Overview

Dataset statistics

Number of variables79
Number of observations4108212
Missing cells244050042
Missing cells (%)75.2%
Total size in memory2.4 GiB
Average record size in memory632.0 B

Variable types

Numeric55
Text23
Unsupported1

Alerts

annualeffectiverate_199L has 4071231 (99.1%) missing valuesMissing
annualeffectiverate_63L has 4035875 (98.2%) missing valuesMissing
contractsum_5085717L has 4108212 (100.0%) missing valuesMissing
credlmt_230A has 4043918 (98.4%) missing valuesMissing
credlmt_935A has 3810159 (92.7%) missing valuesMissing
dateofcredend_289D has 3449815 (84.0%) missing valuesMissing
dateofcredend_353D has 3651494 (88.9%) missing valuesMissing
dateofcredstart_181D has 3651491 (88.9%) missing valuesMissing
dateofcredstart_739D has 3449815 (84.0%) missing valuesMissing
dateofrealrepmt_138D has 3654366 (89.0%) missing valuesMissing
debtoutstand_525A has 3773397 (91.9%) missing valuesMissing
debtoverdue_47A has 3773397 (91.9%) missing valuesMissing
dpdmax_139P has 3452623 (84.0%) missing valuesMissing
dpdmax_757P has 3667919 (89.3%) missing valuesMissing
dpdmaxdatemonth_442T has 3667919 (89.3%) missing valuesMissing
dpdmaxdatemonth_89T has 3452623 (84.0%) missing valuesMissing
dpdmaxdateyear_596T has 3452623 (84.0%) missing valuesMissing
dpdmaxdateyear_896T has 3667919 (89.3%) missing valuesMissing
instlamount_768A has 3813073 (92.8%) missing valuesMissing
instlamount_852A has 4067156 (99.0%) missing valuesMissing
interestrate_508L has 4102975 (99.9%) missing valuesMissing
lastupdate_1112D has 3449815 (84.0%) missing valuesMissing
lastupdate_388D has 3651506 (88.9%) missing valuesMissing
monthlyinstlamount_332A has 3452923 (84.0%) missing valuesMissing
monthlyinstlamount_674A has 3687335 (89.8%) missing valuesMissing
nominalrate_281L has 3863576 (94.0%) missing valuesMissing
nominalrate_498L has 3969804 (96.6%) missing valuesMissing
numberofcontrsvalue_258L has 3770638 (91.8%) missing valuesMissing
numberofcontrsvalue_358L has 3809610 (92.7%) missing valuesMissing
numberofinstls_229L has 3715974 (90.5%) missing valuesMissing
numberofinstls_320L has 3747975 (91.2%) missing valuesMissing
numberofoutstandinstls_520L has 3715663 (90.4%) missing valuesMissing
numberofoutstandinstls_59L has 3747993 (91.2%) missing valuesMissing
numberofoverdueinstlmax_1039L has 3449815 (84.0%) missing valuesMissing
numberofoverdueinstlmax_1151L has 3651491 (88.9%) missing valuesMissing
numberofoverdueinstlmaxdat_148D has 3971294 (96.7%) missing valuesMissing
numberofoverdueinstlmaxdat_641D has 3940253 (95.9%) missing valuesMissing
numberofoverdueinstls_725L has 3452684 (84.0%) missing valuesMissing
numberofoverdueinstls_834L has 3652014 (88.9%) missing valuesMissing
outstandingamount_354A has 3715535 (90.4%) missing valuesMissing
outstandingamount_362A has 3747881 (91.2%) missing valuesMissing
overdueamount_31A has 3651899 (88.9%) missing valuesMissing
overdueamount_659A has 3452681 (84.0%) missing valuesMissing
overdueamountmax2_14A has 3449815 (84.0%) missing valuesMissing
overdueamountmax2_398A has 3651491 (88.9%) missing valuesMissing
overdueamountmax2date_1002D has 3972755 (96.7%) missing valuesMissing
overdueamountmax2date_1142D has 3938602 (95.9%) missing valuesMissing
overdueamountmax_155A has 3449815 (84.0%) missing valuesMissing
overdueamountmax_35A has 3667444 (89.3%) missing valuesMissing
overdueamountmaxdatemonth_284T has 3667444 (89.3%) missing valuesMissing
overdueamountmaxdatemonth_365T has 3449815 (84.0%) missing valuesMissing
overdueamountmaxdateyear_2T has 3449815 (84.0%) missing valuesMissing
overdueamountmaxdateyear_994T has 3667444 (89.3%) missing valuesMissing
periodicityofpmts_1102L has 3748546 (91.2%) missing valuesMissing
periodicityofpmts_837L has 3755452 (91.4%) missing valuesMissing
prolongationcount_1120L has 4081757 (99.4%) missing valuesMissing
prolongationcount_599L has 4102220 (99.9%) missing valuesMissing
refreshdate_3813885D has 1429958 (34.8%) missing valuesMissing
residualamount_488A has 4044452 (98.4%) missing valuesMissing
residualamount_856A has 3813073 (92.8%) missing valuesMissing
totalamount_6A has 3715421 (90.4%) missing valuesMissing
totalamount_996A has 3747868 (91.2%) missing valuesMissing
totaldebtoverduevalue_178A has 3770638 (91.8%) missing valuesMissing
totaldebtoverduevalue_718A has 3809610 (92.7%) missing valuesMissing
totaloutstanddebtvalue_39A has 3770638 (91.8%) missing valuesMissing
totaloutstanddebtvalue_668A has 3809610 (92.7%) missing valuesMissing
annualeffectiverate_63L is highly skewed (γ1 = 35.44180866)Skewed
credlmt_230A is highly skewed (γ1 = 135.1175142)Skewed
credlmt_935A is highly skewed (γ1 = 264.6687613)Skewed
debtoutstand_525A is highly skewed (γ1 = 386.7162334)Skewed
debtoverdue_47A is highly skewed (γ1 = 310.1137253)Skewed
dpdmax_757P is highly skewed (γ1 = 158.2084117)Skewed
interestrate_508L is highly skewed (γ1 = 24.39477055)Skewed
monthlyinstlamount_332A is highly skewed (γ1 = 327.3242395)Skewed
monthlyinstlamount_674A is highly skewed (γ1 = 407.1228449)Skewed
nominalrate_281L is highly skewed (γ1 = 130.5647455)Skewed
nominalrate_498L is highly skewed (γ1 = 56.41200028)Skewed
numberofoutstandinstls_520L is highly skewed (γ1 = 74.83680843)Skewed
numberofoverdueinstlmax_1039L is highly skewed (γ1 = 90.37811423)Skewed
numberofoverdueinstlmax_1151L is highly skewed (γ1 = 280.671289)Skewed
numberofoverdueinstls_725L is highly skewed (γ1 = 20.96103777)Skewed
numberofoverdueinstls_834L is highly skewed (γ1 = 97.95482955)Skewed
outstandingamount_354A is highly skewed (γ1 = 311.8560199)Skewed
outstandingamount_362A is highly skewed (γ1 = 487.0921733)Skewed
overdueamount_31A is highly skewed (γ1 = 144.0515936)Skewed
overdueamount_659A is highly skewed (γ1 = 477.9428866)Skewed
overdueamountmax2_14A is highly skewed (γ1 = 265.4617439)Skewed
overdueamountmax2_398A is highly skewed (γ1 = 213.5484334)Skewed
overdueamountmax_155A is highly skewed (γ1 = 350.8231859)Skewed
overdueamountmax_35A is highly skewed (γ1 = 248.4321213)Skewed
periodicityofpmts_1102L is highly skewed (γ1 = 46.13117812)Skewed
periodicityofpmts_837L is highly skewed (γ1 = 23.45458419)Skewed
residualamount_488A is highly skewed (γ1 = 252.5074256)Skewed
residualamount_856A is highly skewed (γ1 = 234.7795756)Skewed
totalamount_6A is highly skewed (γ1 = 157.8880663)Skewed
totalamount_996A is highly skewed (γ1 = 462.3917867)Skewed
totaldebtoverduevalue_178A is highly skewed (γ1 = 317.0916596)Skewed
totaldebtoverduevalue_718A is highly skewed (γ1 = 62.28840764)Skewed
totaloutstanddebtvalue_39A is highly skewed (γ1 = 409.4556995)Skewed
totaloutstanddebtvalue_668A is highly skewed (γ1 = 487.9555286)Skewed
contractsum_5085717L is an unsupported type, check if it needs cleaning or further analysisUnsupported
credlmt_935A has 93496 (2.3%) zerosZeros
debtoutstand_525A has 46802 (1.1%) zerosZeros
debtoverdue_47A has 317743 (7.7%) zerosZeros
dpdmax_139P has 520988 (12.7%) zerosZeros
dpdmax_757P has 312253 (7.6%) zerosZeros
instlamount_768A has 117602 (2.9%) zerosZeros
monthlyinstlamount_332A has 118642 (2.9%) zerosZeros
monthlyinstlamount_674A has 219375 (5.3%) zerosZeros
num_group1 has 335275 (8.2%) zerosZeros
numberofinstls_229L has 88926 (2.2%) zerosZeros
numberofoutstandinstls_520L has 391852 (9.5%) zerosZeros
numberofoverdueinstlmax_1039L has 490438 (11.9%) zerosZeros
numberofoverdueinstlmax_1151L has 319803 (7.8%) zerosZeros
numberofoverdueinstls_725L has 636359 (15.5%) zerosZeros
numberofoverdueinstls_834L has 455861 (11.1%) zerosZeros
outstandingamount_354A has 392566 (9.6%) zerosZeros
overdueamount_31A has 456195 (11.1%) zerosZeros
overdueamount_659A has 636362 (15.5%) zerosZeros
overdueamountmax2_14A has 488787 (11.9%) zerosZeros
overdueamountmax2_398A has 321264 (7.8%) zerosZeros
overdueamountmax_155A has 521121 (12.7%) zerosZeros
overdueamountmax_35A has 311547 (7.6%) zerosZeros
residualamount_488A has 63759 (1.6%) zerosZeros
residualamount_856A has 122406 (3.0%) zerosZeros
totaldebtoverduevalue_178A has 320411 (7.8%) zerosZeros
totaldebtoverduevalue_718A has 298208 (7.3%) zerosZeros
totaloutstanddebtvalue_668A has 298175 (7.3%) zerosZeros

Reproduction

Analysis started2024-02-13 19:38:19.995630
Analysis finished2024-02-13 19:38:42.338382
Duration22.34 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

case_id
Real number (ℝ)

Distinct335275
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1170392.41
Minimum388
Maximum2588481
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:42.490682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum388
5-th percentile107971
Q1662301
median1290768
Q31382857
95-th percentile2570990
Maximum2588481
Range2588093
Interquartile range (IQR)720556

Descriptive statistics

Standard deviation715611.9713
Coefficient of variation (CV)0.6114290942
Kurtosis-0.1838356875
Mean1170392.41
Median Absolute Deviation (MAD)575065
Skewness0.4645807812
Sum4.808220145 × 1012
Variance5.121004935 × 1011
MonotonicityIncreasing
2024-02-13T20:38:42.667698image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
674781 281
 
< 0.1%
1396592 248
 
< 0.1%
1383556 247
 
< 0.1%
1295017 246
 
< 0.1%
1326462 170
 
< 0.1%
1308352 167
 
< 0.1%
1300000 153
 
< 0.1%
1366678 150
 
< 0.1%
1270558 130
 
< 0.1%
1344712 124
 
< 0.1%
Other values (335265) 4106296
> 99.9%
ValueCountFrequency (%)
388 11
< 0.1%
405 11
< 0.1%
409 11
< 0.1%
410 11
< 0.1%
411 11
< 0.1%
ValueCountFrequency (%)
2588481 11
< 0.1%
2588480 12
< 0.1%
2588479 11
< 0.1%
2588478 11
< 0.1%
2588477 11
< 0.1%

annualeffectiverate_199L
Real number (ℝ)

MISSING 

Distinct3091
Distinct (%)8.4%
Missing4071231
Missing (%)99.1%
Infinite0
Infinite (%)0.0%
Mean612.0496455
Minimum0
Maximum73000
Zeros1397
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:42.825872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12
Q19.13
median35.7
Q396.3
95-th percentile730
Maximum73000
Range73000
Interquartile range (IQR)87.17

Descriptive statistics

Standard deviation5408.498639
Coefficient of variation (CV)8.836699243
Kurtosis141.9981679
Mean612.0496455
Median Absolute Deviation (MAD)32.23
Skewness11.78652567
Sum22634207.94
Variance29251857.53
MonotonicityNot monotonic
2024-02-13T20:38:42.985140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96.3 3855
 
