Overview

Dataset statistics

Number of variables168
Number of observations1003757
Missing cells55521200
Missing cells (%)32.9%
Total size in memory1.2 GiB
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
isdebitcard_729L has constant value ""Constant
lastapprcommoditytypec_5251766M has constant value ""Constant
lastrejectcommodtypec_5251769M 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 (67.8%)Imbalance
equalityempfrom_62L is highly imbalanced (81.5%)Imbalance
isbidproductrequest_292L is highly imbalanced (99.8%)Imbalance
opencred_647L is highly imbalanced (74.4%)Imbalance
actualdpdtolerance_344P has 296686 (29.6%) missing valuesMissing
amtinstpaidbefduel24m_4187115A has 428854 (42.7%) missing valuesMissing
avgdbddpdlast24m_3658932P has 426131 (42.5%) missing valuesMissing
avgdbddpdlast3m_4187120P has 658003 (65.6%) missing valuesMissing
avgdbdtollast24m_4525197P has 785725 (78.3%) missing valuesMissing
avgdpdtolclosure24_3658938P has 329272 (32.8%) missing valuesMissing
avginstallast24m_3658937A has 433721 (43.2%) missing valuesMissing
avglnamtstart24m_4525187A has 933950 (93.0%) missing valuesMissing
avgmaxdpdlast9m_3716943P has 515293 (51.3%) missing valuesMissing
avgoutstandbalancel6m_4187114A has 598994 (59.7%) missing valuesMissing
avgpmtlast12m_4525200A has 807310 (80.4%) missing valuesMissing
bankacctype_710L has 699067 (69.6%) missing valuesMissing
cardtype_51L has 877973 (87.5%) missing valuesMissing
clientscnt_136L has 1003360 (> 99.9%) missing valuesMissing
cntincpaycont9m_3716944L has 322077 (32.1%) missing valuesMissing
cntpmts24_3658933L has 327733 (32.7%) missing valuesMissing
commnoinclast6m_3546845L has 259522 (25.9%) missing valuesMissing
datefirstoffer_1144D has 560713 (55.9%) missing valuesMissing
datelastinstal40dpd_247D has 932264 (92.9%) missing valuesMissing
datelastunpaid_3546854D has 606750 (60.4%) missing valuesMissing
daysoverduetolerancedd_3976961L has 320324 (31.9%) missing valuesMissing
dtlastpmtallstes_4499206D has 758904 (75.6%) missing valuesMissing
eir_270L has 125513 (12.5%) missing valuesMissing
equalitydataagreement_891L has 951445 (94.8%) missing valuesMissing
equalityempfrom_62L has 975352 (97.2%) missing valuesMissing
firstclxcampaign_1125D has 571655 (57.0%) missing valuesMissing
firstdatedue_489D has 339687 (33.8%) missing valuesMissing
inittransactionamount_650A has 877888 (87.5%) missing valuesMissing
interestrate_311L has 125513 (12.5%) missing valuesMissing
interestrategrace_34L has 987174 (98.3%) missing valuesMissing
isbidproductrequest_292L has 995281 (99.2%) missing valuesMissing
isdebitcard_729L has 877888 (87.5%) missing valuesMissing
lastactivateddate_801D has 320842 (32.0%) missing valuesMissing
lastapplicationdate_877D has 220760 (22.0%) missing valuesMissing
lastapprcredamount_781A has 313123 (31.2%) missing valuesMissing
lastapprdate_640D has 313123 (31.2%) missing valuesMissing
lastdelinqdate_224D has 668680 (66.6%) missing valuesMissing
lastdependentsnum_448L has 978699 (97.5%) missing valuesMissing
lastotherinc_902A has 1001529 (99.8%) missing valuesMissing
lastotherlnsexpense_631A has 1001586 (99.8%) missing valuesMissing
lastrejectcredamount_222A has 530899 (52.9%) missing valuesMissing
lastrejectdate_50D has 530899 (52.9%) missing valuesMissing
lastrepayingdate_696D has 1002152 (99.8%) missing valuesMissing
lastst_736L has 220760 (22.0%) missing valuesMissing
maininc_215A has 340950 (34.0%) missing valuesMissing
mastercontrelectronic_519L has 222166 (22.1%) missing valuesMissing
mastercontrexist_109L has 222166 (22.1%) missing valuesMissing
maxannuity_159A has 222166 (22.1%) missing valuesMissing
maxannuity_4075009A has 953635 (95.0%) missing valuesMissing
maxdbddpdlast1m_3658939P has 642917 (64.1%) missing valuesMissing
maxdbddpdtollast12m_3658940P has 483172 (48.1%) missing valuesMissing
maxdbddpdtollast6m_4187119P has 616835 (61.5%) missing valuesMissing
maxdebt4_972A has 222166 (22.1%) missing valuesMissing
maxdpdfrom6mto36m_3546853P has 259522 (25.9%) missing valuesMissing
maxdpdinstldate_3546855D has 567808 (56.6%) missing valuesMissing
maxdpdinstlnum_3546846P has 568985 (56.7%) missing valuesMissing
maxdpdlast12m_727P has 222166 (22.1%) missing valuesMissing
maxdpdlast24m_143P has 222166 (22.1%) missing valuesMissing
maxdpdlast3m_392P has 222166 (22.1%) missing valuesMissing
maxdpdlast6m_474P has 222166 (22.1%) missing valuesMissing
maxdpdlast9m_1059P has 222166 (22.1%) missing valuesMissing
maxdpdtolerance_374P has 222166 (22.1%) missing valuesMissing
maxinstallast24m_3658928A has 433721 (43.2%) missing valuesMissing
maxlnamtstart6m_4525199A has 807461 (80.4%) missing valuesMissing
maxoutstandbalancel12m_4187113A has 555196 (55.3%) missing valuesMissing
maxpmtlast3m_4525190A has 845991 (84.3%) missing valuesMissing
mindbddpdlast24m_3658935P has 426131 (42.5%) missing valuesMissing
mindbdtollast24m_4525191P has 785725 (78.3%) missing valuesMissing
monthsannuity_845L has 320323 (31.9%) missing valuesMissing
numincomingpmts_3546848L has 321992 (32.1%) missing valuesMissing
numinstlallpaidearly3d_817L has 315389 (31.4%) missing valuesMissing
numinstlsallpaid_934L has 315389 (31.4%) missing valuesMissing
numinstlswithdpd10_728L has 322993 (32.2%) missing valuesMissing
numinstlswithdpd5_4187116L has 428854 (42.7%) missing valuesMissing
numinstlswithoutdpd_562L has 322993 (32.2%) missing valuesMissing
numinstmatpaidtearly2d_4499204L has 714921 (71.2%) missing valuesMissing
numinstpaid_4499208L has 714921 (71.2%) missing valuesMissing
numinstpaidearly3d_3546850L has 316703 (31.6%) missing valuesMissing
numinstpaidearly3dest_4493216L has 708376 (70.6%) missing valuesMissing
numinstpaidearly5d_1087L has 320323 (31.9%) missing valuesMissing
numinstpaidearly5dest_4493211L has 708376 (70.6%) missing valuesMissing
numinstpaidearly5dobd_4499205L has 714921 (71.2%) missing valuesMissing
numinstpaidearly_338L has 320323 (31.9%) missing valuesMissing
numinstpaidearlyest_4493214L has 708376 (70.6%) missing valuesMissing
numinstpaidlastcontr_4325080L has 536916 (53.5%) missing valuesMissing
numinstpaidlate1d_3546852L has 320323 (31.9%) missing valuesMissing
numinstregularpaid_973L has 323342 (32.2%) missing valuesMissing
numinstregularpaidest_4493210L has 708376 (70.6%) missing valuesMissing
numinsttopaygr_769L has 320324 (31.9%) missing valuesMissing
numinsttopaygrest_4493213L has 708376 (70.6%) missing valuesMissing
numinstunpaidmax_3546851L has 320324 (31.9%) missing valuesMissing
numinstunpaidmaxest_4493212L has 708376 (70.6%) missing valuesMissing
opencred_647L has 220760 (22.0%) missing valuesMissing
payvacationpostpone_4187118D has 1002290 (99.9%) missing valuesMissing
pctinstlsallpaidearl3d_427L has 324043 (32.3%) missing valuesMissing
pctinstlsallpaidlat10d_839L has 325891 (32.5%) missing valuesMissing
pctinstlsallpaidlate1d_3546856L has 324043 (32.3%) missing valuesMissing
pctinstlsallpaidlate4d_3546849L has 324780 (32.4%) missing valuesMissing
pctinstlsallpaidlate6d_3546844L has 324966 (32.4%) missing valuesMissing
pmtnum_254L has 28654 (2.9%) missing valuesMissing
posfpd10lastmonth_333P has 19192 (1.9%) missing valuesMissing
posfpd30lastmonth_3976960P has 63794 (6.4%) missing valuesMissing
posfstqpd30lastmonth_3976962P has 92454 (9.2%) missing valuesMissing
price_1097A has 134372 (13.4%) missing valuesMissing
sumoutstandtotal_3546847A has 315819 (31.5%) missing valuesMissing
sumoutstandtotalest_4493215A has 708376 (70.6%) missing valuesMissing
totinstallast1m_4525188A has 865065 (86.2%) missing valuesMissing
typesuite_864L has 715237 (71.3%) missing valuesMissing
validfrom_1069D has 884352 (88.1%) missing valuesMissing
actualdpdtolerance_344P is highly skewed (γ1 = 310.5333204)Skewed
applicationcnt_361L is highly skewed (γ1 = 418.5354087)Skewed
applicationscnt_1086L is highly skewed (γ1 = 20.00807847)Skewed
avgmaxdpdlast9m_3716943P is highly skewed (γ1 = 24.43197008)Skewed
clientscnt12m_3712952L is highly skewed (γ1 = 52.13080369)Skewed
clientscnt3m_3712950L is highly skewed (γ1 = 80.02075906)Skewed
clientscnt6m_3712949L is highly skewed (γ1 = 64.93718432)Skewed
clientscnt_100L is highly skewed (γ1 = 76.67377832)Skewed
clientscnt_1022L is highly skewed (γ1 = 57.12276132)Skewed
clientscnt_1130L is highly skewed (γ1 = 38.03268575)Skewed
clientscnt_157L is highly skewed (γ1 = 142.5888772)Skewed
clientscnt_257L is highly skewed (γ1 = 29.21066559)Skewed
clientscnt_304L is highly skewed (γ1 = 196.168309)Skewed
clientscnt_360L is highly skewed (γ1 = 22.38977957)Skewed
clientscnt_493L is highly skewed (γ1 = 328.9606546)Skewed
clientscnt_887L is highly skewed (γ1 = 22.6364637)Skewed
clientscnt_946L is highly skewed (γ1 = 57.65467532)Skewed
downpmt_116A is highly skewed (γ1 = 20.74420913)Skewed
maxannuity_159A is highly skewed (γ1 = 44.10697214)Skewed
maxdpdlast3m_392P is highly skewed (γ1 = 26.29456031)Skewed
maxdpdlast6m_474P is highly skewed (γ1 = 21.19766093)Skewed
case_id has unique valuesUnique
actualdpdtolerance_344P has 704478 (70.2%) zerosZeros
amtinstpaidbefduel24m_4187115A has 99851 (9.9%) zerosZeros
annuitynextmonth_57A has 705978 (70.3%) zerosZeros
applicationcnt_361L has 1003746 (> 99.9%) zerosZeros
applications30d_658L has 904193 (90.1%) zerosZeros
applicationscnt_1086L has 947992 (94.4%) zerosZeros
applicationscnt_464L has 901905 (89.9%) zerosZeros
applicationscnt_629L has 943080 (94.0%) zerosZeros
applicationscnt_867L has 347151 (34.6%) zerosZeros
avgdbddpdlast24m_3658932P has 37164 (3.7%) zerosZeros
avgdbddpdlast3m_4187120P has 35798 (3.6%) zerosZeros
avgdbdtollast24m_4525197P has 14305 (1.4%) zerosZeros
avgdpdtolclosure24_3658938P has 487635 (48.6%) zerosZeros
avgmaxdpdlast9m_3716943P has 390120 (38.9%) zerosZeros
avgoutstandbalancel6m_4187114A has 16140 (1.6%) zerosZeros
clientscnt12m_3712952L has 975460 (97.2%) zerosZeros
clientscnt3m_3712950L has 991571 (98.8%) zerosZeros
clientscnt6m_3712949L has 985333 (98.2%) zerosZeros
clientscnt_100L has 953732 (95.0%) zerosZeros
clientscnt_1022L has 919744 (91.6%) zerosZeros
clientscnt_1071L has 968567 (96.5%) zerosZeros
clientscnt_1130L has 972774 (96.9%) zerosZeros
clientscnt_157L has 949826 (94.6%) zerosZeros
clientscnt_257L has 1000593 (99.7%) zerosZeros
clientscnt_304L has 961656 (95.8%) zerosZeros
clientscnt_360L has 1000314 (99.7%) zerosZeros
clientscnt_493L has 997424 (99.4%) zerosZeros
clientscnt_533L has 932721 (92.9%) zerosZeros
clientscnt_887L has 879172 (87.6%) zerosZeros
clientscnt_946L has 978094 (97.4%) zerosZeros
cntincpaycont9m_3716944L has 192827 (19.2%) zerosZeros
cntpmts24_3658933L has 97045 (9.7%) zerosZeros
commnoinclast6m_3546845L has 744235 (74.1%) zerosZeros
currdebt_22A has 681696 (67.9%) zerosZeros
currdebtcredtyperange_828A has 801429 (79.8%) zerosZeros
daysoverduetolerancedd_3976961L has 213521 (21.3%) zerosZeros
deferredmnthsnum_166L has 1003757 (100.0%) zerosZeros
disbursedcredamount_1113A has 28330 (2.8%) zerosZeros
downpmt_116A has 942641 (93.9%) zerosZeros
eir_270L has 291088 (29.0%) zerosZeros
homephncnt_628L has 557067 (55.5%) zerosZeros
inittransactionamount_650A has 28230 (2.8%) zerosZeros
interestrate_311L has 291088 (29.0%) zerosZeros
interestrategrace_34L has 16583 (1.7%) zerosZeros
lastapprcredamount_781A has 19563 (1.9%) zerosZeros
lastdependentsnum_448L has 17453 (1.7%) zerosZeros
lastrejectcredamount_222A has 27941 (2.8%) zerosZeros
mastercontrelectronic_519L has 781591 (77.9%) zerosZeros
mastercontrexist_109L has 781591 (77.9%) zerosZeros
maxannuity_159A has 99840 (9.9%) zerosZeros
maxdbddpdlast1m_3658939P has 72598 (7.2%) zerosZeros
maxdbddpdtollast12m_3658940P has 176755 (17.6%) zerosZeros
maxdbddpdtollast6m_4187119P has 126300 (12.6%) zerosZeros
maxdebt4_972A has 194255 (19.4%) zerosZeros
maxdpdfrom6mto36m_3546853P has 497585 (49.6%) zerosZeros
maxdpdlast12m_727P has 595142 (59.3%) zerosZeros
maxdpdlast24m_143P has 529537 (52.8%) zerosZeros
maxdpdlast3m_392P has 689934 (68.7%) zerosZeros
maxdpdlast6m_474P has 649000 (64.7%) zerosZeros
maxdpdlast9m_1059P has 618886 (61.7%) zerosZeros
maxdpdtolerance_374P has 391352 (39.0%) zerosZeros
maxoutstandbalancel12m_4187113A has 12184 (1.2%) zerosZeros
mindbddpdlast24m_3658935P has 14059 (1.4%) zerosZeros
numactivecreds_622L has 658295 (65.6%) zerosZeros
numactivecredschannel_414L has 911015 (90.8%) zerosZeros
numactiverelcontr_750L has 800263 (79.7%) zerosZeros
numcontrs3months_479L has 828335 (82.5%) zerosZeros
numinstlallpaidearly3d_817L has 46992 (4.7%) zerosZeros
numinstls_657L has 692532 (69.0%) zerosZeros
numinstlsallpaid_934L has 16756 (1.7%) zerosZeros
numinstlswithdpd10_728L has 498264 (49.6%) zerosZeros
numinstlswithdpd5_4187116L has 363093 (36.2%) zerosZeros
numinstmatpaidtearly2d_4499204L has 14433 (1.4%) zerosZeros
numinstpaidearly3d_3546850L has 47101 (4.7%) zerosZeros
numinstpaidearly3dest_4493216L has 19622 (2.0%) zerosZeros
numinstpaidearly5d_1087L has 203423 (20.3%) zerosZeros
numinstpaidearly5dest_4493211L has 102682 (10.2%) zerosZeros
numinstpaidearly5dobd_4499205L has 37073 (3.7%) zerosZeros
numinstpaidearly_338L has 84721 (8.4%) zerosZeros
numinstpaidearlyest_4493214L has 37809 (3.8%) zerosZeros
numinstpaidlastcontr_4325080L has 23692 (2.4%) zerosZeros
numinstpaidlate1d_3546852L has 247652 (24.7%) zerosZeros
numinsttopaygr_769L has 373481 (37.2%) zerosZeros
numinsttopaygrest_4493213L has 167896 (16.7%) zerosZeros
numinstunpaidmax_3546851L has 373731 (37.2%) zerosZeros
numinstunpaidmaxest_4493212L has 167791 (16.7%) zerosZeros
numnotactivated_1143L has 986706 (98.3%) zerosZeros
numpmtchanneldd_318L has 984151 (98.0%) zerosZeros
numrejects9m_859L has 831405 (82.8%) zerosZeros
pctinstlsallpaidearl3d_427L has 40764 (4.1%) zerosZeros
pctinstlsallpaidlat10d_839L has 394383 (39.3%) zerosZeros
pctinstlsallpaidlate1d_3546856L has 242255 (24.1%) zerosZeros
pctinstlsallpaidlate4d_3546849L has 336772 (33.6%) zerosZeros
pctinstlsallpaidlate6d_3546844L has 363802 (36.2%) zerosZeros
posfpd10lastmonth_333P has 968762 (96.5%) zerosZeros
posfpd30lastmonth_3976960P has 932695 (92.9%) zerosZeros
posfstqpd30lastmonth_3976962P has 884937 (88.2%) zerosZeros
price_1097A has 110171 (11.0%) zerosZeros
sellerplacecnt_915L has 873535 (87.0%) zerosZeros
sellerplacescnt_216L has 409625 (40.8%) zerosZeros
sumoutstandtotal_3546847A has 362441 (36.1%) zerosZeros
sumoutstandtotalest_4493215A has 160173 (16.0%) zerosZeros
totaldebt_9A has 681680 (67.9%) zerosZeros
totalsettled_863A has 323553 (32.2%) zerosZeros

Reproduction

Analysis started2024-02-13 19:54:36.519654
Analysis finished2024-02-13 19:54:54.814811
Duration18.3 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

case_id
Real number (ℝ)

UNIQUE 

Distinct1003757
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1216924.056
Minimum0
Maximum2651092
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:54:54.928429image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile112335.8
Q1725987
median1311700
Q31562639
95-th percentile2600851.2
Maximum2651092
Range2651092
Interquartile range (IQR)836652

Descriptive statistics

Standard deviation696462.4416
Coefficient of variation (CV)0.5723138087
Kurtosis-0.2511746498
Mean1216924.056
Median Absolute Deviation (MAD)455981
Skewness0.3567053677
Sum1.22149604 × 1012
Variance4.850599326 × 1011
MonotonicityStrictly increasing
2024-02-13T20:54:55.142438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
1478984 1
 
< 0.1%
1478986 1
 
< 0.1%
1478987 1
 
< 0.1%
1478988 1
 
< 0.1%
1478989 1
 
< 0.1%
1478990 1
 
< 0.1%
1478991 1
 
< 0.1%
1478992 1
 
< 0.1%
1478993 1
 
< 0.1%
Other values (1003747) 1003747
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
ValueCountFrequency (%)
2651092 1
< 0.1%
2651091 1
< 0.1%
2651090 1
< 0.1%
2651089 1
< 0.1%
2651088 1
< 0.1%

actualdpdtolerance_344P
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct89
Distinct (%)< 0.1%
Missing296686
Missing (%)29.6%
Infinite0
Infinite (%)0.0%
Mean0.05439057747
Minimum0
Maximum3676
Zeros704478
Zeros (%)70.2%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:54:55.297270image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3676
Range3676
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.743984494
Coefficient of variation (CV)160.7628546
Kurtosis109320.9059
Mean0.05439057747
Median Absolute Deviation (MAD)0
Skewness310.5333204
Sum38458
Variance76.45726484
MonotonicityNot monotonic
2024-02-13T20:54:55.459326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 704478
70.2%
1 836
 
0.1%
2 541
 
0.1%
3 332
 
< 0.1%
4 174
 
< 0.1%
5 126
 
< 0.1%
6 70
 
< 0.1%
7 68
 
< 0.1%
8 63
 
< 0.1%
9 40
 
< 0.1%
Other values (79) 343
 
< 0.1%
(Missing) 296686
29.6%
ValueCountFrequency (%)
0 704478
70.2%
1 836
 
0.1%
2 541
 
0.1%
3 332
 
< 0.1%
4 174
 
< 0.1%
ValueCountFrequency (%)
3676 1
< 0.1%
3660 1
< 0.1%
2118 1
< 0.1%
2107 1
< 0.1%
1957 1
< 0.1%

amtinstpaidbefduel24m_4187115A
Real number (ℝ)

MISSING  ZEROS 

Distinct381336
Distinct (%)66.3%
Missing428854
Missing (%)42.7%
Infinite0
Infinite (%)0.0%
Mean50692.54511
Minimum0
Maximum992476.5
Zeros99851
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:54:55.632353image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16452.90015
median26937
Q368960.84
95-th percentile188297.792
Maximum992476.5
Range992476.5
Interquartile range (IQR)62507.93985

Descriptive statistics

Standard deviation65457.93857
Coefficient of variation (CV)1.291273469
Kurtosis7.952461075
Mean50692.54511
Median Absolute Deviation (MAD)25787.164
Skewness2.354064209
Sum2.914329626 × 1010
Variance4284741722
MonotonicityNot monotonic
2024-02-13T20:54:55.796375image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 99851
 
9.9%
4000 52
 
< 0.1%
600 50
 
< 0.1%
6000 46
 
< 0.1%
800 43
 
< 0.1%
2400 42
 
< 0.1%
3000 42
 
< 0.1%
1600 39
 
< 0.1%
13998 37
 
< 0.1%
8000 35
 
< 0.1%
Other values (381326) 474666
47.3%
(Missing) 428854
42.7%
ValueCountFrequency (%)
0 99851
9.9%
0.006 1
 
< 0.1%
0.025999999 1
 
< 0.1%
0.028 1
 
< 0.1%
0.030000001 1
 
< 0.1%
ValueCountFrequency (%)
992476.5 1
< 0.1%
911470.94 1
< 0.1%
894723.2 1
< 0.1%
862195.5 1
< 0.1%
846048.44 1
< 0.1%

annuity_780A
Real number (ℝ)

Distinct67308
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3875.597633
Minimum83
Maximum106007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:54:55.953835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum83
5-th percentile1083.2001
Q11895.8
median3000
Q34989.8003
95-th percentile9417.601
Maximum106007
Range105924
Interquartile range (IQR)3094.0003

Descriptive statistics

Standard deviation2920.237199
Coefficient of variation (CV)0.7534933902
Kurtosis16.11645981
Mean3875.597633
Median Absolute Deviation (MAD)1335
Skewness2.582699075
Sum3890158253
Variance8527785.3
MonotonicityNot monotonic
2024-02-13T20:54:56.120208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6000 9488
 
0.9%
600 3380
 
0.3%
2000 1882
 
0.2%
2472.4001 1633
 
0.2%
1236.2001 1628
 
0.2%
1200 1456
 
0.1%
1666.6 1307
 
0.1%
1218 1304
 
0.1%
2400 1292
 
0.1%
2436 1256
 
0.1%
Other values (67298) 979131
97.5%
ValueCountFrequency (%)
83 1
< 0.1%
133.40001 1
< 0.1%
145.8 1
< 0.1%
149.40001 2
< 0.1%
154.2 1
< 0.1%
ValueCountFrequency (%)
106007 1
< 0.1%
75602 1
< 0.1%
70924.2 1
< 0.1%
70459.4 1
< 0.1%
56290 1
< 0.1%

annuitynextmonth_57A
Real number (ℝ)

ZEROS 

Distinct57214
Distinct (%)5.7%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1348.498702
Minimum0
Maximum85620.805
Zeros705978
Zeros (%)70.3%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:54:56.270315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31866.6
95-th percentile7106.4
Maximum85620.805
Range85620.805
Interquartile range (IQR)1866.6

Descriptive statistics

Standard deviation2687.341477
Coefficient of variation (CV)1.992839498
Kurtosis15.13019873
Mean1348.498702
Median Absolute Deviation (MAD)0
Skewness2.883728506
Sum1353562315
Variance7221804.213
MonotonicityNot monotonic
2024-02-13T20:54:56.637836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 705978
70.3%
2000 235
 
< 0.1%
1666.6 147
 
< 0.1%
6225.2 146
 
< 0.1%
3000 141
 
< 0.1%
1500 123
 
< 0.1%
6478.6 121
 
< 0.1%
2165 119
 
< 0.1%
1998.2001 118
 
< 0.1%
1000 113
 
< 0.1%
Other values (57204) 296514
29.5%
ValueCountFrequency (%)
0 705978
70.3%
19.800001 1
 
< 0.1%
20 2
 
< 0.1%
20.6 1
 
< 0.1%
21 2
 
< 0.1%
ValueCountFrequency (%)
85620.805 1
< 0.1%
74758.805 1
< 0.1%
70924.2 1
< 0.1%
66907.2 1
< 0.1%
61824 1
< 0.1%

applicationcnt_361L
Real number (ℝ)

SKEWED  ZEROS 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.689894068 × 10-5
Minimum0
Maximum5
Zeros1003746
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:54:56.757397image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.009309872567
Coefficient of variation (CV)346.1055466
Kurtosis196132.8399
Mean2.689894068 × 10-5
Median Absolute Deviation (MAD)0
Skewness418.5354087
Sum27
Variance8.667372721 × 10-5
MonotonicityNot monotonic
2024-02-13T20:54:56.886091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
0 1003746
> 99.9%
2 4
 
< 0.1%
1 3
 
< 0.1%
3 2
 
< 0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
0 1003746
> 99.9%
1 3
 
< 0.1%
2 4
 
< 0.1%
3 2
 
< 0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
5 2
 
< 0.1%
3 2
 
< 0.1%
2 4
 
< 0.1%
1 3
 
< 0.1%
0 1003746
> 99.9%

applications30d_658L
Real number (ℝ)

ZEROS 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.126183927
Minimum0
Maximum25
Zeros904193
Zeros (%)90.1%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:54:57.008765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4458153052
Coefficient of variation (CV)3.533059367
Kurtosis107.583413
Mean0.126183927
Median Absolute Deviation (MAD)0
Skewness6.625497235
Sum126658
Variance0.1987512863
MonotonicityNot monotonic
2024-02-13T20:54:57.152117image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 904193
90.1%
1 80736
 
8.0%
2 14026
 
1.4%
3 3162
 
0.3%
4 920
 
0.1%
5 337
 
< 0.1%
6 160
 
< 0.1%
7 86
 
< 0.1%
8 49
 
< 0.1%
9 24
 
< 0.1%
Other values (11) 64
 
< 0.1%
ValueCountFrequency (%)
0 904193
90.1%
1 80736
 
8.0%
2 14026
 
1.4%
3 3162
 
0.3%
4 920
 
0.1%
ValueCountFrequency (%)
25 1
 
< 0.1%
20 5
< 0.1%
18 1
 
< 0.1%
17 2
 
< 0.1%
16 6
< 0.1%

applicationscnt_1086L
Real number (ℝ)

SKEWED  ZEROS 

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.420001056
Minimum0
Maximum443
Zeros947992
Zeros (%)94.4%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:54:57.315043image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum443
Range443
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.683452762
Coefficient of variation (CV)6.389157177
Kurtosis1534.945599
Mean0.420001056
Median Absolute Deviation (MAD)0
Skewness20.00807847
Sum421579
Variance7.200918724
MonotonicityNot monotonic
2024-02-13T20:54:57.468078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 947992
94.4%
1 8441
 
0.8%
2 7014
 
0.7%
3 5759
 
0.6%
4 4819
 
0.5%
5 4120
 
0.4%
6 3469
 
0.3%
7 2970
 
0.3%
8 2625
 
0.3%
9 2186
 
0.2%
Other values (90) 14362
 
1.4%
ValueCountFrequency (%)
0 947992
94.4%
1 8441
 
0.8%
2 7014
 
0.7%
3 5759
 
0.6%
4 4819
 
0.5%
ValueCountFrequency (%)
443 1
< 0.1%
311 1
< 0.1%
303 1
< 0.1%
272 1
< 0.1%
258 1
< 0.1%

applicationscnt_464L
Real number (ℝ)

ZEROS 

Distinct243
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.238873552
Minimum0
Maximum247
Zeros901905
Zeros (%)89.9%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:54:57.616375image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum247
Range247
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.31643395
Coefficient of variation (CV)8.327269504
Kurtosis214.6146123
Mean1.238873552
Median Absolute Deviation (MAD)0
Skewness13.70514459
Sum1243528
Variance106.4288094
MonotonicityNot monotonic
2024-02-13T20:54:57.773036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 901905
89.9%
1 39341
 
3.9%
2 15048
 
1.5%
3 8376
 
0.8%
4 5593
 
0.6%
5 3997
 
0.4%
6 3008
 
0.3%
7 2264
 
0.2%
8 1825
 
0.2%
9 1582
 
0.2%
Other values (233) 20818
 
2.1%
ValueCountFrequency (%)
0 901905
89.9%
1 39341
 
3.9%
2 15048
 
1.5%
3 8376
 
0.8%
4 5593
 
0.6%
ValueCountFrequency (%)
247 2
< 0.1%
246 1
< 0.1%
243 1
< 0.1%
242 1
< 0.1%
239 2
< 0.1%

applicationscnt_629L
Real number (ℝ)

ZEROS 

Distinct78
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3163962991
Minimum0
Maximum77
Zeros943080
Zeros (%)94.0%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:54:57.931075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum77
Range77
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.64809247
Coefficient of variation (CV)8.369543126
Kurtosis238.7195054
Mean0.3163962991
Median Absolute Deviation (MAD)0
Skewness14.18013289
Sum317585
Variance7.01239373
MonotonicityNot monotonic
2024-02-13T20:54:58.098523image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 943080
94.0%
1 29900
 
