42
 
< 0.1%
Other values (159884) 185739
35.5%
(Missing) 129501
24.8%
ValueCountFrequency (%)
-25044 1
 
< 0.1%
0 207109
39.6%
0.002 1
 
< 0.1%
0.006 1
 
< 0.1%
0.008 1
 
< 0.1%
ValueCountFrequency (%)
1029392.8 1
< 0.1%
1022051.44 1
< 0.1%
995935 1
< 0.1%
984399 1
< 0.1%
980540.7 1
< 0.1%

sumoutstandtotalest_4493215A
Real number (ℝ)

MISSING  ZEROS 

Distinct159865
Distinct (%)40.9%
Missing132270
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean30010.46736
Minimum-25044
Maximum1029392.8
Zeros204415
Zeros (%)39.1%
Negative1
Negative (%)< 0.1%
Memory size4.0 MiB
2024-02-13T20:57:14.183091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-25044
5-th percentile0
Q10
median0
Q329975.817
95-th percentile158952.408
Maximum1029392.8
Range1054436.8
Interquartile range (IQR)29975.817

Descriptive statistics

Standard deviation62937.6212
Coefficient of variation (CV)2.097188972
Kurtosis18.36077538
Mean30010.46736
Median Absolute Deviation (MAD)0
Skewness3.631703387
Sum1.172304889 × 1010
Variance3961144162
MonotonicityNot monotonic
2024-02-13T20:57:14.348125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 204415
39.1%
10 88
 
< 0.1%
11998 83
 
< 0.1%
9998 76
 
< 0.1%
7998 62
 
< 0.1%
5998 60
 
< 0.1%
3998 51
 
< 0.1%
8998 45
 
< 0.1%
14998 45
 
< 0.1%
17998 42
 
< 0.1%
Other values (159855) 185665
35.5%
(Missing) 132270
25.3%
ValueCountFrequency (%)
-25044 1
 
< 0.1%
0 204415
39.1%
0.002 1
 
< 0.1%
0.006 1
 
< 0.1%
0.008 1
 
< 0.1%
ValueCountFrequency (%)
1029392.8 1
< 0.1%
1022051.44 1
< 0.1%
995935 1
< 0.1%
984399 1
< 0.1%
980540.7 1
< 0.1%

totaldebt_9A
Real number (ℝ)

ZEROS 

Distinct159976
Distinct (%)30.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean22560.69334
Minimum0
Maximum1029392.8
Zeros338444
Zeros (%)64.7%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:14.509127image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q316637.4
95-th percentile132825.2
Maximum1029392.8
Range1029392.8
Interquartile range (IQR)16637.4

Descriptive statistics

Standard deviation56096.00024
Coefficient of variation (CV)2.486448417
Kurtosis24.49607204
Mean22560.69334
Median Absolute Deviation (MAD)0
Skewness4.211852889
Sum1.179700911 × 1010
Variance3146761243
MonotonicityNot monotonic
2024-02-13T20:57:14.664127image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 338444
64.7%
10 97
 
< 0.1%
9998 63
 
< 0.1%
11998 58
 
< 0.1%
17998 43
 
< 0.1%
5998 41
 
< 0.1%
7998 41
 
< 0.1%
15998 39
 
< 0.1%
3998 36
 
< 0.1%
14998 36
 
< 0.1%
Other values (159966) 184003
35.2%
ValueCountFrequency (%)
0 338444
64.7%
0.002 1
 
< 0.1%
0.004 1
 
< 0.1%
0.006 1
 
< 0.1%
0.008 1
 
< 0.1%
ValueCountFrequency (%)
1029392.8 1
< 0.1%
1022051.44 1
< 0.1%
987535 1
< 0.1%
984399 1
< 0.1%
980540.7 1
< 0.1%

totalsettled_863A
Real number (ℝ)

SKEWED  ZEROS 

Distinct314654
Distinct (%)60.2%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean117119.5274
Minimum0
Maximum48035036
Zeros133695
Zeros (%)25.6%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:14.817603image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median51206.9
Q3155318.15
95-th percentile466467.5365
Maximum48035036
Range48035036
Interquartile range (IQR)155318.15

Descriptive statistics

Standard deviation213717.9827
Coefficient of variation (CV)1.824785221
Kurtosis10056.63181
Mean117119.5274
Median Absolute Deviation (MAD)51206.9
Skewness56.91368198
Sum6.124180086 × 1010
Variance4.567537611 × 1010
MonotonicityNot monotonic
2024-02-13T20:57:14.974108image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 133695
 
25.6%
12000 360
 
0.1%
8000 257
 
< 0.1%
6000 241
 
< 0.1%
4000 224
 
< 0.1%
10000 213
 
< 0.1%
18000 202
 
< 0.1%
14000 201
 
< 0.1%
24000 195
 
< 0.1%
16000 185
 
< 0.1%
Other values (314644) 387127
74.0%
ValueCountFrequency (%)
0 133695
25.6%
0.6 3
 