0.1%
730 2007
 
< 0.1%
98.55 1987
 
< 0.1%
0.12 1711
 
< 0.1%
365 1528
 
< 0.1%
0 1397
 
< 0.1%
438 1075
 
< 0.1%
7.3 1013
 
< 0.1%
55.62 846
 
< 0.1%
5.48 528
 
< 0.1%
Other values (3081) 21034
 
0.5%
(Missing) 4071231
99.1%
ValueCountFrequency (%)
0 1397
< 0.1%
0.08 2
 
< 0.1%
0.09 50
 
< 0.1%
0.1 41
 
< 0.1%
0.11 246
 
< 0.1%
ValueCountFrequency (%)
73000 63
< 0.1%
69350 102
< 0.1%
62415 1
 
< 0.1%
58973.57 1
 
< 0.1%
55480 38
 
< 0.1%

annualeffectiverate_63L
Real number (ℝ)

MISSING  SKEWED 

Distinct4305
Distinct (%)6.0%
Missing4035875
Missing (%)98.2%
Infinite0
Infinite (%)0.0%
Mean84.05078673
Minimum0
Maximum73000
Zeros1976
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:43.142726image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.11
Q10.55
median22.85
Q335.88
95-th percentile51.96
Maximum73000
Range73000
Interquartile range (IQR)35.33

Descriptive statistics

Standard deviation1860.202377
Coefficient of variation (CV)22.13188537
Kurtosis1298.819167
Mean84.05078673
Median Absolute Deviation (MAD)17.73
Skewness35.44180866
Sum6079981.76
Variance3460352.885
MonotonicityNot monotonic
2024-02-13T20:38:43.303689image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.12 11984
 
0.3%
0.11 2010
 
< 0.1%
0 1976
 
< 0.1%
96.3 1562
 
< 0.1%
5.11 1201
 
< 0.1%
26.8 775
 
< 0.1%
0.13 511
 
< 0.1%
68.3 326
 
< 0.1%
0.5 309
 
< 0.1%
0.3 308
 
< 0.1%
Other values (4295) 51375
 
1.3%
(Missing) 4035875
98.2%
ValueCountFrequency (%)
0 1976
< 0.1%
0.01 5
 
< 0.1%
0.08 3
 
< 0.1%
0.09 45
 
< 0.1%
0.1 196
 
< 0.1%
ValueCountFrequency (%)
73000 16
< 0.1%
69350 26
< 0.1%
55480 1
 
< 0.1%
54750 3
 
< 0.1%
48545 4
 
< 0.1%
Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:43.490524image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters32865696
Distinct characters16
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 row4408ff0f
2nd rowea6782cc
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 3450380
84.0%
ea6782cc 573453
 
14.0%
01f63ac8 50212
 
1.2%
00135d9c 23276
 
0.6%
4408ff0f 9503
 
0.2%
1cf4e481 724
 
< 0.1%
be7b251d 461
 
< 0.1%
87bdbcba 89
 
< 0.1%
2c070815 68
 
< 0.1%
4a5a01e3 40
 
< 0.1%
2024-02-13T20:38:43.776932image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 10374991
31.6%
a 4074220
 
12.4%
7 4024451
 
12.2%
1 3525885
 
10.7%
4 3470874
 
10.6%
b 3451569
 
10.5%
c 1221275
 
3.7%
8 634061
 
1.9%
6 623665
 
1.9%
e 574678
 
1.7%
Other values (6) 890027
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23440677
71.3%
Lowercase Letter 9425019
28.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 10374991
44.3%
7 4024451
 
17.2%
1 3525885
 
15.0%
4 3470874
 
14.8%
8 634061
 
2.7%
6 623665
 
2.7%
2 573988
 
2.4%
0 115952
 
0.5%
3 73528
 
0.3%
9 23282
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
a 4074220
43.2%
b 3451569
36.6%
c 1221275
 
13.0%
e 574678
 
6.1%
f 79445
 
0.8%
d 23832
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 23440677
71.3%
Latin 9425019
28.7%

Most frequent character per script

Common
ValueCountFrequency (%)
5 10374991
44.3%
7 4024451
 
17.2%
1 3525885
 
15.0%
4 3470874
 
14.8%
8 634061
 
2.7%
6 623665
 
2.7%
2 573988
 
2.4%
0 115952
 
0.5%
3 73528
 
0.3%
9 23282
 
0.1%
Latin
ValueCountFrequency (%)
a 4074220
43.2%
b 3451569
36.6%
c 1221275
 
13.0%
e 574678
 
6.1%
f 79445
 
0.8%
d 23832
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32865696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 10374991
31.6%
a 4074220
 
12.4%
7 4024451
 
12.2%
1 3525885
 
10.7%
4 3470874
 
10.6%
b 3451569
 
10.5%
c 1221275
 
3.7%
8 634061
 
1.9%
6 623665
 
1.9%
e 574678
 
1.7%
Other values (6) 890027
 
2.7%
Distinct269
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:43.954286image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters32865696
Distinct characters16
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

Unique49 ?
Unique (%)< 0.1%

Sample

1st rowa55475b1
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 3652298
88.9%
ea6782cc 278136
 
6.8%
01f63ac8 58625
 
1.4%
00135d9c 22106
 
0.5%
42a42e75 13884
 
0.3%
9158339f 8060
 
0.2%
4408ff0f 7587
 
0.2%
130920c8 6894
 
0.2%
f0a30139 5473
 
0.1%
e6e56e83 5321
 
0.1%
Other values (259) 49828
 
1.2%
2024-02-13T20:38:44.252208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 11030807
33.6%
a 4026229
 
12.3%
7 3974278
 
12.1%
1 3774844
 
11.5%
4 3729849
 
11.3%
b 3674730
 
11.2%
c 661141
 
2.0%
8 387897
 
1.2%
6 377898
 
1.1%
2 358321
 
1.1%
Other values (6) 869702
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24016574
73.1%
Lowercase Letter 8849122
 
26.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 11030807
45.9%
7 3974278
 
16.5%
1 3774844
 
15.7%
4 3729849
 
15.5%
8 387897
 
1.6%
6 377898
 
1.6%
2 358321
 
1.5%
0 155400
 
0.6%
3 147051
 
0.6%
9 80229
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
a 4026229
45.5%
b 3674730
41.5%
c 661141
 
7.5%
e 334946
 
3.8%
f 102182
 
1.2%
d 49894
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 24016574
73.1%
Latin 8849122
 
26.9%

Most frequent character per script

Common
ValueCountFrequency (%)
5 11030807
45.9%
7 3974278
 
16.5%
1 3774844
 
15.7%
4 3729849
 
15.5%
8 387897
 
1.6%
6 377898
 
1.6%
2 358321
 
1.5%
0 155400
 
0.6%
3 147051
 
0.6%
9 80229
 
0.3%
Latin
ValueCountFrequency (%)
a 4026229
45.5%
b 3674730
41.5%
c 661141
 
7.5%
e 334946
 
3.8%
f 102182
 
1.2%
d 49894
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32865696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 11030807
33.6%
a 4026229
 
12.3%
7 3974278
 
12.1%
1 3774844
 
11.5%
4 3729849
 
11.3%
b 3674730
 
11.2%
c 661141
 
2.0%
8 387897
 
1.2%
6 377898
 
1.1%
2 358321
 
1.1%
Other values (6) 869702
 
2.6%
Distinct43
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:44.430408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters32865696
Distinct characters16
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

Unique7 ?
Unique (%)< 0.1%

Sample

1st row7241344e
2nd row7241344e
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 3451448
84.0%
7241344e 635548
 
15.5%
8f3a197f 6330
 
0.2%
0dc85f9d 5667
 
0.1%
dd67cff0 1935
 
< 0.1%
a52d5641 1690
 
< 0.1%
b919198c 1652
 
< 0.1%
885ce291 970
 
< 0.1%
7640edc3 541
 
< 0.1%
83931972 458
 
< 0.1%
Other values (33) 1973
 
< 0.1%
2024-02-13T20:38:44.768102image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 10365152
31.5%
4 5361453
16.3%
1 4100315
 
12.5%
7 4097024
 
12.5%
a 3460003
 
10.5%
b 3453665
 
10.5%
3 644779
 
2.0%
2 640125
 
1.9%
e 638788
 
1.9%
f 22824
 
0.1%
Other values (6) 81568
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25260342
76.9%
Lowercase Letter 7605354
 
23.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 10365152
41.0%
4 5361453
21.2%
1 4100315
 
16.2%
7 4097024
 
16.2%
3 644779
 
2.6%
2 640125
 
2.5%
9 20554
 
0.1%
8 16885
 
0.1%
0 8826
 
< 0.1%
6 5229
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 3460003
45.5%
b 3453665
45.4%
e 638788
 
8.4%
f 22824
 
0.3%
d 18965
 
0.2%
c 11109
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 25260342
76.9%
Latin 7605354
 
23.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 10365152
41.0%
4 5361453
21.2%
1 4100315
 
16.2%
7 4097024
 
16.2%
3 644779
 
2.6%
2 640125
 
2.5%
9 20554
 
0.1%
8 16885
 
0.1%
0 8826
 
< 0.1%
6 5229
 
< 0.1%
Latin
ValueCountFrequency (%)
a 3460003
45.5%
b 3453665
45.4%
e 638788
 
8.4%
f 22824
 
0.3%
d 18965
 
0.2%
c 11109
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32865696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 10365152
31.5%
4 5361453
16.3%
1 4100315
 
12.5%
7 4097024
 
12.5%
a 3460003
 
10.5%
b 3453665
 
10.5%
3 644779
 
2.0%
2 640125
 
1.9%
e 638788
 
1.9%
f 22824
 
0.1%
Other values (6) 81568
 
0.2%
Distinct156
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:44.954324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters32865696
Distinct characters16
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

Unique31 ?
Unique (%)< 0.1%

Sample

1st rowa55475b1
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 3651620
88.9%
7241344e 424070
 
10.3%
8f3a197f 8369
 
0.2%
a3386307 4866
 
0.1%
8260bab9 3278
 
0.1%
d7416962 3135
 
0.1%
b83056f9 1554
 
< 0.1%
4476359f 1335
 
< 0.1%
3dc5f434 1314
 
< 0.1%
41694615 1044
 
< 0.1%
Other values (146) 7627
 
0.2%
2024-02-13T20:38:45.243121image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 10963709
33.4%
4 4936405
15.0%
7 4098013
 
12.5%
1 4093314
 
12.5%
a 3670856
 
11.2%
b 3664019
 
11.1%
3 454872
 
1.4%
2 432743
 
1.3%
e 426722
 
1.3%
8 24968
 
0.1%
Other values (6) 100075
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25064816
76.3%
Lowercase Letter 7800880
 
23.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 10963709
43.7%
4 4936405
19.7%
7 4098013
 
16.3%
1 4093314
 
16.3%
3 454872
 
1.8%
2 432743
 
1.7%
8 24968
 
0.1%
6 24956
 
0.1%
9 23599
 
0.1%
0 12237
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 3670856
47.1%
b 3664019
47.0%
e 426722
 
5.5%
f 24866
 
0.3%
d 8482
 
0.1%
c 5935
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 25064816
76.3%
Latin 7800880
 
23.7%

Most frequent character per script

Common
ValueCountFrequency (%)
5 10963709
43.7%
4 4936405
19.7%
7 4098013
 
16.3%
1 4093314
 
16.3%
3 454872
 
1.8%
2 432743
 
1.7%
8 24968
 
0.1%
6 24956
 
0.1%
9 23599
 
0.1%
0 12237
 
< 0.1%
Latin
ValueCountFrequency (%)
a 3670856
47.1%
b 3664019
47.0%
e 426722
 
5.5%
f 24866
 
0.3%
d 8482
 
0.1%
c 5935
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32865696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 10963709
33.4%
4 4936405
15.0%
7 4098013
 
12.5%
1 4093314
 
12.5%
a 3670856
 
11.2%
b 3664019
 
11.1%
3 454872
 
1.4%
2 432743
 
1.3%
e 426722
 
1.3%
8 24968
 
0.1%
Other values (6) 100075
 
0.3%

contractsum_5085717L
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4108212
Missing (%)100.0%
Memory size31.3 MiB

credlmt_230A
Real number (ℝ)

MISSING  SKEWED 

Distinct5700
Distinct (%)8.9%
Missing4043918
Missing (%)98.4%
Infinite0
Infinite (%)0.0%
Mean35626.14772
Minimum0
Maximum100000000
Zeros23584
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:45.403917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12785.9
Q340000
95-th percentile96000
Maximum100000000
Range100000000
Interquartile range (IQR)40000

Descriptive statistics

Standard deviation551563.3402
Coefficient of variation (CV)15.48198095
Kurtosis21113.22989
Mean35626.14772
Median Absolute Deviation (MAD)12785.9
Skewness135.1175142
Sum2290547541
Variance3.042221183 × 1011
MonotonicityNot monotonic
2024-02-13T20:38:45.576923image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23584
 