3.0%
2 9157
 
0.9%
3 4627
 
0.5%
4 2831
 
0.3%
5 1919
 
0.2%
6 1406
 
0.1%
7 1034
 
0.1%
8 863
 
0.1%
9 733
 
0.1%
Other values (68) 8207
 
0.8%
ValueCountFrequency (%)
0 943080
94.0%
1 29900
 
3.0%
2 9157
 
0.9%
3 4627
 
0.5%
4 2831
 
0.3%
ValueCountFrequency (%)
77 1
 
< 0.1%
76 1
 
< 0.1%
75 2
< 0.1%
74 1
 
< 0.1%
73 4
< 0.1%

applicationscnt_867L
Real number (ℝ)

ZEROS 

Distinct78
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.54688037
Minimum0
Maximum97
Zeros347151
Zeros (%)34.6%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:54:58.264511image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile10
Maximum97
Range97
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.741651159
Coefficient of variation (CV)1.469111468
Kurtosis21.8632115
Mean2.54688037
Median Absolute Deviation (MAD)1
Skewness3.420540499
Sum2556449
Variance13.9999534
MonotonicityNot monotonic
2024-02-13T20:54:58.420568image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 347151
34.6%
1 192211
19.1%
2 128593
 
12.8%
3 89349
 
8.9%
4 62576
 
6.2%
5 45036
 
4.5%
6 32654
 
3.3%
7 24049
 
2.4%
8 17997
 
1.8%
9 13776
 
1.4%
Other values (68) 50365
 
5.0%
ValueCountFrequency (%)
0 347151
34.6%
1 192211
19.1%
2 128593
 
12.8%
3 89349
 
8.9%
4 62576
 
6.2%
ValueCountFrequency (%)
97 1
< 0.1%
89 2
< 0.1%
79 1
< 0.1%
77 1
< 0.1%
76 2
< 0.1%

avgdbddpdlast24m_3658932P
Real number (ℝ)

MISSING  ZEROS 

Distinct3972
Distinct (%)0.7%
Missing426131
Missing (%)42.5%
Infinite0
Infinite (%)0.0%
Mean29.61587082
Minimum-1176
Maximum4467
Zeros37164
Zeros (%)3.7%
Negative464480
Negative (%)46.3%
Memory size7.7 MiB
2024-02-13T20:54:58.565551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1176
5-th percentile-32
Q1-11
median-5
Q3-1
95-th percentile7
Maximum4467
Range5643
Interquartile range (IQR)10

Descriptive statistics

Standard deviation316.840868
Coefficient of variation (CV)10.69834718
Kurtosis117.8702603
Mean29.61587082
Median Absolute Deviation (MAD)4
Skewness10.41734487
Sum17106897
Variance100388.1357
MonotonicityNot monotonic
2024-02-13T20:54:58.711551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 47251
 
4.7%
-2 45561
 
4.5%
-3 40160
 
4.0%
0 37164
 
3.7%
-4 35334
 
3.5%
-5 30958
 
3.1%
-6 27668
 
2.8%
-7 24831
 
2.5%
-8 21785
 
2.2%
-9 19995
 
2.0%
Other values (3962) 246919
24.6%
(Missing) 426131
42.5%
ValueCountFrequency (%)
-1176 1
< 0.1%
-1135 1
< 0.1%
-1082 1
< 0.1%
-1019 1
< 0.1%
-1005 1
< 0.1%
ValueCountFrequency (%)
4467 1
< 0.1%
4464 1
< 0.1%
4448 1
< 0.1%
4443 1
< 0.1%
4432 1
< 0.1%

avgdbddpdlast3m_4187120P
Real number (ℝ)

MISSING  ZEROS 

Distinct3090
Distinct (%)0.9%
Missing658003
Missing (%)65.6%
Infinite0
Infinite (%)0.0%
Mean29.94085969
Minimum-908
Maximum4467
Zeros35798
Zeros (%)3.6%
Negative265417
Negative (%)26.4%
Memory size7.7 MiB
2024-02-13T20:54:58.870589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-908
5-th percentile-42
Q1-11
median-4
Q3-1
95-th percentile6
Maximum4467
Range5375
Interquartile range (IQR)10

Descriptive statistics

Standard deviation363.7941832
Coefficient of variation (CV)12.15042544
Kurtosis102.9795268
Mean29.94085969
Median Absolute Deviation (MAD)4
Skewness9.993970112
Sum10352172
Variance132346.2077
MonotonicityNot monotonic
2024-02-13T20:54:59.051783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 38213
 
3.8%
0 35798
 
3.6%
-2 29504
 
2.9%
-3 23163
 
2.3%
-4 18533
 
1.8%
-5 15215
 
1.5%
-6 13252
 
1.3%
-7 11698
 
1.2%
1 11639
 
1.2%
-8 10459
 
1.0%
Other values (3080) 138280
 
13.8%
(Missing) 658003
65.6%
ValueCountFrequency (%)
-908 1
< 0.1%
-887 1
< 0.1%
-850 1
< 0.1%
-657 1
< 0.1%
-531 1
< 0.1%
ValueCountFrequency (%)
4467 1
< 0.1%
4464 1
< 0.1%
4462 1
< 0.1%
4448 1
< 0.1%
4443 1
< 0.1%

avgdbdtollast24m_4525197P
Real number (ℝ)

MISSING  ZEROS 

Distinct2948
Distinct (%)1.4%
Missing785725
Missing (%)78.3%
Infinite0
Infinite (%)0.0%
Mean31.97039425
Minimum-1176
Maximum4467
Zeros14305
Zeros (%)1.4%
Negative181083
Negative (%)18.0%
Memory size7.7 MiB
2024-02-13T20:54:59.208962image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1176
5-th percentile-35
Q1-11
median-5
Q3-1
95-th percentile5
Maximum4467
Range5643
Interquartile range (IQR)10

Descriptive statistics

Standard deviation328.8512729
Coefficient of variation (CV)10.28611878
Kurtosis111.1424249
Mean31.97039425
Median Absolute Deviation (MAD)4
Skewness10.09403594
Sum6970569
Variance108143.1597
MonotonicityNot monotonic
2024-02-13T20:54:59.353301image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 18707
 
1.9%
-2 17812
 
1.8%
-3 15518
 
1.5%
0 14305
 
1.4%
-4 13756
 
1.4%
-5 11826
 
1.2%
-6 10518
 
1.0%
-7 9599
 
1.0%
-8 8388
 
0.8%
-9 8334
 
0.8%
Other values (2938) 89269
 
8.9%
(Missing) 785725
78.3%
ValueCountFrequency (%)
-1176 1
< 0.1%
-1136 1
< 0.1%
-942 1
< 0.1%
-925 1
< 0.1%
-853 2
< 0.1%
ValueCountFrequency (%)
4467 1
< 0.1%
4464 1
< 0.1%
4448 1
< 0.1%
4443 1
< 0.1%
4432 1
< 0.1%

avgdpdtolclosure24_3658938P
Real number (ℝ)

MISSING  ZEROS 

Distinct3876
Distinct (%)0.6%
Missing329272
Missing (%)32.8%
Infinite0
Infinite (%)0.0%
Mean44.52924676
Minimum0
Maximum4467
Zeros487635
Zeros (%)48.6%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:54:59.509303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile17
Maximum4467
Range4467
Interquartile range (IQR)1

Descriptive statistics

Standard deviation315.5864038
Coefficient of variation (CV)7.087171394
Kurtosis105.4095912
Mean44.52924676
Median Absolute Deviation (MAD)0
Skewness9.698604027
Sum30034309
Variance99594.77828
MonotonicityNot monotonic
2024-02-13T20:54:59.661445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 487635
48.6%
1 80304
 
8.0%
2 25459
 
2.5%
3 13655
 
1.4%
4 8410
 
0.8%
5 5798
 
0.6%
6 4165
 
0.4%
7 3143
 
0.3%
8 2506
 
0.2%
9 2009
 
0.2%
Other values (3866) 41401
 
4.1%
(Missing) 329272
32.8%
ValueCountFrequency (%)
0 487635
48.6%
1 80304
 
8.0%
2 25459
 
2.5%
3 13655
 
1.4%
4 8410
 
0.8%
ValueCountFrequency (%)
4467 1
< 0.1%
4464 1
< 0.1%
4448 1
< 0.1%
4443 1
< 0.1%
4432 1
< 0.1%

avginstallast24m_3658937A
Real number (ℝ)

MISSING 

Distinct78236
Distinct (%)13.7%
Missing433721
Missing (%)43.2%
Infinite0
Infinite (%)0.0%
Mean5117.126961
Minimum0
Maximum400000
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:54:59.815409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1164.8
Q12445
median3894
Q36209.650075
95-th percentile11945.65075
Maximum400000
Range400000
Interquartile range (IQR)3764.650075

Descriptive statistics

Standard deviation6142.688624
Coefficient of variation (CV)1.200417474
Kurtosis405.3169623
Mean5117.126961
Median Absolute Deviation (MAD)1715
Skewness14.43640009
Sum2916946585
Variance37732623.53
MonotonicityNot monotonic
2024-02-13T20:54:59.980340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
800 150
 
< 0.1%
600 147
 
< 0.1%
667.2 142
 
< 0.1%
2000 124
 
< 0.1%
1999.8 106
 
< 0.1%
2666.6 97
 
< 0.1%
3000 87
 
< 0.1%
3333.4001 79
 
< 0.1%
2000.4 78
 
< 0.1%
1998.2001 74
 
< 0.1%
Other values (78226) 568952
56.7%
(Missing) 433721
43.2%
ValueCountFrequency (%)
0 5
< 0.1%
0.2 3
< 0.1%
0.4 3
< 0.1%
0.6 5
< 0.1%
0.8 2
 
< 0.1%
ValueCountFrequency (%)
400000 1
< 0.1%
391795.22 1
< 0.1%
318163 1
< 0.1%
301035.6 1
< 0.1%
290726 1
< 0.1%

avglnamtstart24m_4525187A
Real number (ℝ)

MISSING 

Distinct30266
Distinct (%)43.4%
Missing933950
Missing (%)93.0%
Infinite0
Infinite (%)0.0%
Mean43699.38738
Minimum0
Maximum513520
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:00.156338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7301.4
Q115646
median27978
Q354445.6
95-th percentile134126.61
Maximum513520
Range513520
Interquartile range (IQR)38799.6

Descriptive statistics

Standard deviation43990.70108
Coefficient of variation (CV)1.006666311
Kurtosis8.458835632
Mean43699.38738
Median Absolute Deviation (MAD)15480
Skewness2.424435991
Sum3050523135
Variance1935181782
MonotonicityNot monotonic
2024-02-13T20:55:00.317523image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 1081
 
0.1%
20000 442
 
< 0.1%
40000 438
 
< 0.1%
150000 343
 
< 0.1%
60000 327
 
< 0.1%
23682.8 288
 
< 0.1%
21841.4 280
 
< 0.1%
10920.8 273
 
< 0.1%
30000 269
 
< 0.1%
200000 200
 
< 0.1%
Other values (30256) 65866
 
6.6%
(Missing) 933950
93.0%
ValueCountFrequency (%)
0 12
< 0.1%
2000 7
< 0.1%
2016 1
 
< 0.1%
2040 1
 
< 0.1%
2049.8 1
 
< 0.1%
ValueCountFrequency (%)
513520 2
< 0.1%
496148.8 1
 
< 0.1%
495251.22 3
< 0.1%
494899.22 1
 
< 0.1%
464022.4 1
 
< 0.1%

avgmaxdpdlast9m_3716943P
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct204
Distinct (%)< 0.1%
Missing515293
Missing (%)51.3%
Infinite0
Infinite (%)0.0%
Mean0.7226080121
Minimum0
Maximum236
Zeros390120
Zeros (%)38.9%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:00.466520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum236
Range236
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.639908114
Coefficient of variation (CV)6.421058218
Kurtosis792.4803873
Mean0.7226080121
Median Absolute Deviation (MAD)0
Skewness24.43197008
Sum352968
Variance21.52874731
MonotonicityNot monotonic
2024-02-13T20:55:00.611526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 390120
38.9%
1 50740
 
5.1%
2 17131
 
1.7%
3 8935
 
0.9%
4 5414
 
0.5%
5 3549
 
0.4%
6 2467
 
0.2%
7 1850
 
0.2%
8 1404
 
0.1%
9 1046
 
0.1%
Other values (194) 5808
 
0.6%
(Missing) 515293
51.3%
ValueCountFrequency (%)
0 390120
38.9%
1 50740
 
5.1%
2 17131
 
1.7%
3 8935
 
0.9%
4 5414
 
0.5%
ValueCountFrequency (%)
236 1
< 0.1%
231 1
< 0.1%
229 1
< 0.1%
223 1
< 0.1%
218 2
< 0.1%

avgoutstandbalancel6m_4187114A
Real number (ℝ)

MISSING  ZEROS 

Distinct339858
Distinct (%)84.0%
Missing598994
Missing (%)59.7%
Infinite0
Infinite (%)0.0%
Mean44570.30155
Minimum-7588198.5
Maximum1131135.9
Zeros16140
Zeros (%)1.6%
Negative6100
Negative (%)0.6%
Memory size7.7 MiB
2024-02-13T20:55:00.761432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-7588198.5
5-th percentile0
Q18864.464
median22685.715
Q353911.106
95-th percentile168442.832
Maximum1131135.9
Range8719334.4
Interquartile range (IQR)45046.642

Descriptive statistics

Standard deviation61632.94976
Coefficient of variation (CV)1.382825505
Kurtosis594.8184126
Mean44570.30155
Median Absolute Deviation (MAD)17088.815
Skewness-1.805207822
Sum1.804040897 × 1010
Variance3798620496
MonotonicityNot monotonic
2024-02-13T20:55:00.914880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16140
 
1.6%
-2 376
 
< 0.1%
-4 177
 
< 0.1%
3000 167
 
< 0.1%
2000 146
 
< 0.1%
6000 134
 
< 0.1%
4000 114
 
< 0.1%
2400 112
 
< 0.1%
1500 105
 
< 0.1%
1000 104
 
< 0.1%
Other values (339848) 387188
38.6%
(Missing) 598994
59.7%
ValueCountFrequency (%)
-7588198.5 1
< 0.1%
-216271.78 1
< 0.1%
-160700 1
< 0.1%
-111002.89 1
< 0.1%
-78050.2 1
< 0.1%
ValueCountFrequency (%)
1131135.9 1
< 0.1%
1128721.2 1
< 0.1%
1102620.4 1
< 0.1%
1088508 1
< 0.1%
1079704.9 1
< 0.1%

avgpmtlast12m_4525200A
Real number (ℝ)

MISSING 

Distinct63869
Distinct (%)32.5%
Missing807310
Missing (%)80.4%
Infinite0
Infinite (%)0.0%
Mean5935.065595
Minimum0
Maximum391795.22
Zeros388
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:01.069257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1067.2001
Q12451.30005
median4114.8003
Q36977.2
95-th percentile14832.08
Maximum391795.22
Range391795.22
Interquartile range (IQR)4525.89995

Descriptive statistics

Standard deviation8646.086046
Coefficient of variation (CV)1.4567802
Kurtosis251.9117487
Mean5935.065595
Median Absolute Deviation (MAD)2004.4002
Skewness12.22061291
Sum1165925831
Variance74754803.92
MonotonicityNot monotonic
2024-02-13T20:55:01.256556image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 388
 
< 0.1%
2000 70
 
< 0.1%
600 63
 
< 0.1%
2999.6 46
 
< 0.1%
2165 46
 
< 0.1%
1665 45
 
< 0.1%
1998.2001 44
 
< 0.1%
1666.6 42
 
< 0.1%
1500 42
 
< 0.1%
2166.6 42
 
< 0.1%
Other values (63859) 195619
 
19.5%
(Missing) 807310
80.4%
ValueCountFrequency (%)
0 388
< 0.1%
0.2 27
 
< 0.1%
0.4 22
 
< 0.1%
0.6 16
 
< 0.1%
0.8 18
 
< 0.1%
ValueCountFrequency (%)
391795.22 1
< 0.1%
379675.22 1
< 0.1%
318163 1
< 0.1%
314949.22 1
< 0.1%
262954.22 1
< 0.1%

bankacctype_710L
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing699067
Missing (%)69.6%
Memory size7.7 MiB
2024-02-13T20:55:01.355479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters609380
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 304690
100.0%
2024-02-13T20:55:01.560473image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 304690
50.0%
A 304690
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 609380
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 304690
50.0%
A 304690
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 609380
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 304690
50.0%
A 304690
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 609380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 304690
50.0%
A 304690
50.0%

cardtype_51L
Text

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing877973
Missing (%)87.5%
Memory size7.7 MiB
2024-02-13T20:55:01.707253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length7
Mean length7.076917573
Min length7

Characters and Unicode

Total characters890163
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 123849
98.5%
personalized 1935
 
1.5%
2024-02-13T20:55:01.980860image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 249633
28.0%
T 247698
27.8%
I 125784
14.1%
S 125784
14.1%
A 125784
14.1%
E 3870
 
0.4%
P 1935
 
0.2%
R 1935
 
0.2%
O 1935
 
0.2%
L 1935
 
0.2%
Other values (2) 3870
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 890163
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 249633
28.0%
T 247698
27.8%
I 125784
14.1%
S 125784
14.1%
A 125784
14.1%
E 3870
 
0.4%
P 1935
 
0.2%
R 1935
 
0.2%
O 1935
 
0.2%
L 1935
 
0.2%
Other values (2) 3870
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 890163
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 249633
28.0%
T 247698
27.8%
I 125784
14.1%
S 125784
14.1%
A 125784
14.1%
E 3870
 
0.4%
P 1935
 
0.2%
R 1935
 
0.2%
O 1935
 
0.2%
L 1935
 
0.2%
Other values (2) 3870
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 890163
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 249633
28.0%
T 247698
27.8%
I 125784
14.1%
S 125784
14.1%
A 125784
14.1%
E 3870
 
0.4%
P 1935
 
0.2%
R 1935
 
0.2%
O 1935
 
0.2%
L 1935
 
0.2%
Other values (2) 3870
 
0.4%

clientscnt12m_3712952L
Real number (ℝ)

SKEWED  ZEROS 

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03199479555
Minimum0
Maximum47
Zeros975460
Zeros (%)97.2%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:02.125667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum47
Range47
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2772198323
Coefficient of variation (CV)8.664528948
Kurtosis5493.737192
Mean0.03199479555
Median Absolute Deviation (MAD)0
Skewness52.13080369
Sum32115
Variance0.0768508354
MonotonicityNot monotonic
2024-02-13T20:55:02.269668image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 975460
97.2%
1 26952
 
2.7%
2 915
 
0.1%
3 137
 
< 0.1%
4 65
 
< 0.1%
5 40
 
< 0.1%
6 29
 
< 0.1%
8 20
 
< 0.1%
7 20
 
< 0.1%
13 14
 
< 0.1%
Other values (29) 105
 
< 0.1%
ValueCountFrequency (%)
0 975460
97.2%
1 26952
 
2.7%
2 915
 
0.1%
3 137
 
< 0.1%
4 65
 
< 0.1%
ValueCountFrequency (%)
47 1
< 0.1%
46 1
< 0.1%
45 1
< 0.1%
38 1
< 0.1%
35 1
< 0.1%

clientscnt3m_3712950L
Real number (ℝ)

SKEWED  ZEROS 

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01480836497
Minimum0
Maximum47
Zeros991571
Zeros (%)98.8%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:02.405458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum47
Range47
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2281499063
Coefficient of variation (CV)15.40682625
Kurtosis10836.73608
Mean0.01480836497
Median Absolute Deviation (MAD)0
Skewness80.02075906
Sum14864
Variance0.05205237972
MonotonicityNot monotonic
2024-02-13T20:55:02.542421image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 991571
98.8%
1 11548
 
1.2%
2 311
 
< 0.1%
3 71
 
< 0.1%
4 54
 
< 0.1%
5 38
 
< 0.1%
6 26
 
< 0.1%
7 21
 
< 0.1%
8 20
 
< 0.1%
9 13
 
< 0.1%
Other values (28) 84
 
< 0.1%
ValueCountFrequency (%)
0 991571
98.8%
1 11548
 
1.2%
2 311
 
< 0.1%
3 71
 
< 0.1%
4 54
 
< 0.1%
ValueCountFrequency (%)
47 1
< 0.1%
46 1
< 0.1%
45 1
< 0.1%
38 1
< 0.1%
35 1
< 0.1%

clientscnt6m_3712949L
Real number (ℝ)

SKEWED  ZEROS 

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02148029852
Minimum0
Maximum47
Zeros985333
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:02.904553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum47
Range47
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2480860615
Coefficient of variation (CV)11.54946991
Kurtosis7865.262972
Mean0.02148029852
Median Absolute Deviation (MAD)0
Skewness64.93718432
Sum21561
Variance0.0615466939
MonotonicityNot monotonic
2024-02-13T20:55:03.041567image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 985333
98.2%
1 17543
 
1.7%
2 505
 
0.1%
3 98
 
< 0.1%
4 58
 
< 0.1%
5 36
 
< 0.1%
6 29
 
< 0.1%
8 24
 
< 0.1%
7 19
 
< 0.1%
9 15
 
< 0.1%
Other values (28) 97
 
< 0.1%
ValueCountFrequency (%)
0 985333
98.2%
1 17543
 
1.7%
2 505
 
0.1%
3 98
 
< 0.1%
4 58
 
< 0.1%
ValueCountFrequency (%)
47 1
< 0.1%
46 1
< 0.1%
45 1
< 0.1%
38 1
< 0.1%
35 1
< 0.1%

clientscnt_100L
Real number (ℝ)

SKEWED  ZEROS 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0541425863
Minimum0
Maximum95
Zeros953732
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:03.162402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum95
Range95
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2884321302
Coefficient of variation (CV)5.327269159
Kurtosis20422.93871
Mean0.0541425863
Median Absolute Deviation (MAD)0
Skewness76.67377832
Sum54346
Variance0.08319309374
MonotonicityNot monotonic
2024-02-13T20:55:03.288070image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 953732
95.0%
1 46443
 
4.6%
2 3245
 
0.3%
3 294
 
< 0.1%
4 22
 
< 0.1%
6 6
 
< 0.1%
67 3
 
< 0.1%
5 3
 
< 0.1%
12 2
 
< 0.1%
8 2
 
< 0.1%
Other values (4) 5
 
< 0.1%
ValueCountFrequency (%)
0 953732
95.0%
1 46443
 
4.6%
2 3245
 
0.3%
3 294
 
< 0.1%
4 22
 
< 0.1%
ValueCountFrequency (%)
95 1
 
< 0.1%
67 3
< 0.1%
19 1
 
< 0.1%
15 1
 
< 0.1%
12 2
< 0.1%

clientscnt_1022L
Real number (ℝ)

SKEWED  ZEROS 

Distinct41
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09371292056
Minimum0
Maximum102
Zeros919744
Zeros (%)91.6%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:03.434496image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum102
Range102
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4184212083
Coefficient of variation (CV)4.464925495
Kurtosis10800.0823
Mean0.09371292056
Median Absolute Deviation (MAD)0
Skewness57.12276132
Sum94065
Variance0.1750763075
MonotonicityNot monotonic
2024-02-13T20:55:03.585859image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 919744
91.6%
1 77534
 
7.7%
2 5501
 
0.5%
3 553
 
0.1%
4 144
 
< 0.1%
5 60
 
< 0.1%
6 38
 
< 0.1%
7 30
 
< 0.1%
8 23
 
< 0.1%
9 18
 
< 0.1%
Other values (31) 112
 
< 0.1%
ValueCountFrequency (%)
0 919744
91.6%
1 77534
 
7.7%
2 5501
 
0.5%
3 553
 
0.1%
4 144
 
< 0.1%
ValueCountFrequency (%)
102 1
< 0.1%
101 1
< 0.1%
97 1
< 0.1%
47 1
< 0.1%
46 1
< 0.1%

clientscnt_1071L
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03643112825
Minimum0
Maximum11
Zeros968567
Zeros (%)96.5%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:03.706169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1956324021
Coefficient of variation (CV)5.369924332
Kurtosis51.15686252
Mean0.03643112825
Median Absolute Deviation (MAD)0
Skewness5.910147846
Sum36568
Variance0.03827203673
MonotonicityNot monotonic
2024-02-13T20:55:03.819172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 968567
96.5%
1 33940
 
3.4%
2 1156
 
0.1%
3 76
 
< 0.1%
4 14
 
< 0.1%
7 1
 
< 0.1%
11 1
 
< 0.1%
9 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 968567
96.5%
1 33940
 
3.4%
2 1156
 
0.1%
3 76
 
< 0.1%
4 14
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
9 1
 
< 0.1%
7 1
 
< 0.1%
5 1
 
< 0.1%
4 14
< 0.1%

clientscnt_1130L
Real number (ℝ)

SKEWED  ZEROS 

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03425729534
Minimum0
Maximum31
Zeros972774
Zeros (%)96.9%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:03.937165image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum31
Range31
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.245287678
Coefficient of variation (CV)7.160158896
Kurtosis3462.203729
Mean0.03425729534
Median Absolute Deviation (MAD)0
Skewness38.03268575
Sum34386
Variance0.06016604498
MonotonicityNot monotonic
2024-02-13T20:55:04.069635image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 972774
96.9%
1 28925
 
2.9%
2 1731
 
0.2%
3 190
 
< 0.1%
4 44
 
< 0.1%
5 18
 
< 0.1%
6 10
 
< 0.1%
7 7
 
< 0.1%
8 5
 
< 0.1%
22 5
 
< 0.1%
Other values (19) 48
 
< 0.1%
ValueCountFrequency (%)
0 972774
96.9%
1 28925
 
2.9%
2 1731
 
0.2%
3 190
 
< 0.1%
4 44
 
< 0.1%
ValueCountFrequency (%)
31 1
 
< 0.1%
29 3
< 0.1%
28 2
< 0.1%
27 3
< 0.1%
26 2
< 0.1%

clientscnt_136L
Real number (ℝ)

MISSING 

Distinct5
Distinct (%)1.3%
Missing1003360
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean0.05289672544
Minimum0
Maximum4
Zeros386
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:04.190160image/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 deviation0.3549404871
Coefficient of variation (CV)6.7100654
Kurtosis68.17204627
Mean0.05289672544
Median Absolute Deviation (MAD)0
Skewness7.890824434
Sum21
Variance0.1259827494
MonotonicityNot monotonic
2024-02-13T20:55:04.309132image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
0 386
 
< 0.1%
1 5
 
< 0.1%
2 3
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
(Missing) 1003360
> 99.9%
ValueCountFrequency (%)
0 386
< 0.1%
1 5
 
< 0.1%
2 3
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
ValueCountFrequency (%)
4 1
 
< 0.1%
3 2
 
< 0.1%
2 3
 
< 0.1%
1 5
 
< 0.1%
0 386
< 0.1%

clientscnt_157L
Real number (ℝ)

SKEWED  ZEROS 

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08863499831
Minimum0
Maximum241
Zeros949826
Zeros (%)94.6%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:04.448133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum241
Range241
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5948774255
Coefficient of variation (CV)6.711541003
Kurtosis49475.28389
Mean0.08863499831
Median Absolute Deviation (MAD)0
Skewness142.5888772
Sum88968
Variance0.3538791514
MonotonicityNot monotonic
2024-02-13T20:55:04.588132image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 949826
94.6%
1 34249
 
3.4%
2 10953
 
1.1%
3 5894
 
0.6%
4 1532
 
0.2%
5 752
 
0.1%
6 234
 
< 0.1%
7 122
 
< 0.1%
9 61
 
< 0.1%
8 56
 
< 0.1%
Other values (22) 78
 
< 0.1%
ValueCountFrequency (%)
0 949826
94.6%
1 34249
 
3.4%
2 10953
 
1.1%
3 5894
 
0.6%
4 1532
 
0.2%
ValueCountFrequency (%)
241 1
< 0.1%
194 1
< 0.1%
193 1
< 0.1%
74 1
< 0.1%
40 1
< 0.1%

clientscnt_257L
Real number (ℝ)

SKEWED  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003270711935
Minimum0
Maximum13
Zeros1000593
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:04.707134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.06081423493
Coefficient of variation (CV)18.59357722
Kurtosis2627.042293
Mean0.003270711935
Median Absolute Deviation (MAD)0
Skewness29.21066559
Sum3283
Variance0.003698371171
MonotonicityNot monotonic
2024-02-13T20:55:05.080041image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1000593
99.7%
1 3078
 
0.3%
2 72
 
< 0.1%
3 8
 
< 0.1%
5 4
 
< 0.1%
4 1
 
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
0 1000593
99.7%
1 3078
 
0.3%
2 72
 
< 0.1%
3 8
 
< 0.1%
4 1
 
< 0.1%
ValueCountFrequency (%)
13 1
 
< 0.1%
5 4
 
< 0.1%
4 1
 
< 0.1%
3 8
 
< 0.1%
2 72
< 0.1%

clientscnt_304L
Real number (ℝ)

SKEWED  ZEROS 

Distinct55
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08116506286
Minimum0
Maximum345
Zeros961656
Zeros (%)95.8%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:05.219113image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum345
Range345
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8385359141
Coefficient of variation (CV)10.3312421
Kurtosis60997.31238
Mean0.08116506286
Median Absolute Deviation (MAD)0
Skewness196.168309
Sum81470
Variance0.7031424792
MonotonicityNot monotonic
2024-02-13T20:55:05.374078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 961656
95.8%
1 21345
 
2.1%
2 11523
 
1.1%
3 5434
 
0.5%
4 2301
 
0.2%
5 898
 
0.1%
6 324
 
< 0.1%
7 138
 
< 0.1%
8 43
 
< 0.1%
9 17
 
< 0.1%
Other values (45) 78
 
< 0.1%
ValueCountFrequency (%)
0 961656
95.8%
1 21345
 
2.1%
2 11523
 
1.1%
3 5434
 
0.5%
4 2301
 
0.2%
ValueCountFrequency (%)
345 1
< 0.1%
249 1
< 0.1%
241 1
< 0.1%
228 1
< 0.1%
218 1
< 0.1%

clientscnt_360L
Real number (ℝ)