< 0.1%
2 1
 
< 0.1%
3.6000001 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
48035036 1
< 0.1%
41504444 1
< 0.1%
36672144 1
< 0.1%
23079422 1
< 0.1%
22062280 1
< 0.1%

totinstallast1m_4525188A
Real number (ℝ)

MISSING 

Distinct95012
Distinct (%)44.4%
Missing309146
Missing (%)59.1%
Infinite0
Infinite (%)0.0%
Mean10304.44011
Minimum0.21400002
Maximum543734.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-13T20:57:15.125165image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.21400002
5-th percentile1357.55
Q13409
median6343
Q311726.05
95-th percentile28575.4
Maximum543734.2
Range543733.986
Interquartile range (IQR)8317.05

Descriptive statistics

Standard deviation16136.15145
Coefficient of variation (CV)1.565941601
Kurtosis110.4728989
Mean10304.44011
Median Absolute Deviation (MAD)3567
Skewness8.192548774
Sum2202635901
Variance260375383.6
MonotonicityNot monotonic
2024-02-13T20:57:15.302298image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 412
 
0.1%
1200 282
 
0.1%
2000 140
 
< 0.1%
3000 109
 
< 0.1%
4000 94
 
< 0.1%
3333.2 91
 
< 0.1%
1000 75
 
< 0.1%
1500 72
 
< 0.1%
5000 71
 
< 0.1%
1833.2001 68
 
< 0.1%
Other values (95002) 212342
40.6%
(Missing) 309146
59.1%
ValueCountFrequency (%)
0.21400002 1
< 0.1%
0.222 1
< 0.1%
0.382 1
< 0.1%
0.42 1
< 0.1%
0.466 1
< 0.1%
ValueCountFrequency (%)
543734.2 1
< 0.1%
513520 1
< 0.1%
497634.22 1
< 0.1%
475046 1
< 0.1%
453385.1 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing46
Missing (%)< 0.1%
Memory size4.0 MiB
2024-02-13T20:57:15.414878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1045712
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFO
2nd rowFO
3rd rowFO
4th rowFO
5th rowFO
ValueCountFrequency (%)
fo 503581
96.3%
bo 19275
 
3.7%
2024-02-13T20:57:15.637080image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 522856
50.0%
F 503581
48.2%
B 19275
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1045712
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 522856
50.0%
F 503581
48.2%
B 19275
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 1045712
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 522856
50.0%
F 503581
48.2%
B 19275
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1045712
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 522856
50.0%
F 503581
48.2%
B 19275
 
1.8%

typesuite_864L
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing406268
Missing (%)77.7%
Memory size4.0 MiB
2024-02-13T20:57:15.716834image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters233268
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAL
2nd rowAL
3rd rowAL
4th rowAL
5th rowAL
ValueCountFrequency (%)
al 116634
100.0%
2024-02-13T20:57:15.922512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 116634
50.0%
L 116634
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 233268
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 116634
50.0%
L 116634
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 233268
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 116634
50.0%
L 116634
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 233268
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 116634
50.0%
L 116634
50.0%

validfrom_1069D
Text

MISSING 

Distinct257
Distinct (%)0.8%
Missing490534
Missing (%)93.8%
Memory size4.0 MiB
2024-02-13T20:57:16.287740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters323680
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st row2019-11-12
2nd row2019-12-31
3rd row2019-12-15
4th row2019-12-27
5th row2020-01-01
ValueCountFrequency (%)
2019-11-28 2266
 
7.0%
2020-02-24 1450
 
4.5%
2019-11-30 1297
 
4.0%
2020-02-21 1246
 
3.8%
2020-02-12 1124
 
3.5%
2019-10-16 1086
 
3.4%
2020-02-04 856
 
2.6%
2019-11-12 850
 
2.6%
2020-05-05 743
 
2.3%
2020-03-30 611
 
1.9%
Other values (247) 20839
64.4%
2024-02-13T20:57:16.775480image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 88957
27.5%
2 82420
25.5%
- 64736
20.0%
1 45084
13.9%
9 12948
 
4.0%
3 9974
 
3.1%
4 4780
 
1.5%
5 4780
 
1.5%
8 4040
 
1.2%
6 4026
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258944
80.0%
Dash Punctuation 64736
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 88957
34.4%
2 82420
31.8%
1 45084
17.4%
9 12948
 
5.0%
3 9974
 
3.9%
4 4780
 
1.8%
5 4780
 
1.8%
8 4040
 
1.6%
6 4026
 
1.6%
7 1935
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 64736
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 323680
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 88957
27.5%
2 82420
25.5%
- 64736
20.0%
1 45084
13.9%
9 12948
 
4.0%
3 9974
 
3.1%
4 4780
 
1.5%
5 4780
 
1.5%
8 4040
 
1.2%
6 4026
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 323680
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 88957
27.5%
2 82420
25.5%
- 64736
20.0%
1 45084
13.9%
9 12948
 
4.0%
3 9974
 
3.1%
4 4780
 
1.5%
5 4780
 
1.5%
8 4040
 
1.2%
6 4026
 
1.2%