0.6%
10000 4237
 
0.1%
20000 3390
 
0.1%
30000 1568
 
< 0.1%
40000 1265
 
< 0.1%
60000 941
 
< 0.1%
58000 773
 
< 0.1%
50000 726
 
< 0.1%
4000 711
 
< 0.1%
6000 704
 
< 0.1%
Other values (5690) 26395
 
0.6%
(Missing) 4043918
98.4%
ValueCountFrequency (%)
0 23584
0.6%
0.2 65
 
< 0.1%
10 1
 
< 0.1%
10.594001 1
 
< 0.1%
17.6 1
 
< 0.1%
ValueCountFrequency (%)
100000000 1
< 0.1%
60000000 1
< 0.1%
59000000 1
< 0.1%
25000000 1
< 0.1%
22011000 1
< 0.1%

credlmt_935A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct46716
Distinct (%)15.7%
Missing3810159
Missing (%)92.7%
Infinite0
Infinite (%)0.0%
Mean99771.40784
Minimum0
Maximum1848000100
Zeros93496
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:45.743468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20000
Q353865.8
95-th percentile210000
Maximum1848000100
Range1848000100
Interquartile range (IQR)53865.8

Descriptive statistics

Standard deviation4577044.59
Coefficient of variation (CV)45.87531327
Kurtosis94381.52349
Mean99771.40784
Median Absolute Deviation (MAD)20000
Skewness264.6687613
Sum2.973716742 × 1010
Variance2.094933718 × 1013
MonotonicityNot monotonic
2024-02-13T20:38:45.908611image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 93496
 
2.3%
10000 26676
 
0.6%
20000 22351
 
0.5%
30000 10958
 
0.3%
200000 4369
 
0.1%
100000 4308
 
0.1%
40000 3601
 
0.1%
62000 2457
 
0.1%
5000 1952
 
< 0.1%
60000 1672
 
< 0.1%
Other values (46706) 126213
 
3.1%
(Missing) 3810159
92.7%
ValueCountFrequency (%)
0 93496
2.3%
0.2 12
 
< 0.1%
10 1
 
< 0.1%
10.682 1
 
< 0.1%
11.51 1
 
< 0.1%
ValueCountFrequency (%)
1848000100 1
< 0.1%
740000000 1
< 0.1%
600000000 1
< 0.1%
540000000 1
< 0.1%
500000000 1
< 0.1%

dateofcredend_289D
Text

MISSING 

Distinct8025
Distinct (%)1.2%
Missing3449815
Missing (%)84.0%
Memory size31.3 MiB
2024-02-13T20:38:46.304360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters6583970
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

Unique2073 ?
Unique (%)0.3%

Sample

1st row2023-06-20
2nd row2020-08-06
3rd row2020-06-06
4th row2022-11-06
5th row2019-09-09
ValueCountFrequency (%)
2021-10-14 2121
 
0.3%
2021-09-14 1965
 
0.3%
2020-01-14 1778
 
0.3%
2021-11-14 1694
 
0.3%
2021-05-14 1591
 
0.2%
2020-05-14 1458
 
0.2%
2020-03-14 1432
 
0.2%
2019-11-15 1388
 
0.2%
2020-02-14 1317
 
0.2%
2019-08-15 1316
 
0.2%
Other values (8015) 642337
97.6%
2024-02-13T20:38:47.092097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1718284
26.1%
2 1496379
22.7%
- 1316794
20.0%
1 908967
13.8%
9 339575
 
5.2%
3 166144
 
2.5%
4 141096
 
2.1%
8 128402
 
2.0%
5 128246
 
1.9%
7 120828
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5267176
80.0%
Dash Punctuation 1316794
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1718284
32.6%
2 1496379
28.4%
1 908967
17.3%
9 339575
 
6.4%
3 166144
 
3.2%
4 141096
 
2.7%
8 128402
 
2.4%
5 128246
 
2.4%
7 120828
 
2.3%
6 119255
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 1316794
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6583970
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1718284
26.1%
2 1496379
22.7%
- 1316794
20.0%
1 908967
13.8%
9 339575
 
5.2%
3 166144
 
2.5%
4 141096
 
2.1%
8 128402
 
2.0%
5 128246
 
1.9%
7 120828
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6583970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1718284
26.1%
2 1496379
22.7%
- 1316794
20.0%
1 908967
13.8%
9 339575
 
5.2%
3 166144
 
2.5%
4 141096
 
2.1%
8 128402
 
2.0%
5 128246
 
1.9%
7 120828
 
1.8%

dateofcredend_353D
Text

MISSING 

Distinct8062
Distinct (%)1.8%
Missing3651494
Missing (%)88.9%
Memory size31.3 MiB
2024-02-13T20:38:47.477277image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters4567180
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

Unique888 ?
Unique (%)0.2%

Sample

1st row2006-03-26
2nd row2007-02-24
3rd row2010-01-04
4th row2009-12-27
5th row2009-07-17
ValueCountFrequency (%)
2019-06-14 543
 
0.1%
2019-03-14 457
 
0.1%
2013-06-29 445
 
0.1%
2018-12-14 445
 
0.1%
2019-01-15 440
 
0.1%
2013-07-29 439
 
0.1%
2018-12-15 437
 
0.1%
2019-04-15 433
 
0.1%
2013-10-29 428
 
0.1%
2013-09-29 416
 
0.1%
Other values (8052) 452235
99.0%
2024-02-13T20:38:48.019352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1107482
24.2%
- 913436
20.0%
1 775080
17.0%
2 767654
16.8%
8 177924
 
3.9%
9 172842
 
3.8%
7 145159
 
3.2%
5 132358
 
2.9%
6 127471
 
2.8%
3 125350
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3653744
80.0%
Dash Punctuation 913436
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1107482
30.3%
1 775080
21.2%
2 767654
21.0%
8 177924
 
4.9%
9 172842
 
4.7%
7 145159
 
4.0%
5 132358
 
3.6%
6 127471
 
3.5%
3 125350
 
3.4%
4 122424
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 913436
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4567180
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1107482
24.2%
- 913436
20.0%
1 775080
17.0%
2 767654
16.8%
8 177924
 
3.9%
9 172842
 
3.8%
7 145159
 
3.2%
5 132358
 
2.9%
6 127471
 
2.8%
3 125350
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4567180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1107482
24.2%
- 913436
20.0%
1 775080
17.0%
2 767654
16.8%
8 177924
 
3.9%
9 172842
 
3.8%
7 145159
 
3.2%
5 132358
 
2.9%
6 127471
 
2.8%
3 125350
 
2.7%

dateofcredstart_181D
Text

MISSING 

Distinct5696
Distinct (%)1.2%
Missing3651491
Missing (%)88.9%
Memory size31.3 MiB
2024-02-13T20:38:48.435409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters4567210
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

Unique200 ?
Unique (%)< 0.1%

Sample

1st row2005-09-28
2nd row2006-03-02
3rd row2007-01-04
4th row2007-12-27
5th row2008-07-17
ValueCountFrequency (%)
2018-01-12 379
 
0.1%
2018-01-13 376
 
0.1%
2018-04-03 341
 
0.1%
2017-12-08 333
 
0.1%
2018-01-05 321
 
0.1%
2018-01-08 315
 
0.1%
2018-05-04 300
 
0.1%
2018-03-21 296
 
0.1%
2017-12-01 294
 
0.1%
2017-12-25 285
 
0.1%
Other values (5686) 453481
99.3%
2024-02-13T20:38:48.973528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1132497
24.8%
- 913442
20.0%
1 782530
17.1%
2 756887
16.6%
7 174629
 
3.8%
8 165803
 
3.6%
6 147121
 
3.2%
3 142392
 
3.1%
5 124701
 
2.7%
4 122258
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3653768
80.0%
Dash Punctuation 913442
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1132497
31.0%
1 782530
21.4%
2 756887
20.7%
7 174629
 
4.8%
8 165803
 
4.5%
6 147121
 
4.0%
3 142392
 
3.9%
5 124701
 
3.4%
4 122258
 
3.3%
9 104950
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 913442
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4567210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1132497
24.8%
- 913442
20.0%
1 782530
17.1%
2 756887
16.6%
7 174629
 
3.8%
8 165803
 
3.6%
6 147121
 
3.2%
3 142392
 
3.1%
5 124701
 
2.7%
4 122258
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4567210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1132497
24.8%
- 913442
20.0%
1 782530
17.1%
2 756887
16.6%
7 174629
 
3.8%
8 165803
 
3.6%
6 147121
 
3.2%
3 142392
 
3.1%
5 124701
 
2.7%
4 122258
 
2.7%

dateofcredstart_739D
Text

MISSING 

Distinct4514
Distinct (%)0.7%
Missing3449815
Missing (%)84.0%
Memory size31.3 MiB
2024-02-13T20:38:49.365610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters6583970
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

Unique384 ?
Unique (%)0.1%

Sample

1st row2018-06-20
2nd row2018-08-06
3rd row2012-06-06
4th row2017-11-06
5th row2016-09-09
ValueCountFrequency (%)
2018-12-07 1963
 
0.3%
2018-12-28 1674
 
0.3%
2018-11-30 1624
 
0.2%
2019-01-04 1621
 
0.2%
2019-01-02 1576
 
0.2%
2019-01-11 1526
 
0.2%
2019-01-07 1516
 
0.2%
2019-01-03 1514
 
0.2%
2018-08-06 1487
 
0.2%
2018-12-24 1485
 
0.2%
Other values (4504) 642411
97.6%
2024-02-13T20:38:49.923201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1461845
22.2%
- 1316794
20.0%
1 1255837
19.1%
2 1071679
16.3%
8 423756
 
6.4%
9 219596
 
3.3%
7 214200
 
3.3%
3 177607
 
2.7%
6 156228
 
2.4%
4 143354
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5267176
80.0%
Dash Punctuation 1316794
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1461845
27.8%
1 1255837
23.8%
2 1071679
20.3%
8 423756
 
8.0%
9 219596
 
4.2%
7 214200
 
4.1%
3 177607
 
3.4%
6 156228
 
3.0%
4 143354
 
2.7%
5 143074
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 1316794
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6583970
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1461845
22.2%
- 1316794
20.0%
1 1255837
19.1%
2 1071679
16.3%
8 423756
 
6.4%
9 219596
 
3.3%
7 214200
 
3.3%
3 177607
 
2.7%
6 156228
 
2.4%
4 143354
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6583970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1461845
22.2%
- 1316794
20.0%
1 1255837
19.1%
2 1071679
16.3%
8 423756
 
6.4%
9 219596
 
3.3%
7 214200
 
3.3%
3 177607
 
2.7%
6 156228
 
2.4%
4 143354
 
2.2%

dateofrealrepmt_138D
Text

MISSING 

Distinct5303
Distinct (%)1.2%
Missing3654366
Missing (%)89.0%
Memory size31.3 MiB
2024-02-13T20:38:50.285378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters4538460
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

Unique161 ?
Unique (%)< 0.1%

Sample

1st row2006-02-24
2nd row2007-01-09
3rd row2007-12-19
4th row2011-08-12
5th row2009-07-17
ValueCountFrequency (%)
2018-08-10 5113
 
1.1%
2011-08-12 2952
 
0.7%
2015-06-29 1322
 
0.3%
2012-11-15 1127
 
0.2%
2008-12-12 1020
 
0.2%
2015-02-23 1001
 
0.2%
2016-03-25 631
 
0.1%
2012-06-29 518
 
0.1%
2018-12-19 501
 
0.1%
2012-03-11 500
 
0.1%
Other values (5293) 439161
96.8%
2024-02-13T20:38:50.769839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1085421
23.9%
- 907692
20.0%
1 787567
17.4%
2 757734
16.7%
8 195402
 
4.3%
7 152402
 
3.4%
9 151078
 
3.3%
6 132473
 
2.9%
3 128237
 
2.8%
5 123150
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3630768
80.0%
Dash Punctuation 907692
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1085421
29.9%
1 787567
21.7%
2 757734
20.9%
8 195402
 
5.4%
7 152402
 
4.2%
9 151078
 
4.2%
6 132473
 
3.6%
3 128237
 
3.5%
5 123150
 
3.4%
4 117304
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 907692
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4538460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1085421
23.9%
- 907692
20.0%
1 787567
17.4%
2 757734
16.7%
8 195402
 
4.3%
7 152402
 
3.4%
9 151078
 
3.3%
6 132473
 
2.9%
3 128237
 
2.8%
5 123150
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4538460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1085421
23.9%
- 907692
20.0%
1 787567
17.4%
2 757734
16.7%
8 195402
 
4.3%
7 152402
 
3.4%
9 151078
 
3.3%
6 132473
 
2.9%
3 128237
 
2.8%
5 123150
 
2.7%

debtoutstand_525A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct279341
Distinct (%)83.4%
Missing3773397
Missing (%)91.9%
Infinite0
Infinite (%)0.0%
Mean198169.0396
Minimum0
Maximum1688617600
Zeros46802
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:50.957258image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112336.0625
median54350.105
Q3159301.7
95-th percentile730875.812
Maximum1688617600
Range1688617600
Interquartile range (IQR)146965.6375