SKEWED  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003574570339
Minimum0
Maximum8
Zeros1000314
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:05.527544image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation0.06307322888
Coefficient of variation (CV)17.64498188
Kurtosis932.2488199
Mean0.003574570339
Median Absolute Deviation (MAD)0
Skewness22.38977957
Sum3588
Variance0.003978232201
MonotonicityNot monotonic
2024-02-13T20:55:05.651908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1000314
99.7%
1 3326
 
0.3%
2 103
 
< 0.1%
3 10
 
< 0.1%
7 2
 
< 0.1%
4 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 1000314
99.7%
1 3326
 
0.3%
2 103
 
< 0.1%
3 10
 
< 0.1%
4 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 2
 
< 0.1%
4 1
 
< 0.1%
3 10
 
< 0.1%
2 103
< 0.1%

clientscnt_493L
Real number (ℝ)

SKEWED  ZEROS 

Distinct56
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0120118714
Minimum0
Maximum329
Zeros997424
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:05.792372image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum329
Range329
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6568255174
Coefficient of variation (CV)54.68136442
Kurtosis144532.4372
Mean0.0120118714
Median Absolute Deviation (MAD)0
Skewness328.9606546
Sum12057
Variance0.4314197602
MonotonicityNot monotonic
2024-02-13T20:55:05.942197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 997424
99.4%
1 5286
 
0.5%
2 620
 
0.1%
3 138
 
< 0.1%
4 59
 
< 0.1%
5 35
 
< 0.1%
6 22
 
< 0.1%
9 18
 
< 0.1%
7 16
 
< 0.1%
8 16
 
< 0.1%
Other values (46) 123
 
< 0.1%
ValueCountFrequency (%)
0 997424
99.4%
1 5286
 
0.5%
2 620
 
0.1%
3 138
 
< 0.1%
4 59
 
< 0.1%
ValueCountFrequency (%)
329 1
< 0.1%
328 1
< 0.1%
229 1
< 0.1%
133 1
< 0.1%
101 1
< 0.1%

clientscnt_533L
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0755700832
Minimum0
Maximum8
Zeros932721
Zeros (%)92.9%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:06.071143image/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 deviation0.2826687045
Coefficient of variation (CV)3.740484231
Kurtosis17.201166
Mean0.0755700832
Median Absolute Deviation (MAD)0
Skewness3.934116553
Sum75854
Variance0.07990159651
MonotonicityNot monotonic
2024-02-13T20:55:06.192570image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 932721
92.9%
1 66403
 
6.6%
2 4470
 
0.4%
3 149
 
< 0.1%
4 10
 
< 0.1%
5 2
 
< 0.1%
8 1
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 932721
92.9%
1 66403
 
6.6%
2 4470
 
0.4%
3 149
 
< 0.1%
4 10
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
6 1
 
< 0.1%
5 2
 
< 0.1%
4 10
 
< 0.1%
3 149
< 0.1%

clientscnt_887L
Real number (ℝ)

SKEWED  ZEROS 

Distinct938
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.602130795
Minimum0
Maximum1165
Zeros879172
Zeros (%)87.6%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:06.335319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum1165
Range1165
Interquartile range (IQR)0

Descriptive statistics

Standard deviation38.75907088
Coefficient of variation (CV)14.89512786
Kurtosis548.5385666
Mean2.602130795
Median Absolute Deviation (MAD)0
Skewness22.6364637
Sum2611907
Variance1502.265576
MonotonicityNot monotonic
2024-02-13T20:55:06.492197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 879172
87.6%
1 53735
 
5.4%
2 19989
 
2.0%
3 11330
 
1.1%
4 7218
 
0.7%
5 4770
 
0.5%
6 3312
 
0.3%
7 2577
 
0.3%
8 2000
 
0.2%
9 1553
 
0.2%
Other values (928) 18101
 
1.8%
ValueCountFrequency (%)
0 879172
87.6%
1 53735
 
5.4%
2 19989
 
2.0%
3 11330
 
1.1%
4 7218
 
0.7%
ValueCountFrequency (%)
1165 2
< 0.1%
1164 3
< 0.1%
1163 3
< 0.1%
1162 2
< 0.1%
1161 3
< 0.1%

clientscnt_946L
Real number (ℝ)

SKEWED  ZEROS 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0277268303
Minimum0
Maximum72
Zeros978094
Zeros (%)97.4%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:06.623233image/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 deviation0.1954682225
Coefficient of variation (CV)7.049786086
Kurtosis18502.14845
Mean0.0277268303
Median Absolute Deviation (MAD)0
Skewness57.65467532
Sum27831
Variance0.03820782599
MonotonicityNot monotonic
2024-02-13T20:55:06.745253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 978094
97.4%
1 24032
 
2.4%
2 1347
 
0.1%
3 202
 
< 0.1%
4 48
 
< 0.1%
5 12
 
< 0.1%
7 6
 
< 0.1%
6 6
 
< 0.1%
8 3
 
< 0.1%
72 1
 
< 0.1%
Other values (6) 6
 
< 0.1%
ValueCountFrequency (%)
0 978094
97.4%
1 24032
 
2.4%
2 1347
 
0.1%
3 202
 
< 0.1%
4 48
 
< 0.1%
ValueCountFrequency (%)
72 1
< 0.1%
17 1
< 0.1%
14 1
< 0.1%
12 1
< 0.1%
11 1
< 0.1%

cntincpaycont9m_3716944L
Real number (ℝ)

MISSING  ZEROS 

Distinct79
Distinct (%)< 0.1%
Missing322077
Missing (%)32.1%
Infinite0
Infinite (%)0.0%
Mean6.042675449
Minimum0
Maximum318
Zeros192827
Zeros (%)19.2%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:06.887197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q39
95-th percentile18
Maximum318
Range318
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.939101522
Coefficient of variation (CV)0.9828595913
Kurtosis13.98827073
Mean6.042675449
Median Absolute Deviation (MAD)5
Skewness1.35510909
Sum4119171
Variance35.27292689
MonotonicityNot monotonic
2024-02-13T20:55:07.033939image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 192827
19.2%
9 60016
 
6.0%
8 44896
 
4.5%
6 38773
 
3.9%
3 37364
 
3.7%
7 34229
 
3.4%
5 33737
 
3.4%
4 32632
 
3.3%
10 28926
 
2.9%
2 27695
 
2.8%
Other values (69) 150585
15.0%
(Missing) 322077
32.1%
ValueCountFrequency (%)
0 192827
19.2%
1 25259
 
2.5%
2 27695
 
2.8%
3 37364
 
3.7%
4 32632
 
3.3%
ValueCountFrequency (%)
318 1
< 0.1%
152 1
< 0.1%
128 1
< 0.1%
110 1
< 0.1%
105 1
< 0.1%

cntpmts24_3658933L
Real number (ℝ)

MISSING  ZEROS 

Distinct26
Distinct (%)< 0.1%
Missing327733
Missing (%)32.7%
Infinite0
Infinite (%)0.0%
Mean10.25728968
Minimum0
Maximum25
Zeros97045
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:07.169284image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median9
Q316
95-th percentile24
Maximum25
Range25
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.740604773
Coefficient of variation (CV)0.7546442592
Kurtosis-1.037172183
Mean10.25728968
Median Absolute Deviation (MAD)6
Skewness0.3397070825
Sum6934174
Variance59.91696226
MonotonicityNot monotonic
2024-02-13T20:55:07.302252image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 97045
 
9.7%
6 38914
 
3.9%
12 35163
 
3.5%
3 34077
 
3.4%
11 29829
 
3.0%
9 29644
 
3.0%
5 28746
 
2.9%
10 27444
 
2.7%
8 24774
 
2.5%
4 24592
 
2.4%
Other values (16) 305796
30.5%
(Missing) 327733
32.7%
ValueCountFrequency (%)
0 97045
9.7%
1 17562
 
1.7%
2 21032
 
2.1%
3 34077
 
3.4%
4 24592
 
2.4%
ValueCountFrequency (%)
25 23128
2.3%
24 23648
2.4%
23 17351
1.7%
22 16310
1.6%
21 15964
1.6%

commnoinclast6m_3546845L
Real number (ℝ)

CONSTANT  MISSING  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing259522
Missing (%)25.9%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros744235
Zeros (%)74.1%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:07.413253image/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
2024-02-13T20:55:07.516251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 744235
74.1%
(Missing) 259522
 
25.9%
ValueCountFrequency (%)
0 744235
74.1%
ValueCountFrequency (%)
0 744235
74.1%

credamount_770A
Real number (ℝ)

Distinct140247
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47891.0614
Minimum2000
Maximum470592
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:07.672527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile10000
Q119472
median33796
Q360000
95-th percentile140000
Maximum470592
Range468592
Interquartile range (IQR)40528

Descriptive statistics

Standard deviation42199.0818
Coefficient of variation (CV)0.8811473491
Kurtosis7.325678902
Mean47891.0614
Median Absolute Deviation (MAD)17796
Skewness2.179813039
Sum4.807098812 × 1010
Variance1780762505
MonotonicityNot monotonic
2024-02-13T20:55:07.821114image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 37135
 
3.7%
40000 22829
 
2.3%
60000 20139
 
2.0%
20000 18692
 
1.9%
30000 16097
 
1.6%
150000 15705
 
1.6%
50000 11381
 
1.1%
80000 8807
 
0.9%
10000 6641
 
0.7%
70000 6478
 
0.6%
Other values (140237) 839853
83.7%
ValueCountFrequency (%)
2000 58
< 0.1%
2002 1
 
< 0.1%
2016 1
 
< 0.1%
2016.6 1
 
< 0.1%
2030 2
 
< 0.1%
ValueCountFrequency (%)
470592 1
< 0.1%
441667.4 1
< 0.1%
432659 1
< 0.1%
421000 1
< 0.1%
415510 1
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size7.7 MiB
2024-02-13T20:55:07.941037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3011268
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 661574
65.9%
cal 216313
 
21.6%
rel 125869
 
12.5%
2024-02-13T20:55:08.274439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 1003756
33.3%
C 877887
29.2%
O 661574
22.0%
A 216313
 
7.2%
R 125869
 
4.2%
E 125869
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3011268
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 1003756
33.3%
C 877887
29.2%
O 661574
22.0%
A 216313
 
7.2%
R 125869
 
4.2%
E 125869
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 3011268
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 1003756
33.3%
C 877887
29.2%
O 661574
22.0%
A 216313
 
7.2%
R 125869
 
4.2%
E 125869
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3011268
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 1003756
33.3%
C 877887
29.2%
O 661574
22.0%
A 216313
 
7.2%
R 125869
 
4.2%
E 125869
 
4.2%

currdebt_22A
Real number (ℝ)

ZEROS 

Distinct250573
Distinct (%)25.0%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean18183.49999
Minimum0
Maximum1210629.1
Zeros681696
Zeros (%)67.9%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:08.429103image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q311967.3
95-th percentile107299.2625
Maximum1210629.1
Range1210629.1
Interquartile range (IQR)11967.3

Descriptive statistics

Standard deviation47798.57166
Coefficient of variation (CV)2.628678291
Kurtosis32.58827587
Mean18183.49999
Median Absolute Deviation (MAD)0
Skewness4.685446529
Sum1.825177903 × 1010
Variance2284703453
MonotonicityNot monotonic
2024-02-13T20:55:08.591388image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 681696
67.9%
10 110
 
< 0.1%
19998 62
 
< 0.1%
7998 53
 
< 0.1%
14998 52
 
< 0.1%
17998 49
 
< 0.1%
9978 48
 
< 0.1%
9998 47
 
< 0.1%
13998 46
 
< 0.1%
15998 45
 
< 0.1%
Other values (250563) 321547
32.0%
ValueCountFrequency (%)
0 681696
67.9%
0.020000001 1
 
< 0.1%
0.025999999 2
 
< 0.1%
0.030000001 1
 
< 0.1%
0.048 1
 
< 0.1%
ValueCountFrequency (%)
1210629.1 1
< 0.1%
1192100.9 1
< 0.1%
1092393 1
< 0.1%
1085048.1 1
< 0.1%
1071760.9 1
< 0.1%

currdebtcredtyperange_828A
Real number (ℝ)

ZEROS 

Distinct163569
Distinct (%)16.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean10328.77838
Minimum0
Maximum1028338.2
Zeros801429
Zeros (%)79.8%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:08.753385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile66564.1215
Maximum1028338.2
Range1028338.2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation34360.63304
Coefficient of variation (CV)3.326688963
Kurtosis45.30900375
Mean10328.77838
Median Absolute Deviation (MAD)0
Skewness5.637595891
Sum1.036756294 × 1010
Variance1180653103
MonotonicityNot monotonic
2024-02-13T20:55:08.911603image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 801429
79.8%
7998 43
 
< 0.1%
17998 40
 
< 0.1%
19998 38
 
< 0.1%
11998 36
 
< 0.1%
19978 34
 
< 0.1%
14998 33
 
< 0.1%
8998 31
 
< 0.1%
9978 31
 
< 0.1%
15998 31
 
< 0.1%
Other values (163559) 202009
 
20.1%
ValueCountFrequency (%)
0 801429
79.8%
0.2 3
 
< 0.1%
0.4 2
 
< 0.1%
0.6 5
 
< 0.1%
0.8 2
 
< 0.1%
ValueCountFrequency (%)
1028338.2 1
< 0.1%
873632.44 1
< 0.1%
865120 1
< 0.1%
861237.8 1
< 0.1%
824982.8 1
< 0.1%

datefirstoffer_1144D
Text

MISSING 

Distinct1228
Distinct (%)0.3%
Missing560713
Missing (%)55.9%
Memory size7.7 MiB
2024-02-13T20:55:09.258012image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters4430440
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

Unique35 ?
Unique (%)< 0.1%

Sample

1st row2019-01-07
2nd row2019-01-25
3rd row2018-12-31
4th row2019-01-19
5th row2008-04-03
ValueCountFrequency (%)
2008-04-03 90002
 
20.3%
2007-10-29 71945
 
16.2%
2014-02-19 11387
 
2.6%
2016-03-28 5493
 
1.2%
2017-07-25 4329
 
1.0%
2017-07-29 4110
 
0.9%
2017-07-21 4040
 
0.9%
2014-03-08 3999
 
0.9%
2017-03-31 3781
 
0.9%
2016-11-28 3624
 
0.8%
Other values (1218) 240334
54.2%
2024-02-13T20:55:09.699147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1204121
27.2%
- 886088
20.0%
2 681593
15.4%
1 581265
13.1%
7 208977
 
4.7%
8 188104
 
4.2%
4 176575
 
4.0%
3 167140
 
3.8%
9 155661
 
3.5%
6 98134
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3544352
80.0%
Dash Punctuation 886088
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1204121
34.0%
2 681593
19.2%
1 581265
16.4%
7 208977
 
5.9%
8 188104
 
5.3%
4 176575
 
5.0%
3 167140
 
4.7%
9 155661
 
4.4%
6 98134
 
2.8%
5 82782
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 886088
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4430440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1204121
27.2%
- 886088
20.0%
2 681593
15.4%
1 581265
13.1%
7 208977
 
4.7%
8 188104
 
4.2%
4 176575
 
4.0%
3 167140
 
3.8%
9 155661
 
3.5%
6 98134
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4430440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1204121
27.2%
- 886088
20.0%
2 681593
15.4%
1 581265
13.1%
7 208977
 
4.7%
8 188104
 
4.2%
4 176575
 
4.0%
3 167140
 
3.8%
9 155661
 
3.5%
6 98134
 
2.2%
Distinct4825
Distinct (%)6.7%
Missing932264
Missing (%)92.9%
Memory size7.7 MiB
2024-02-13T20:55:10.126728image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters714930
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

Unique201 ?
Unique (%)0.3%

Sample

1st row2016-02-15
2nd row2017-07-25
3rd row2007-06-06
4th row2007-06-30
5th row2015-01-05
ValueCountFrequency (%)
2017-06-14 180
 
0.3%
2008-02-14 156
 
0.2%
2017-05-30 146
 
0.2%
2017-05-25 143
 
0.2%
2017-05-23 142
 
0.2%
2017-05-20 137
 
0.2%
2017-05-29 135
 
0.2%
2007-04-14 133
 
0.2%
2017-05-17 132
 
0.2%
2017-05-24 130
 
0.2%
Other values (4815) 70059
98.0%
2024-02-13T20:55:10.632802image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 179905
25.2%
- 142986
20.0%
1 114715
16.0%
2 113954
15.9%
7 31293
 
4.4%
8 26879
 
3.8%
6 23624
 
3.3%
5 23205
 
3.2%
9 19984
 
2.8%
3 19565
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 571944
80.0%
Dash Punctuation 142986
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 179905
31.5%
1 114715
20.1%
2 113954
19.9%
7 31293
 
5.5%
8 26879
 
4.7%
6 23624
 
4.1%
5 23205
 
4.1%
9 19984
 
3.5%
3 19565
 
3.4%
4 18820
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 142986
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 714930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 179905
25.2%
- 142986
20.0%
1 114715
16.0%
2 113954
15.9%
7 31293
 
4.4%
8 26879
 
3.8%
6 23624
 
3.3%
5 23205
 
3.2%
9 19984
 
2.8%
3 19565
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 714930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 179905
25.2%
- 142986
20.0%
1 114715
16.0%
2 113954
15.9%
7 31293
 
4.4%
8 26879
 
3.8%
6 23624
 
3.3%
5 23205
 
3.2%
9 19984
 
2.8%
3 19565
 
2.7%
Distinct4936
Distinct (%)1.2%
Missing606750
Missing (%)60.4%
Memory size7.7 MiB
2024-02-13T20:55:11.034414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3970070
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

Unique58 ?
Unique (%)< 0.1%

Sample

1st row2018-12-31
2nd row2018-12-11
3rd row2019-01-02
4th row2018-11-25
5th row2018-12-31
ValueCountFrequency (%)
2019-08-11 1074
 
0.3%
2019-04-07 1062
 
0.3%
2019-09-15 991
 
0.2%
2019-05-12 984
 
0.2%
2019-07-28 984
 
0.2%
2019-08-18 979
 
0.2%
2019-01-15 978
 
0.2%
2019-02-11 967
 
0.2%
2019-05-15 935
 
0.2%
2018-12-18 873
 
0.2%
Other values (4926) 387180
97.5%
2024-02-13T20:55:11.548015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 922024
23.2%
- 794014
20.0%
1 703908
17.7%
2 635759
16.0%
9 206775
 
5.2%
8 188076
 
4.7%
7 141041
 
3.6%
5 110102
 
2.8%
6 102136
 
2.6%
3 89450
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3176056
80.0%
Dash Punctuation 794014
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 922024
29.0%
1 703908
22.2%
2 635759
20.0%
9 206775
 
6.5%
8 188076
 
5.9%
7 141041
 
4.4%
5 110102
 
3.5%
6 102136
 
3.2%
3 89450
 
2.8%
4 76785
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 794014
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3970070
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 922024
23.2%
- 794014
20.0%
1 703908
17.7%
2 635759
16.0%
9 206775
 
5.2%
8 188076
 
4.7%
7 141041
 
3.6%
5 110102
 
2.8%
6 102136
 
2.6%
3 89450
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3970070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 922024
23.2%
- 794014
20.0%
1 703908
17.7%
2 635759
16.0%
9 206775
 
5.2%
8 188076
 
4.7%
7 141041
 
3.6%
5 110102
 
2.8%
6 102136
 
2.6%
3 89450
 
2.3%

daysoverduetolerancedd_3976961L
Real number (ℝ)

MISSING  ZEROS 

Distinct4390
Distinct (%)0.6%
Missing320324
Missing (%)31.9%
Infinite0
Infinite (%)0.0%
Mean87.61516784
Minimum0
Maximum5020
Zeros213521
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:11.713026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q313
95-th percentile233
Maximum5020
Range5020
Interquartile range (IQR)13

Descriptive statistics

Standard deviation458.4981354
Coefficient of variation (CV)5.233090876
Kurtosis66.83667251
Mean87.61516784
Median Absolute Deviation (MAD)2
Skewness7.815554598
Sum59879097
Variance210220.5402
MonotonicityNot monotonic
2024-02-13T20:55:11.882990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 213521
21.3%
1 115840
 
11.5%
2 38461
 
3.8%
3 30138
 
3.0%
4 22641
 
2.3%
5 17890
 
1.8%
6 14633
 
1.5%
7 12858
 
1.3%
8 11250
 
1.1%
9 10091
 
1.0%
Other values (4380) 196110
19.5%
(Missing) 320324
31.9%
ValueCountFrequency (%)
0 213521
21.3%
1 115840
11.5%
2 38461
 
3.8%
3 30138
 
3.0%
4 22641
 
2.3%
ValueCountFrequency (%)
5020 1
< 0.1%
5018 1
< 0.1%
5012 1
< 0.1%
5009 1
< 0.1%
5005 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
Zeros1003757
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:12.020098image/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
2024-02-13T20:55:12.124106image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 1003757
100.0%
ValueCountFrequency (%)
0 1003757
100.0%
ValueCountFrequency (%)
0 1003757
100.0%

disbursedcredamount_1113A
Real number (ℝ)

ZEROS 

Distinct153694
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44174.94211
Minimum0
Maximum400000
Zeros28330
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:12.259336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7998
Q117998
median30354
Q357439.258
95-th percentile125962.496
Maximum400000
Range400000
Interquartile range (IQR)39441.258

Descriptive statistics

Standard deviation40505.84844
Coefficient of variation (CV)0.9169417436
Kurtosis8.445003777
Mean44174.94211
Median Absolute Deviation (MAD)15972
Skewness2.319259717
Sum4.434090737 × 1010
Variance1640723758
MonotonicityNot monotonic
2024-02-13T20:55:12.424869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28330
 
2.8%
100000 26305
 
2.6%
40000 21459
 
2.1%
60000 19025
 
1.9%
20000 17511
 
1.7%
30000 15569
 
1.6%
150000 11086
 
1.1%
50000 10996
 
1.1%
80000 8251
 
0.8%
70000 6191
 
0.6%
Other values (153684) 839034
83.6%
ValueCountFrequency (%)
0 28330
2.8%
1798 1
 
< 0.1%
1998 1
 
< 0.1%
2000 58
 
< 0.1%
2000.036 1
 
< 0.1%
ValueCountFrequency (%)
400000 488
< 0.1%
399999.9 1
 
< 0.1%
399999.88 1
 
< 0.1%
399999.84 2
 
< 0.1%
399856.62 1
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing618
Missing (%)0.1%
Memory size7.7 MiB
2024-02-13T20:55:12.557342image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.971983942
Min length2

Characters and Unicode

Total characters2981313
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 758802
75.6%
gba 216233
 
21.6%
dd 28104
 
2.8%
2024-02-13T20:55:12.805249image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 975035
32.7%
A 975035
32.7%
S 758802
25.5%
G 216233
 
7.3%
D 56208
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2981313
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 975035
32.7%
A 975035
32.7%
S 758802
25.5%
G 216233
 
7.3%
D 56208
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 2981313
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 975035
32.7%
A 975035
32.7%
S 758802
25.5%
G 216233
 
7.3%
D 56208
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2981313
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 975035
32.7%
A 975035
32.7%
S 758802
25.5%
G 216233
 
7.3%
D 56208
 
1.9%

downpmt_116A
Real number (ℝ)

SKEWED  ZEROS 

Distinct7125
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean529.0191908
Minimum0
Maximum461867.4
Zeros942641
Zeros (%)93.9%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:12.955217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1000
Maximum461867.4
Range461867.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3973.15189
Coefficient of variation (CV)7.510411642
Kurtosis948.4845577
Mean529.0191908
Median Absolute Deviation (MAD)0
Skewness20.74420913
Sum531006715.9
Variance15785935.94
MonotonicityNot monotonic
2024-02-13T20:55:13.107249image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 942641
93.9%
2000 7129
 
0.7%
4000 5747
 
0.6%
20000 3768
 
0.4%
10000 3638
 
0.4%
6000 3290
 
0.3%
1000 2861
 
0.3%
8000 1950
 
0.2%
200 1923
 
0.2%
400 1577
 
0.2%
Other values (7115) 29233
 
2.9%
ValueCountFrequency (%)
0 942641
93.9%
0.2 55
 
< 0.1%
0.4 17
 
< 0.1%
0.6 4
 
< 0.1%
0.8 1
 
< 0.1%
ValueCountFrequency (%)
461867.4 1
< 0.1%
420400 1
< 0.1%
333442.4 1
< 0.1%
320400 1
< 0.1%
309994 1
< 0.1%
Distinct4097
Distinct (%)1.7%
Missing758904
Missing (%)75.6%
Memory size7.7 MiB
2024-02-13T20:55:13.432695image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2448530
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

Unique152 ?
Unique (%)0.1%

Sample

1st row2019-08-29
2nd row2019-08-23
3rd row2019-08-24
4th row2019-06-28
5th row2019-07-27
ValueCountFrequency (%)
2019-09-16 8759
 
3.6%
2019-09-19 1577
 
0.6%
2019-11-19 1560
 
0.6%
2019-10-21 1536
 
0.6%
2019-11-25 1536
 
0.6%
2019-08-26 1526
 
0.6%
2019-10-28 1498
 
0.6%
2019-08-28 1494
 
0.6%
2019-11-20 1479
 
0.6%
2019-11-22 1473
 
0.6%
Other values (4087) 222415
90.8%
2024-02-13T20:55:13.894944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 523397
21.4%
- 489706
20.0%
1 488462
19.9%
2 392804
16.0%
9 244352
10.0%
8 84334
 
3.4%
7 52901
 
2.2%
6 50753
 
2.1%
3 45863
 
1.9%
5 40160
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1958824
80.0%
Dash Punctuation 489706
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 523397
26.7%
1 488462
24.9%
2 392804
20.1%
9 244352
12.5%
8 84334
 
4.3%
7 52901
 
2.7%
6 50753
 
2.6%
3 45863
 
2.3%
5 40160
 
2.1%
4 35798
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 489706
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2448530
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 523397
21.4%
- 489706
20.0%
1 488462
19.9%
2 392804
16.0%
9 244352
10.0%
8 84334
 
3.4%
7 52901
 
2.2%
6 50753
 
2.1%
3 45863
 
1.9%
5 40160
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2448530
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 523397
21.4%
- 489706
20.0%
1 488462
19.9%
2 392804
16.0%
9 244352
10.0%
8 84334
 
3.4%
7 52901
 
2.2%
6 50753
 
2.1%
3 45863
 
1.9%
5 40160
 
1.6%

eir_270L
Real number (ℝ)

MISSING  ZEROS 

Distinct79
Distinct (%)< 0.1%
Missing125513
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean0.2740746145
Minimum0
Maximum0.45
Zeros291088
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:14.080988image/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 deviation0.1995002238
Coefficient of variation (CV)0.7279047867
Kurtosis-1.577804933
Mean0.2740746145
Median Absolute Deviation (MAD)0.0495
Skewness-0.5721079569
Sum240704.3857
Variance0.03980033929
MonotonicityNot monotonic
2024-02-13T20:55:14.246531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 291088
29.0%
0.45 205763
20.5%
0.42 97257
 
9.7%
0.433 56208
 
5.6%
0.39 47295
 
4.7%
0.4005 42216
 
4.2%
0.4175 24321
 
2.4%
0.4 12509
 
1.2%
0.181 11869
 
1.2%
0.425 8840
 
0.9%
Other values (69) 80878
 
8.1%
(Missing) 125513
12.5%
ValueCountFrequency (%)
0 291088
29.0%
0.0012 934
 
0.1%
0.05 56
 
< 0.1%
0.1 9
 
< 0.1%
0.15 11
 
< 0.1%
ValueCountFrequency (%)
0.45 205763
20.5%
0.4483 1
 
< 0.1%
0.4474 1
 
< 0.1%
0.4472 89
 
< 0.1%
0.4471 1
 
< 0.1%

equalitydataagreement_891L
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing951445
Missing (%)94.8%
Memory size7.7 MiB
True
 
49240
False
 
3072
(Missing)
951445 
ValueCountFrequency (%)
True 49240
 
4.9%
False 3072
 
0.3%
(Missing) 951445
94.8%
2024-02-13T20:55:14.365538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

equalityempfrom_62L
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing975352
Missing (%)97.2%
Memory size7.7 MiB
True
 
27608
False
 
797
(Missing)
975352 
ValueCountFrequency (%)
True 27608
 
2.8%
False 797
 
0.1%
(Missing) 975352
97.2%
2024-02-13T20:55:14.453456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Distinct1071
Distinct (%)0.2%
Missing571655
Missing (%)57.0%
Memory size7.7 MiB
2024-02-13T20:55:14.818687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters4321020
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

Unique5 ?
Unique (%)< 0.1%

Sample

1st row2019-01-07
2nd row2019-01-28
3rd row2019-02-06
4th row2019-02-06
5th row2019-02-05
ValueCountFrequency (%)
2016-01-31 149363
34.6%
2017-07-24 19638
 
4.5%
2016-03-27 11160
 
2.6%
2016-02-01 10782
 
2.5%
2017-04-18 6396
 
1.5%
2017-07-20 5589
 
1.3%
2016-11-27 5366
 
1.2%
2016-03-23 5361
 
1.2%
2017-09-06 5268
 
1.2%
2017-03-16 4663
 
1.1%
Other values (1061) 208516
48.3%
2024-02-13T20:55:15.353054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 941738
21.8%
1 917226
21.2%
- 864204
20.0%
2 603802
14.0%
6 305819
 
7.1%
3 239805
 
5.5%
7 186437
 
4.3%
9 88031
 
2.0%
8 77431
 
1.8%
4 59806
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3456816
80.0%
Dash Punctuation 864204
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 941738
27.2%
1 917226
26.5%
2 603802
17.5%
6 305819
 
8.8%
3 239805
 
6.9%
7 186437
 
5.4%
9 88031
 
2.5%
8 77431
 
2.2%
4 59806
 
1.7%
5 36721
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 864204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4321020
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 941738
21.8%
1 917226
21.2%
- 864204
20.0%
2 603802
14.0%
6 305819
 
7.1%
3 239805
 
5.5%
7 186437
 
4.3%
9 88031
 
2.0%
8 77431
 
1.8%
4 59806
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4321020
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 941738
21.8%
1 917226
21.2%
- 864204
20.0%
2 603802
14.0%
6 305819
 
7.1%
3 239805
 
5.5%
7 186437
 
4.3%
9 88031
 
2.0%
8 77431
 
1.8%
4 59806
 
1.4%

firstdatedue_489D
Text

MISSING 

Distinct4898
Distinct (%)0.7%
Missing339687
Missing (%)33.8%
Memory size7.7 MiB
2024-02-13T20:55:15.744659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters6640700
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

Unique84 ?
Unique (%)< 0.1%

Sample

1st row2018-11-10
2nd row2018-12-03
3rd row2018-10-31
4th row2018-12-12
5th row2018-11-15
ValueCountFrequency (%)
2018-02-15 1682
 
0.3%
2018-02-11 1278
 
0.2%
2018-03-14 1048
 
0.2%
2018-06-15 986
 
0.1%
2018-06-11 953
 
0.1%
2018-01-15 931
 
0.1%
2019-02-15 926
 
0.1%
2018-12-15 904
 
0.1%
2018-09-15 860
 
0.1%
2017-12-15 855
 
0.1%
Other values (4888) 653647
98.4%
2024-02-13T20:55:16.268991image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1601386
24.1%
- 1328140
20.0%
1 1187071
17.9%
2 1115920
16.8%
8 269346
 
4.1%
7 229692
 
3.5%
5 193719
 
2.9%
6 192854
 
2.9%
3 187567
 
2.8%
9 187528
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5312560
80.0%
Dash Punctuation 1328140
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1601386
30.1%
1 1187071
22.3%
2 1115920
21.0%
8 269346
 
5.1%
7 229692
 
4.3%
5 193719
 
3.6%
6 192854
 
3.6%
3 187567
 
3.5%
9 187528
 
3.5%
4 147477
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 1328140
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6640700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1601386
24.1%
- 1328140
20.0%
1 1187071
17.9%
2 1115920
16.8%
8 269346
 
4.1%
7 229692
 
3.5%
5 193719
 
2.9%
6 192854
 
2.9%
3 187567
 
2.8%
9 187528
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6640700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1601386
24.1%
- 1328140
20.0%
1 1187071
17.9%
2 1115920
16.8%
8 269346
 
4.1%
7 229692
 
3.5%
5 193719
 
2.9%
6 192854
 
2.9%
3 187567
 
2.8%
9 187528
 
2.8%

homephncnt_628L
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6202228229
Minimum0
Maximum11
Zeros557067
Zeros (%)55.5%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:16.420350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8366637304
Coefficient of variation (CV)1.34897282
Kurtosis3.206086533
Mean0.6202228229
Median Absolute Deviation (MAD)0
Skewness1.567003789
Sum622553
Variance0.7000061977
MonotonicityNot monotonic
2024-02-13T20:55:16.536384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 557067
55.5%
1 314830
31.4%
2 98053
 
9.8%
3 25930
 
2.6%
4 6094
 
0.6%
5 1388
 
0.1%
6 287
 
< 0.1%
7 83
 
< 0.1%
8 19
 
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
0 557067
55.5%
1 314830
31.4%
2 98053
 
9.8%
3 25930
 
2.6%
4 6094
 
0.6%
ValueCountFrequency (%)
11 1
 
< 0.1%
9 5
 
< 0.1%
8 19
 
< 0.1%
7 83
 
< 0.1%
6 287
< 0.1%

inittransactionamount_650A
Real number (ℝ)

MISSING  ZEROS 

Distinct32321
Distinct (%)25.7%
Missing877888
Missing (%)87.5%
Infinite0
Infinite (%)0.0%
Mean29097.18246
Minimum0
Maximum200000
Zeros28230
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:16.672717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18994
median22654
Q339998
95-th percentile85396
Maximum200000
Range200000
Interquartile range (IQR)31004

Descriptive statistics

Standard deviation29757.58608
Coefficient of variation (CV)1.02269648
Kurtosis5.250490231
Mean29097.18246
Median Absolute Deviation (MAD)15775
Skewness1.884857881
Sum3662433259
Variance885513929.1
MonotonicityNot monotonic
2024-02-13T20:55:16.839035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28230
 
2.8%
23998 934
 
0.1%
17998 864
 
0.1%
19998 769
 
0.1%
13998 714
 
0.1%
11998 705
 
0.1%
23978 697
 
0.1%
15998 671
 
0.1%
29998 644
 
0.1%
25998 635
 
0.1%
Other values (32311) 91006
 
9.1%
(Missing) 877888
87.5%
ValueCountFrequency (%)
0 28230
2.8%
1798 1
 
< 0.1%
1998 1
 
< 0.1%
2000 6
 
< 0.1%
2002 1
 
< 0.1%
ValueCountFrequency (%)
200000 37
< 0.1%
199998 28
< 0.1%
199997.8 2
 
< 0.1%
199997.4 1
 
< 0.1%
199996.8 1
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size7.7 MiB
2024-02-13T20:55:16.959505image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.215503569
Min length3

Characters and Unicode

Total characters3227581
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 759213
75.6%
cash 216313
 
21.6%
ndf 28230
 
2.8%
2024-02-13T20:55:17.184639image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 975526
30.2%
P 759213
23.5%
O 759213
23.5%
C 216313
 
6.7%
A 216313
 
6.7%
H 216313
 
6.7%
N 28230
 
0.9%
D 28230
 
0.9%
F 28230
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3227581
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 975526
30.2%
P 759213
23.5%
O 759213
23.5%
C 216313
 
6.7%
A 216313
 
6.7%
H 216313
 
6.7%
N 28230
 
0.9%
D 28230
 
0.9%
F 28230
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 3227581
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 975526
30.2%
P 759213
23.5%
O 759213
23.5%
C 216313
 
6.7%
A 216313
 
6.7%
H 216313
 
6.7%
N 28230
 
0.9%
D 28230
 
0.9%
F 28230
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3227581
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 975526
30.2%
P 759213
23.5%
O 759213
23.5%
C 216313
 
6.7%
A 216313
 
6.7%
H 216313
 
6.7%
N 28230
 
0.9%
D 28230
 
0.9%
F 28230
 
0.9%

interestrate_311L
Real number (ℝ)

MISSING  ZEROS 

Distinct79
Distinct (%)< 0.1%
Missing125513
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean0.2740746145
Minimum0
Maximum0.45
Zeros291088
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:17.345634image/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 deviation0.1995002238
Coefficient of variation (CV)0.7279047867
Kurtosis-1.577804933
Mean0.2740746145
Median Absolute Deviation (MAD)0.0495
Skewness-0.5721079569
Sum240704.3857
Variance0.03980033929
MonotonicityNot monotonic
2024-02-13T20:55:17.501635image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 291088
29.0%
0.45 205763
20.5%
0.42 97257
 
9.7%
0.433 56208
 
5.6%
0.39 47295
 
4.7%
0.4005 42216
 
4.2%
0.4175 24321
 
2.4%
0.4 12509
 
1.2%
0.181 11869
 
1.2%
0.425 8840
 
0.9%
Other values (69) 80878
 
8.1%
(Missing) 125513
12.5%
ValueCountFrequency (%)
0 291088
29.0%
0.0012 934
 
0.1%
0.05 56
 
< 0.1%
0.1 9
 
< 0.1%
0.15 11
 
< 0.1%
ValueCountFrequency (%)
0.45 205763
20.5%
0.4483 1
 
< 0.1%
0.4474 1
 
< 0.1%
0.4472 89
 
< 0.1%
0.4471 1
 
< 0.1%

interestrategrace_34L
Real number (ℝ)

CONSTANT  MISSING  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing987174
Missing (%)98.3%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros16583
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:17.619630image/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
2024-02-13T20:55:17.720171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 16583
 
1.7%
(Missing) 987174
98.3%
ValueCountFrequency (%)
0 16583
1.7%
ValueCountFrequency (%)
0 16583
1.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size980.4 KiB
False
884333 
True
119424 
ValueCountFrequency (%)
False 884333
88.1%
True 119424
 
11.9%
2024-02-13T20:55:18.087421image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

isbidproductrequest_292L
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing995281
Missing (%)99.2%
Memory size7.7 MiB
False
 
8475
True
 
1
(Missing)
995281 
ValueCountFrequency (%)
False 8475
 
0.8%
True 1
 
< 0.1%
(Missing) 995281
99.2%
2024-02-13T20:55:18.186420image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

isdebitcard_729L
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing877888
Missing (%)87.5%
Memory size7.7 MiB
False
125869 
(Missing)
877888 
ValueCountFrequency (%)
False 125869
 
12.5%
(Missing) 877888
87.5%
2024-02-13T20:55:18.280679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Distinct3866
Distinct (%)0.6%
Missing320842
Missing (%)32.0%
Memory size7.7 MiB
2024-02-13T20:55:18.665916image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters6829150
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

Unique54 ?
Unique (%)< 0.1%

Sample

1st row2018-10-19
2nd row2018-11-07
3rd row2018-12-28
4th row2018-10-09
5th row2018-11-16
ValueCountFrequency (%)
2019-01-31 2925
 
0.4%
2019-01-11 2265
 
0.3%
2019-01-09 2180
 
0.3%
2019-01-30 2148
 
0.3%
2019-01-10 2113
 
0.3%
2019-02-06 2020
 
0.3%
2019-01-24 2014
 
0.3%
2019-01-04 2005
 
0.3%
2018-10-17 1991
 
0.3%
2018-12-26 1931
 
0.3%
Other values (3856) 661323
96.8%
2024-02-13T20:55:19.215064image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1536690
22.5%
- 1365830
20.0%
1 1291628
18.9%
2 1087339
15.9%
8 407805
 
6.0%
9 340885
 
5.0%
7 214691
 
3.1%
3 158954
 
2.3%
6 156191
 
2.3%
5 136746
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5463320
80.0%
Dash Punctuation 1365830
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1536690
28.1%
1 1291628
23.6%
2 1087339
19.9%
8 407805
 
7.5%
9 340885
 
6.2%
7 214691
 
3.9%
3 158954
 
2.9%
6 156191
 
2.9%
5 136746
 
2.5%
4 132391
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 1365830
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6829150
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1536690
22.5%
- 1365830
20.0%
1 1291628
18.9%
2 1087339
15.9%
8 407805
 
6.0%
9 340885
 
5.0%
7 214691
 
3.1%
3 158954
 
2.3%
6 156191
 
2.3%
5 136746
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6829150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1536690
22.5%
- 1365830
20.0%
1 1291628
18.9%
2 1087339
15.9%
8 407805
 
6.0%
9 340885
 
5.0%
7 214691
 
3.1%
3 158954
 
2.3%
6 156191
 
2.3%
5 136746
 
2.0%
Distinct5083
Distinct (%)0.6%
Missing220760
Missing (%)22.0%
Memory size7.7 MiB
2024-02-13T20:55:19.640318image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters7829970
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

Unique61 ?
Unique (%)< 0.1%

Sample

1st row2013-04-03
2nd row2019-01-07
3rd row2019-01-08
4th row2019-01-16
5th row2018-09-12
ValueCountFrequency (%)
2018-12-07 2289
 
0.3%
2019-01-13 1895
 
0.2%
2018-12-08 1836
 
0.2%
2018-12-28 1744
 
0.2%
2018-12-29 1742
 
0.2%
2019-01-11 1690
 
0.2%
2019-01-04 1683
 
0.2%
2019-06-28 1666
 
0.2%
2019-01-12 1651
 
0.2%
2019-01-25 1651
 
0.2%
Other values (5073) 765150
97.7%
2024-02-13T20:55:20.160141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1751466
22.4%
- 1565994
20.0%
1 1460703
18.7%
2 1258530
16.1%
9 477423
 
6.1%
8 424818
 
5.4%
7 225275
 
2.9%
3 190050
 
2.4%
6 169796
 
2.2%
5 154115
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6263976
80.0%
Dash Punctuation 1565994
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1751466
28.0%
1 1460703
23.3%
2 1258530
20.1%
9 477423
 
7.6%
8 424818
 
6.8%
7 225275
 
3.6%
3 190050
 
3.0%
6 169796
 
2.7%
5 154115
 
2.5%
4 151800
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 1565994
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7829970
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1751466
22.4%
- 1565994
20.0%
1 1460703
18.7%
2 1258530
16.1%
9 477423
 
6.1%
8 424818
 
5.4%
7 225275
 
2.9%
3 190050
 
2.4%
6 169796
 
2.2%
5 154115
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7829970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1751466
22.4%
- 1565994
20.0%
1 1460703
18.7%
2 1258530
16.1%
9 477423
 
6.1%
8 424818
 
5.4%
7 225275
 
2.9%
3 190050
 
2.4%
6 169796
 
2.2%
5 154115
 
2.0%
Distinct43
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:20.360297image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length9.092625008
Min length8

Characters and Unicode

Total characters9126786
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 517454
51.6%
p159_130_59 184373
 
18.4%
p12_6_178 112401
 
11.2%
p148_110_5 46317
 
4.6%
p109_133_183 40017
 
4.0%
p53_45_92 22622
 
2.3%
p52_56_90 15842
 
1.6%
p33_29_177 14772
 
1.5%
p100_96_175 13435
 
1.3%
p21_79_33 13291
 
1.3%
Other values (33) 23233
 
2.3%
2024-02-13T20:55:20.727748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 2061684
22.6%
1 1467753
16.1%
_ 972606
10.7%
7 703581
 
7.7%
4 607543
 
6.7%
a 517454
 
5.7%
b 517454
 
5.7%
9 505421
 
5.5%
P 486303
 
5.3%
3 397276
 
4.4%
Other values (4) 889711
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6632969
72.7%
Lowercase Letter 1034908
 
11.3%
Connector Punctuation 972606
 
10.7%
Uppercase Letter 486303
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2061684
31.1%
1 1467753
22.1%
7 703581
 
10.6%
4 607543
 
9.2%
9 505421
 
7.6%
3 397276
 
6.0%
0 320012
 
4.8%
8 219676
 
3.3%
2 194421
 
2.9%
6 155602
 
2.3%
Lowercase Letter
ValueCountFrequency (%)
a 517454
50.0%
b 517454
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 972606
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 486303
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7605575
83.3%
Latin 1521211
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
5 2061684
27.1%
1 1467753
19.3%
_ 972606
12.8%
7 703581
 
9.3%
4 607543
 
8.0%
9 505421
 
6.6%
3 397276
 
5.2%
0 320012
 
4.2%
8 219676
 
2.9%
2 194421
 
2.6%
Latin
ValueCountFrequency (%)
a 517454
34.0%
b 517454
34.0%
P 486303
32.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9126786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 2061684
22.6%
1 1467753
16.1%
_ 972606
10.7%
7 703581
 
7.7%
4 607543
 
6.7%
a 517454
 
5.7%
b 517454
 
5.7%
9 505421
 
5.5%
P 486303
 
5.3%
3 397276
 
4.4%
Other values (4) 889711
9.7%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:20.886646image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8030056
Distinct characters6
Distinct categories2 ?
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 1003757
100.0%
2024-02-13T20:55:21.157798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 3011271
37.5%
a 1003757
 
12.5%
4 1003757
 
12.5%
7 1003757
 
12.5%
b 1003757
 
12.5%
1 1003757
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6022542
75.0%
Lowercase Letter 2007514
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 3011271
50.0%
4 1003757
 
16.7%
7 1003757
 
16.7%
1 1003757
 
16.7%
Lowercase Letter
ValueCountFrequency (%)
a 1003757
50.0%
b 1003757
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6022542
75.0%
Latin 2007514
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 3011271
50.0%
4 1003757
 
16.7%
7 1003757
 
16.7%
1 1003757
 
16.7%
Latin
ValueCountFrequency (%)
a 1003757
50.0%
b 1003757
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8030056
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 3011271
37.5%
a 1003757
 
12.5%
4 1003757
 
12.5%
7 1003757
 
12.5%
b 1003757
 
12.5%
1 1003757
 
12.5%

lastapprcredamount_781A
Real number (ℝ)

MISSING  ZEROS 

Distinct97841
Distinct (%)14.2%
Missing313123
Missing (%)31.2%
Infinite0
Infinite (%)0.0%
Mean36896.67615
Minimum0
Maximum400000
Zeros19563
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:21.308823image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4464
Q113434
median25135.3005
Q347900
95-th percentile100000
Maximum400000
Range400000
Interquartile range (IQR)34466

Descriptive statistics

Standard deviation35843.43169
Coefficient of variation (CV)0.9714542186
Kurtosis8.532920423
Mean36896.67615
Median Absolute Deviation (MAD)14655.3005
Skewness2.329308615
Sum2.548209903 × 1010
Variance1284751596
MonotonicityNot monotonic
2024-02-13T20:55:21.475228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 24217
 
2.4%
0 19563
 
1.9%
20000 15827
 
1.6%
40000 15046
 
1.5%
60000 13159
 
1.3%
30000 12377
 
1.2%
150000 6510
 
0.6%
24000 6203
 
0.6%
50000 5826
 
0.6%
10000 5568
 
0.6%
Other values (97831) 566338
56.4%
(Missing) 313123
31.2%
ValueCountFrequency (%)
0 19563
1.9%
0.2 121
 
< 0.1%
200 1
 
< 0.1%
2000 248
 
< 0.1%
2002 5
 
< 0.1%
ValueCountFrequency (%)
400000 126
< 0.1%
398000 3
 
< 0.1%
394000 1
 
< 0.1%
392800 1
 
< 0.1%
392000 1
 
< 0.1%

lastapprdate_640D
Text

MISSING 

Distinct5089
Distinct (%)0.7%
Missing313123
Missing (%)31.2%
Memory size7.7 MiB
2024-02-13T20:55:21.913134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters6906340
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

Unique30 ?
Unique (%)< 0.1%

Sample

1st row2019-01-11
2nd row2018-10-11
3rd row2018-12-31
4th row2018-11-02
5th row2018-12-11
ValueCountFrequency (%)
2018-12-07 2137
 
0.3%
2019-01-13 1761
 
0.3%
2018-12-08 1751
 
0.3%
2018-12-29 1652
 
0.2%
2018-12-28 1568
 
0.2%
2018-12-09 1479
 
0.2%
2018-11-24 1457
 
0.2%
2019-01-12 1456
 
0.2%
2018-12-30 1442
 
0.2%
2018-12-01 1420
 
0.2%
Other values (5079) 674511
97.7%
2024-02-13T20:55:22.410841image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1551741
22.5%
- 1381268
20.0%
1 1288877
18.7%
2 1112127
16.1%
8 407477
 
5.9%
9 348978
 
5.1%
7 216996
 
3.1%
3 171647
 
2.5%
6 155906
 
2.3%
5 137109
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5525072
80.0%
Dash Punctuation 1381268
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1551741
28.1%
1 1288877
23.3%
2 1112127
20.1%
8 407477
 
7.4%
9 348978
 
6.3%
7 216996
 
3.9%
3 171647
 
3.1%
6 155906
 
2.8%
5 137109
 
2.5%
4 134214
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 1381268
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6906340
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1551741
22.5%
- 1381268
20.0%
1 1288877
18.7%
2 1112127
16.1%
8 407477
 
5.9%
9 348978
 
5.1%
7 216996
 
3.1%
3 171647
 
2.5%
6 155906
 
2.3%
5 137109
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6906340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1551741
22.5%
- 1381268
20.0%
1 1288877
18.7%
2 1112127
16.1%
8 407477
 
5.9%
9 348978
 
5.1%
7 216996
 
3.1%
3 171647
 
2.5%
6 155906
 
2.3%
5 137109
 
2.0%
Distinct67
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:22.610101image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.784209724
Min length8

Characters and Unicode

Total characters8817212
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

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowa55475b1
2nd rowa55475b1
3rd rowa55475b1
4th rowP94_109_143
5th rowP24_27_36
ValueCountFrequency (%)
a55475b1 721831
71.9%
p94_109_143 188780
 
18.8%
p85_114_140 14833
 
1.5%
p198_89_166 11760
 
1.2%
p30_86_84 9265
 
0.9%
p73_130_169 7076
 
0.7%
p180_60_137 6740
 
0.7%
p24_27_36 6433
 
0.6%
p11_56_131 5237
 
0.5%
p52_67_90 4586
 
0.5%
Other values (57) 27216
 
2.7%
2024-02-13T20:55:22.988246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 2205023
25.0%
1 1274736
14.5%
4 1157247
13.1%
7 756269
 
8.6%
a 721831
 
8.2%
b 721831
 
8.2%
_ 563852
 
6.4%
9 429248
 
4.9%
P 281926
 
3.2%
0 258398
 
2.9%
Other values (4) 446851
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6527772
74.0%
Lowercase Letter 1443662
 
16.4%
Connector Punctuation 563852
 
6.4%
Uppercase Letter 281926
 
3.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2205023
33.8%
1 1274736
19.5%
4 1157247
17.7%
7 756269
 
11.6%
9 429248
 
6.6%
0 258398
 
4.0%
3 246296
 
3.8%
6 87507
 
1.3%
8 75064
 
1.1%
2 37984
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
a 721831
50.0%
b 721831
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 563852
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 281926
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7091624
80.4%
Latin 1725588
 
19.6%

Most frequent character per script

Common
ValueCountFrequency (%)
5 2205023
31.1%
1 1274736
18.0%
4 1157247
16.3%
7 756269
 
10.7%
_ 563852
 
8.0%
9 429248
 
6.1%
0 258398
 
3.6%
3 246296
 
3.5%
6 87507
 
1.2%
8 75064
 
1.1%
Latin
ValueCountFrequency (%)
a 721831
41.8%
b 721831
41.8%
P 281926
 
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8817212
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 2205023
25.0%
1 1274736
14.5%
4 1157247
13.1%
7 756269
 
8.6%
a 721831
 
8.2%
b 721831
 
8.2%
_ 563852
 
6.4%
9 429248
 
4.9%
P 281926
 
3.2%
0 258398
 
2.9%
Other values (4) 446851
 
5.1%

lastdelinqdate_224D
Text

MISSING 

Distinct3826
Distinct (%)1.1%
Missing668680
Missing (%)66.6%
Memory size7.7 MiB
2024-02-13T20:55:23.365043image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3350770
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

Unique505 ?
Unique (%)0.2%

Sample

1st row2018-12-31
2nd row2018-12-15
3rd row2018-12-11
4th row2019-01-02
5th row2018-11-25
ValueCountFrequency (%)
2019-08-11 1086
 
0.3%
2019-04-07 1028
 
0.3%
2019-09-15 1022
 
0.3%
2019-05-12 990
 
0.3%
2019-08-18 977
 
0.3%
2019-01-15 976
 
0.3%
2019-07-28 948
 
0.3%
2019-05-15 944
 
0.3%
2019-08-09 937
 
0.3%
2019-02-11 932
 
0.3%
Other values (3816) 325237
97.1%
2024-02-13T20:55:23.871688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 747861
22.3%
- 670154
20.0%
1 617546
18.4%
2 529548
15.8%
9 191645
 
5.7%
8 164302
 
4.9%
7 117651
 
3.5%
5 97684
 
2.9%
6 87454
 
2.6%
3 67372
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2680616
80.0%
Dash Punctuation 670154
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 747861
27.9%
1 617546
23.0%
2 529548
19.8%
9 191645
 
7.1%
8 164302
 
6.1%
7 117651
 
4.4%
5 97684
 
3.6%
6 87454
 
3.3%
3 67372
 
2.5%
4 59553
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 670154
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3350770
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 747861
22.3%
- 670154
20.0%
1 617546
18.4%
2 529548
15.8%
9 191645
 
5.7%
8 164302
 
4.9%
7 117651
 
3.5%
5 97684
 
2.9%
6 87454
 
2.6%
3 67372
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3350770
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 747861
22.3%
- 670154
20.0%
1 617546
18.4%
2 529548
15.8%
9 191645
 
5.7%
8 164302
 
4.9%
7 117651
 
3.5%
5 97684
 
2.9%
6 87454
 
2.6%
3 67372
 
2.0%

lastdependentsnum_448L
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)0.1%
Missing978699
Missing (%)97.5%
Infinite0
Infinite (%)0.0%
Mean0.5776199218
Minimum0
Maximum14
Zeros17453
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:24.006692image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.043636836
Coefficient of variation (CV)1.80678816
Kurtosis5.732376508
Mean0.5776199218
Median Absolute Deviation (MAD)0
Skewness2.105550232
Sum14474
Variance1.089177845
MonotonicityNot monotonic
2024-02-13T20:55:24.119691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 17453
 
1.7%
1 3273
 
0.3%
2 2590
 
0.3%
3 1178
 
0.1%
4 402
 
< 0.1%
5 124
 
< 0.1%
6 26
 
< 0.1%
7 7
 
< 0.1%
12 1
 
< 0.1%
9 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
(Missing) 978699
97.5%
ValueCountFrequency (%)
0 17453
1.7%
1 3273
 
0.3%
2 2590
 
0.3%
3 1178
 
0.1%
4 402
 
< 0.1%
ValueCountFrequency (%)
14 1
< 0.1%
12 1
< 0.1%
11 1
< 0.1%
9 1
< 0.1%
8 1
< 0.1%

lastotherinc_902A
Real number (ℝ)

MISSING 

Distinct98
Distinct (%)4.4%
Missing1001529
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean891.6773205
Minimum0
Maximum30000
Zeros362
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:24.257597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2
median0.2
Q30.2
95-th percentile6124
Maximum30000
Range30000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3692.173326
Coefficient of variation (CV)4.140705658
Kurtosis36.11703199
Mean891.6773205
Median Absolute Deviation (MAD)0
Skewness5.669192396
Sum1986657.07
Variance13632143.87
MonotonicityNot monotonic
2024-02-13T20:55:24.407154image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 1656
 
0.2%
0 362
 
< 0.1%
3000 18
 
< 0.1%
30000 17
 
< 0.1%
6000 17
 
< 0.1%
10000 14
 
< 0.1%
4000 11
 
< 0.1%
2000 8
 
< 0.1%
20000 7
 
< 0.1%
16000 7
 
< 0.1%
Other values (88) 111
 
< 0.1%
(Missing) 1001529
99.8%
ValueCountFrequency (%)
0 362
 
< 0.1%
0.2 1656
0.2%
0.6 1
 
< 0.1%
329.4 1
 
< 0.1%
1400 1
 
< 0.1%
ValueCountFrequency (%)
30000 17
< 0.1%
27200 1
 
< 0.1%
26000 1
 
< 0.1%
24000 3
 
< 0.1%
21400 1
 
< 0.1%

lastotherlnsexpense_631A
Real number (ℝ)

MISSING 

Distinct193
Distinct (%)8.9%
Missing1001586
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean8914.16877
Minimum0
Maximum270000
Zeros1317
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:24.560150image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34400
95-th percentile50000
Maximum270000
Range270000
Interquartile range (IQR)4400

Descriptive statistics

Standard deviation19249.10979
Coefficient of variation (CV)2.159383593
Kurtosis27.36935459
Mean8914.16877
Median Absolute Deviation (MAD)0
Skewness3.735110933
Sum19352660.4
Variance370528227.6
MonotonicityNot monotonic
2024-02-13T20:55:24.716231image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1317
 
0.1%
30000 54
 
< 0.1%
200 54
 
< 0.1%
20000 45
 
< 0.1%
400 43
 
< 0.1%
40000 29
 
< 0.1%
24000 23
 
< 0.1%
1000 23
 
< 0.1%
300 19
 
< 0.1%
50000 18
 
< 0.1%
Other values (183) 546
 
0.1%
(Missing) 1001586
99.8%
ValueCountFrequency (%)
0 1317
0.1%
0.2 17
 
< 0.1%
0.4 2
 
< 0.1%
2 1
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
270000 1
< 0.1%
200000 2
< 0.1%
140000 1
< 0.1%
122000 1
< 0.1%
100000 1
< 0.1%
Distinct44
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:24.917222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.474423591
Min length8

Characters and Unicode

Total characters8506262
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 rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 789334
78.6%
p159_130_59 71239
 
7.1%
p12_6_178 42465
 
4.2%
p148_110_5 28832
 
2.9%
p109_133_183 18791
 
1.9%
p53_45_92 13473
 
1.3%
p52_56_90 11641
 
1.2%
p100_96_175 6580
 
0.7%
p33_29_177 6439
 
0.6%
p21_79_33 4544
 
0.5%
Other values (34) 10419
 
1.0%
2024-02-13T20:55:25.251755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 2598387
30.5%
1 1203139
14.1%
7 864551
 
10.2%
4 840770
 
9.9%
a 789334
 
9.3%
b 789334
 
9.3%
_ 428846
 
5.0%
P 214423
 
2.5%
9 210740
 
2.5%
3 168868
 
2.0%
Other values (4) 397870
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6284325
73.9%
Lowercase Letter 1578668
 
18.6%
Connector Punctuation 428846
 
5.0%
Uppercase Letter 214423
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2598387
41.3%
1 1203139
19.1%
7 864551
 
13.8%
4 840770
 
13.4%
9 210740
 
3.4%
3 168868
 
2.7%
0 146697
 
2.3%
8 99435
 
1.6%
2 85145
 
1.4%
6 66593
 
1.1%
Lowercase Letter
ValueCountFrequency (%)
a 789334
50.0%
b 789334
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 428846
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 214423
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6713171
78.9%
Latin 1793091
 
21.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 2598387
38.7%
1 1203139
17.9%
7 864551
 
12.9%
4 840770
 
12.5%
_ 428846
 
6.4%
9 210740
 
3.1%
3 168868
 
2.5%
0 146697
 
2.2%
8 99435
 
1.5%
2 85145
 
1.3%
Latin
ValueCountFrequency (%)
a 789334
44.0%
b 789334
44.0%
P 214423
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8506262
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 2598387
30.5%
1 1203139
14.1%
7 864551
 
10.2%
4 840770
 
9.9%
a 789334
 
9.3%
b 789334
 
9.3%
_ 428846
 
5.0%
P 214423
 
2.5%
9 210740
 
2.5%
3 168868
 
2.0%
Other values (4) 397870
 
4.7%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:25.405341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8030056
Distinct characters6
Distinct categories2 ?
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 1003757
100.0%
2024-02-13T20:55:25.669787image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 3011271
37.5%
a 1003757
 
12.5%
4 1003757
 
12.5%
7 1003757
 
12.5%
b 1003757
 
12.5%
1 1003757
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6022542
75.0%
Lowercase Letter 2007514
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 3011271
50.0%
4 1003757
 
16.7%
7 1003757
 
16.7%
1 1003757
 
16.7%
Lowercase Letter
ValueCountFrequency (%)
a 1003757
50.0%
b 1003757
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6022542
75.0%
Latin 2007514
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 3011271
50.0%
4 1003757
 