Descriptive statistics

Standard deviation3552509.021
Coefficient of variation (CV)17.92666013
Kurtosis167348.5207
Mean198169.0396
Median Absolute Deviation (MAD)51501.725
Skewness386.7162334
Sum6.634996698 × 1010
Variance1.262032034 × 1013
MonotonicityNot monotonic
2024-02-13T20:38:51.127352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46802
 
1.1%
200000 108
 
< 0.1%
100000 100
 
< 0.1%
20000 90
 
< 0.1%
10000 83
 
< 0.1%
40000 78
 
< 0.1%
4000 64
 
< 0.1%
30000 62
 
< 0.1%
60000 49
 
< 0.1%
62000 45
 
< 0.1%
Other values (279331) 287334
 
7.0%
(Missing) 3773397
91.9%
ValueCountFrequency (%)
0 46802
1.1%
0.002 3
 
< 0.1%
0.004 2
 
< 0.1%
0.018000001 2
 
< 0.1%
0.022 1
 
< 0.1%
ValueCountFrequency (%)
1688617600 1
< 0.1%
863046850 1
< 0.1%
702172300 1
< 0.1%
89586820 1
< 0.1%
75439480 1
< 0.1%

debtoverdue_47A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct15243
Distinct (%)4.6%
Missing3773397
Missing (%)91.9%
Infinite0
Infinite (%)0.0%
Mean1819.273038
Minimum0
Maximum49005736
Zeros317743
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:51.287016image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.6
Maximum49005736
Range49005736
Interquartile range (IQR)0

Descriptive statistics

Standard deviation109560.5174
Coefficient of variation (CV)60.22214101
Kurtosis125246.3482
Mean1819.273038
Median Absolute Deviation (MAD)0
Skewness310.1137253
Sum609119902.2
Variance1.200350698 × 1010
MonotonicityNot monotonic
2024-02-13T20:38:51.447440image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 317743
 
7.7%
2 36
 
< 0.1%
10 36
 
< 0.1%
4 35
 
< 0.1%
0.4 31
 
< 0.1%
0.8 31
 
< 0.1%
14 27
 
< 0.1%
1.2 27
 
< 0.1%
0.2 25
 
< 0.1%
8 23
 
< 0.1%
Other values (15233) 16801
 
0.4%
(Missing) 3773397
91.9%
ValueCountFrequency (%)
0 317743
7.7%
0.002 4
 
< 0.1%
0.004 3
 
< 0.1%
0.006 2
 
< 0.1%
0.008 1
 
< 0.1%
ValueCountFrequency (%)
49005736 1
< 0.1%
17978344 1
< 0.1%
15792015 1
< 0.1%
15233466 1
< 0.1%
13135123 1
< 0.1%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:51.616717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters32865696
Distinct characters16
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 4090814
99.6%
6da7c7ed 4102
 
0.1%
95decc86 2545
 
0.1%
f8e51f8d 2068
 
0.1%
0349102c 2039
 
< 0.1%
53179c19 1982
 
< 0.1%
1d89fa48 1587
 
< 0.1%
18e98e64 1553
 
< 0.1%
8a7423d5 781
 
< 0.1%
0cb4d552 517
 
< 0.1%
Other values (2) 224
 
< 0.1%
2024-02-13T20:38:51.905334image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 12281076
37.4%
1 4102045
 
12.5%
7 4101781
 
12.5%
a 4097294
 
12.5%
4 4097291
 
12.5%
b 4091759
 
12.4%
d 15702
 
< 0.1%
8 13956
 
< 0.1%
c 13740
 
< 0.1%
e 11821
 
< 0.1%
Other values (6) 39231
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24629005
74.9%
Lowercase Letter 8236691
 
25.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 12281076
49.9%
1 4102045
 
16.7%
7 4101781
 
16.7%
4 4097291
 
16.6%
8 13956
 
0.1%
9 11688
 
< 0.1%
6 8210
 
< 0.1%
3 4812
 
< 0.1%
0 4809
 
< 0.1%
2 3337
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 4097294
49.7%
b 4091759
49.7%
d 15702
 
0.2%
c 13740
 
0.2%
e 11821
 
0.1%
f 6375
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 24629005
74.9%
Latin 8236691
 
25.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 12281076
49.9%
1 4102045
 
16.7%
7 4101781
 
16.7%
4 4097291
 
16.6%
8 13956
 
0.1%
9 11688
 
< 0.1%
6 8210
 
< 0.1%
3 4812
 
< 0.1%
0 4809
 
< 0.1%
2 3337
 
< 0.1%
Latin
ValueCountFrequency (%)
a 4097294
49.7%
b 4091759
49.7%
d 15702
 
0.2%
c 13740
 
0.2%
e 11821
 
0.1%
f 6375
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32865696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 12281076
37.4%
1 4102045
 
12.5%
7 4101781
 
12.5%
a 4097294
 
12.5%
4 4097291
 
12.5%
b 4091759
 
12.4%
d 15702
 
< 0.1%
8 13956
 
< 0.1%
c 13740
 
< 0.1%
e 11821
 
< 0.1%
Other values (6) 39231
 
0.1%

dpdmax_139P
Real number (ℝ)

MISSING  ZEROS 

Distinct2275
Distinct (%)0.3%
Missing3452623
Missing (%)84.0%
Infinite0
Infinite (%)0.0%
Mean12.86329087
Minimum0
Maximum4877
Zeros520988
Zeros (%)12.7%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:52.065304image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile22
Maximum4877
Range4877
Interquartile range (IQR)0

Descriptive statistics

Standard deviation127.5708841
Coefficient of variation (CV)9.917437566
Kurtosis378.9647317
Mean12.86329087
Median Absolute Deviation (MAD)0
Skewness17.54663618
Sum8433032
Variance16274.33048
MonotonicityNot monotonic
2024-02-13T20:38:52.224161image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 520988
 
12.7%
1 26601
 
0.6%
2 9306
 
0.2%
3 8983
 
0.2%
4 8281
 
0.2%
7 4695
 
0.1%
5 4689
 
0.1%
6 4054
 
0.1%
8 3343
 
0.1%
9 3250
 
0.1%
Other values (2265) 61399
 
1.5%
(Missing) 3452623
84.0%
ValueCountFrequency (%)
0 520988
12.7%
1 26601
 
0.6%
2 9306
 
0.2%
3 8983
 
0.2%
4 8281
 
0.2%
ValueCountFrequency (%)
4877 1
< 0.1%
4800 1
< 0.1%
4733 1
< 0.1%
4608 1
< 0.1%
4590 1
< 0.1%

dpdmax_757P
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct2845
Distinct (%)0.6%
Missing3667919
Missing (%)89.3%
Infinite0
Infinite (%)0.0%
Mean44.05819307
Minimum-9
Maximum117000
Zeros312253
Zeros (%)7.6%
Negative81
Negative (%)< 0.1%
Memory size31.3 MiB
2024-02-13T20:38:52.376161image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile0
Q10
median0
Q31
95-th percentile113
Maximum117000
Range117009
Interquartile range (IQR)1

Descriptive statistics

Standard deviation321.35541
Coefficient of variation (CV)7.293885373
Kurtosis51300.07664
Mean44.05819307
Median Absolute Deviation (MAD)0
Skewness158.2084117
Sum19398514
Variance103269.2995
MonotonicityNot monotonic
2024-02-13T20:38:52.536667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 312253
 
7.6%
1 22794
 
0.6%
2 7647
 
0.2%
3 7406
 
0.2%
4 5932
 
0.1%
6 4446
 
0.1%
5 3871
 
0.1%
7 3790
 
0.1%
8 2541
 
0.1%
9 2518
 
0.1%
Other values (2835) 67095
 
1.6%
(Missing) 3667919
89.3%
ValueCountFrequency (%)
-9 2
< 0.1%
-8 1
< 0.1%
-7 1
< 0.1%
-6 1
< 0.1%
-5 2
< 0.1%
ValueCountFrequency (%)
117000 1
< 0.1%
84560 1
< 0.1%
40438 1
< 0.1%
5174 1
< 0.1%
4897 1
< 0.1%

dpdmaxdatemonth_442T
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing3667919
Missing (%)89.3%
Infinite0
Infinite (%)0.0%
Mean6.498047891
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:52.666728image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.442545651
Coefficient of variation (CV)0.5297815142
Kurtosis-1.192646146
Mean6.498047891
Median Absolute Deviation (MAD)3
Skewness-0.03588026489
Sum2861045
Variance11.85112056
MonotonicityNot monotonic
2024-02-13T20:38:52.782597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 40851
 
1.0%
1 40488
 
1.0%
6 38947
 
0.9%
7 38561
 
0.9%
11 38120
 
0.9%
9 36206
 
0.9%
10 35753
 
0.9%
3 34987
 
0.9%
2 34826
 
0.8%
5 34663
 
0.8%
Other values (2) 66891
 
1.6%
(Missing) 3667919
89.3%
ValueCountFrequency (%)
1 40488
1.0%
2 34826
0.8%
3 34987
0.9%
4 32898
0.8%
5 34663
0.8%
ValueCountFrequency (%)
12 33993
0.8%
11 38120
0.9%
10 35753
0.9%
9 36206
0.9%
8 40851
1.0%

dpdmaxdatemonth_89T
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing3452623
Missing (%)84.0%
Infinite0
Infinite (%)0.0%
Mean6.232970657
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:52.892686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4071543
Coefficient of variation (CV)0.5466340991
Kurtosis-1.138789864
Mean6.232970657
Median Absolute Deviation (MAD)3
Skewness0.1121041042
Sum4086267
Variance11.60870042
MonotonicityNot monotonic
2024-02-13T20:38:53.013055image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 63929
 
1.6%
1 61572
 
1.5%
5 60904
 
1.5%
7 60089
 
1.5%
4 57285
 
1.4%
2 55733
 
1.4%
3 55446
 
1.3%
8 51443
 
1.3%
10 50952
 
1.2%
12 50012
 
1.2%
Other values (2) 88224
 
2.1%
(Missing) 3452623
84.0%
ValueCountFrequency (%)
1 61572
1.5%
2 55733
1.4%
3 55446
1.3%
4 57285
1.4%
5 60904
1.5%
ValueCountFrequency (%)
12 50012
1.2%
11 46905
1.1%
10 50952
1.2%
9 41319
1.0%
8 51443
1.3%

dpdmaxdateyear_596T
Real number (ℝ)

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing3452623
Missing (%)84.0%
Infinite0
Infinite (%)0.0%
Mean2017.988769
Minimum2015
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:53.146082image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2017
Q12017
median2018
Q32019
95-th percentile2019
Maximum2019
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.7392021802
Coefficient of variation (CV)0.0003663063896
Kurtosis-1.160151972
Mean2017.988769
Median Absolute Deviation (MAD)1
Skewness0.01487827015
Sum1322971239
Variance0.5464198632
MonotonicityNot monotonic
2024-02-13T20:38:53.273874image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
2018 297600
 
7.2%
2017 182548
 
4.4%
2019 175361
 
4.3%
2016 64
 
< 0.1%
2015 16
 
< 0.1%
(Missing) 3452623
84.0%
ValueCountFrequency (%)
2015 16
 
< 0.1%
2016 64
 
< 0.1%
2017 182548
4.4%
2018 297600
7.2%
2019 175361
4.3%
ValueCountFrequency (%)
2019 175361
4.3%
2018 297600
7.2%
2017 182548
4.4%
2016 64
 
< 0.1%
2015 16
 
< 0.1%

dpdmaxdateyear_896T
Real number (ℝ)

MISSING 

Distinct17
Distinct (%)< 0.1%
Missing3667919
Missing (%)89.3%
Infinite0
Infinite (%)0.0%
Mean2014.058541
Minimum2003
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:53.394889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2003
5-th percentile2007
Q12012
median2015
Q32017
95-th percentile2018
Maximum2019
Range16
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.736322258
Coefficient of variation (CV)0.001855120982
Kurtosis-0.6368962043
Mean2014.058541
Median Absolute Deviation (MAD)3
Skewness-0.6863947581
Sum886775877
Variance13.96010402
MonotonicityNot monotonic
2024-02-13T20:38:53.521468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2018 74615
 
1.8%
2017 62742
 
1.5%
2016 44484
 
1.1%
2015 39753
 
1.0%
2014 35178
 
0.9%
2013 30525
 
0.7%
2012 25667
 
0.6%
2011 23221
 
0.6%
2007 23134
 
0.6%
2019 17641
 
0.4%
Other values (7) 63333
 
1.5%
(Missing) 3667919
89.3%
ValueCountFrequency (%)
2003 1
 
< 0.1%
2004 147
 
< 0.1%
2005 3224
 
0.1%
2006 11789
0.3%
2007 23134
0.6%
ValueCountFrequency (%)
2019 17641
 
0.4%
2018 74615
1.8%
2017 62742
1.5%
2016 44484
1.1%
2015 39753
1.0%
Distinct211
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:53.963184image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.227314462
Min length8