16.7%
7 1003757
 
16.7%
1 1003757
 
16.7%
Latin
ValueCountFrequency (%)
a 1003757
50.0%
b 1003757
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8030056
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 3011271
37.5%
a 1003757
 
12.5%
4 1003757
 
12.5%
7 1003757
 
12.5%
b 1003757
 
12.5%
1 1003757
 
12.5%

lastrejectcredamount_222A
Real number (ℝ)

MISSING  ZEROS 

Distinct58463
Distinct (%)12.4%
Missing530899
Missing (%)52.9%
Infinite0
Infinite (%)0.0%
Mean48122.94521
Minimum0
Maximum715392
Zeros27941
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:25.813889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115965.55
median31998
Q360894
95-th percentile150000
Maximum715392
Range715392
Interquartile range (IQR)44928.45

Descriptive statistics

Standard deviation48134.88167
Coefficient of variation (CV)1.000248041
Kurtosis7.373672129
Mean48122.94521
Median Absolute Deviation (MAD)21198
Skewness2.155180339
Sum2.275531963 × 1010
Variance2316966833
MonotonicityNot monotonic
2024-02-13T20:55:25.972369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 34047
 
3.4%
0 27941
 
2.8%
60000 26294
 
2.6%
40000 24570
 
2.4%
20000 22933
 
2.3%
30000 17736
 
1.8%
200000 10635
 
1.1%
50000 9119
 
0.9%
80000 8299
 
0.8%
150000 8014
 
0.8%
Other values (58453) 283270
28.2%
(Missing) 530899
52.9%
ValueCountFrequency (%)
0 27941
2.8%
0.2 3
 
< 0.1%
2000 240
 
< 0.1%
2000.2001 1
 
< 0.1%
2002 1
 
< 0.1%
ValueCountFrequency (%)
715392 2
< 0.1%
550000 1
< 0.1%
501422.22 2
< 0.1%
493000 1
< 0.1%
479614 1
< 0.1%

lastrejectdate_50D
Text

MISSING 

Distinct5070
Distinct (%)1.1%
Missing530899
Missing (%)52.9%
Memory size7.7 MiB
2024-02-13T20:55:26.430306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters4728580
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

Unique27 ?
Unique (%)< 0.1%

Sample

1st row2013-04-03
2nd row2019-01-07
3rd row2018-09-12
4th row2018-08-20
5th row2018-12-12
ValueCountFrequency (%)
2019-02-06 807
 
0.2%
2019-01-04 796
 
0.2%
2018-12-07 782
 
0.2%
2019-02-05 765
 
0.2%
2019-04-01 755
 
0.2%
2019-01-25 752
 
0.2%
2019-01-02 744
 
0.2%
2019-03-28 740
 
0.2%
2019-01-07 721
 
0.2%
2019-01-28 720
 
0.2%
Other values (5060) 465276
98.4%
2024-02-13T20:55:26.988331image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1076569
22.8%
- 945716
20.0%
1 867882
18.4%
2 765515
16.2%
9 226727
 
4.8%
8 224487
 
4.7%
7 153702
 
3.3%
3 126562
 
2.7%
6 120401
 
2.5%
5 111204
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3782864
80.0%
Dash Punctuation 945716
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1076569
28.5%
1 867882
22.9%
2 765515
20.2%
9 226727
 
6.0%
8 224487
 
5.9%
7 153702
 
4.1%
3 126562
 
3.3%
6 120401
 
3.2%
5 111204
 
2.9%
4 109815
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 945716
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4728580
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1076569
22.8%
- 945716
20.0%
1 867882
18.4%
2 765515
16.2%
9 226727
 
4.8%
8 224487
 
4.7%
7 153702
 
3.3%
3 126562
 
2.7%
6 120401
 
2.5%
5 111204
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4728580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1076569
22.8%
- 945716
20.0%
1 867882
18.4%
2 765515
16.2%
9 226727
 
4.8%
8 224487
 
4.7%
7 153702
 
3.3%
3 126562
 
2.7%
6 120401
 
2.5%
5 111204
 
2.4%
Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:27.199646image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length8.821819424
Min length8

Characters and Unicode

Total characters8854963
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 rowP94_109_143
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 652000
65.0%
p99_56_166 148550
 
14.8%
p94_109_143 124619
 
12.4%
p45_84_106 37020
 
3.7%
p198_131_9 33322
 
3.3%
p30_86_84 2229
 
0.2%
p48_22_32 1755
 
0.2%
p52_67_90 1053
 
0.1%
p121_60_164 977
 
0.1%
p196_88_176 922
 
0.1%
Other values (8) 1310
 
0.1%
2024-02-13T20:55:27.522118image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 2142892
24.2%
1 1194145
13.5%
4 981096
11.1%
_ 703514
 
7.9%
7 654480
 
7.4%
a 652000
 
7.4%
b 652000
 
7.4%
9 615754
 
7.0%
6 491295
 
5.5%
P 351757
 
4.0%
Other values (4) 416030
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6495692
73.4%
Lowercase Letter 1304000
 
14.7%
Connector Punctuation 703514
 
7.9%
Uppercase Letter 351757
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2142892
33.0%
1 1194145
18.4%
4 981096
15.1%
7 654480
 
10.1%
9 615754
 
9.5%
6 491295
 
7.6%
0 166295
 
2.6%
3 162128
 
2.5%
8 79133
 
1.2%
2 8474
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
a 652000
50.0%
b 652000
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 703514
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 351757
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7199206
81.3%
Latin 1655757
 
18.7%

Most frequent character per script

Common
ValueCountFrequency (%)
5 2142892
29.8%
1 1194145
16.6%
4 981096
13.6%
_ 703514
 
9.8%
7 654480
 
9.1%
9 615754
 
8.6%
6 491295
 
6.8%
0 166295
 
2.3%
3 162128
 
2.3%
8 79133
 
1.1%
Latin
ValueCountFrequency (%)
a 652000
39.4%
b 652000
39.4%
P 351757
21.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8854963
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 2142892
24.2%
1 1194145
13.5%
4 981096
11.1%
_ 703514
 
7.9%
7 654480
 
7.4%
a 652000
 
7.4%
b 652000
 
7.4%
9 615754
 
7.0%
6 491295
 
5.5%
P 351757
 
4.0%
Other values (4) 416030
 
4.7%
Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:27.689254image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length8.899070193
Min length8

Characters and Unicode

Total characters8932504
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 685515
68.3%
p94_109_143 285563
28.4%
p30_86_84 15717
 
1.6%
p52_67_90 5928
 
0.6%
p129_162_80 3894
 
0.4%
p69_72_116 3809
 
0.4%
p84_14_61 1818
 
0.2%
p64_121_167 542
 
0.1%
p19_25_34 432
 
< 0.1%
p5_143_178 399
 
< 0.1%
2024-02-13T20:55:27.993618image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 2063584
23.1%
1 1278819
14.3%
4 1277367
14.3%
7 696193
 
7.8%
a 685515
 
7.7%
b 685515
 
7.7%
_ 636484
 
7.1%
9 585189
 
6.6%
P 318242
 
3.6%
0 311242
 
3.5%
Other values (4) 394354
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6606748
74.0%
Lowercase Letter 1371030
 
15.3%
Connector Punctuation 636484
 
7.1%
Uppercase Letter 318242
 
3.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2063584
31.2%
1 1278819
19.4%
4 1277367
19.3%
7 696193
 
10.5%
9 585189
 
8.9%
0 311242
 
4.7%
3 302251
 
4.6%
8 37545
 
0.6%
6 36059
 
0.5%
2 18499
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
a 685515
50.0%
b 685515
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 636484
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 318242
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7243232
81.1%
Latin 1689272
 
18.9%

Most frequent character per script

Common
ValueCountFrequency (%)
5 2063584
28.5%
1 1278819
17.7%
4 1277367
17.6%
7 696193
 
9.6%
_ 636484
 
8.8%
9 585189
 
8.1%
0 311242
 
4.3%
3 302251
 
4.2%
8 37545
 
0.5%
6 36059
 
0.5%
Latin
ValueCountFrequency (%)
a 685515
40.6%
b 685515
40.6%
P 318242
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8932504
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 2063584
23.1%
1 1278819
14.3%
4 1277367
14.3%
7 696193
 
7.8%
a 685515
 
7.7%
b 685515
 
7.7%
_ 636484
 
7.1%
9 585189
 
6.6%
P 318242
 
3.6%
0 311242
 
3.5%
Other values (4) 394354
 
4.4%

lastrepayingdate_696D
Text

MISSING 

Distinct331
Distinct (%)20.6%
Missing1002152
Missing (%)99.8%
Memory size7.7 MiB
2024-02-13T20:55:28.400424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters16050
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

Unique40 ?
Unique (%)2.5%

Sample

1st row2019-03-09
2nd row2019-01-17
3rd row2019-01-16
4th row2019-01-19
5th row2019-01-20
ValueCountFrequency (%)
2019-11-07 20
 
1.2%
2019-12-15 19
 
1.2%
2019-07-12 18
 
1.1%
2019-09-11 14
 
0.9%
2019-08-11 13
 
0.8%
2019-10-28 13
 
0.8%
2019-09-15 13
 
0.8%
2019-07-28 13
 
0.8%
2019-08-15 12
 
0.7%
2019-01-15 12
 
0.7%
Other values (321) 1458
90.8%
2024-02-13T20:55:28.946971image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3511
21.9%
- 3210
20.0%
1 3069
19.1%
2 2480
15.5%
9 1920
12.0%
8 408
 
2.5%
7 369
 
2.3%
5 327
 
2.0%
3 292
 
1.8%
6 285
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12840
80.0%
Dash Punctuation 3210
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3511
27.3%
1 3069
23.9%
2 2480
19.3%
9 1920
15.0%
8 408
 
3.2%
7 369
 
2.9%
5 327
 
2.5%
3 292
 
2.3%
6 285
 
2.2%
4 179
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 3210
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16050
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3511
21.9%
- 3210
20.0%
1 3069
19.1%
2 2480
15.5%
9 1920
12.0%
8 408
 
2.5%
7 369
 
2.3%
5 327
 
2.0%
3 292
 
1.8%
6 285
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3511
21.9%
- 3210
20.0%
1 3069
19.1%
2 2480
15.5%
9 1920
12.0%
8 408
 
2.5%
7 369
 
2.3%
5 327
 
2.0%
3 292
 
1.8%
6 285
 
1.8%

lastst_736L
Text

MISSING 

Distinct11
Distinct (%)< 0.1%
Missing220760
Missing (%)22.0%
Memory size7.7 MiB
2024-02-13T20:55:29.090046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters782997
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 rowT
4th rowT
5th rowD
ValueCountFrequency (%)
d 243667
31.1%
a 241163
30.8%
k 210769
26.9%
t 71598
 
9.1%
n 12585
 
1.6%
s 2083
 
0.3%
q 846
 
0.1%
l 189
 
< 0.1%
h 85
 
< 0.1%
p 8
 
< 0.1%
2024-02-13T20:55:29.348381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 243667
31.1%
A 241163
30.8%
K 210769
26.9%
T 71598
 
9.1%
N 12585
 
1.6%
S 2083
 
0.3%
Q 846
 
0.1%
L 189
 
< 0.1%
H 85
 
< 0.1%
P 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 782997
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 243667
31.1%
A 241163
30.8%
K 210769
26.9%
T 71598
 
9.1%
N 12585
 
1.6%
S 2083
 
0.3%
Q 846
 
0.1%
L 189
 
< 0.1%
H 85
 
< 0.1%
P 8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 782997
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 243667
31.1%
A 241163
30.8%
K 210769
26.9%
T 71598
 
9.1%
N 12585
 
1.6%
S 2083
 
0.3%
Q 846
 
0.1%
L 189
 
< 0.1%
H 85
 
< 0.1%
P 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 782997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 243667
31.1%
A 241163
30.8%
K 210769
26.9%
T 71598
 
9.1%
N 12585
 
1.6%
S 2083
 
0.3%
Q 846
 
0.1%
L 189
 
< 0.1%
H 85
 
< 0.1%
P 8
 
< 0.1%

maininc_215A
Real number (ℝ)

MISSING 

Distinct5906
Distinct (%)0.9%
Missing340950
Missing (%)34.0%
Infinite0
Infinite (%)0.0%
Mean48482.53574
Minimum0
Maximum200000
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:29.504388image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10000
Q130000
median40000
Q360000
95-th percentile102000
Maximum200000
Range200000
Interquartile range (IQR)30000

Descriptive statistics

Standard deviation31946.5982
Coefficient of variation (CV)0.6589300191
Kurtosis4.969698256
Mean48482.53574
Median Absolute Deviation (MAD)16000
Skewness1.820819142
Sum3.213456406 × 1010
Variance1020585136
MonotonicityNot monotonic
2024-02-13T20:55:29.669830image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40000 62065
 
6.2%
30000 57020
 
5.7%
50000 54431
 
5.4%
60000 44475
 
4.4%
70000 27897
 
2.8%
20000 24625
 
2.5%
36000 22690
 
2.3%
24000 21386
 
2.1%
80000 14989
 
1.5%
100000 14049
 
1.4%
Other values (5896) 319180
31.8%
(Missing) 340950
34.0%
ValueCountFrequency (%)
0 4
 
< 0.1%
0.038 1
 
< 0.1%
0.2 20
< 0.1%
0.4 1
 
< 0.1%
0.6 1
 
< 0.1%
ValueCountFrequency (%)
200000 3887
0.4%
199999.8 2
 
< 0.1%
199998 3
 
< 0.1%
199980 5
 
< 0.1%
199820 1
 
< 0.1%

mastercontrelectronic_519L
Real number (ℝ)

CONSTANT  MISSING  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing222166
Missing (%)22.1%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros781591
Zeros (%)77.9%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:29.796112image/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
2024-02-13T20:55:29.894129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 781591
77.9%
(Missing) 222166
 
22.1%
ValueCountFrequency (%)
0 781591
77.9%
ValueCountFrequency (%)
0 781591
77.9%

mastercontrexist_109L
Real number (ℝ)

CONSTANT  MISSING  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing222166
Missing (%)22.1%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros781591
Zeros (%)77.9%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:29.993707image/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
2024-02-13T20:55:30.090749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 781591
77.9%
(Missing) 222166
 
22.1%
ValueCountFrequency (%)
0 781591
77.9%
ValueCountFrequency (%)
0 781591
77.9%

maxannuity_159A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct236907
Distinct (%)30.3%
Missing222166
Missing (%)22.1%
Infinite0
Infinite (%)0.0%
Mean28968.84927
Minimum0
Maximum18788770
Zeros99840
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:30.223687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13523.9
median9534.71
Q325160
95-th percentile121627.7675
Maximum18788770
Range18788770
Interquartile range (IQR)21636.1

Descriptive statistics

Standard deviation80835.7106
Coefficient of variation (CV)2.79043568
Kurtosis6119.968325
Mean28968.84927
Median Absolute Deviation (MAD)7548.71
Skewness44.10697214
Sum2.264179187 × 1010
Variance6534412108
MonotonicityNot monotonic
2024-02-13T20:55:30.383686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 99840
 
9.9%
4000 7554
 
0.8%
2000 5927
 
0.6%
6000 5623
 
0.6%
3000 5286
 
0.5%
8000 4364
 
0.4%
5000 4047
 
0.4%
2400 3454
 
0.3%
10000 3434
 
0.3%
12000 2776
 
0.3%
Other values (236897) 639286
63.7%
(Missing) 222166
 
22.1%
ValueCountFrequency (%)
0 99840
9.9%
8 1
 
< 0.1%
10 7
 
< 0.1%
20 4
 
< 0.1%
30 2
 
< 0.1%
ValueCountFrequency (%)
18788770 1
< 0.1%
11919068 1
< 0.1%
10348000 1
< 0.1%
10082258 1
< 0.1%
8240500 2
< 0.1%

maxannuity_4075009A
Real number (ℝ)

MISSING 

Distinct3265
Distinct (%)6.5%
Missing953635
Missing (%)95.0%
Infinite0
Infinite (%)0.0%
Mean52090.59535
Minimum620
Maximum550000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:30.536100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum620
5-th percentile4900
Q110200
median15020
Q324115
95-th percentile300000
Maximum550000
Range549380
Interquartile range (IQR)13915

Descriptive statistics

Standard deviation102636.8759
Coefficient of variation (CV)1.970353289
Kurtosis10.31555203
Mean52090.59535
Median Absolute Deviation (MAD)5940
Skewness3.22508275
Sum2610884820
Variance1.053432829 × 1010
MonotonicityNot monotonic
2024-02-13T20:55:30.678979image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150000 2009
 
0.2%
200000 1247
 
0.1%
14440 965
 
0.1%
16060 948
 
0.1%
400000 832
 
0.1%
300000 773
 
0.1%
15000 548
 
0.1%
21400 517
 
0.1%
26760 450
 
< 0.1%
510000 437
 
< 0.1%
Other values (3255) 41396
 
4.1%
(Missing) 953635
95.0%
ValueCountFrequency (%)
620 2
< 0.1%
640 4
< 0.1%
660 1
 
< 0.1%
680 2
< 0.1%
700 1
 
< 0.1%
ValueCountFrequency (%)
550000 220
< 0.1%
530000 302
< 0.1%
510000 437
< 0.1%
490000 256
< 0.1%
445620 1
 
< 0.1%

maxdbddpdlast1m_3658939P
Real number (ℝ)

MISSING  ZEROS 

Distinct1867
Distinct (%)0.5%
Missing642917
Missing (%)64.1%
Infinite0
Infinite (%)0.0%
Mean43.81255681
Minimum-1237
Maximum4518
Zeros72598
Zeros (%)7.2%
Negative230690
Negative (%)23.0%
Memory size7.7 MiB
2024-02-13T20:55:30.819243image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1237
5-th percentile-37
Q1-9
median-2
Q30
95-th percentile8
Maximum4518
Range5755
Interquartile range (IQR)9

Descriptive statistics

Standard deviation452.9996141
Coefficient of variation (CV)10.33949276
Kurtosis75.49127702
Mean43.81255681
Median Absolute Deviation (MAD)3
Skewness8.765709159
Sum15809323
Variance205208.6504
MonotonicityNot monotonic
2024-02-13T20:55:30.964354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 72598
 
7.2%
-1 41311
 
4.1%
-2 25145
 
2.5%
-3 19701
 
2.0%
-4 15925
 
1.6%
1 15185
 
1.5%
-5 12100
 
1.2%
-6 9858
 
1.0%
-7 8539
 
0.9%
2 8306
 
0.8%
Other values (1857) 132172
 
13.2%
(Missing) 642917
64.1%
ValueCountFrequency (%)
-1237 1
< 0.1%
-1209 1
< 0.1%
-877 1
< 0.1%
-512 1
< 0.1%
-504 1
< 0.1%
ValueCountFrequency (%)
4518 1
< 0.1%
4516 1
< 0.1%
4510 1
< 0.1%
4508 1
< 0.1%
4504 1
< 0.1%

maxdbddpdtollast12m_3658940P
Real number (ℝ)

MISSING  ZEROS 

Distinct3534
Distinct (%)0.7%
Missing483172
Missing (%)48.1%
Infinite0
Infinite (%)0.0%
Mean51.33827329
Minimum-1199
Maximum4518
Zeros176755
Zeros (%)17.6%
Negative155997
Negative (%)15.5%
Memory size7.7 MiB
2024-02-13T20:55:31.105108image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1199
5-th percentile-13
Q1-1
median0
Q32
95-th percentile19
Maximum4518
Range5717
Interquartile range (IQR)3

Descriptive statistics

Standard deviation414.9543391
Coefficient of variation (CV)8.082748259
Kurtosis81.6415173
Mean51.33827329
Median Absolute Deviation (MAD)1
Skewness8.96057304
Sum26725935
Variance172187.1035
MonotonicityNot monotonic
2024-02-13T20:55:31.254107image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 176755
 
17.6%
1 46875
 
4.7%
-1 46430
 
4.6%
2 28123
 
2.8%
-2 20618
 
2.1%
3 20294
 
2.0%
-3 14226
 
1.4%
4 13766
 
1.4%
-4 10485
 
1.0%
5 10096
 
1.0%
Other values (3524) 132917
 
13.2%
(Missing) 483172
48.1%
ValueCountFrequency (%)
-1199 1
< 0.1%
-1142 1
< 0.1%
-1103 1
< 0.1%
-914 1
< 0.1%
-889 1
< 0.1%
ValueCountFrequency (%)
4518 1
< 0.1%
4516 1
< 0.1%
4510 1
< 0.1%
4508 1
< 0.1%
4504 1
< 0.1%

maxdbddpdtollast6m_4187119P
Real number (ℝ)

MISSING  ZEROS 

Distinct2896
Distinct (%)0.7%
Missing616835
Missing (%)61.5%
Infinite0
Infinite (%)0.0%
Mean48.54350231
Minimum-741
Maximum4518
Zeros126300
Zeros (%)12.6%
Negative148070
Negative (%)14.8%
Memory size7.7 MiB
2024-02-13T20:55:31.428045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-741
5-th percentile-19
Q1-2
median0
Q31
95-th percentile12
Maximum4518
Range5259
Interquartile range (IQR)3

Descriptive statistics

Standard deviation428.5097896
Coefficient of variation (CV)8.827335672
Kurtosis80.90095869
Mean48.54350231
Median Absolute Deviation (MAD)1
Skewness8.988851838
Sum18782549
Variance183620.6398
MonotonicityNot monotonic
2024-02-13T20:55:31.590338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 126300
 
12.6%
-1 41126
 
4.1%
1 30685
 
3.1%
-2 18751
 
1.9%
2 17312
 
1.7%
-3 13094
 
1.3%
3 12599
 
1.3%
-4 9496
 
0.9%
4 8596
 
0.9%
-5 7016
 
0.7%
Other values (2886) 101947
 
10.2%
(Missing) 616835
61.5%
ValueCountFrequency (%)
-741 1
< 0.1%
-628 1
< 0.1%
-531 1
< 0.1%
-469 1
< 0.1%
-456 1
< 0.1%
ValueCountFrequency (%)
4518 1
< 0.1%
4516 1
< 0.1%
4510 1
< 0.1%
4508 1
< 0.1%
4504 1
< 0.1%

maxdebt4_972A
Real number (ℝ)

MISSING  ZEROS 

Distinct300246
Distinct (%)38.4%
Missing222166
Missing (%)22.1%
Infinite0
Infinite (%)0.0%
Mean44295.9571
Minimum0
Maximum913520
Zeros194255
Zeros (%)19.4%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:31.746314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1682.8
median29998
Q364240.8475
95-th percentile145150.05
Maximum913520
Range913520
Interquartile range (IQR)63558.0475

Descriptive statistics

Standard deviation49612.78689
Coefficient of variation (CV)1.120029685
Kurtosis4.788266009
Mean44295.9571
Median Absolute Deviation (MAD)29998
Skewness1.790737504
Sum3.462132141 × 1010
Variance2461428623
MonotonicityNot monotonic
2024-02-13T20:55:31.898811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 194255
 
19.4%
100000 3137
 
0.3%
103980 2507
 
0.2%
150000 1737
 
0.2%
40000 1592
 
0.2%
60000 1251
 
0.1%
20000 1209
 
0.1%
72014 1051
 
0.1%
30000 965
 
0.1%
75128.6 956
 
0.1%
Other values (300236) 572931
57.1%
(Missing) 222166
 
22.1%
ValueCountFrequency (%)
0 194255
19.4%
0.002 6
 
< 0.1%
0.004 7
 
< 0.1%
0.006 1
 
< 0.1%
0.010000001 1
 
< 0.1%
ValueCountFrequency (%)
913520 1
< 0.1%
850201.44 1
< 0.1%
655435.8 1
< 0.1%
638061.44 1
< 0.1%
633719.94 1
< 0.1%

maxdpdfrom6mto36m_3546853P
Real number (ℝ)

MISSING  ZEROS 

Distinct3424
Distinct (%)0.5%
Missing259522
Missing (%)25.9%
Infinite0
Infinite (%)0.0%
Mean28.88493957
Minimum0
Maximum4266
Zeros497585
Zeros (%)49.6%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:32.049831image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile26
Maximum4266
Range4266
Interquartile range (IQR)2

Descriptive statistics

Standard deviation228.8375329
Coefficient of variation (CV)7.922382261
Kurtosis163.4264136
Mean28.88493957
Median Absolute Deviation (MAD)0
Skewness11.94721775
Sum21497183
Variance52366.61644
MonotonicityNot monotonic
2024-02-13T20:55:32.202647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 497585
49.6%
1 50757
 
5.1%
2 33539
 
3.3%
3 23753
 
2.4%
4 16687
 
1.7%
5 12461
 
1.2%
6 10172
 
1.0%
7 8246
 
0.8%
8 6877
 
0.7%
9 6184
 
0.6%
Other values (3414) 77974
 
7.8%
(Missing) 259522
25.9%
ValueCountFrequency (%)
0 497585
49.6%
1 50757
 
5.1%
2 33539
 
3.3%
3 23753
 
2.4%
4 16687
 
1.7%
ValueCountFrequency (%)
4266 1
< 0.1%
4244 1
< 0.1%
4233 1
< 0.1%
4224 1
< 0.1%
4212 1
< 0.1%
Distinct4975
Distinct (%)1.1%
Missing567808
Missing (%)56.6%
Memory size7.7 MiB
2024-02-13T20:55:32.651234image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters4359490
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

Unique52 ?
Unique (%)< 0.1%

Sample

1st row2018-12-01
2nd row2018-12-11
3rd row2018-12-03
4th row2018-11-25
5th row2018-12-31
ValueCountFrequency (%)
2018-11-11 998
 
0.2%
2019-05-12 993
 
0.2%
2019-01-15 988
 
0.2%
2019-04-07 981
 
0.2%
2019-02-11 967
 
0.2%
2018-12-15 900
 
0.2%
2019-01-11 848
 
0.2%
2019-04-28 838
 
0.2%
2019-08-11 832
 
0.2%
2019-02-07 812
 
0.2%
Other values (4965) 426792
97.9%
2024-02-13T20:55:33.486744image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1020033
23.4%
- 871898
20.0%
1 778132
17.8%
2 694551
15.9%
8 213206
 
4.9%
9 200528
 
4.6%
7 151013
 
3.5%
5 124535
 
2.9%
6 111886
 
2.6%
3 103757
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3487592
80.0%
Dash Punctuation 871898
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1020033
29.2%
1 778132
22.3%
2 694551
19.9%
8 213206
 
6.1%
9 200528
 
5.7%
7 151013
 
4.3%
5 124535
 
3.6%
6 111886
 
3.2%
3 103757
 
3.0%
4 89951
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 871898
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4359490
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1020033
23.4%
- 871898
20.0%
1 778132
17.8%
2 694551
15.9%
8 213206
 
4.9%
9 200528
 
4.6%
7 151013
 
3.5%
5 124535
 
2.9%
6 111886
 
2.6%
3 103757
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4359490
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1020033
23.4%
- 871898
20.0%
1 778132
17.8%
2 694551
15.9%
8 213206
 
4.9%
9 200528
 
4.6%
7 151013
 
3.5%
5 124535
 
2.9%
6 111886
 
2.6%
3 103757
 
2.4%

maxdpdinstlnum_3546846P
Real number (ℝ)

MISSING 

Distinct75
Distinct (%)< 0.1%
Missing568985
Missing (%)56.7%
Infinite0
Infinite (%)0.0%
Mean8.070275915
Minimum0
Maximum105
Zeros2310
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:33.661187image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median7
Q310
95-th percentile20
Maximum105
Range105
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.93266562
Coefficient of variation (CV)0.7351255004
Kurtosis4.767511376
Mean8.070275915
Median Absolute Deviation (MAD)3
Skewness1.589030429
Sum3508730
Variance35.19652136
MonotonicityNot monotonic
2024-02-13T20:55:33.814556image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 43124
 
4.3%
4 39427
 
3.9%
3 36663
 
3.7%
6 34383
 
3.4%
2 30235
 
3.0%
9 29817
 
3.0%
7 28927
 
2.9%
8 28497
 
2.8%
10 26566
 
2.6%
1 26227
 
2.6%
Other values (65) 110906
 
11.0%
(Missing) 568985
56.7%
ValueCountFrequency (%)
0 2310
 
0.2%
1 26227
2.6%
2 30235
3.0%
3 36663
3.7%
4 39427
3.9%
ValueCountFrequency (%)
105 1
 
< 0.1%
101 1
 
< 0.1%
95 1
 
< 0.1%
93 1
 
< 0.1%
92 3
< 0.1%

maxdpdlast12m_727P
Real number (ℝ)

MISSING  ZEROS 

Distinct2951
Distinct (%)0.4%
Missing222166
Missing (%)22.1%
Infinite0
Infinite (%)0.0%
Mean15.81442724
Minimum0
Maximum4430
Zeros595142
Zeros (%)59.3%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:33.962790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11
Maximum4430
Range4430
Interquartile range (IQR)0

Descriptive statistics

Standard deviation185.8768575
Coefficient of variation (CV)11.75362564
Kurtosis310.8289581
Mean15.81442724
Median Absolute Deviation (MAD)0
Skewness16.71288044
Sum12360414
Variance34550.20616
MonotonicityNot monotonic
2024-02-13T20:55:34.106469image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 595142
59.3%
1 47290
 
4.7%
2 28520
 
2.8%
3 20511
 
2.0%
4 13898
 
1.4%
5 10199
 
1.0%
6 7836
 
0.8%
7 6198
 
0.6%
8 4773
 
0.5%
9 4371
 
0.4%
Other values (2941) 42853
 
4.3%
(Missing) 222166
 
22.1%
ValueCountFrequency (%)
0 595142
59.3%
1 47290
 
4.7%
2 28520
 
2.8%
3 20511
 
2.0%
4 13898
 
1.4%
ValueCountFrequency (%)
4430 1
< 0.1%
4408 1
< 0.1%
4398 1
< 0.1%
4390 1
< 0.1%
4376 2
< 0.1%

maxdpdlast24m_143P
Real number (ℝ)

MISSING  ZEROS 

Distinct3296
Distinct (%)0.4%
Missing222166
Missing (%)22.1%
Infinite0
Infinite (%)0.0%
Mean23.41533615
Minimum0
Maximum4430
Zeros529537
Zeros (%)52.8%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:34.249617image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile18.5
Maximum4430
Range4430
Interquartile range (IQR)1

Descriptive statistics

Standard deviation214.5304754
Coefficient of variation (CV)9.161964362
Kurtosis208.6112337
Mean23.41533615
Median Absolute Deviation (MAD)0
Skewness13.56288425
Sum18301216
Variance46023.32486
MonotonicityNot monotonic
2024-02-13T20:55:34.397662image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 529537
52.8%
1 57694
 
5.7%
2 36629
 
3.6%
3 26001
 
2.6%
4 17901
 
1.8%
5 13416
 
1.3%
6 10595
 
1.1%
7 8445
 
0.8%
8 6902
 
0.7%
9 6124
 
0.6%
Other values (3286) 68347
 
6.8%
(Missing) 222166
22.1%
ValueCountFrequency (%)
0 529537
52.8%
1 57694
 
5.7%
2 36629
 
3.6%
3 26001
 
2.6%
4 17901
 
1.8%
ValueCountFrequency (%)
4430 1
< 0.1%
4408 1
< 0.1%
4398 1
< 0.1%
4390 1
< 0.1%
4376 2
< 0.1%

maxdpdlast3m_392P
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct1826
Distinct (%)0.2%
Missing222166
Missing (%)22.1%
Infinite0
Infinite (%)0.0%
Mean6.674389802
Minimum0
Maximum4430
Zeros689934
Zeros (%)68.7%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:34.551915image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum4430
Range4430
Interquartile range (IQR)0

Descriptive statistics

Standard deviation135.6014651
Coefficient of variation (CV)20.31668349
Kurtosis733.7615863
Mean6.674389802
Median Absolute Deviation (MAD)0
Skewness26.29456031
Sum5216643
Variance18387.75734
MonotonicityNot monotonic
2024-02-13T20:55:34.698733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 689934
68.7%
1 27602
 
2.7%
2 15573
 
1.6%
3 10896
 
1.1%
4 7205
 
0.7%
5 5022
 
0.5%
6 3708
 
0.4%
7 2843
 
0.3%
8 2230
 
0.2%
9 1948
 
0.2%
Other values (1816) 14630
 
1.5%
(Missing) 222166
 
22.1%
ValueCountFrequency (%)
0 689934
68.7%
1 27602
 
2.7%
2 15573
 
1.6%
3 10896
 
1.1%
4 7205
 
0.7%
ValueCountFrequency (%)
4430 1
< 0.1%
4408 1
< 0.1%
4398 1
< 0.1%
4390 1
< 0.1%
4376 2
< 0.1%

maxdpdlast6m_474P
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct2444
Distinct (%)0.3%
Missing222166
Missing (%)22.1%
Infinite0
Infinite (%)0.0%
Mean10.1246803
Minimum0
Maximum4430
Zeros649000
Zeros (%)64.7%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:34.846250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum4430
Range4430
Interquartile range (IQR)0

Descriptive statistics

Standard deviation157.2876841
Coefficient of variation (CV)15.53507661
Kurtosis488.6960506
Mean10.1246803
Median Absolute Deviation (MAD)0
Skewness21.19766093
Sum7913359
Variance24739.41558
MonotonicityNot monotonic
2024-02-13T20:55:34.990250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 649000
64.7%
1 37110
 
3.7%
2 21152
 
2.1%
3 15257
 
1.5%
4 10373
 
1.0%
5 7220
 
0.7%
6 5561
 
0.6%
7 4317
 
0.4%
8 3290
 
0.3%
9 2993
 
0.3%
Other values (2434) 25318
 
2.5%
(Missing) 222166
 
22.1%
ValueCountFrequency (%)
0 649000
64.7%
1 37110
 
3.7%
2 21152
 
2.1%
3 15257
 
1.5%
4 10373
 
1.0%
ValueCountFrequency (%)
4430 1
< 0.1%
4408 1
< 0.1%
4398 1
< 0.1%
4390 1
< 0.1%
4376 2
< 0.1%

maxdpdlast9m_1059P
Real number (ℝ)

MISSING  ZEROS 

Distinct2765
Distinct (%)0.4%
Missing222166
Missing (%)22.1%
Infinite0
Infinite (%)0.0%
Mean13.16329896
Minimum0
Maximum4430
Zeros618886
Zeros (%)61.7%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:35.135363image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8
Maximum4430
Range4430
Interquartile range (IQR)0

Descriptive statistics

Standard deviation173.0484193
Coefficient of variation (CV)13.14628041
Kurtosis375.5240503
Mean13.16329896
Median Absolute Deviation (MAD)0
Skewness18.43819422
Sum10288316
Variance29945.75543
MonotonicityNot monotonic
2024-02-13T20:55:35.281360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 618886
61.7%
1 42791
 
4.3%
2 25327
 
2.5%
3 18290
 
1.8%
4 12485
 
1.2%
5 8937
 
0.9%
6 6795
 
0.7%
7 5384
 
0.5%
8 4141
 
0.4%
9 3742
 
0.4%
Other values (2755) 34813
 
3.5%
(Missing) 222166
 
22.1%
ValueCountFrequency (%)
0 618886
61.7%
1 42791
 
4.3%
2 25327
 
2.5%
3 18290
 
1.8%
4 12485
 
1.2%
ValueCountFrequency (%)
4430 1
< 0.1%
4408 1
< 0.1%
4398 1
< 0.1%
4390 1
< 0.1%
4376 2
< 0.1%

maxdpdtolerance_374P
Real number (ℝ)

MISSING  ZEROS 

Distinct3686
Distinct (%)0.5%
Missing222166
Missing (%)22.1%
Infinite0
Infinite (%)0.0%
Mean46.5906414
Minimum0
Maximum4430
Zeros391352
Zeros (%)39.0%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:35.426358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile86
Maximum4430
Range4430
Interquartile range (IQR)8

Descriptive statistics

Standard deviation261.7293232
Coefficient of variation (CV)5.617637263
Kurtosis106.4093331
Mean46.5906414
Median Absolute Deviation (MAD)0
Skewness9.409877399
Sum36414826
Variance68502.23862
MonotonicityNot monotonic
2024-02-13T20:55:35.594149image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 391352
39.0%
1 56026
 
5.6%
2 39844
 
4.0%
3 30429
 
3.0%
4 22758
 
2.3%
5 17679
 
1.8%
6 14838
 
1.5%
7 12873
 
1.3%
8 11378
 
1.1%
9 10400
 
1.0%
Other values (3676) 174014
17.3%
(Missing) 222166
22.1%
ValueCountFrequency (%)
0 391352
39.0%
1 56026
 
5.6%
2 39844
 
4.0%
3 30429
 
3.0%
4 22758
 
2.3%
ValueCountFrequency (%)
4430 1
< 0.1%
4408 1
< 0.1%
4398 1
< 0.1%
4390 1
< 0.1%
4376 2
< 0.1%

maxinstallast24m_3658928A
Real number (ℝ)

MISSING 

Distinct157173
Distinct (%)27.6%
Missing433721
Missing (%)43.2%
Infinite0
Infinite (%)0.0%
Mean13589.06875
Minimum0
Maximum612969.6
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:35.751874image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1300.150075
Q13061.15
median6089.8003
Q313135.65075
95-th percentile56182.65
Maximum612969.6
Range612969.6
Interquartile range (IQR)10074.50075

Descriptive statistics

Standard deviation22870.15091
Coefficient of variation (CV)1.68298147
Kurtosis38.2306353
Mean13589.06875
Median Absolute Deviation (MAD)3727.4002
Skewness4.874831988
Sum7746258396
Variance523043802.6
MonotonicityNot monotonic
2024-02-13T20:55:35.902869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
800 1435
 
0.1%
600 435
 
< 0.1%
1000 307
 
< 0.1%
2000 191
 
< 0.1%
3000 112
 
< 0.1%
3333.6 108
 
< 0.1%
4000.4001 96
 
< 0.1%
2180.6 95
 
< 0.1%
4000 90
 
< 0.1%
2666.8 87
 
< 0.1%
Other values (157163) 567080
56.5%
(Missing) 433721
43.2%
ValueCountFrequency (%)
0 5
< 0.1%
0.2 3
< 0.1%
0.4 3
< 0.1%
0.6 5
< 0.1%
0.8 2
 
< 0.1%
ValueCountFrequency (%)
612969.6 1
< 0.1%
541288.8 1
< 0.1%
534779.44 2
< 0.1%
520648.6 1
< 0.1%
506425.22 1
< 0.1%

maxlnamtstart6m_4525199A
Real number (ℝ)

MISSING 

Distinct116661
Distinct (%)59.4%
Missing807461
Missing (%)80.4%
Infinite0
Infinite (%)0.0%
Mean42299.6445
Minimum0
Maximum517345.8
Zeros647
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:36.054847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8869.75025
Q118617.35025
median31261.7
Q354460.15
95-th percentile111342.2
Maximum517345.8
Range517345.8
Interquartile range (IQR)35842.79975

Descriptive statistics

Standard deviation35619.53689
Coefficient of variation (CV)0.842076507
Kurtosis9.438091092
Mean42299.6445
Median Absolute Deviation (MAD)15591.801
Skewness2.331294995
Sum8303251017
Variance1268751408
MonotonicityNot monotonic
2024-02-13T20:55:36.208401image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 1453
 
0.1%
0 647
 
0.1%
103980 578
 
0.1%
40000 504
 
0.1%
20000 496
 
< 0.1%
60000 441
 
< 0.1%
150000 408
 
< 0.1%
30000 344
 
< 0.1%
23682.8 235
 
< 0.1%
63980 226
 
< 0.1%
Other values (116651) 190964
 
19.0%
(Missing) 807461
80.4%
ValueCountFrequency (%)
0 647
0.1%
0.6 1
 
< 0.1%
3.6000001 1
 
< 0.1%
200 3
 
< 0.1%
474.4 1
 
< 0.1%
ValueCountFrequency (%)
517345.8 1
< 0.1%
513520 2
< 0.1%
511270.6 1
< 0.1%
442168.8 1
< 0.1%
430047.4 1
< 0.1%

maxoutstandbalancel12m_4187113A
Real number (ℝ)

MISSING  ZEROS 

Distinct312353
Distinct (%)69.6%
Missing555196
Missing (%)55.3%
Infinite0
Infinite (%)0.0%
Mean67797.52852
Minimum-7588198.5
Maximum1234095.9
Zeros12184
Zeros (%)1.2%
Negative2134
Negative (%)0.2%
Memory size7.7 MiB
2024-02-13T20:55:36.360983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-7588198.5
5-th percentile4300
Q119476.4
median40680
Q385043
95-th percentile228157
Maximum1234095.9
Range8822294.4
Interquartile range (IQR)65566.6

Descriptive statistics

Standard deviation78054.79074
Coefficient of variation (CV)1.151292568
Kurtosis216.2264614
Mean67797.52852
Median Absolute Deviation (MAD)26293.2
Skewness0.4060212885
Sum3.041132719 × 1010
Variance6092550357
MonotonicityNot monotonic
2024-02-13T20:55:36.515852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12184
 
1.2%
12000 264
 
< 0.1%
13998 217
 
< 0.1%
6000 212
 
< 0.1%
10000 202
 
< 0.1%
19998 199
 
< 0.1%
20000 190
 
< 0.1%
-2 189
 
< 0.1%
17998 184
 
< 0.1%
18000 179
 
< 0.1%
Other values (312343) 434541
43.3%
(Missing) 555196
55.3%
ValueCountFrequency (%)
-7588198.5 1
< 0.1%
-44762.555 1
< 0.1%
-26000 1
< 0.1%
-22628.105 1
< 0.1%
-20513 1
< 0.1%
ValueCountFrequency (%)
1234095.9 1
< 0.1%
1212684.6 1
< 0.1%
1212300.9 1
< 0.1%
1210416.6 1
< 0.1%
1175381.2 1
< 0.1%

maxpmtlast3m_4525190A
Real number (ℝ)

MISSING 

Distinct81184
Distinct (%)51.5%
Missing845991
Missing (%)84.3%
Infinite0
Infinite (%)0.0%
Mean10645.70459
Minimum0
Maximum506425.22
Zeros457
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:36.666800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1045.6
Q12519
median4893.90015
Q39862.3715
95-th percentile43568.4555
Maximum506425.22
Range506425.22
Interquartile range (IQR)7343.3715

Descriptive statistics

Standard deviation19072.98637
Coefficient of variation (CV)1.791613342
Kurtosis66.03882009
Mean10645.70459
Median Absolute Deviation (MAD)2848.90015
Skewness6.160019412
Sum1679530230
Variance363778809.1
MonotonicityNot monotonic
2024-02-13T20:55:36.829837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 603
 
0.1%
0 457
 
< 0.1%
800 106
 
< 0.1%
2000 95
 
< 0.1%
1000 72
 
< 0.1%
2999.6 56
 
< 0.1%
1666.6 56
 
< 0.1%
2165 55
 
< 0.1%
1665 55
 
< 0.1%
2166.6 54
 
< 0.1%
Other values (81174) 156157
 
15.6%
(Missing) 845991
84.3%
ValueCountFrequency (%)
0 457
< 0.1%
0.018000001 1
 
< 0.1%
0.15 1
 
< 0.1%
0.386 1
 
< 0.1%
0.6 6
 
< 0.1%
ValueCountFrequency (%)
506425.22 1
 
< 0.1%
495251.22 3
< 0.1%
494899.22 1
 
< 0.1%
494643.6 1
 
< 0.1%
394824.38 1
 
< 0.1%

mindbddpdlast24m_3658935P
Real number (ℝ)

MISSING  ZEROS 

Distinct4069
Distinct (%)0.7%
Missing426131
Missing (%)42.5%
Infinite0
Infinite (%)0.0%
Mean1.093454935
Minimum-1284
Maximum4427
Zeros14059
Zeros (%)1.4%
Negative547985
Negative (%)54.6%
Memory size7.7 MiB
2024-02-13T20:55:36.977571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1284
5-th percentile-106
Q1-35
median-16
Q3-6
95-th percentile0
Maximum4427
Range5711
Interquartile range (IQR)29

Descriptive statistics

Standard deviation304.6834071
Coefficient of variation (CV)278.6428571
Kurtosis133.4752084
Mean1.093454935
Median Absolute Deviation (MAD)12
Skewness11.08489165
Sum631608
Variance92831.97855
MonotonicityNot monotonic
2024-02-13T20:55:37.122212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4 21026
 
2.1%
-3 20993
 
2.1%
-2 19131
 
1.9%
-1 19069
 
1.9%
-5 18869
 
1.9%
-9 18280
 
1.8%
-6 16472
 
1.6%
-7 15757
 
1.6%
-8 15729
 
1.6%
-10 15503
 
1.5%
Other values (4059) 396797
39.5%
(Missing) 426131
42.5%
ValueCountFrequency (%)
-1284 1
< 0.1%
-1282 2
< 0.1%
-1272 1
< 0.1%
-1269 1
< 0.1%
-1267 2
< 0.1%
ValueCountFrequency (%)
4427 1
< 0.1%
4410 1
< 0.1%
4409 1
< 0.1%
4408 1
< 0.1%
4396 1
< 0.1%

mindbdtollast24m_4525191P
Real number (ℝ)

MISSING 

Distinct2750
Distinct (%)1.3%
Missing785725
Missing (%)78.3%
Infinite0
Infinite (%)0.0%
Mean1.893093674
Minimum-1284
Maximum4427
Zeros5373
Zeros (%)0.5%
Negative206890
Negative (%)20.6%
Memory size7.7 MiB
2024-02-13T20:55:37.261223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1284
5-th percentile-110
Q1-37
median-17
Q3-6
95-th percentile0
Maximum4427
Range5711
Interquartile range (IQR)31

Descriptive statistics

Standard deviation314.4113472
Coefficient of variation (CV)166.0833542
Kurtosis128.6348666
Mean1.893093674
Median Absolute Deviation (MAD)13
Skewness10.8811819
Sum412755
Variance98854.49523
MonotonicityNot monotonic
2024-02-13T20:55:37.409133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3 8156
 
0.8%
-4 8024
 
0.8%
-1 7498
 
0.7%
-2 7346
 
0.7%
-5 7128
 
0.7%
-9 6630
 
0.7%
-6 6128
 
0.6%
-7 5843
 
0.6%
-10 5754
 
0.6%
-8 5746
 
0.6%
Other values (2740) 149779
 
14.9%
(Missing) 785725
78.3%
ValueCountFrequency (%)
-1284 1
< 0.1%
-1282 1
< 0.1%
-1269 1
< 0.1%
-1267 1
< 0.1%
-1232 1
< 0.1%
ValueCountFrequency (%)
4427 1
< 0.1%
4410 1
< 0.1%
4409 1
< 0.1%
4408 1
< 0.1%
4396 1
< 0.1%

mobilephncnt_593L
Real number (ℝ)

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.726694808
Minimum0
Maximum23
Zeros710
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:37.543133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum23
Range23
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.089127302
Coefficient of variation (CV)0.6307584275
Kurtosis8.271000492
Mean1.726694808
Median Absolute Deviation (MAD)0
Skewness2.242292076
Sum1733182
Variance1.186198279
MonotonicityNot monotonic
2024-02-13T20:55:37.663104image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 564510
56.2%
2 263922
26.3%
3 105709
 
10.5%
4 41275
 
4.1%
5 16282
 
1.6%
6 6482
 
0.6%
7 2705
 
0.3%
8 1154
 
0.1%
0 710
 
0.1%
9 517
 
0.1%
Other values (13) 491
 
< 0.1%
ValueCountFrequency (%)
0 710
 
0.1%
1 564510
56.2%
2 263922
26.3%
3 105709
 
10.5%
4 41275
 
4.1%
ValueCountFrequency (%)
23 1
< 0.1%
21 1
< 0.1%
20 1
< 0.1%
19 2
< 0.1%
18 1
< 0.1%

monthsannuity_845L
Real number (ℝ)

MISSING 

Distinct157
Distinct (%)< 0.1%
Missing320323
Missing (%)31.9%
Infinite0
Infinite (%)0.0%
Mean24.50028679
Minimum0
Maximum156
Zeros6790
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:37.823784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q19
median18
Q333
95-th percentile71
Maximum156
Range156
Interquartile range (IQR)24

Descriptive statistics

Standard deviation21.96047764
Coefficient of variation (CV)0.8963355338
Kurtosis2.773413285
Mean24.50028679
Median Absolute Deviation (MAD)11
Skewness1.606331947
Sum16744329
Variance482.262578
MonotonicityNot monotonic
2024-02-13T20:55:37.982224image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 44522
 
4.4%
6 44323
 
4.4%
3 23752
 
2.4%
9 22716
 
2.3%
18 21558
 
2.1%
11 20012
 
2.0%
10 19850
 
2.0%
8 17565
 
1.7%
4 17184
 
1.7%
5 17096
 
1.7%
Other values (147) 434856
43.3%
(Missing) 320323
31.9%
ValueCountFrequency (%)
0 6790
 
0.7%
1 9629
1.0%
2 9508
0.9%
3 23752
2.4%
4 17184
1.7%
ValueCountFrequency (%)
156 3
< 0.1%
155 6
< 0.1%
154 4
< 0.1%
153 5
< 0.1%
152 2
 
< 0.1%

numactivecreds_622L
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4306361002
Minimum0
Maximum7
Zeros658295
Zeros (%)65.6%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:38.110223image/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 deviation0.6596328746
Coefficient of variation (CV)1.531763998
Kurtosis1.700740289
Mean0.4306361002
Median Absolute Deviation (MAD)0
Skewness1.445177349
Sum432254
Variance0.4351155292
MonotonicityNot monotonic
2024-02-13T20:55:38.223678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 658295
65.6%
1 266410
26.5%
2 72003
 
7.2%
3 6442
 
0.6%
4 533
 
0.1%
5 67
 
< 0.1%
6 4
 
< 0.1%
7 3
 
< 0.1%
ValueCountFrequency (%)
0 658295
65.6%
1 266410
26.5%
2 72003
 
7.2%
3 6442
 
0.6%
4 533
 
0.1%
ValueCountFrequency (%)
7 3
 
< 0.1%
6 4
 
< 0.1%
5 67
 
< 0.1%
4 533
 
0.1%
3 6442
0.6%

numactivecredschannel_414L
Real number (ℝ)

ZEROS 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0993128815
Minimum0
Maximum4
Zeros911015
Zeros (%)90.8%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:38.340877image/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 deviation0.3215579691
Coefficient of variation (CV)3.237827402
Kurtosis10.96081176
Mean0.0993128815
Median Absolute Deviation (MAD)0
Skewness3.300028877
Sum99686
Variance0.1033995275
MonotonicityNot monotonic
2024-02-13T20:55:38.454876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
0 911015
90.8%
1 85854
 
8.6%
2 6833
 
0.7%
3 54
 
< 0.1%
4 1
 
< 0.1%
ValueCountFrequency (%)
0 911015
90.8%
1 85854
 
8.6%
2 6833
 
0.7%
3 54
 
< 0.1%
4 1
 
< 0.1%
ValueCountFrequency (%)
4 1
 
< 0.1%
3 54
 
< 0.1%
2 6833
 
0.7%
1 85854
 
8.6%
0 911015
90.8%

numactiverelcontr_750L
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2107013949
Minimum0
Maximum8
Zeros800263
Zeros (%)79.7%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:38.575670image/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 deviation0.4285804746
Coefficient of variation (CV)2.034065673
Kurtosis2.854168577
Mean0.2107013949
Median Absolute Deviation (MAD)0
Skewness1.810935688
Sum211493
Variance0.1836812232
MonotonicityNot monotonic
2024-02-13T20:55:38.694669image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 800263
79.7%
1 196110
 
19.5%
2 6849
 
0.7%
3 470
 
< 0.1%
4 57
 
< 0.1%
5 4
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 800263
79.7%
1 196110
 
19.5%
2 6849
 
0.7%
3 470
 
< 0.1%
4 57
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 1
 
< 0.1%
6 2
 
< 0.1%
5 4
 
< 0.1%
4 57
< 0.1%

numcontrs3months_479L
Real number (ℝ)

ZEROS 

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2472132199
Minimum0
Maximum34
Zeros828335
Zeros (%)82.5%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:38.827678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum34
Range34
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6951539194
Coefficient of variation (CV)2.811960944
Kurtosis134.9887285
Mean0.2472132199
Median Absolute Deviation (MAD)0
Skewness7.216580287
Sum248142
Variance0.4832389716
MonotonicityNot monotonic
2024-02-13T20:55:38.968723image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 828335
82.5%
1 131335
 
13.1%
2 29919
 
3.0%
3 8320
 
0.8%
4 2946
 
0.3%
5 1216
 
0.1%
6 642
 
0.1%
7 351
 
< 0.1%
8 201
 
< 0.1%
9 140
 
< 0.1%
Other values (24) 352
 
< 0.1%
ValueCountFrequency (%)
0 828335
82.5%
1 131335
 
13.1%
2 29919
 
3.0%
3 8320
 
0.8%
4 2946
 
0.3%
ValueCountFrequency (%)
34 2
< 0.1%
33 1
< 0.1%
31 1
< 0.1%
30 1
< 0.1%
29 2
< 0.1%

numincomingpmts_3546848L
Real number (ℝ)

MISSING 

Distinct310
Distinct (%)< 0.1%
Missing321992
Missing (%)32.1%
Infinite0
Infinite (%)0.0%
Mean30.29841661
Minimum0
Maximum843
Zeros497
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:39.119678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q110
median20
Q340
95-th percentile94
Maximum843
Range843
Interquartile range (IQR)30

Descriptive statistics

Standard deviation30.97189062
Coefficient of variation (CV)1.022228027
Kurtosis7.988207948
Mean30.29841661
Median Absolute Deviation (MAD)13
Skewness2.242338304
Sum20656400
Variance959.2580088
MonotonicityNot monotonic
2024-02-13T20:55:39.267678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 35824
 
3.6%
12 29927
 
3.0%
3 23176
 
2.3%
9 19963
 
2.0%
11 19710
 
2.0%
10 18804
 
1.9%
7 18010
 
1.8%
5 17376
 
1.7%
13 17191
 
1.7%
4 16997
 
1.7%
Other values (300) 464787
46.3%
(Missing) 321992
32.1%
ValueCountFrequency (%)
0 497
 
< 0.1%
1 7370
 
0.7%
2 10465
1.0%
3 23176
2.3%
4 16997
1.7%
ValueCountFrequency (%)
843 1
< 0.1%
790 1
< 0.1%
640 1
< 0.1%
567 1
< 0.1%
561 1
< 0.1%

numinstlallpaidearly3d_817L
Real number (ℝ)

MISSING  ZEROS 

Distinct271
Distinct (%)< 0.1%
Missing315389
Missing (%)31.4%
Infinite0
Infinite (%)0.0%
Mean19.45884905
Minimum0
Maximum377
Zeros46992
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:39.410678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median11
Q325
95-th percentile69
Maximum377
Range377
Interquartile range (IQR)21

Descriptive statistics

Standard deviation24.36088539
Coefficient of variation (CV)1.251918103
Kurtosis9.96239964
Mean19.45884905
Median Absolute Deviation (MAD)8
Skewness2.671470993
Sum13394849
Variance593.4527372
MonotonicityNot monotonic
2024-02-13T20:55:39.566680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46992
 
4.7%
1 36124
 
3.6%
6 34617
 
3.4%
3 34613
 
3.4%
2 32837
 
3.3%
4 29632
 
3.0%
5 28867
 
2.9%
12 24606
 
2.5%
7 22584
 
2.2%
8 22566
 
2.2%
Other values (261) 374930
37.4%
(Missing) 315389
31.4%
ValueCountFrequency (%)
0 46992
4.7%
1 36124
3.6%
2 32837
3.3%
3 34613
3.4%
4 29632
3.0%
ValueCountFrequency (%)
377 1
< 0.1%
321 1
< 0.1%
318 1
< 0.1%
300 1
< 0.1%
297 2
< 0.1%

numinstls_657L
Real number (ℝ)

ZEROS 

Distinct249
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean7.225366748
Minimum0
Maximum442
Zeros692532
Zeros (%)69.0%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:39.724978image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile36
Maximum442
Range442
Interquartile range (IQR)12

Descriptive statistics

Standard deviation14.44784701
Coefficient of variation (CV)1.999600506
Kurtosis21.47112943
Mean7.225366748
Median Absolute Deviation (MAD)0
Skewness3.292043483
Sum7252498
Variance208.7402831
MonotonicityNot monotonic
2024-02-13T20:55:39.880038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 692532
69.0%
12 67468
 
6.7%
24 49889
 
5.0%
16 28754
 
2.9%
36 23102
 
2.3%
6 22285
 
2.2%
18 16368
 
1.6%
48 14786
 
1.5%
30 13045
 
1.3%
3 6922
 
0.7%
Other values (239) 68604
 
6.8%
ValueCountFrequency (%)
0 692532
69.0%
3 6922
 
0.7%
4 3137
 
0.3%
5 819
 
0.1%
6 22285
 
2.2%
ValueCountFrequency (%)
442 1
< 0.1%
413 1
< 0.1%
338 1
< 0.1%
330 1
< 0.1%
315 1
< 0.1%

numinstlsallpaid_934L
Real number (ℝ)

MISSING  ZEROS 

Distinct285
Distinct (%)< 0.1%
Missing315389
Missing (%)31.4%
Infinite0
Infinite (%)0.0%
Mean25.58423547
Minimum0
Maximum382
Zeros16756
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:40.048988image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median16
Q333
95-th percentile83
Maximum382
Range382
Interquartile range (IQR)26

Descriptive statistics

Standard deviation28.04942355
Coefficient of variation (CV)1.096355745
Kurtosis7.593504111
Mean25.58423547
Median Absolute Deviation (MAD)11
Skewness2.359777562
Sum17611369
Variance786.7701613
MonotonicityNot monotonic
2024-02-13T20:55:40.213976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 36386
 
3.6%
3 28803
 
2.9%
12 28031
 
2.8%
5 23971
 
2.4%
4 21590
 
2.2%
9 21330
 
2.1%
10 21318
 
2.1%
11 19980
 
2.0%
13 19126
 
1.9%
8 18687
 
1.9%
Other values (275) 449146
44.7%
(Missing) 315389
31.4%
ValueCountFrequency (%)
0 16756
1.7%
1 15350
1.5%
2 17839
1.8%
3 28803
2.9%
4 21590
2.2%
ValueCountFrequency (%)
382 1
< 0.1%
347 1
< 0.1%
327 1
< 0.1%
325 1
< 0.1%
321 1
< 0.1%

numinstlswithdpd10_728L
Real number (ℝ)

MISSING  ZEROS 

Distinct236
Distinct (%)< 0.1%
Missing322993
Missing (%)32.2%
Infinite0
Infinite (%)0.0%
Mean3.765642719
Minimum0
Maximum330
Zeros498264
Zeros (%)49.6%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:40.373548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile23
Maximum330
Range330
Interquartile range (IQR)1

Descriptive statistics

Standard deviation12.22341527
Coefficient of variation (CV)3.246036912
Kurtosis50.02323014
Mean3.765642719
Median Absolute Deviation (MAD)0
Skewness5.87977116
Sum2563514
Variance149.4118807
MonotonicityNot monotonic
2024-02-13T20:55:40.791584image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 498264
49.6%
1 36085
 
3.6%
2 15887
 
1.6%
3 13535
 
1.3%
4 11288
 
1.1%
5 10163
 
1.0%
6 8109
 
0.8%
7 7448
 
0.7%
8 6169
 
0.6%
9 4641
 
0.5%
Other values (226) 69175
 
6.9%
(Missing) 322993
32.2%
ValueCountFrequency (%)
0 498264
49.6%
1 36085
 
3.6%
2 15887
 
1.6%
3 13535
 
1.3%
4 11288
 
1.1%
ValueCountFrequency (%)
330 2
< 0.1%
279 2
< 0.1%
273 1
< 0.1%
272 1
< 0.1%
264 1
< 0.1%

numinstlswithdpd5_4187116L
Real number (ℝ)

MISSING  ZEROS 

Distinct76
Distinct (%)< 0.1%
Missing428854
Missing (%)42.7%
Infinite0
Infinite (%)0.0%
Mean2.103290468
Minimum0
Maximum89
Zeros363093
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:40.949585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile11
Maximum89
Range89
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.615294027
Coefficient of variation (CV)2.194320802
Kurtosis20.49190688
Mean2.103290468
Median Absolute Deviation (MAD)0
Skewness3.764237205
Sum1209188
Variance21.30093895
MonotonicityNot monotonic
2024-02-13T20:55:41.098616image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 363093
36.2%
1 53555
 