Characters and Unicode

Total characters33799552
Distinct characters26
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)< 0.1%

Sample

1st rowa55475b1
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 3651491
85.9%
home 142353
 
3.3%
credit 142353
 
3.3%
p133_127_114 50358
 
1.2%
p150_136_157 42631
 
1.0%
b619fa46 36699
 
0.9%
p204_66_73 29355
 
0.7%
p40_52_135 25861
 
0.6%
d6a7d943 13359
 
0.3%
9a93e20f 12896
 
0.3%
Other values (202) 103209
 
2.4%
2024-02-13T20:38:54.559381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 11180200
33.1%
1 4079709
 
12.1%
4 3850167
 
11.4%
7 3814979
 
11.3%
a 3752710
 
11.1%
b 3750616
 
11.1%
e 332641
 
1.0%
_ 324562
 
1.0%
3 265955
 
0.8%
d 226405
 
0.7%
Other values (16) 2221608
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23970642
70.9%
Lowercase Letter 8915008
 
26.4%
Uppercase Letter 446987
 
1.3%
Connector Punctuation 324562
 
1.0%
Space Separator 142353
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3752710
42.1%
b 3750616
42.1%
e 332641
 
3.7%
d 226405
 
2.5%
r 142353
 
1.6%
m 142353
 
1.6%
i 142353
 
1.6%
t 142353
 
1.6%
o 142353
 
1.6%
f 81902
 
0.9%
Decimal Number
ValueCountFrequency (%)
5 11180200
46.6%
1 4079709
 
17.0%
4 3850167
 
16.1%
7 3814979
 
15.9%
3 265955
 
1.1%
6 211507
 
0.9%
2 196359
 
0.8%
0 164567
 
0.7%
9 147556
 
0.6%
8 59643
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
P 162281
36.3%
C 142353
31.8%
H 142353
31.8%
Connector Punctuation
ValueCountFrequency (%)
_ 324562
100.0%
Space Separator
ValueCountFrequency (%)
142353
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24437557
72.3%
Latin 9361995
 
27.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3752710
40.1%
b 3750616
40.1%
e 332641
 
3.6%
d 226405
 
2.4%
P 162281
 
1.7%
C 142353
 
1.5%
r 142353
 
1.5%
m 142353
 
1.5%
i 142353
 
1.5%
t 142353
 
1.5%
Other values (4) 425577
 
4.5%
Common
ValueCountFrequency (%)
5 11180200
45.8%
1 4079709
 
16.7%
4 3850167
 
15.8%
7 3814979
 
15.6%
_ 324562
 
1.3%
3 265955
 
1.1%
6 211507
 
0.9%
2 196359
 
0.8%
0 164567
 
0.7%
9 147556
 
0.6%
Other values (2) 201996
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33799552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 11180200
33.1%
1 4079709
 
12.1%
4 3850167
 
11.4%
7 3814979
 
11.3%
a 3752710
 
11.1%
b 3750616
 
11.1%
e 332641
 
1.0%
_ 324562
 
1.0%
3 265955
 
0.8%
d 226405
 
0.7%
Other values (16) 2221608
 
6.6%
Distinct137
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:54.771442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.382243175
Min length8

Characters and Unicode

Total characters34436032
Distinct characters26
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st row55b002a9
2nd rowP204_66_73
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 3449815
80.6%
p204_66_73 254397
 
5.9%
home 172605
 
4.0%
credit 172605
 
4.0%
p133_127_114 62452
 
1.5%
p150_136_157 59528
 
1.4%
d6a7d943 21538
 
0.5%
50babcd4 17280
 
0.4%
p102_97_118 12928
 
0.3%
0d39f5db 11915
 
0.3%
Other values (128) 45754
 
1.1%
2024-02-13T20:38:55.132770image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 10535217
30.6%
1 3950423
 
11.5%
7 3878293
 
11.3%
4 3820775
 
11.1%
b 3523715
 
10.2%
a 3506747
 
10.2%
_ 795044
 
2.3%
6 600876
 
1.7%
3 505315
 
1.5%
P 397522
 
1.2%
Other values (16) 2922105
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24133329
70.1%
Lowercase Letter 8592322
 
25.0%
Connector Punctuation 795044
 
2.3%
Uppercase Letter 742732
 
2.2%
Space Separator 172605
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 3523715
41.0%
a 3506747
40.8%
e 364708
 
4.2%
d 272705
 
3.2%
r 172605
 
2.0%
t 172605
 
2.0%
i 172605
 
2.0%
o 172605
 
2.0%
m 172605
 
2.0%
f 32393
 
0.4%
Decimal Number
ValueCountFrequency (%)
5 10535217
43.7%
1 3950423
 
16.4%
7 3878293
 
16.1%
4 3820775
 
15.8%
6 600876
 
2.5%
3 505315
 
2.1%
0 384673
 
1.6%
2 369391
 
1.5%
9 65029
 
0.3%
8 23337
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
P 397522
53.5%
C 172605
23.2%
H 172605
23.2%
Connector Punctuation
ValueCountFrequency (%)
_ 795044
100.0%
Space Separator
ValueCountFrequency (%)
172605
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25100978
72.9%
Latin 9335054
 
27.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 3523715
37.7%
a 3506747
37.6%
P 397522
 
4.3%
e 364708
 
3.9%
d 272705
 
2.9%
r 172605
 
1.8%
t 172605
 
1.8%
i 172605
 
1.8%
o 172605
 
1.8%
C 172605
 
1.8%
Other values (4) 406632
 
4.4%
Common
ValueCountFrequency (%)
5 10535217
42.0%
1 3950423
 
15.7%
7 3878293
 
15.5%
4 3820775
 
15.2%
_ 795044
 
3.2%
6 600876
 
2.4%
3 505315
 
2.0%
0 384673
 
1.5%
2 369391
 
1.5%
172605
 
0.7%
Other values (2) 88366
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34436032
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 10535217
30.6%
1 3950423
 
11.5%
7 3878293
 
11.3%
4 3820775
 
11.1%
b 3523715
 
10.2%
a 3506747
 
10.2%
_ 795044
 
2.3%
6 600876
 
1.7%
3 505315
 
1.5%
P 397522
 
1.2%
Other values (16) 2922105
 
8.5%

instlamount_768A
Real number (ℝ)

MISSING  ZEROS 

Distinct74103
Distinct (%)25.1%
Missing3813073
Missing (%)92.8%
Infinite0
Infinite (%)0.0%
Mean3259.767351
Minimum0
Maximum376510.5
Zeros117602
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:55.312130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1131
Q34591.511
95-th percentile13068.04
Maximum376510.5
Range376510.5
Interquartile range (IQR)4591.511

Descriptive statistics

Standard deviation5205.889166
Coefficient of variation (CV)1.597012488
Kurtosis115.5997326
Mean3259.767351
Median Absolute Deviation (MAD)1131
Skewness4.60359567
Sum962084476.1
Variance27101282.01
MonotonicityNot monotonic
2024-02-13T20:38:55.469810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 117602
 
2.9%
400 6971
 
0.2%
600 1077
 
< 0.1%
1000 996
 
< 0.1%
2000 521
 
< 0.1%
9067.8 425
 
< 0.1%
1200 401
 
< 0.1%
2510 383
 
< 0.1%
800 355
 
< 0.1%
3000 283
 
< 0.1%
Other values (74093) 166125
 
4.0%
(Missing) 3813073
92.8%
ValueCountFrequency (%)
0 117602
2.9%
0.002 3
 
< 0.1%
0.006 2
 
< 0.1%
0.008 1
 
< 0.1%
0.014 1
 
< 0.1%
ValueCountFrequency (%)
376510.5 1
< 0.1%
178554.2 1
< 0.1%
159796.92 1
< 0.1%
131119.2 1
< 0.1%
96758.4 1
< 0.1%

instlamount_852A
Real number (ℝ)

MISSING 

Distinct8811
Distinct (%)21.5%
Missing4067156
Missing (%)99.0%
Infinite0
Infinite (%)0.0%
Mean575.6078159
Minimum0
Maximum139346.98
Zeros26836
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:55.620345image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3353.512
95-th percentile3393.6501
Maximum139346.98
Range139346.98
Interquartile range (IQR)353.512

Descriptive statistics

Standard deviation1800.802469
Coefficient of variation (CV)3.128523309
Kurtosis1049.596696
Mean575.6078159
Median Absolute Deviation (MAD)0
Skewness19.21682787
Sum23632154.49
Variance3242889.533
MonotonicityNot monotonic
2024-02-13T20:38:55.772807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26836
 
0.7%
400 1136
 
< 0.1%
1000 878
 
< 0.1%
1200 282
 
< 0.1%
2000 212
 
< 0.1%
800 201
 
< 0.1%
1540 193
 
< 0.1%
2260 173
 
< 0.1%
3000 168
 
< 0.1%
3700 125
 
< 0.1%
Other values (8801) 10852
 
0.3%
(Missing) 4067156
99.0%
ValueCountFrequency (%)
0 26836
0.7%
0.002 1
 
< 0.1%
0.004 3
 
< 0.1%
0.006 5
 
< 0.1%
0.010000001 1
 
< 0.1%
ValueCountFrequency (%)
139346.98 1
< 0.1%
74248.195 1
< 0.1%
72495.57 1
< 0.1%
41270.426 1
< 0.1%
41094.34 1
< 0.1%

interestrate_508L
Real number (ℝ)

MISSING  SKEWED 

Distinct110
Distinct (%)2.1%
Missing4102975
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean66.49442429
Minimum0
Maximum32917
Zeros55
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:55.925740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q118
median20
Q324
95-th percentile35
Maximum32917
Range32917
Interquartile range (IQR)6

Descriptive statistics

Standard deviation830.6236595
Coefficient of variation (CV)12.49162871
Kurtosis736.5198114
Mean66.49442429
Median Absolute Deviation (MAD)4
Skewness24.39477055
Sum348231.3
Variance689935.6637
MonotonicityNot monotonic
2024-02-13T20:38:56.078000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 899
 
< 0.1%
24 692
 
< 0.1%
18 311
 
< 0.1%
22 300
 
< 0.1%
20 299
 
< 0.1%
35 297
 
< 0.1%
17 240
 
< 0.1%
16 229
 
< 0.1%
25 196
 
< 0.1%
26 185
 
< 0.1%
Other values (100) 1589
 
< 0.1%
(Missing) 4102975
99.9%
ValueCountFrequency (%)
0 55
< 0.1%
4 1
 
< 0.1%
5 8
 
< 0.1%
5.3 1
 
< 0.1%
5.5 5
 
< 0.1%
ValueCountFrequency (%)
32917 1
 
< 0.1%
25786 1
 
< 0.1%
15000 1
 
< 0.1%
12000 6
< 0.1%
10000 6
< 0.1%

lastupdate_1112D
Text

MISSING 

Distinct231
Distinct (%)< 0.1%
Missing3449815
Missing (%)84.0%
Memory size31.3 MiB
2024-02-13T20:38:56.423349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters6583970
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

Unique16 ?
Unique (%)< 0.1%

Sample

1st row2019-01-24
2nd row2019-01-11
3rd row2019-01-11
4th row2019-01-17
5th row2019-01-03
ValueCountFrequency (%)
2019-03-26 44338
 
6.7%
2019-05-07 34599
 
5.3%
2019-06-10 23981
 
3.6%
2019-06-07 22653
 
3.4%
2019-04-23 21895
 
3.3%
2019-02-26 21379
 
3.2%
2019-06-18 21020
 
3.2%
2019-03-05 19567
 
3.0%
2019-04-10 18953
 
2.9%
2019-05-21 18078
 
2.7%
Other values (221) 411934
62.6%
2024-02-13T20:38:56.907833image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1552817
23.6%
- 1316794
20.0%
2 1092622
16.6%
1 1016273
15.4%
9 700562
10.6%
6 226026
 
3.4%
3 194011
 
2.9%
5 161033
 
2.4%
4 145817
 
2.2%
7 107540
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5267176
80.0%
Dash Punctuation 1316794
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1552817
29.5%
2 1092622
20.7%
1 1016273
19.3%
9 700562
13.3%
6 226026
 
4.3%
3 194011
 
3.7%
5 161033
 
3.1%
4 145817
 
2.8%
7 107540
 
2.0%
8 70475
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 1316794
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6583970
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1552817
23.6%
- 1316794
20.0%
2 1092622
16.6%
1 1016273
15.4%
9 700562
10.6%
6 226026
 
3.4%
3 194011
 
2.9%
5 161033
 
2.4%
4 145817
 
2.2%
7 107540
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6583970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1552817
23.6%
- 1316794
20.0%
2 1092622
16.6%
1 1016273
15.4%
9 700562
10.6%
6 226026
 