5.3%
2 28384
 
2.8%
3 19990
 
2.0%
4 18886
 
1.9%
5 15497
 
1.5%
6 12415
 
1.2%
7 9826
 
1.0%
8 8263
 
0.8%
9 6850
 
0.7%
Other values (66) 38144
 
3.8%
(Missing) 428854
42.7%
ValueCountFrequency (%)
0 363093
36.2%
1 53555
 
5.3%
2 28384
 
2.8%
3 19990
 
2.0%
4 18886
 
1.9%
ValueCountFrequency (%)
89 1
< 0.1%
80 1
< 0.1%
79 1
< 0.1%
78 1
< 0.1%
74 1
< 0.1%

numinstlswithoutdpd_562L
Real number (ℝ)

MISSING 

Distinct319
Distinct (%)< 0.1%
Missing322993
Missing (%)32.2%
Infinite0
Infinite (%)0.0%
Mean29.52455476
Minimum0
Maximum557
Zeros3653
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:41.238621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q19
median19
Q339
95-th percentile92
Maximum557
Range557
Interquartile range (IQR)30

Descriptive statistics

Standard deviation30.57099427
Coefficient of variation (CV)1.035443024
Kurtosis7.103173867
Mean29.52455476
Median Absolute Deviation (MAD)12
Skewness2.234946861
Sum20099254
Variance934.5856907
MonotonicityNot monotonic
2024-02-13T20:55:41.382617image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 28112
 
2.8%
4 26135
 
2.6%
13 23285
 
2.3%
6 23255
 
2.3%
11 20313
 
2.0%
5 19417
 
1.9%
12 19270
 
1.9%
10 18829
 
1.9%
14 18023
 
1.8%
8 17176
 
1.7%
Other values (309) 466949
46.5%
(Missing) 322993
32.2%
ValueCountFrequency (%)
0 3653
 
0.4%
1 11549
1.2%
2 14190
1.4%
3 17154
1.7%
4 26135
2.6%
ValueCountFrequency (%)
557 1
< 0.1%
486 1
< 0.1%
464 1
< 0.1%
408 1
< 0.1%
407 1
< 0.1%

numinstmatpaidtearly2d_4499204L
Real number (ℝ)

MISSING  ZEROS 

Distinct253
Distinct (%)0.1%
Missing714921
Missing (%)71.2%
Infinite0
Infinite (%)0.0%
Mean20.46983409
Minimum0
Maximum321
Zeros14433
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:41.525837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median12
Q326
95-th percentile71
Maximum321
Range321
Interquartile range (IQR)21

Descriptive statistics

Standard deviation24.85818786
Coefficient of variation (CV)1.214381501
Kurtosis9.888397679
Mean20.46983409
Median Absolute Deviation (MAD)9
Skewness2.658425067
Sum5912425
Variance617.9295036
MonotonicityNot monotonic
2024-02-13T20:55:41.698560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14433
 
1.4%
3 14252
 
1.4%
6 14020
 
1.4%
1 13593
 
1.4%
2 13068
 
1.3%
4 12213
 
1.2%
5 11984
 
1.2%
12 10667
 
1.1%
9 9587
 
1.0%
8 9510
 
0.9%
Other values (243) 165509
 
16.5%
(Missing) 714921
71.2%
ValueCountFrequency (%)
0 14433
1.4%
1 13593
1.4%
2 13068
1.3%
3 14252
1.4%
4 12213
1.2%
ValueCountFrequency (%)
321 1
< 0.1%
298 1
< 0.1%
293 1
< 0.1%
288 1
< 0.1%
285 1
< 0.1%

numinstpaid_4499208L
Real number (ℝ)

MISSING 

Distinct289
Distinct (%)0.1%
Missing714921
Missing (%)71.2%
Infinite0
Infinite (%)0.0%
Mean30.29261588
Minimum0
Maximum341
Zeros1599
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:41.848598image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q110
median20
Q340
95-th percentile94
Maximum341
Range341
Interquartile range (IQR)30

Descriptive statistics

Standard deviation30.93811387
Coefficient of variation (CV)1.021308757
Kurtosis6.735860723
Mean30.29261588
Median Absolute Deviation (MAD)13
Skewness2.230382431
Sum8749598
Variance957.1668901
MonotonicityNot monotonic
2024-02-13T20:55:42.007579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 17676
 
1.8%
12 17590
 
1.8%
3 10091
 
1.0%
18 8916
 
0.9%
9 7992
 
0.8%
10 7703
 
0.8%
24 7701
 
0.8%
13 7565
 
0.8%
4 6676
 
0.7%
5 6163
 
0.6%
Other values (279) 190763
 
19.0%
(Missing) 714921
71.2%
ValueCountFrequency (%)
0 1599
 
0.2%
1 3336
 
0.3%
2 3734
 
0.4%
3 10091
1.0%
4 6676
0.7%
ValueCountFrequency (%)
341 1
< 0.1%
324 1
< 0.1%
320 1
< 0.1%
317 1
< 0.1%
316 1
< 0.1%

numinstpaidearly3d_3546850L
Real number (ℝ)

MISSING  ZEROS 

Distinct264
Distinct (%)< 0.1%
Missing316703
Missing (%)31.6%
Infinite0
Infinite (%)0.0%
Mean18.75880207
Minimum0
Maximum373
Zeros47101
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:42.183467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median11
Q324
95-th percentile66
Maximum373
Range373
Interquartile range (IQR)20

Descriptive statistics

Standard deviation23.4363941
Coefficient of variation (CV)1.249354517
Kurtosis10.12670245
Mean18.75880207
Median Absolute Deviation (MAD)8
Skewness2.681212918
Sum12888310
Variance549.2645686
MonotonicityNot monotonic
2024-02-13T20:55:42.340069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47101
 
4.7%
1 37134
 
3.7%
3 35573
 
3.5%
2 34343
 
3.4%
6 33809
 
3.4%
4 30430
 
3.0%
5 30244
 
3.0%
12 23462
 
2.3%
7 23312
 
2.3%
8 22941
 
2.3%
Other values (254) 368705
36.7%
(Missing) 316703
31.6%
ValueCountFrequency (%)
0 47101
4.7%
1 37134
3.7%
2 34343
3.4%
3 35573
3.5%
4 30430
3.0%
ValueCountFrequency (%)
373 1
< 0.1%
310 1
< 0.1%
307 1
< 0.1%
300 1
< 0.1%
296 1
< 0.1%

numinstpaidearly3dest_4493216L
Real number (ℝ)

MISSING  ZEROS 

Distinct248
Distinct (%)0.1%
Missing708376
Missing (%)70.6%
Infinite0
Infinite (%)0.0%
Mean18.51262607
Minimum0
Maximum310
Zeros19622
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:42.496738image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median11
Q324
95-th percentile65
Maximum310
Range310
Interquartile range (IQR)20

Descriptive statistics

Standard deviation23.10367583
Coefficient of variation (CV)1.247995597
Kurtosis10.61135237
Mean18.51262607
Median Absolute Deviation (MAD)8
Skewness2.733319763
Sum5468278
Variance533.7798367
MonotonicityNot monotonic
2024-02-13T20:55:42.651653image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19622
 
2.0%
1 16699
 
1.7%
3 15315
 
1.5%
2 14855
 
1.5%
6 14347
 
1.4%
4 13200
 
1.3%
5 12712
 
1.3%
12 10181
 
1.0%
7 9932
 
1.0%
8 9872
 
1.0%
Other values (238) 158646
 
15.8%
(Missing) 708376
70.6%
ValueCountFrequency (%)
0 19622
2.0%
1 16699
1.7%
2 14855
1.5%
3 15315
1.5%
4 13200
1.3%
ValueCountFrequency (%)
310 1
< 0.1%
300 1
< 0.1%
289 1
< 0.1%
283 1
< 0.1%
280 1
< 0.1%

numinstpaidearly5d_1087L
Real number (ℝ)

MISSING  ZEROS 

Distinct209
Distinct (%)< 0.1%
Missing320323
Missing (%)31.9%
Infinite0
Infinite (%)0.0%
Mean5.318613063
Minimum0
Maximum278
Zeros203423
Zeros (%)20.3%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:42.798832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile20
Maximum278
Range278
Interquartile range (IQR)6

Descriptive statistics

Standard deviation10.42698788
Coefficient of variation (CV)1.960471227
Kurtosis53.34944416
Mean5.318613063
Median Absolute Deviation (MAD)2
Skewness5.898745154
Sum3634921
Variance108.7220762
MonotonicityNot monotonic
2024-02-13T20:55:42.943847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 203423
20.3%
1 89046
 
8.9%
2 64287
 
6.4%
3 55808
 
5.6%
4 41217
 
4.1%
6 36416
 
3.6%
5 36168
 
3.6%
7 19112
 
1.9%
8 17649
 
1.8%
9 16197
 
1.6%
Other values (199) 104111
 
10.4%
(Missing) 320323
31.9%
ValueCountFrequency (%)
0 203423
20.3%
1 89046
8.9%
2 64287
 
6.4%
3 55808
 
5.6%
4 41217
 
4.1%
ValueCountFrequency (%)
278 1
< 0.1%
247 1
< 0.1%
245 1
< 0.1%
240 1
< 0.1%
218 1
< 0.1%

numinstpaidearly5dest_4493211L
Real number (ℝ)

MISSING  ZEROS 

Distinct50
Distinct (%)< 0.1%
Missing708376
Missing (%)70.6%
Infinite0
Infinite (%)0.0%
Mean3.230790064
Minimum0
Maximum49
Zeros102682
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:43.097821image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile12
Maximum49
Range49
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.362924873
Coefficient of variation (CV)1.350420419
Kurtosis8.58758244
Mean3.230790064
Median Absolute Deviation (MAD)2
Skewness2.357233239
Sum954314
Variance19.03511345
MonotonicityNot monotonic
2024-02-13T20:55:43.257802image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 102682
 
10.2%
1 41721
 
4.2%
2 29525
 
2.9%
3 25152
 
2.5%
4 17945
 
1.8%
5 15247
 
1.5%
6 14935
 
1.5%
7 7554
 
0.8%
8 6953
 
0.7%
9 6482
 
0.6%
Other values (40) 27185
 
2.7%
(Missing) 708376
70.6%
ValueCountFrequency (%)
0 102682
10.2%
1 41721
4.2%
2 29525
 
2.9%
3 25152
 
2.5%
4 17945
 
1.8%
ValueCountFrequency (%)
49 1
 
< 0.1%
48 7
< 0.1%
47 15
< 0.1%
46 2
 
< 0.1%
45 8
< 0.1%

numinstpaidearly5dobd_4499205L
Real number (ℝ)

MISSING  ZEROS 

Distinct231
Distinct (%)0.1%
Missing714921
Missing (%)71.2%
Infinite0
Infinite (%)0.0%
Mean14.91951142
Minimum0
Maximum300
Zeros37073
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:43.407058image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q319
95-th percentile55
Maximum300
Range300
Interquartile range (IQR)17

Descriptive statistics

Standard deviation20.43784537
Coefficient of variation (CV)1.369873637
Kurtosis12.67152087
Mean14.91951142
Median Absolute Deviation (MAD)7
Skewness2.963866549
Sum4309292
Variance417.7055232
MonotonicityNot monotonic
2024-02-13T20:55:43.561307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37073
 
3.7%
1 21030
 
2.1%
2 16888
 
1.7%
3 15722
 
1.6%
6 13775
 
1.4%
4 13622
 
1.4%
5 12937
 
1.3%
7 9583
 
1.0%
8 9362
 
0.9%
9 8898
 
0.9%
Other values (221) 129946
 
12.9%
(Missing) 714921
71.2%
ValueCountFrequency (%)
0 37073
3.7%
1 21030
2.1%
2 16888
1.7%
3 15722
1.6%
4 13622
 
1.4%
ValueCountFrequency (%)
300 1
< 0.1%
291 1
< 0.1%
263 1
< 0.1%
257 1
< 0.1%
255 1
< 0.1%

numinstpaidearly_338L
Real number (ℝ)

MISSING  ZEROS 

Distinct251
Distinct (%)< 0.1%
Missing320323
Missing (%)31.9%
Infinite0
Infinite (%)0.0%
Mean15.31695818
Minimum0
Maximum373
Zeros84721
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:43.719070image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q319
95-th percentile57
Maximum373
Range373
Interquartile range (IQR)17

Descriptive statistics

Standard deviation20.8821172
Coefficient of variation (CV)1.363333173
Kurtosis12.04376163
Mean15.31695818
Median Absolute Deviation (MAD)7
Skewness2.896958908
Sum10468130
Variance436.0628189
MonotonicityNot monotonic
2024-02-13T20:55:43.875306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 84721
 
8.4%
1 49124
 
4.9%
2 39613
 
3.9%
3 37513
 
3.7%
6 32986
 
3.3%
4 32184
 
3.2%
5 30835
 
3.1%
7 22808
 
2.3%
8 22075
 
2.2%
9 21030
 
2.1%
Other values (241) 310545
30.9%
(Missing) 320323
31.9%
ValueCountFrequency (%)
0 84721
8.4%
1 49124
4.9%
2 39613
3.9%
3 37513
3.7%
4 32184
 
3.2%
ValueCountFrequency (%)
373 1
< 0.1%
300 1
< 0.1%
291 1
< 0.1%
290 1
< 0.1%
289 1
< 0.1%

numinstpaidearlyest_4493214L
Real number (ℝ)

MISSING  ZEROS 

Distinct232
Distinct (%)0.1%
Missing708376
Missing (%)70.6%
Infinite0
Infinite (%)0.0%
Mean14.97108142
Minimum0
Maximum300
Zeros37809
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:44.028305image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q319
95-th percentile56
Maximum300
Range300
Interquartile range (IQR)17

Descriptive statistics

Standard deviation20.49291422
Coefficient of variation (CV)1.368833263
Kurtosis12.54707087
Mean14.97108142
Median Absolute Deviation (MAD)7
Skewness2.952989812
Sum4422173
Variance419.9595334
MonotonicityNot monotonic
2024-02-13T20:55:44.190833image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37809
 
3.8%
1 21470
 
2.1%
2 17241
 
1.7%
3 16031
 
1.6%
6 14064
 
1.4%
4 13934
 
1.4%
5 13201
 
1.3%
7 9800
 
1.0%
8 9583
 
1.0%
9 9095
 
0.9%
Other values (222) 133153
 
13.3%
(Missing) 708376
70.6%
ValueCountFrequency (%)
0 37809
3.8%
1 21470
2.1%
2 17241
1.7%
3 16031
1.6%
4 13934
 
1.4%
ValueCountFrequency (%)
300 1
< 0.1%
291 1
< 0.1%
263 1
< 0.1%
257 1
< 0.1%
255 1
< 0.1%

numinstpaidlastcontr_4325080L
Real number (ℝ)

MISSING  ZEROS 

Distinct70
Distinct (%)< 0.1%
Missing536916
Missing (%)53.5%
Infinite0
Infinite (%)0.0%
Mean7.761349153
Minimum0
Maximum74
Zeros23692
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:44.361291image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q312
95-th percentile18
Maximum74
Range74
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.984406849
Coefficient of variation (CV)0.7710523945
Kurtosis5.01549812
Mean7.761349153
Median Absolute Deviation (MAD)3
Skewness1.641654837
Sum3623316
Variance35.81312534
MonotonicityNot monotonic
2024-02-13T20:55:44.514272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 70655
 
7.0%
3 53807
 
5.4%
12 48469
 
4.8%
4 31738
 
3.2%
5 28351
 
2.8%
0 23692
 
2.4%
1 22982
 
2.3%
2 22504
 
2.2%
10 21751
 
2.2%
9 20517
 
2.0%
Other values (60) 122375
 
12.2%
(Missing) 536916
53.5%
ValueCountFrequency (%)
0 23692
2.4%
1 22982
2.3%
2 22504
2.2%
3 53807
5.4%
4 31738
3.2%
ValueCountFrequency (%)
74 3
< 0.1%
73 2
< 0.1%
72 2
< 0.1%
71 1
 
< 0.1%
70 3
< 0.1%

numinstpaidlate1d_3546852L
Real number (ℝ)

MISSING  ZEROS 

Distinct113
Distinct (%)< 0.1%
Missing320323
Missing (%)31.9%
Infinite0
Infinite (%)0.0%
Mean5.193794865
Minimum0
Maximum137
Zeros247652
Zeros (%)24.7%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:44.665272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile22
Maximum137
Range137
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.324354359
Coefficient of variation (CV)1.60274993
Kurtosis12.34144632
Mean5.193794865
Median Absolute Deviation (MAD)2
Skewness2.924094713
Sum3549616
Variance69.2948755
MonotonicityNot monotonic
2024-02-13T20:55:44.813819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 247652
24.7%
1 83860
 
8.4%
2 50680
 
5.0%
3 37695
 
3.8%
4 33168
 
3.3%
5 28293
 
2.8%
6 24105
 
2.4%
7 19605
 
2.0%
8 17420
 
1.7%
9 15303
 
1.5%
Other values (103) 125653
 
12.5%
(Missing) 320323
31.9%
ValueCountFrequency (%)
0 247652
24.7%
1 83860
 
8.4%
2 50680
 
5.0%
3 37695
 
3.8%
4 33168
 
3.3%
ValueCountFrequency (%)
137 1
< 0.1%
117 1
< 0.1%
115 1
< 0.1%
113 1
< 0.1%
109 1
< 0.1%

numinstregularpaid_973L
Real number (ℝ)

MISSING 

Distinct301
Distinct (%)< 0.1%
Missing323342
Missing (%)32.2%
Infinite0
Infinite (%)0.0%
Mean26.71216831
Minimum0
Maximum356
Zeros6047
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:44.964355image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q17
median16
Q335
95-th percentile88
Maximum356
Range356
Interquartile range (IQR)28

Descriptive statistics

Standard deviation29.64100128
Coefficient of variation (CV)1.109644149
Kurtosis7.704104571
Mean26.71216831
Median Absolute Deviation (MAD)10
Skewness2.400914754
Sum18175360
Variance878.588957
MonotonicityNot monotonic
2024-02-13T20:55:45.121423image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 55320
 
5.5%
12 46040
 
4.6%
3 33350
 
3.3%
9 21539
 
2.1%
10 20846
 
2.1%
4 20595
 
2.1%
18 20385
 
2.0%
5 19613
 
2.0%
13 17751
 
1.8%
24 17081
 
1.7%
Other values (291) 407895
40.6%
(Missing) 323342
32.2%
ValueCountFrequency (%)
0 6047
 
0.6%
1 10838
 
1.1%
2 10848
 
1.1%
3 33350
3.3%
4 20595
2.1%
ValueCountFrequency (%)
356 1
< 0.1%
341 1
< 0.1%
337 1
< 0.1%
329 1
< 0.1%
324 1
< 0.1%

numinstregularpaidest_4493210L
Real number (ℝ)

MISSING 

Distinct290
Distinct (%)0.1%
Missing708376
Missing (%)70.6%
Infinite0
Infinite (%)0.0%
Mean30.3952827
Minimum0
Maximum341
Zeros1618
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:45.281144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q110
median20
Q340
95-th percentile94
Maximum341
Range341
Interquartile range (IQR)30

Descriptive statistics

Standard deviation31.03615136
Coefficient of variation (CV)1.021084478
Kurtosis6.709349254
Mean30.3952827
Median Absolute Deviation (MAD)13
Skewness2.227196455
Sum8978189
Variance963.2426914
MonotonicityNot monotonic
2024-02-13T20:55:45.440564image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 18102
 
1.8%
12 18053
 
1.8%
3 10269
 
1.0%
18 9115
 
0.9%
9 8136
 
0.8%
24 7926
 
0.8%
10 7849
 
0.8%
13 7712
 
0.8%
4 6786
 
0.7%
5 6264
 
0.6%
Other values (280) 195169
 
19.4%
(Missing) 708376
70.6%
ValueCountFrequency (%)
0 1618
 
0.2%
1 3395
 
0.3%
2 3781
 
0.4%
3 10269
1.0%
4 6786
0.7%
ValueCountFrequency (%)
341 1
< 0.1%
324 1
< 0.1%
320 1
< 0.1%
317 1
< 0.1%
316 1
< 0.1%

numinsttopaygr_769L
Real number (ℝ)

MISSING  ZEROS 

Distinct95
Distinct (%)< 0.1%
Missing320324
Missing (%)31.9%
Infinite0
Infinite (%)0.0%
Mean5.926073222
Minimum0
Maximum141
Zeros373481
Zeros (%)37.2%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:45.592997image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39
95-th percentile28
Maximum141
Range141
Interquartile range (IQR)9

Descriptive statistics

Standard deviation10.05012713
Coefficient of variation (CV)1.695916799
Kurtosis6.165106092
Mean5.926073222
Median Absolute Deviation (MAD)0
Skewness2.291161697
Sum4050074
Variance101.0050553
MonotonicityNot monotonic
2024-02-13T20:55:45.744959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 373481
37.2%
1 26124
 
2.6%
2 16742
 
1.7%
4 16418
 
1.6%
6 16140
 
1.6%
3 15575
 
1.6%
5 15446
 
1.5%
9 15358
 
1.5%
8 14830
 
1.5%
7 14612
 
1.5%
Other values (85) 158707
15.8%
(Missing) 320324
31.9%
ValueCountFrequency (%)
0 373481
37.2%
1 26124
 
2.6%
2 16742
 
1.7%
3 15575
 
1.6%
4 16418
 
1.6%
ValueCountFrequency (%)
141 1
< 0.1%
99 1
< 0.1%
94 1
< 0.1%
93 1
< 0.1%
91 1
< 0.1%

numinsttopaygrest_4493213L
Real number (ℝ)

MISSING  ZEROS 

Distinct91
Distinct (%)< 0.1%
Missing708376
Missing (%)70.6%
Infinite0
Infinite (%)0.0%
Mean5.627281376
Minimum0
Maximum99
Zeros167896
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:45.900455image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile27
Maximum99
Range99
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.888504978
Coefficient of variation (CV)1.757243741
Kurtosis6.943685797
Mean5.627281376
Median Absolute Deviation (MAD)0
Skewness2.413761139
Sum1662192
Variance97.78253069
MonotonicityNot monotonic
2024-02-13T20:55:46.056453image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 167896
 
16.7%
1 10729
 
1.1%
9 6945
 
0.7%
2 6744
 
0.7%
8 6621
 
0.7%
6 6532
 
0.7%
4 6473
 
0.6%
3 6254
 
0.6%
7 6115
 
0.6%
5 6080
 
0.6%
Other values (81) 64992
 
6.5%
(Missing) 708376
70.6%
ValueCountFrequency (%)
0 167896
16.7%
1 10729
 
1.1%
2 6744
 
0.7%
3 6254
 
0.6%
4 6473
 
0.6%
ValueCountFrequency (%)
99 1
 
< 0.1%
91 1
 
< 0.1%
89 4
< 0.1%
88 1
 
< 0.1%
87 2
< 0.1%

numinstunpaidmax_3546851L
Real number (ℝ)

MISSING  ZEROS 

Distinct62
Distinct (%)< 0.1%
Missing320324
Missing (%)31.9%
Infinite0
Infinite (%)0.0%
Mean5.391900889
Minimum0
Maximum67
Zeros373731
Zeros (%)37.2%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:46.203331image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile24
Maximum67
Range67
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.845945686
Coefficient of variation (CV)1.640598718
Kurtosis4.345398435
Mean5.391900889
Median Absolute Deviation (MAD)0
Skewness2.058283713
Sum3685003
Variance78.25075509
MonotonicityNot monotonic
2024-02-13T20:55:46.366412image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 373731
37.2%
1 26657
 
2.7%
6 17244
 
1.7%
5 16908
 
1.7%
2 16771
 
1.7%
9 16616
 
1.7%
4 16488
 
1.6%
3 16003
 
1.6%
8 15816
 
1.6%
7 15688
 
1.6%
Other values (52) 151511
15.1%
(Missing) 320324
31.9%
ValueCountFrequency (%)
0 373731
37.2%
1 26657
 
2.7%
2 16771
 
1.7%
3 16003
 
1.6%
4 16488
 
1.6%
ValueCountFrequency (%)
67 1
 
< 0.1%
60 15
< 0.1%
59 23
< 0.1%
58 31
< 0.1%
57 37
< 0.1%

numinstunpaidmaxest_4493212L
Real number (ℝ)

MISSING  ZEROS 

Distinct61
Distinct (%)< 0.1%
Missing708376
Missing (%)70.6%
Infinite0
Infinite (%)0.0%
Mean5.111564386
Minimum0
Maximum60
Zeros167791
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:46.527043image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile23
Maximum60
Range60
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.676134203
Coefficient of variation (CV)1.697353989
Kurtosis4.962397088
Mean5.111564386
Median Absolute Deviation (MAD)0
Skewness2.167273836
Sum1509859
Variance75.27530472
MonotonicityNot monotonic
2024-02-13T20:55:46.685044image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 167791
 
16.7%
1 11099
 
1.1%
9 7507
 
0.7%
8 7178
 
0.7%
6 7071
 
0.7%
2 6784
 
0.7%
5 6651
 
0.7%
7 6605
 
0.7%
4 6445
 
0.6%
3 6410
 
0.6%
Other values (51) 61840
 
6.2%
(Missing) 708376
70.6%
ValueCountFrequency (%)
0 167791
16.7%
1 11099
 
1.1%
2 6784
 
0.7%
3 6410
 
0.6%
4 6445
 
0.6%
ValueCountFrequency (%)
60 7
 
< 0.1%
59 13
< 0.1%
58 16
< 0.1%
57 19
< 0.1%
56 23
< 0.1%

numnotactivated_1143L
Real number (ℝ)

ZEROS 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01795055975
Minimum0
Maximum4
Zeros986706
Zeros (%)98.3%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:46.813244image/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 deviation0.1401029653
Coefficient of variation (CV)7.804935739
Kurtosis79.43576839
Mean0.01795055975
Median Absolute Deviation (MAD)0
Skewness8.410130695
Sum18018
Variance0.01962884089
MonotonicityNot monotonic
2024-02-13T20:55:46.929315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
0 986706
98.3%
1 16118
 
1.6%
2 902
 
0.1%
3 28
 
< 0.1%
4 3
 
< 0.1%
ValueCountFrequency (%)
0 986706
98.3%
1 16118
 
1.6%
2 902
 
0.1%
3 28
 
< 0.1%
4 3
 
< 0.1%
ValueCountFrequency (%)
4 3
 
< 0.1%
3 28
 
< 0.1%
2 902
 
0.1%
1 16118
 
1.6%
0 986706
98.3%

numpmtchanneldd_318L
Real number (ℝ)

ZEROS 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02035353178
Minimum0
Maximum4
Zeros984151
Zeros (%)98.0%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:47.045200image/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 deviation0.1473790853
Coefficient of variation (CV)7.240958813
Kurtosis68.25612671
Mean0.02035353178
Median Absolute Deviation (MAD)0
Skewness7.752536064
Sum20430
Variance0.02172059479
MonotonicityNot monotonic
2024-02-13T20:55:47.160898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
0 984151
98.0%
1 18846
 
1.9%
2 702
 
0.1%
3 52
 
< 0.1%
4 6
 
< 0.1%
ValueCountFrequency (%)
0 984151
98.0%
1 18846
 
1.9%
2 702
 
0.1%
3 52
 
< 0.1%
4 6
 
< 0.1%
ValueCountFrequency (%)
4 6
 
< 0.1%
3 52
 
< 0.1%
2 702
 
0.1%
1 18846
 
1.9%
0 984151
98.0%

numrejects9m_859L
Real number (ℝ)

ZEROS 

Distinct44
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3214951427
Minimum0
Maximum61
Zeros831405
Zeros (%)82.8%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:47.296885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum61
Range61
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.031167191
Coefficient of variation (CV)3.207411417
Kurtosis151.7471374
Mean0.3214951427
Median Absolute Deviation (MAD)0
Skewness8.209477612
Sum322703
Variance1.063305776
MonotonicityNot monotonic
2024-02-13T20:55:47.718886image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 831405
82.8%
1 105290
 
10.5%
2 34743
 
3.5%
3 15071
 
1.5%
4 6844
 
0.7%
5 3982
 
0.4%
6 2146
 
0.2%
7 1322
 
0.1%
8 826
 
0.1%
9 587
 
0.1%
Other values (34) 1541
 
0.2%
ValueCountFrequency (%)
0 831405
82.8%
1 105290
 
10.5%
2 34743
 
3.5%
3 15071
 
1.5%
4 6844
 
0.7%
ValueCountFrequency (%)
61 1
 
< 0.1%
60 1
 
< 0.1%
52 1
 
< 0.1%
48 3
< 0.1%
46 1
 
< 0.1%

opencred_647L
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing220760
Missing (%)22.0%
Memory size7.7 MiB
False
749320 
True
 
33677
(Missing)
220760 
ValueCountFrequency (%)
False 749320
74.7%
True 33677
 
3.4%
(Missing) 220760
 
22.0%
2024-02-13T20:55:47.840877image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

paytype1st_925L
Text

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing1061
Missing (%)0.1%
Memory size7.7 MiB
2024-02-13T20:55:47.946107image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5013480
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 1002696
100.0%
2024-02-13T20:55:48.185948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 1002696
20.0%
T 1002696
20.0%
H 1002696
20.0%
E 1002696
20.0%
R 1002696
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5013480
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 1002696
20.0%
T 1002696
20.0%
H 1002696
20.0%
E 1002696
20.0%
R 1002696
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5013480
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 1002696
20.0%
T 1002696
20.0%
H 1002696
20.0%
E 1002696
20.0%
R 1002696
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5013480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 1002696
20.0%
T 1002696
20.0%
H 1002696
20.0%
E 1002696
20.0%
R 1002696
20.0%

paytype_783L
Text

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing1061
Missing (%)0.1%
Memory size7.7 MiB
2024-02-13T20:55:48.312946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5013480
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 1002696
100.0%
2024-02-13T20:55:48.583368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 1002696
20.0%
T 1002696
20.0%
H 1002696
20.0%
E 1002696
20.0%
R 1002696
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5013480
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 1002696
20.0%
T 1002696
20.0%
H 1002696
20.0%
E 1002696
20.0%
R 1002696
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5013480
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 1002696
20.0%
T 1002696
20.0%
H 1002696
20.0%
E 1002696
20.0%
R 1002696
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5013480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 1002696
20.0%
T 1002696
20.0%
H 1002696
20.0%
E 1002696
20.0%
R 1002696
20.0%
Distinct310
Distinct (%)21.1%
Missing1002290
Missing (%)99.9%
Memory size7.7 MiB
2024-02-13T20:55:48.936428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters14670
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