3.4%
3 194011
 
2.9%
5 161033
 
2.4%
4 145817
 
2.2%
7 107540
 
1.6%

lastupdate_388D
Text

MISSING 

Distinct4275
Distinct (%)0.9%
Missing3651506
Missing (%)88.9%
Memory size31.3 MiB
2024-02-13T20:38:57.274921image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters4567060
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

Unique254 ?
Unique (%)0.1%

Sample

1st row2006-02-24
2nd row2007-01-09
3rd row2007-12-26
4th row2015-04-10
5th row2009-07-21
ValueCountFrequency (%)
2007-09-25 7566
 
1.7%
2008-06-13 4153
 
0.9%
2008-11-12 3316
 
0.7%
2015-04-10 3279
 
0.7%
2013-03-07 3073
 
0.7%
2018-08-10 2967
 
0.6%
2018-12-28 2952
 
0.6%
2018-08-11 2623
 
0.6%
2018-12-29 2236
 
0.5%
2013-10-02 2232
 
0.5%
Other values (4265) 422309
92.5%
2024-02-13T20:38:57.785855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1087323
23.8%
- 913412
20.0%
1 812057
17.8%
2 744807
16.3%
8 191846
 
4.2%
9 151631
 
3.3%
7 148113
 
3.2%
6 139404
 
3.1%
5 133481
 
2.9%
3 132919
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3653648
80.0%
Dash Punctuation 913412
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1087323
29.8%
1 812057
22.2%
2 744807
20.4%
8 191846
 
5.3%
9 151631
 
4.2%
7 148113
 
4.1%
6 139404
 
3.8%
5 133481
 
3.7%
3 132919
 
3.6%
4 112067
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 913412
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4567060
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1087323
23.8%
- 913412
20.0%
1 812057
17.8%
2 744807
16.3%
8 191846
 
4.2%
9 151631
 
3.3%
7 148113
 
3.2%
6 139404
 
3.1%
5 133481
 
2.9%
3 132919
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4567060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1087323
23.8%
- 913412
20.0%
1 812057
17.8%
2 744807
16.3%
8 191846
 
4.2%
9 151631
 
3.3%
7 148113
 
3.2%
6 139404
 
3.1%
5 133481
 
2.9%
3 132919
 
2.9%

monthlyinstlamount_332A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct175380
Distinct (%)26.8%
Missing3452923
Missing (%)84.0%
Infinite0
Infinite (%)0.0%
Mean5226.822745
Minimum0
Maximum20000000
Zeros118642
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:57.954817image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11165.2001
median3167.2
Q36297.2
95-th percentile15379.894
Maximum20000000
Range20000000
Interquartile range (IQR)5131.9999

Descriptive statistics

Standard deviation42058.77261
Coefficient of variation (CV)8.046718755
Kurtosis130243.8083
Mean5226.822745
Median Absolute Deviation (MAD)2469
Skewness327.3242395
Sum3425079450
Variance1768940354
MonotonicityNot monotonic
2024-02-13T20:38:58.123102image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118642
 
2.9%
400 6979
 
0.2%
600 1085
 
< 0.1%
1000 1062
 
< 0.1%
2000 713
 
< 0.1%
3000 433
 
< 0.1%
1200 428
 
< 0.1%
9067.8 426
 
< 0.1%
2510 391
 
< 0.1%
4200.492 387
 
< 0.1%
Other values (175370) 524743
 
12.8%
(Missing) 3452923
84.0%
ValueCountFrequency (%)
0 118642
2.9%
0.002 4
 
< 0.1%
0.006 2
 
< 0.1%
0.008 2
 
< 0.1%
0.014 1
 
< 0.1%
ValueCountFrequency (%)
20000000 1
< 0.1%
16000000 1
< 0.1%
12400155 1
< 0.1%
11238104 1
< 0.1%
5856598.5 1
< 0.1%

monthlyinstlamount_674A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct104583
Distinct (%)24.8%
Missing3687335
Missing (%)89.8%
Infinite0
Infinite (%)0.0%
Mean5896.002702
Minimum0
Maximum59077588
Zeros219375
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:58.286226image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33292.8
95-th percentile16269
Maximum59077588
Range59077588
Interquartile range (IQR)3292.8

Descriptive statistics

Standard deviation107159.2225
Coefficient of variation (CV)18.17489373
Kurtosis219873.458
Mean5896.002702
Median Absolute Deviation (MAD)0
Skewness407.1228449
Sum2481491929
Variance1.148309897 × 1010
MonotonicityNot monotonic
2024-02-13T20:38:58.440998image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 219375
 
5.3%
400 1139
 
< 0.1%
1000 888
 
< 0.1%
2100 413
 
< 0.1%
4200 388
 
< 0.1%
3150 380
 
< 0.1%
2000 311
 
< 0.1%
1200 296
 
< 0.1%
0.1 294
 
< 0.1%
800 214
 
< 0.1%
Other values (104573) 197179
 
4.8%
(Missing) 3687335
89.8%
ValueCountFrequency (%)
0 219375
5.3%
0.002 6
 
< 0.1%
0.004 5
 
< 0.1%
0.006 5
 
< 0.1%
0.008 4
 
< 0.1%
ValueCountFrequency (%)
59077588 1
< 0.1%
9125000 1
< 0.1%
7913549 1
< 0.1%
6866641.5 1
< 0.1%
6656173.5 1
< 0.1%

nominalrate_281L
Real number (ℝ)

MISSING  SKEWED 

Distinct792
Distinct (%)0.3%
Missing3863576
Missing (%)94.0%
Infinite0
Infinite (%)0.0%
Mean33.53551407
Minimum0
Maximum30341.1
Zeros19001
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:58.596984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118.1
median40
Q343.3
95-th percentile45
Maximum30341.1
Range30341.1
Interquartile range (IQR)25.2

Descriptive statistics

Standard deviation153.4282827
Coefficient of variation (CV)4.575098578
Kurtosis22173.53986
Mean33.53551407
Median Absolute Deviation (MAD)5
Skewness130.5647455
Sum8203994.02
Variance23540.23794
MonotonicityNot monotonic
2024-02-13T20:38:58.753329image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 46376
 
1.1%
42 31340
 
0.8%
0.12 24952
 
0.6%
0 19001
 
0.5%
39 17707
 
0.4%
43.3 17448
 
0.4%
40 7732
 
0.2%
43 7435
 
0.2%
29.4 3785
 
0.1%
18.1 3728
 
0.1%
Other values (782) 65132
 
1.6%
(Missing) 3863576
94.0%
ValueCountFrequency (%)
0 19001
0.5%
0.01 5
 
< 0.1%
0.06 2
 
< 0.1%
0.12 24952
0.6%
0.27 8
 
< 0.1%
ValueCountFrequency (%)
30341.1 3
 
< 0.1%
19020.6 3
 
< 0.1%
11690.1 2
 
< 0.1%
9891.4 1
 
< 0.1%
6200 10
< 0.1%

nominalrate_498L
Real number (ℝ)

MISSING  SKEWED 

Distinct897
Distinct (%)0.6%
Missing3969804
Missing (%)96.6%
Infinite0
Infinite (%)0.0%
Mean65.41531198
Minimum0
Maximum30341.1
Zeros3389
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:58.910446image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12
Q118.2
median43.3
Q345
95-th percentile98.6
Maximum30341.1
Range30341.1
Interquartile range (IQR)26.8

Descriptive statistics

Standard deviation300.5630324
Coefficient of variation (CV)4.594689276
Kurtosis4956.575166
Mean65.41531198
Median Absolute Deviation (MAD)3.25
Skewness56.41200028
Sum9054002.5
Variance90338.13645
MonotonicityNot monotonic
2024-02-13T20:38:59.067130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 52753
 
1.3%
0.12 23590
 
0.6%
42 4046
 
0.1%
96.3 3855
 
0.1%
0 3389
 
0.1%
35 2518
 
0.1%
43.3 2497
 
0.1%
40.05 2302
 
0.1%
42.5 2229
 
0.1%
730 2003
 
< 0.1%
Other values (887) 39226
 
1.0%
(Missing) 3969804
96.6%
ValueCountFrequency (%)
0 3389
 
0.1%
0.06 2
 
< 0.1%
0.1 15
 
< 0.1%
0.11 1
 
< 0.1%
0.12 23590
0.6%
ValueCountFrequency (%)
30341.1 6
< 0.1%
19020.6 3
< 0.1%
11690.1 3
< 0.1%
10000 1
 
< 0.1%
9891.4 1
 
< 0.1%

num_group1
Real number (ℝ)

ZEROS 

Distinct281
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.25046833
Minimum0
Maximum280
Zeros335275
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:59.216524image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q39
95-th percentile15
Maximum280
Range280
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.853159129
Coefficient of variation (CV)0.9364352908
Kurtosis196.1316419
Mean6.25046833
Median Absolute Deviation (MAD)3
Skewness7.786715634
Sum25678249
Variance34.25947179
MonotonicityNot monotonic
2024-02-13T20:38:59.359500image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 335275
8.2%
1 335119
8.2%
2 334896
8.2%
7 334789
8.1%
10 334789
8.1%
9 334789
8.1%
8 334789
8.1%
6 334789
8.1%
5 334789
8.1%
4 334789
8.1%
Other values (271) 759399
18.5%
ValueCountFrequency (%)
0 335275
8.2%
1 335119
8.2%
2 334896
8.2%
3 334789
8.1%
4 334789
8.1%
ValueCountFrequency (%)
280 1
< 0.1%
279 1
< 0.1%
278 1
< 0.1%
277 1
< 0.1%
276 1
< 0.1%

numberofcontrsvalue_258L
Real number (ℝ)

MISSING 

Distinct22
Distinct (%)< 0.1%
Missing3770638
Missing (%)91.8%
Infinite0
Infinite (%)0.0%
Mean1.941740181
Minimum0
Maximum46
Zeros804
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:38:59.495479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum46
Range46
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.076937412
Coefficient of variation (CV)0.5546248786
Kurtosis24.86325146
Mean1.941740181
Median Absolute Deviation (MAD)1
Skewness1.913367751
Sum655481
Variance1.15979419
MonotonicityNot monotonic
2024-02-13T20:38:59.877276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 142893
 
3.5%
2 109712
 
2.7%
3 55302
 
1.3%
4 20772
 
0.5%
5 5582
 
0.1%
6 1815
 
< 0.1%
0 804
 
< 0.1%
7 493
 
< 0.1%
8 131
 
< 0.1%
9 37
 
< 0.1%
Other values (12) 33
 
< 0.1%
(Missing) 3770638
91.8%
ValueCountFrequency (%)
0 804
 
< 0.1%
1 142893
3.5%
2 109712
2.7%
3 55302
 
1.3%
4 20772
 
0.5%
ValueCountFrequency (%)
46 1
< 0.1%
44 1
< 0.1%
35 1
< 0.1%
32 2
< 0.1%
27 1
< 0.1%

numberofcontrsvalue_358L
Real number (ℝ)

MISSING 

Distinct106
Distinct (%)< 0.1%
Missing3809610
Missing (%)92.7%
Infinite0
Infinite (%)0.0%
Mean5.973958647
Minimum0
Maximum280
Zeros24
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:00.006975image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q38
95-th percentile16
Maximum280
Range280
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.647614059
Coefficient of variation (CV)0.9453721381
Kurtosis67.81134802
Mean5.973958647
Median Absolute Deviation (MAD)3
Skewness3.881083823
Sum1783836
Variance31.89554456
MonotonicityNot monotonic
2024-02-13T20:39:00.154979image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 51583
 
1.3%
2 39501
 
1.0%
3 33180
 
0.8%
4 28290
 
0.7%
5 24317
 
0.6%
6 20591
 
0.5%
7 17464
 
0.4%
8 14744
 
0.4%
9 12180
 
0.3%
10 10125
 
0.2%
Other values (96) 46627
 
1.1%
(Missing) 3809610
92.7%
ValueCountFrequency (%)
0 24
 
< 0.1%
1 51583
1.3%
2 39501
1.0%
3 33180
0.8%
4 28290
0.7%
ValueCountFrequency (%)
280 1
< 0.1%
238 1
< 0.1%
232 2
< 0.1%
158 1
< 0.1%
156 1
< 0.1%

numberofinstls_229L
Real number (ℝ)

MISSING  ZEROS 

Distinct304
Distinct (%)0.1%
Missing3715974
Missing (%)90.5%
Infinite0
Infinite (%)0.0%
Mean12.57407747
Minimum0
Maximum600
Zeros88926
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:00.306648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q313
95-th percentile36
Maximum600
Range600
Interquartile range (IQR)12

Descriptive statistics

Standard deviation21.61935747
Coefficient of variation (CV)1.719359334
Kurtosis72.16797855
Mean12.57407747
Median Absolute Deviation (MAD)6
Skewness6.679096004
Sum4932031
Variance467.3966176
MonotonicityNot monotonic
2024-02-13T20:39:00.456481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 88926
 
2.2%
12 64830
 
1.6%
6 46041
 
1.1%
1 31791
 
0.8%
24 24289
 
0.6%
3 15798
 
0.4%
18 15256
 
0.4%
36 14364
 
0.3%
10 9334
 
0.2%
4 6604
 
0.2%
Other values (294) 75005
 
1.8%
(Missing) 3715974
90.5%
ValueCountFrequency (%)
0 88926
2.2%
1 31791
 
0.8%
2 1804
 
< 0.1%
3 15798
 
0.4%
4 6604
 
0.2%
ValueCountFrequency (%)
600 2
< 0.1%
494 1
< 0.1%
489 1
< 0.1%
488 1
< 0.1%
484 1
< 0.1%

numberofinstls_320L
Real number (ℝ)

MISSING 

Distinct312
Distinct (%)0.1%
Missing3747975
Missing (%)91.2%
Infinite0
Infinite (%)0.0%
Mean30.62740918
Minimum0
Maximum479
Zeros119
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:00.613480image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q112
median24
Q336
95-th percentile73
Maximum479
Range479
Interquartile range (IQR)24

Descriptive statistics

Standard deviation33.93290141
Coefficient of variation (CV)1.10792595
Kurtosis19.80796099
Mean30.62740918
Median Absolute Deviation (MAD)12
Skewness3.867630426
Sum11033126
Variance1151.441798
MonotonicityNot monotonic
2024-02-13T20:39:00.775486image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 73405
 
1.8%
24 49271
 
1.2%
36 30666
 
0.7%
48 26609
 
0.6%
6 22323
 
0.5%
16 21738
 
0.5%
18 19200
 
0.5%
60 15034
 
0.4%
30 8567
 
0.2%
1 6054
 
0.1%
Other values (302) 87370
 
2.1%
(Missing) 3747975
91.2%
ValueCountFrequency (%)
0 119
 
< 0.1%
1 6054
0.1%
2 36
 
< 0.1%
3 5818
0.1%
4 1401
 
< 0.1%
ValueCountFrequency (%)
479 1
 
< 0.1%
363 1
 
< 0.1%
362 1
 
< 0.1%
361 1
 
< 0.1%
360 5
< 0.1%

numberofoutstandinstls_520L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct121
Distinct (%)< 0.1%
Missing3715663
Missing (%)90.4%
Infinite0
Infinite (%)0.0%
Mean0.0818139901
Minimum0
Maximum525
Zeros391852
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:00.933496image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum525
Range525
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.636760514
Coefficient of variation (CV)44.45157252
Kurtosis6822.723145
Mean0.0818139901
Median Absolute Deviation (MAD)0
Skewness74.83680843
Sum32116
Variance13.22602704
MonotonicityNot monotonic
2024-02-13T20:39:01.084474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 391852
 
9.5%
1 71
 
< 0.1%
2 47
 
< 0.1%
6 27
 
< 0.1%
4 25
 
< 0.1%
12 25
 
< 0.1%
22 23
 
< 0.1%
14 21
 
< 0.1%
8 21
 
< 0.1%
26 20
 
< 0.1%
Other values (111) 417
 
< 0.1%
(Missing) 3715663
90.4%
ValueCountFrequency (%)
0 391852
9.5%
1 71
 
< 0.1%
2 47
 
< 0.1%
3 7
 
< 0.1%
4 25
 
< 0.1%
ValueCountFrequency (%)
525 1
< 0.1%
452 1
< 0.1%
446 1
< 0.1%
346 1
< 0.1%
344 2
< 0.1%

numberofoutstandinstls_59L
Real number (ℝ)

MISSING 

Distinct284
Distinct (%)0.1%
Missing3747993
Missing (%)91.2%
Infinite0
Infinite (%)0.0%
Mean20.66127828
Minimum0
Maximum318
Zeros3116
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:01.228535image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median12
Q325
95-th percentile59
Maximum318
Range318
Interquartile range (IQR)20

Descriptive statistics

Standard deviation28.78415537
Coefficient of variation (CV)1.393144944
Kurtosis26.50819911
Mean20.66127828
Median Absolute Deviation (MAD)8
Skewness4.370460057
Sum7442585
Variance828.5276005
MonotonicityNot monotonic
2024-02-13T20:39:01.382541image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 23283
 
0.6%
6 16808
 
0.4%
2 16649
 
0.4%
3 16645
 
0.4%
5 16524
 
0.4%
4 15213
 
0.4%
7 14766
 
0.4%
8 14050
 
0.3%
9 13852
 
0.3%
10 13397
 
0.3%
Other values (274) 199032
 
4.8%
(Missing) 3747993
91.2%
ValueCountFrequency (%)
0 3116
 
0.1%
1 23283
0.6%
2 16649
0.4%
3 16645
0.4%
4 15213
0.4%
ValueCountFrequency (%)
318 1
< 0.1%
313 1
< 0.1%
307 2
< 0.1%
305 1
< 0.1%
304 1
< 0.1%

numberofoverdueinstlmax_1039L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct2466
Distinct (%)0.4%
Missing3449815
Missing (%)84.0%
Infinite0
Infinite (%)0.0%
Mean16.54613554
Minimum0
Maximum61133
Zeros490438
Zeros (%)11.9%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:01.532627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile35
Maximum61133
Range61133
Interquartile range (IQR)1

Descriptive statistics

Standard deviation164.5328464
Coefficient of variation (CV)9.943883633
Kurtosis29202.09089
Mean16.54613554
Median Absolute Deviation (MAD)0
Skewness90.37811423
Sum10893926
Variance27071.05753
MonotonicityNot monotonic
2024-02-13T20:39:01.703394image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 490438
 
11.9%
1 28382
 
0.7%
2 10335
 
0.3%
4 10093
 
0.2%
3 8828
 
0.2%
5 7079
 
0.2%
8 4723
 
0.1%
7 4424
 
0.1%
6 3618
 
0.1%
18 3577
 
0.1%
Other values (2456) 86900
 
2.1%
(Missing) 3449815
84.0%
ValueCountFrequency (%)
0 490438
11.9%
1 28382
 
0.7%
2 10335
 
0.3%
3 8828
 
0.2%
4 10093
 
0.2%
ValueCountFrequency (%)
61133 1
< 0.1%
13674 1
< 0.1%
5419 1
< 0.1%
5333 1
< 0.1%
5259 1
< 0.1%

numberofoverdueinstlmax_1151L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct3141
Distinct (%)0.7%
Missing3651491
Missing (%)88.9%
Infinite0
Infinite (%)0.0%
Mean51.7859678
Minimum0
Maximum260000
Zeros319803
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:01.870371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile139
Maximum260000
Range260000
Interquartile range (IQR)1

Descriptive statistics

Standard deviation553.7247198
Coefficient of variation (CV)10.69256293
Kurtosis116887.5369
Mean51.7859678
Median Absolute Deviation (MAD)0
Skewness280.671289
Sum23651739
Variance306611.0654
MonotonicityNot monotonic
2024-02-13T20:39:02.025599image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 319803
 
7.8%
1 24931
 
0.6%
2 7534
 
0.2%
4 7059
 
0.2%
3 6873
 
0.2%
7 4643
 
0.1%
5 3984
 
0.1%
6 3370
 
0.1%
8 3028
 
0.1%
9 2317
 
0.1%
Other values (3131) 73179
 
1.8%
(Missing) 3651491
88.9%
ValueCountFrequency (%)
0 319803
7.8%
1 24931
 
0.6%
2 7534
 
0.2%
3 6873
 
0.2%
4 7059
 
0.2%
ValueCountFrequency (%)
260000 1
< 0.1%
130000 1
< 0.1%
93972 1
< 0.1%
93956 1
< 0.1%
44931 1
< 0.1%
Distinct3940
Distinct (%)2.9%
Missing3971294
Missing (%)96.7%
Memory size31.3 MiB
2024-02-13T20:39:02.324790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1369180
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

Unique354 ?
Unique (%)0.3%

Sample

1st row2011-09-04
2nd row2017-01-21
3rd row2015-02-16
4th row2015-12-29
5th row2018-04-26
ValueCountFrequency (%)
2007-07-31 5448
 
4.0%
2011-08-24 1607
 
1.2%
2007-07-05 1236
 
0.9%
2012-03-04 1202
 
0.9%
2008-10-15 819
 
0.6%
2011-09-04 730
 
0.5%
2010-01-07 537
 
0.4%
2015-06-26 521
 
0.4%
2018-08-02 494
 
0.4%
2018-09-17 462
 
0.3%
Other values (3930) 123862
90.5%
2024-02-13T20:39:02.803871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 334332
24.4%
- 273836
20.0%
1 236075
17.2%
2 217499
15.9%
7 54599
 
4.0%
8 50047
 
3.7%
5 42883
 
3.1%
4 42501
 
3.1%
3 41299
 
3.0%
6 40732
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1095344
80.0%
Dash Punctuation 273836
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 334332
30.5%
1 236075
21.6%
2 217499
19.9%
7 54599
 
5.0%
8 50047
 
4.6%
5 42883
 
3.9%
4 42501
 
3.9%
3 41299
 
3.8%
6 40732
 
3.7%
9 35377
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 273836
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1369180
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 334332
24.4%
- 273836
20.0%
1 236075
17.2%
2 217499
15.9%
7 54599
 
4.0%
8 50047
 
3.7%
5 42883
 
3.1%
4 42501
 
3.1%
3 41299
 
3.0%
6 40732
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1369180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 334332
24.4%
- 273836
20.0%
1 236075
17.2%
2 217499
15.9%
7 54599
 
4.0%
8 50047
 
3.7%
5 42883
 
3.1%
4 42501
 
3.1%
3 41299
 
3.0%
6 40732
 
3.0%
Distinct1988
Distinct (%)1.2%
Missing3940253
Missing (%)95.9%
Memory size31.3 MiB
2024-02-13T20:39:03.210855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1679590
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

Unique433 ?
Unique (%)0.3%

Sample

1st row2014-01-21
2nd row2018-07-12
3rd row2018-11-12
4th row2018-07-12
5th row2018-12-15
ValueCountFrequency (%)
2019-02-07 2197
 
1.3%
2018-12-20 2197
 
1.3%
2018-11-20 2151
 
1.3%
2019-03-26 2149
 
1.3%
2019-01-11 1727
 
1.0%
2019-01-23 1694
 
1.0%
2019-01-24 1457
 
0.9%
2019-01-07 1450
 
0.9%
2019-03-05 1447
 
0.9%
2019-02-12 1438
 
0.9%
Other values (1978) 150052
89.3%
2024-02-13T20:39:03.773594image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 375355
22.3%
- 335918
20.0%
1 315401
18.8%
2 278067
16.6%
8 87898
 
5.2%
9 72598
 
4.3%
6 47075
 
2.8%
7 46231
 
2.8%
5 42888
 
2.6%
4 39842
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1343672
80.0%
Dash Punctuation 335918
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 375355
27.9%
1 315401
23.5%
2 278067
20.7%
8 87898
 
6.5%
9 72598
 
5.4%
6 47075
 
3.5%
7 46231
 
3.4%
5 42888
 
3.2%
4 39842
 
3.0%
3 38317
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 335918
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1679590
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 375355
22.3%
- 335918
20.0%
1 315401
18.8%
2 278067
16.6%
8 87898
 
5.2%
9 72598
 
4.3%
6 47075
 
2.8%
7 46231
 
2.8%
5 42888
 
2.6%
4 39842
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1679590
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 375355
22.3%
- 335918
20.0%
1 315401
18.8%
2 278067
16.6%
8 87898
 
5.2%
9 72598
 
4.3%
6 47075
 
2.8%
7 46231
 
2.8%
5 42888
 
2.6%
4 39842
 
2.4%

numberofoverdueinstls_725L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct2124
Distinct (%)0.3%
Missing3452684
Missing (%)84.0%
Infinite0
Infinite (%)0.0%
Mean8.684010447
Minimum0
Maximum5419
Zeros636359
Zeros (%)15.5%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:03.962375image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5419
Range5419
Interquartile range (IQR)0

Descriptive statistics

Standard deviation124.942852
Coefficient of variation (CV)14.3876902
Kurtosis532.221482
Mean8.684010447
Median Absolute Deviation (MAD)0
Skewness20.96103777
Sum5692612
Variance15610.71626
MonotonicityNot monotonic
2024-02-13T20:39:04.121753image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 636359
 