Unique43 ?
Unique (%)2.9%

Sample

1st row2019-07-25
2nd row2019-03-04
3rd row2019-02-19
4th row2019-01-23
5th row2019-02-12
ValueCountFrequency (%)
2019-08-10 23
 
1.6%
2019-09-06 18
 
1.2%
2019-08-27 18
 
1.2%
2019-08-26 16
 
1.1%
2019-08-06 16
 
1.1%
2019-07-20 15
 
1.0%
2019-08-18 14
 
1.0%
2019-06-06 14
 
1.0%
2019-08-24 13
 
0.9%
2019-05-28 13
 
0.9%
Other values (300) 1307
89.1%
2024-02-13T20:55:49.434687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3399
23.2%
- 2934
20.0%
1 2349
16.0%
2 2321
15.8%
9 1682
11.5%
8 410
 
2.8%
3 360
 
2.5%
4 338
 
2.3%
7 296
 
2.0%
6 293
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11736
80.0%
Dash Punctuation 2934
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3399
29.0%
1 2349
20.0%
2 2321
19.8%
9 1682
14.3%
8 410
 
3.5%
3 360
 
3.1%
4 338
 
2.9%
7 296
 
2.5%
6 293
 
2.5%
5 288
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 2934
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14670
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3399
23.2%
- 2934
20.0%
1 2349
16.0%
2 2321
15.8%
9 1682
11.5%
8 410
 
2.8%
3 360
 
2.5%
4 338
 
2.3%
7 296
 
2.0%
6 293
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14670
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3399
23.2%
- 2934
20.0%
1 2349
16.0%
2 2321
15.8%
9 1682
11.5%
8 410
 
2.8%
3 360
 
2.5%
4 338
 
2.3%
7 296
 
2.0%
6 293
 
2.0%

pctinstlsallpaidearl3d_427L
Real number (ℝ)

MISSING  ZEROS 

Distinct8828
Distinct (%)1.3%
Missing324043
Missing (%)32.3%
Infinite0
Infinite (%)0.0%
Mean0.603787798
Minimum0
Maximum23
Zeros40764
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:49.604695image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.34615
median0.625
Q30.85714
95-th percentile1
Maximum23
Range23
Interquartile range (IQR)0.51099

Descriptive statistics

Standard deviation0.358679894
Coefficient of variation (CV)0.5940495903
Kurtosis78.35428744
Mean0.603787798
Median Absolute Deviation (MAD)0.25
Skewness3.019505287
Sum410403.0193
Variance0.1286512664
MonotonicityNot monotonic
2024-02-13T20:55:49.762722image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 63977
 
6.4%
0 40764
 
4.1%
0.5 22628
 
2.3%
0.66667 20053
 
2.0%
0.33333 18679
 
1.9%
0.83333 11734
 
1.2%
0.75 10541
 
1.1%
0.16667 10304
 
1.0%
0.25 8862
 
0.9%
0.8 7033
 
0.7%
Other values (8818) 465139
46.3%
(Missing) 324043
32.3%
ValueCountFrequency (%)
0 40764
4.1%
0.0119 1
 
< 0.1%
0.01266 1
 
< 0.1%
0.01333 1
 
< 0.1%
0.01389 1
 
< 0.1%
ValueCountFrequency (%)
23 1
 
< 0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
12.5 1
 
< 0.1%
12 13
< 0.1%

pctinstlsallpaidlat10d_839L
Real number (ℝ)

MISSING  ZEROS 

Distinct4296
Distinct (%)0.6%
Missing325891
Missing (%)32.5%
Infinite0
Infinite (%)0.0%
Mean0.07932438472
Minimum0
Maximum1.11111
Zeros394383
Zeros (%)39.3%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:49.913870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.08333
95-th percentile0.41667
Maximum1.11111
Range1.11111
Interquartile range (IQR)0.08333

Descriptive statistics

Standard deviation0.162354342
Coefficient of variation (CV)2.046714167
Kurtosis10.58633832
Mean0.07932438472
Median Absolute Deviation (MAD)0
Skewness3.056789321
Sum53771.30337
Variance0.02635893236
MonotonicityNot monotonic
2024-02-13T20:55:50.065068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 394383
39.3%
0.16667 9602
 
1.0%
0.08333 6915
 
0.7%
0.33333 6862
 
0.7%
0.25 5503
 
0.5%
0.5 4910
 
0.5%
0.11111 4645
 
0.5%
1 4077
 
0.4%
0.125 3919
 
0.4%
0.05556 3873
 
0.4%
Other values (4286) 233177
23.2%
(Missing) 325891
32.5%
ValueCountFrequency (%)
0 394383
39.3%
0.00294 1
 
< 0.1%
0.00299 1
 
< 0.1%
0.00305 1
 
< 0.1%
0.0037 1
 
< 0.1%
ValueCountFrequency (%)
1.11111 1
 
< 0.1%
1 4077
0.4%
0.96667 1
 
< 0.1%
0.96154 1
 
< 0.1%
0.96 2
 
< 0.1%

pctinstlsallpaidlate1d_3546856L
Real number (ℝ)

MISSING  ZEROS 

Distinct6320
Distinct (%)0.9%
Missing324043
Missing (%)32.3%
Infinite0
Infinite (%)0.0%
Mean0.1774964648
Minimum0
Maximum1
Zeros242255
Zeros (%)24.1%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:50.215876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.08333
Q30.2766
95-th percentile0.66667
Maximum1
Range1
Interquartile range (IQR)0.2766

Descriptive statistics

Standard deviation0.2300687894
Coefficient of variation (CV)1.296188009
Kurtosis2.013875356
Mean0.1774964648
Median Absolute Deviation (MAD)0.08333
Skewness1.572027535
Sum120646.8321
Variance0.05293164785
MonotonicityNot monotonic
2024-02-13T20:55:50.370882image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 242255
24.1%
0.33333 15530
 
1.5%
0.16667 14884
 
1.5%
0.5 11783
 
1.2%
0.25 9986
 
1.0%
1 8879
 
0.9%
0.08333 8182
 
0.8%
0.66667 7418
 
0.7%
0.2 7172
 
0.7%
0.11111 6562
 
0.7%
Other values (6310) 347063
34.6%
(Missing) 324043
32.3%
ValueCountFrequency (%)
0 242255
24.1%
0.0036 1
 
< 0.1%
0.00376 1
 
< 0.1%
0.00391 1
 
< 0.1%
0.00415 1
 
< 0.1%
ValueCountFrequency (%)
1 8879
0.9%
0.97917 1
 
< 0.1%
0.97222 5
 
< 0.1%
0.97059 2
 
< 0.1%
0.9697 2
 
< 0.1%

pctinstlsallpaidlate4d_3546849L
Real number (ℝ)

MISSING  ZEROS 

Distinct5325
Distinct (%)0.8%
Missing324780
Missing (%)32.4%
Infinite0
Infinite (%)0.0%
Mean0.1133795344
Minimum0
Maximum2
Zeros336772
Zeros (%)33.6%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:50.522839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01042
Q30.15385
95-th percentile0.53704
Maximum2
Range2
Interquartile range (IQR)0.15385

Descriptive statistics

Standard deviation0.1916141574
Coefficient of variation (CV)1.690024205
Kurtosis5.672218778
Mean0.1133795344
Median Absolute Deviation (MAD)0.01042
Skewness2.325918869
Sum76982.09611
Variance0.03671598532
MonotonicityNot monotonic
2024-02-13T20:55:50.705877image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 336772
33.6%
0.16667 11609
 
1.2%
0.33333 9924
 
1.0%
0.08333 7432
 
0.7%
0.5 7254
 
0.7%
0.25 7238
 
0.7%
1 5526
 
0.6%
0.11111 5460
 
0.5%
0.2 4954
 
0.5%
0.125 4738
 
0.5%
Other values (5315) 278070
27.7%
(Missing) 324780
32.4%
ValueCountFrequency (%)
0 336772
33.6%
0.00305 1
 
< 0.1%
0.00328 1
 
< 0.1%
0.00339 1
 
< 0.1%
0.00377 1
 
< 0.1%
ValueCountFrequency (%)
2 1
 
< 0.1%
1.33333 1
 
< 0.1%
1.2 1
 
< 0.1%
1 5526
0.6%
0.97917 1
 
< 0.1%

pctinstlsallpaidlate6d_3546844L
Real number (ℝ)

MISSING  ZEROS 

Distinct4901
Distinct (%)0.7%
Missing324966
Missing (%)32.4%
Infinite0
Infinite (%)0.0%
Mean0.09696087349
Minimum0
Maximum1
Zeros363802
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:50.878665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.11765
95-th percentile0.5
Maximum1
Range1
Interquartile range (IQR)0.11765

Descriptive statistics

Standard deviation0.1783831676
Coefficient of variation (CV)1.839743818
Kurtosis7.539928229
Mean0.09696087349
Median Absolute Deviation (MAD)0
Skewness2.627743967
Sum65816.16828
Variance0.03182055448
MonotonicityNot monotonic
2024-02-13T20:55:51.037696image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 363802
36.2%
0.16667 10617
 
1.1%
0.33333 8536
 
0.9%
0.08333 7136
 
0.7%
0.25 6368
 
0.6%
0.5 6113
 
0.6%
0.11111 5072
 
0.5%
1 4806
 
0.5%
0.2 4375
 
0.4%
0.125 4366
 
0.4%
Other values (4891) 257600
25.7%
(Missing) 324966
32.4%
ValueCountFrequency (%)
0 363802
36.2%
0.00305 1
 
< 0.1%
0.00377 1
 
< 0.1%
0.00402 1
 
< 0.1%
0.00403 1
 
< 0.1%
ValueCountFrequency (%)
1 4806
0.5%
0.97917 1
 
< 0.1%
0.97222 1
 
< 0.1%
0.96667 2
 
< 0.1%
0.96429 2
 
< 0.1%

pmtnum_254L
Real number (ℝ)

MISSING 

Distinct40
Distinct (%)< 0.1%
Missing28654
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean17.06412041
Minimum3
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:51.180682image/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 deviation9.589546396
Coefficient of variation (CV)0.5619713274
Kurtosis1.837644147
Mean17.06412041
Median Absolute Deviation (MAD)6
Skewness1.318467371
Sum16639275
Variance91.95940008
MonotonicityNot monotonic
2024-02-13T20:55:51.317565image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
12 342938
34.2%
24 199161
19.8%
6 135397
 
13.5%
18 68841
 
6.9%
16 61009
 
6.1%
36 54972
 
5.5%
48 23403
 
2.3%
30 20405
 
2.0%
9 18275
 
1.8%
11 12612
 
1.3%
Other values (30) 38090
 
3.8%
(Missing) 28654
 
2.9%
ValueCountFrequency (%)
3 356
 
< 0.1%
4 197
 
< 0.1%
5 29
 
< 0.1%
6 135397
13.5%
7 1014
 
0.1%
ValueCountFrequency (%)
60 1914
0.2%
58 33
 
< 0.1%
56 18
 
< 0.1%
54 61
 
< 0.1%
52 14
 
< 0.1%

posfpd10lastmonth_333P
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing19192
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean0.01605074322
Minimum0
Maximum1
Zeros968762
Zeros (%)96.5%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:51.441552image/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 deviation0.1256707321
Coefficient of variation (CV)7.829589593
Kurtosis57.31902069
Mean0.01605074322
Median Absolute Deviation (MAD)0
Skewness7.701876671
Sum15803
Variance0.0157931329
MonotonicityNot monotonic
2024-02-13T20:55:51.549140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 968762
96.5%
1 15803
 
1.6%
(Missing) 19192
 
1.9%
ValueCountFrequency (%)
0 968762
96.5%
1 15803
 
1.6%
ValueCountFrequency (%)
1 15803
 
1.6%
0 968762
96.5%

posfpd30lastmonth_3976960P
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing63794
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean0.007732219247
Minimum0
Maximum1
Zeros932695
Zeros (%)92.9%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:51.658103image/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 deviation0.08759246654
Coefficient of variation (CV)11.32824403
Kurtosis124.3374366
Mean0.007732219247
Median Absolute Deviation (MAD)0
Skewness11.23998096
Sum7268
Variance0.007672440195
MonotonicityNot monotonic
2024-02-13T20:55:51.771139image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 932695
92.9%
1 7268
 
0.7%
(Missing) 63794
 
6.4%
ValueCountFrequency (%)
0 932695
92.9%
1 7268
 
0.7%
ValueCountFrequency (%)
1 7268
 
0.7%
0 932695
92.9%

posfstqpd30lastmonth_3976962P
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing92454
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean0.02893219928
Minimum0
Maximum1
Zeros884937
Zeros (%)88.2%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:51.878888image/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 deviation0.1676161029
Coefficient of variation (CV)5.793410355
Kurtosis29.59352988
Mean0.02893219928
Median Absolute Deviation (MAD)0
Skewness5.620806431
Sum26366
Variance0.02809515795
MonotonicityNot monotonic
2024-02-13T20:55:51.988372image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 884937
88.2%
1 26366
 
2.6%
(Missing) 92454
 
9.2%
ValueCountFrequency (%)
0 884937
88.2%
1 26366
 
2.6%
ValueCountFrequency (%)
1 26366
 
2.6%
0 884937
88.2%
Distinct199
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:52.289251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length9.791960604
Min length8

Characters and Unicode

Total characters9828749
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 344396
34.3%
p131_33_167 41235
 
4.1%
p197_47_166 35119
 
3.5%
p123_6_84 27411
 
2.7%
p98_137_111 21953
 
2.2%
p204_99_158 18281
 
1.8%
p159_143_123 16949
 
1.7%
p62_144_102 16263
 
1.6%
p147_21_170 15238
 
1.5%
p178_112_160 14509
 
1.4%
Other values (189) 452403
45.1%
2024-02-13T20:55:52.810327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1973026
20.1%
5 1368849
13.9%
_ 1318722
13.4%
7 872670
8.9%
4 757269
 
7.7%
P 659361
 
6.7%
6 441589
 
4.5%
3 429971
 
4.4%
2 374926
 
3.8%
9 352385
 
3.6%
Other values (4) 1279981
13.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7161874
72.9%
Connector Punctuation 1318722
 
13.4%
Lowercase Letter 688792
 
7.0%
Uppercase Letter 659361
 
6.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1973026
27.5%
5 1368849
19.1%
7 872670
12.2%
4 757269
 
10.6%
6 441589
 
6.2%
3 429971
 
6.0%
2 374926
 
5.2%
9 352385
 
4.9%
8 346491
 
4.8%
0 244698
 
3.4%
Lowercase Letter
ValueCountFrequency (%)
a 344396
50.0%
b 344396
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1318722
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 659361
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8480596
86.3%
Latin 1348153
 
13.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1973026
23.3%
5 1368849
16.1%
_ 1318722
15.5%
7 872670
10.3%
4 757269
 
8.9%
6 441589
 
5.2%
3 429971
 
5.1%
2 374926
 
4.4%
9 352385
 
4.2%
8 346491
 
4.1%
Latin
ValueCountFrequency (%)
P 659361
48.9%
a 344396
25.5%
b 344396
25.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9828749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1973026
20.1%
5 1368849
13.9%
_ 1318722
13.4%
7 872670
8.9%
4 757269
 
7.7%
P 659361
 
6.7%
6 441589
 
4.5%
3 429971
 
4.4%
2 374926
 
3.8%
9 352385
 
3.6%
Other values (4) 1279981
13.0%

price_1097A
Real number (ℝ)

MISSING  ZEROS 

Distinct132402
Distinct (%)15.2%
Missing134372
Missing (%)13.4%
Infinite0
Infinite (%)0.0%
Mean33339.94149
Minimum0
Maximum761867.44
Zeros110171
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:52.982057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113400
median24478
Q343956
95-th percentile94000
Maximum761867.44
Range761867.44
Interquartile range (IQR)30556

Descriptive statistics

Standard deviation33206.10212
Coefficient of variation (CV)0.9959856146
Kurtosis12.17756226
Mean33339.94149
Median Absolute Deviation (MAD)13480
Skewness2.637250542
Sum2.898524503 × 1010
Variance1102645218
MonotonicityNot monotonic
2024-02-13T20:55:53.135762image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 110171
 
11.0%
23998 3818
 
0.4%
23978 3577
 
0.4%
17998 3549
 
0.4%
13998 3383
 
0.3%
11998 3376
 
0.3%
19998 3325
 
0.3%
25978 3090
 
0.3%
15998 2820
 
0.3%
19978 2775
 
0.3%
Other values (132392) 729501
72.7%
(Missing) 134372
 
13.4%
ValueCountFrequency (%)
0 110171
11.0%
1998 1
 
< 0.1%
2000 10
 
< 0.1%
2002 2
 
< 0.1%
2016 1
 
< 0.1%
ValueCountFrequency (%)
761867.44 1
< 0.1%
620400 2
< 0.1%
600000 1
< 0.1%
555569.2 1
< 0.1%
533442.44 1
< 0.1%

sellerplacecnt_915L
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1537563374
Minimum0
Maximum8
Zeros873535
Zeros (%)87.0%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:53.269424image/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 deviation0.4337780856
Coefficient of variation (CV)2.821204595
Kurtosis14.59779182
Mean0.1537563374
Median Absolute Deviation (MAD)0
Skewness3.364672077
Sum154334
Variance0.1881634276
MonotonicityNot monotonic
2024-02-13T20:55:53.386227image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 873535
87.0%
1 110190
 
11.0%
2 16693
 
1.7%
3 2753
 
0.3%
4 468
 
< 0.1%
5 88
 
< 0.1%
6 24
 
< 0.1%
7 5
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 873535
87.0%
1 110190
 
11.0%
2 16693
 
1.7%
3 2753
 
0.3%
4 468
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 5
 
< 0.1%
6 24
 
< 0.1%
5 88
 
< 0.1%
4 468
< 0.1%

sellerplacescnt_216L
Real number (ℝ)

ZEROS 

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.449693502
Minimum0
Maximum33
Zeros409625
Zeros (%)40.8%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:53.514787image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum33
Range33
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.859990021
Coefficient of variation (CV)1.283022942
Kurtosis6.881468903
Mean1.449693502
Median Absolute Deviation (MAD)1
Skewness2.05999473
Sum1455140
Variance3.45956288
MonotonicityNot monotonic
2024-02-13T20:55:53.648757image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 409625
40.8%
1 235622
23.5%
2 146839
 
14.6%
3 88765
 
8.8%
4 52125
 
5.2%
5 30319
 
3.0%
6 17286
 
1.7%
7 9793
 
1.0%
8 5493
 
0.5%
9 3202
 
0.3%
Other values (20) 4688
 
0.5%
ValueCountFrequency (%)
0 409625
40.8%
1 235622
23.5%
2 146839
 
14.6%
3 88765
 
8.8%
4 52125
 
5.2%
ValueCountFrequency (%)
33 2
< 0.1%
31 1
< 0.1%
30 2
< 0.1%
28 1
< 0.1%
27 2
< 0.1%

sumoutstandtotal_3546847A
Real number (ℝ)

MISSING  ZEROS 

Distinct250374
Distinct (%)36.4%
Missing315819
Missing (%)31.5%
Infinite0
Infinite (%)0.0%
Mean26465.08578
Minimum-2405
Maximum1210629.1
Zeros362441
Zeros (%)36.1%
Negative4
Negative (%)< 0.1%
Memory size7.7 MiB
2024-02-13T20:55:53.806780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-2405
5-th percentile0
Q10
median0
Q327182.95
95-th percentile136811.8
Maximum1210629.1
Range1213034.1
Interquartile range (IQR)27182.95

Descriptive statistics

Standard deviation55719.20946
Coefficient of variation (CV)2.105385561
Kurtosis22.63980425
Mean26465.08578
Median Absolute Deviation (MAD)0
Skewness3.873267155
Sum1.820633818 × 1010
Variance3104630303
MonotonicityNot monotonic
2024-02-13T20:55:53.972799image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 362441
36.1%
10 87
 
< 0.1%
7998 79
 
< 0.1%
15998 72
 
< 0.1%
11998 70
 
< 0.1%
19998 69
 
< 0.1%
9998 68
 
< 0.1%
5998 68
 
< 0.1%
9978 66
 
< 0.1%
14998 66
 
< 0.1%
Other values (250364) 324852
32.4%
(Missing) 315819
31.5%
ValueCountFrequency (%)
-2405 1
 
< 0.1%
-326 1
 
< 0.1%
-26.4 1
 
< 0.1%
-2.8 1
 
< 0.1%
0 362441
36.1%
ValueCountFrequency (%)
1210629.1 1
< 0.1%
1192100.9 1
< 0.1%
1092393 1
< 0.1%
1085048.1 1
< 0.1%
1071760.9 1
< 0.1%

sumoutstandtotalest_4493215A
Real number (ℝ)

MISSING  ZEROS 

Distinct117176
Distinct (%)39.7%
Missing708376
Missing (%)70.6%
Infinite0
Infinite (%)0.0%
Mean26060.57437
Minimum-2405
Maximum1085048.1
Zeros160173
Zeros (%)16.0%
Negative4
Negative (%)< 0.1%
Memory size7.7 MiB
2024-02-13T20:55:54.134986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-2405
5-th percentile0
Q10
median0
Q325700.916
95-th percentile137213.8
Maximum1085048.1
Range1087453.1
Interquartile range (IQR)25700.916

Descriptive statistics

Standard deviation57039.30692
Coefficient of variation (CV)2.188720253
Kurtosis24.63821983
Mean26060.57437
Median Absolute Deviation (MAD)0
Skewness4.069710434
Sum7697798517
Variance3253482534
MonotonicityNot monotonic
2024-02-13T20:55:54.292984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 160173
 
16.0%
10 60
 
< 0.1%
7998 46
 
< 0.1%
15998 44
 
< 0.1%
5998 42
 
< 0.1%
9998 42
 
< 0.1%
19998 41
 
< 0.1%
11998 41
 
< 0.1%
9978 40
 
< 0.1%
14998 38
 
< 0.1%
Other values (117166) 134814
 
13.4%
(Missing) 708376
70.6%
ValueCountFrequency (%)
-2405 1
 
< 0.1%
-326 1
 
< 0.1%
-26.4 1
 
< 0.1%
-2.8 1
 
< 0.1%
0 160173
16.0%
ValueCountFrequency (%)
1085048.1 1
< 0.1%
1071760.9 1
< 0.1%
1059281.4 1
< 0.1%
1028338.2 1
< 0.1%
967626.8 1
< 0.1%

totaldebt_9A
Real number (ℝ)

ZEROS 

Distinct250622
Distinct (%)25.0%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean18184.05553
Minimum0
Maximum1210629.1
Zeros681680
Zeros (%)67.9%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:54.446983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q311967.7005
95-th percentile107300.8125
Maximum1210629.1
Range1210629.1
Interquartile range (IQR)11967.7005

Descriptive statistics

Standard deviation47798.8946
Coefficient of variation (CV)2.628615741
Kurtosis32.58741811
Mean18184.05553
Median Absolute Deviation (MAD)0
Skewness4.68537112
Sum1.825233666 × 1010
Variance2284734325
MonotonicityNot monotonic
2024-02-13T20:55:54.605992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 681680
67.9%
10 110
 
< 0.1%
19998 62
 
< 0.1%
7998 53
 
< 0.1%
14998 52
 
< 0.1%
17998 49
 
< 0.1%
9978 48
 
< 0.1%
9998 47
 
< 0.1%
13998 46
 
< 0.1%
15998 45
 
< 0.1%
Other values (250612) 321563
32.0%
ValueCountFrequency (%)
0 681680
67.9%
0.020000001 1
 
< 0.1%
0.025999999 3
 
< 0.1%
0.030000001 1
 
< 0.1%
0.048 1
 
< 0.1%
ValueCountFrequency (%)
1210629.1 1
< 0.1%
1192100.9 1
< 0.1%
1092393 1
< 0.1%
1085048.1 1
< 0.1%
1071760.9 1
< 0.1%

totalsettled_863A
Real number (ℝ)

ZEROS 

Distinct479298
Distinct (%)47.8%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean79276.37877
Minimum0
Maximum7988198.5
Zeros323553
Zeros (%)32.2%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:54.764049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median29780
Q3101873.785
95-th percentile339027.804
Maximum7988198.5
Range7988198.5
Interquartile range (IQR)101873.785

Descriptive statistics

Standard deviation125684.1403
Coefficient of variation (CV)1.585392045
Kurtosis70.81590065
Mean79276.37877
Median Absolute Deviation (MAD)29780
Skewness3.886839846
Sum7.957406157 × 1010
Variance1.579650311 × 1010
MonotonicityNot monotonic
2024-02-13T20:55:54.936335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 323553
32.2%
12000 714
 
0.1%
6000 668
 
0.1%
8000 518
 
0.1%
4000 500
 
< 0.1%
10000 423
 
< 0.1%
18000 393
 
< 0.1%
14000 375
 
< 0.1%
9000 356
 
< 0.1%
16000 346
 
< 0.1%
Other values (479288) 675909
67.3%
ValueCountFrequency (%)
0 323553
32.2%
10 4
 
< 0.1%
20 3
 
< 0.1%
30 1
 
< 0.1%
40 6
 
< 0.1%
ValueCountFrequency (%)
7988198.5 1
< 0.1%
7753567.5 1
< 0.1%
7234491.5 1
< 0.1%
6815196 1
< 0.1%
5103817 1
< 0.1%

totinstallast1m_4525188A
Real number (ℝ)

MISSING 

Distinct76529
Distinct (%)55.2%
Missing865065
Missing (%)86.2%
Infinite0
Infinite (%)0.0%
Mean10576.19257
Minimum0.222
Maximum794899.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2024-02-13T20:55:55.118457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.222
5-th percentile1219
Q13142.4001
median6008.5
Q311627.30025
95-th percentile34611.44035
Maximum794899.2
Range794898.978
Interquartile range (IQR)8484.90015

Descriptive statistics

Standard deviation16354.41783
Coefficient of variation (CV)1.546342668
Kurtosis118.6385326
Mean10576.19257
Median Absolute Deviation (MAD)3508.0999
Skewness7.407880726
Sum1466833299
Variance267466982.6
MonotonicityNot monotonic
2024-02-13T20:55:55.283629image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 363
 
< 0.1%
1200 179
 
< 0.1%
2000 79
 
< 0.1%
4000 64
 
< 0.1%
3000 60
 
< 0.1%
1600 53
 
< 0.1%
3333.2 45
 
< 0.1%
800 45
 
< 0.1%
1000 42
 
< 0.1%
2500 39
 
< 0.1%
Other values (76519) 137723
 
13.7%
(Missing) 865065
86.2%
ValueCountFrequency (%)
0.222 1
< 0.1%
0.23 1
< 0.1%
0.332 1
< 0.1%
0.458 2
< 0.1%
0.546 1
< 0.1%
ValueCountFrequency (%)
794899.2 1
< 0.1%
511210.6 1
< 0.1%
427946 1
< 0.1%
423576.22 1
< 0.1%
394824.38 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing45
Missing (%)< 0.1%
Memory size7.7 MiB
2024-02-13T20:55:55.399693image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2007424
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 rowBO
2nd rowBO
3rd rowBO
4th rowBO
5th rowBO
ValueCountFrequency (%)
fo 977890
97.4%
bo 25822
 
2.6%
2024-02-13T20:55:55.614693image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 1003712
50.0%
F 977890
48.7%
B 25822
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2007424
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 1003712
50.0%
F 977890
48.7%
B 25822
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 2007424
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 1003712
50.0%
F 977890
48.7%
B 25822
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2007424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 1003712
50.0%
F 977890
48.7%
B 25822
 
1.3%

typesuite_864L
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing715237
Missing (%)71.3%
Memory size7.7 MiB
2024-02-13T20:55:55.699132image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters577040
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 288520
100.0%
2024-02-13T20:55:55.902958image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 288520
50.0%
L 288520
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 577040
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 288520
50.0%
L 288520
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 577040
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 288520
50.0%
L 288520
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 577040
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 288520
50.0%
L 288520
50.0%

validfrom_1069D
Text

MISSING 

Distinct449
Distinct (%)0.4%
Missing884352
Missing (%)88.1%
Memory size7.7 MiB
2024-02-13T20:55:56.247527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1194050
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

Unique7 ?
Unique (%)< 0.1%

Sample

1st row2019-02-06
2nd row2019-01-11
3rd row2019-08-30
4th row2019-11-01
5th row2019-12-23
ValueCountFrequency (%)
2019-06-18 15175
 
12.7%
2019-02-11 9114
 
7.6%
2019-10-16 8338
 
7.0%
2019-10-02 4314
 
3.6%
2019-05-08 2787
 
2.3%
2019-08-08 2454
 
2.1%
2019-07-04 1932
 
1.6%
2019-01-28 1919
 
1.6%
2019-11-28 1758
 
1.5%
2019-04-24 1682
 
1.4%
Other values (439) 69932
58.6%
2024-02-13T20:55:56.716449image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 261493
21.9%
1 241443
20.2%
- 238810
20.0%
2 180981
15.2%
9 125379
10.5%
8 45899
 
3.8%
6 33127
 
2.8%
3 20582
 
1.7%
4 18052
 
1.5%
7 14363
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 955240
80.0%
Dash Punctuation 238810
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 261493
27.4%
1 241443
25.3%
2 180981
18.9%
9 125379
13.1%
8 45899
 
4.8%
6 33127
 
3.5%
3 20582
 
2.2%
4 18052
 
1.9%
7 14363
 
1.5%
5 13921
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 238810
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1194050
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 261493
21.9%
1 241443
20.2%
- 238810
20.0%
2 180981
15.2%
9 125379
10.5%
8 45899
 
3.8%
6 33127
 
2.8%
3 20582
 
1.7%
4 18052
 
1.5%
7 14363
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1194050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 261493
21.9%
1 241443
20.2%
- 238810
20.0%
2 180981
15.2%
9 125379
10.5%
8 45899
 
3.8%
6 33127
 
2.8%
3 20582
 
1.7%
4 18052
 
1.5%
7 14363
 
1.2%