15.5%
1 2901
 
0.1%
4 938
 
< 0.1%
2 806
 
< 0.1%
3 745
 
< 0.1%
5 560
 
< 0.1%
8 539
 
< 0.1%
7 537
 
< 0.1%
11 464
 
< 0.1%
6 393
 
< 0.1%
Other values (2114) 11286
 
0.3%
(Missing) 3452684
84.0%
ValueCountFrequency (%)
0 636359
15.5%
1 2901
 
0.1%
2 806
 
< 0.1%
3 745
 
< 0.1%
4 938
 
< 0.1%
ValueCountFrequency (%)
5419 1
< 0.1%
5333 1
< 0.1%
5259 1
< 0.1%
5120 1
< 0.1%
5100 1
< 0.1%

numberofoverdueinstls_834L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct70
Distinct (%)< 0.1%
Missing3652014
Missing (%)88.9%
Infinite0
Infinite (%)0.0%
Mean0.05372886334
Minimum0
Maximum868
Zeros455861
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:04.271082image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum868
Range868
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.523912172
Coefficient of variation (CV)84.19891825
Kurtosis10841.28853
Mean0.05372886334
Median Absolute Deviation (MAD)0
Skewness97.95482955
Sum24511
Variance20.46578134
MonotonicityNot monotonic
2024-02-13T20:39:04.417547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 455861
 
11.1%
1 109
 
< 0.1%
2 54
 
< 0.1%
5 22
 
< 0.1%
3 22
 
< 0.1%
4 12
 
< 0.1%
7 11
 
< 0.1%
6 10
 
< 0.1%
11 7
 
< 0.1%
15 5
 
< 0.1%
Other values (60) 85
 
< 0.1%
(Missing) 3652014
88.9%
ValueCountFrequency (%)
0 455861
11.1%
1 109
 
< 0.1%
2 54
 
< 0.1%
3 22
 
< 0.1%
4 12
 
< 0.1%
ValueCountFrequency (%)
868 1
< 0.1%
604 1
< 0.1%
586 1
< 0.1%
518 1
< 0.1%
503 1
< 0.1%

outstandingamount_354A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct86
Distinct (%)< 0.1%
Missing3715535
Missing (%)90.4%
Infinite0
Infinite (%)0.0%
Mean4.065994422
Minimum0
Maximum351940.3
Zeros392566
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:04.560541image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum351940.3
Range351940.3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation815.2670955
Coefficient of variation (CV)200.5086606
Kurtosis112662.2592
Mean4.065994422
Median Absolute Deviation (MAD)0
Skewness311.8560199
Sum1596622.492
Variance664660.437
MonotonicityNot monotonic
2024-02-13T20:39:04.715493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 392566
 
9.6%
0.4 7
 
< 0.1%
0.2 5
 
< 0.1%
1.2 4
 
< 0.1%
1.6 3
 
< 0.1%
1 3
 
< 0.1%
6000 2
 
< 0.1%
1786.4 2
 
< 0.1%
0.6 2
 
< 0.1%
8315.664 2
 
< 0.1%
Other values (76) 81
 
< 0.1%
(Missing) 3715535
90.4%
ValueCountFrequency (%)
0 392566
9.6%
0.2 5
 
< 0.1%
0.4 7
 
< 0.1%
0.6 2
 
< 0.1%
0.8 2
 
< 0.1%
ValueCountFrequency (%)
351940.3 1
< 0.1%
214592.48 1
< 0.1%
196028.58 1
< 0.1%
134000 1
< 0.1%
115627.766 1
< 0.1%

outstandingamount_362A
Real number (ℝ)

MISSING  SKEWED 

Distinct340220
Distinct (%)94.4%
Missing3747881
Missing (%)91.2%
Infinite0
Infinite (%)0.0%
Mean160062.0501
Minimum0
Maximum1688617600
Zeros3366
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:04.873121image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3250.0481
Q113903.734
median37362.926
Q3108526.2
95-th percentile600000
Maximum1688617600
Range1688617600
Interquartile range (IQR)94622.466

Descriptive statistics

Standard deviation3052952.991
Coefficient of variation (CV)19.07355921
Kurtosis262302.7884
Mean160062.0501
Median Absolute Deviation (MAD)29577.032
Skewness487.0921733
Sum5.767531859 × 1010
Variance9.320521967 × 1012
MonotonicityNot monotonic
2024-02-13T20:39:05.041421image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3366
 
0.1%
100000 187
 
< 0.1%
40000 182
 
< 0.1%
20000 177
 
< 0.1%
60000 164
 
< 0.1%
200000 123
 
< 0.1%
2114 120
 
< 0.1%
30000 115
 
< 0.1%
10000 101
 
< 0.1%
4000 92
 
< 0.1%
Other values (340210) 355704
 
8.7%
(Missing) 3747881
91.2%
ValueCountFrequency (%)
0 3366
0.1%
0.002 12
 
< 0.1%
0.004 5
 
< 0.1%
0.006 2
 
< 0.1%
0.008 1
 
< 0.1%
ValueCountFrequency (%)
1688617600 1
< 0.1%
505571330 1
< 0.1%
365137500 1
< 0.1%
88925784 1
< 0.1%
73752440 1
< 0.1%

overdueamount_31A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct100
Distinct (%)< 0.1%
Missing3651899
Missing (%)88.9%
Infinite0
Infinite (%)0.0%
Mean17.79835196
Minimum0
Maximum421656.6
Zeros456195
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:05.200398image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum421656.6
Range421656.6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1847.876089
Coefficient of variation (CV)103.822876
Kurtosis25040.6172
Mean17.79835196
Median Absolute Deviation (MAD)0
Skewness144.0515936
Sum8121619.378
Variance3414646.041
MonotonicityNot monotonic
2024-02-13T20:39:05.344435image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 456195
 
11.1%
0.4 6
 
< 0.1%
1.2 4
 
< 0.1%
0.2 3
 
< 0.1%
1.6 3
 
< 0.1%
1 2
 
< 0.1%
2.2 2
 
< 0.1%
1.4 2
 
< 0.1%
135375 2
 
< 0.1%
0.6 2
 
< 0.1%
Other values (90) 92
 
< 0.1%
(Missing) 3651899
88.9%
ValueCountFrequency (%)
0 456195
11.1%
0.128 1
 
< 0.1%
0.2 3
 
< 0.1%
0.4 6
 
< 0.1%
0.6 2
 
< 0.1%
ValueCountFrequency (%)
421656.6 1
< 0.1%
415876 1
< 0.1%
406000 1
< 0.1%
282475 1
< 0.1%
281860 1
< 0.1%

overdueamount_659A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct16932
Distinct (%)2.6%
Missing3452681
Missing (%)84.0%
Infinite0
Infinite (%)0.0%
Mean932.6235511
Minimum0
Maximum49005736
Zeros636362
Zeros (%)15.5%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:05.491361image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum49005736
Range49005736
Interquartile range (IQR)0

Descriptive statistics

Standard deviation74356.73592
Coefficient of variation (CV)79.7285634
Kurtosis294623.2297
Mean932.6235511
Median Absolute Deviation (MAD)0
Skewness477.9428866
Sum611363649.1
Variance5528924177
MonotonicityNot monotonic
2024-02-13T20:39:05.650053image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 636362
 
15.5%
4 38
 
< 0.1%
10 37
 
< 0.1%
2 37
 
< 0.1%
0.4 34
 
< 0.1%
0.8 33
 
< 0.1%
0.2 30
 
< 0.1%
14 28
 
< 0.1%
1.2 28
 
< 0.1%
8 27
 
< 0.1%
Other values (16922) 18877
 
0.5%
(Missing) 3452681
84.0%
ValueCountFrequency (%)
0 636362
15.5%
0.002 5
 
< 0.1%
0.004 3
 
< 0.1%
0.006 2
 
< 0.1%
0.008 1
 
< 0.1%
ValueCountFrequency (%)
49005736 1
< 0.1%
15233466 1
< 0.1%
14662000 1
< 0.1%
12144611 1
< 0.1%
8581935 1
< 0.1%

overdueamountmax2_14A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct135322
Distinct (%)20.6%
Missing3449815
Missing (%)84.0%
Infinite0
Infinite (%)0.0%
Mean3275.328112
Minimum0
Maximum49261336
Zeros488787
Zeros (%)11.9%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:05.803191image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35.8
95-th percentile9207.1532
Maximum49261336
Range49261336
Interquartile range (IQR)5.8

Descriptive statistics

Standard deviation116946.836
Coefficient of variation (CV)35.70538034
Kurtosis88712.1697
Mean3275.328112
Median Absolute Deviation (MAD)0
Skewness265.4617439
Sum2156466203
Variance1.367656245 × 1010
MonotonicityNot monotonic
2024-02-13T20:39:05.970349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 488787
 
11.9%
0.4 178
 
< 0.1%
0.2 178
 
< 0.1%
800 175
 
< 0.1%
10 172
 
< 0.1%
2 164
 
< 0.1%
4 162
 
< 0.1%
0.8 154
 
< 0.1%
400 143
 
< 0.1%
8 138
 
< 0.1%
Other values (135312) 168146
 
4.1%
(Missing) 3449815
84.0%
ValueCountFrequency (%)
0 488787
11.9%
0.002 16
 
< 0.1%
0.004 12
 
< 0.1%
0.006 11
 
< 0.1%
0.008 16
 
< 0.1%
ValueCountFrequency (%)
49261336 1
< 0.1%
38414330 1
< 0.1%
33724950 1
< 0.1%
33003988 1
< 0.1%
15991722 2
< 0.1%

overdueamountmax2_398A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct98047
Distinct (%)21.5%
Missing3651491
Missing (%)88.9%
Infinite0
Infinite (%)0.0%
Mean5635.960605
Minimum0
Maximum60940892
Zeros321264
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:06.136547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3695.4
95-th percentile14966.565
Maximum60940892
Range60940892
Interquartile range (IQR)695.4

Descriptive statistics

Standard deviation169765.1202
Coefficient of variation (CV)30.12177197
Kurtosis57971.97163
Mean5635.960605
Median Absolute Deviation (MAD)0
Skewness213.5484334
Sum2574061563
Variance2.882019603 × 1010
MonotonicityNot monotonic
2024-02-13T20:39:06.291254image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 321264
 
7.8%
0.2 622
 
< 0.1%
0.4 346
 
< 0.1%
0.8 223
 
< 0.1%
1.6 206
 
< 0.1%
2 205
 
< 0.1%
1 185
 
< 0.1%
0.6 182
 
< 0.1%
1.2 161
 
< 0.1%
4 146
 
< 0.1%
Other values (98037) 133181
 
3.2%
(Missing) 3651491
88.9%
ValueCountFrequency (%)
0 321264
7.8%
0.002 13
 
< 0.1%
0.004 9
 
< 0.1%
0.006 8
 
< 0.1%
0.008 7
 
< 0.1%
ValueCountFrequency (%)
60940892 1
< 0.1%
42066460 1
< 0.1%
38038588 1
< 0.1%
30147584 1
< 0.1%
27719088 1
< 0.1%
Distinct3856
Distinct (%)2.8%
Missing3972755
Missing (%)96.7%
Memory size31.3 MiB
2024-02-13T20:39:06.611159image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1354570
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique312 ?
Unique (%)0.2%

Sample

1st row2011-04-20
2nd row2016-08-26
3rd row2015-02-16
4th row2016-01-27
5th row2018-04-26
ValueCountFrequency (%)
2011-10-06 2069
 
1.5%
2008-10-15 804
 
0.6%
2015-07-07 640
 
0.5%
2010-01-07 607
 
0.4%
2008-11-27 429
 
0.3%
2018-04-26 411
 
0.3%
2018-03-16 405
 
0.3%
2017-08-24 403
 
0.3%
2007-07-31 401
 
0.3%
2018-05-16 390
 
0.3%
Other values (3846) 128898
95.2%
2024-02-13T20:39:07.056493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 329853
24.4%
- 270914
20.0%
1 239738
17.7%
2 218153
16.1%
8 49734
 
3.7%
7 45101
 
3.3%
6 43116
 
3.2%
5 43061
 
3.2%
4 40096
 
3.0%
3 39179
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1083656
80.0%
Dash Punctuation 270914
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 329853
30.4%
1 239738
22.1%
2 218153
20.1%
8 49734
 
4.6%
7 45101
 
4.2%
6 43116
 
4.0%
5 43061
 
4.0%
4 40096
 
3.7%
3 39179
 
3.6%
9 35625
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 270914
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1354570
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 329853
24.4%
- 270914
20.0%
1 239738
17.7%
2 218153
16.1%
8 49734
 
3.7%
7 45101
 
3.3%
6 43116
 
3.2%
5 43061
 
3.2%
4 40096
 
3.0%
3 39179
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1354570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 329853
24.4%
- 270914
20.0%
1 239738
17.7%
2 218153
16.1%
8 49734
 
3.7%
7 45101
 
3.3%
6 43116
 
3.2%
5 43061
 
3.2%
4 40096
 
3.0%
3 39179
 
2.9%
Distinct2259
Distinct (%)1.3%
Missing3938602
Missing (%)95.9%
Memory size31.3 MiB
2024-02-13T20:39:07.468719image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1696100
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

Unique488 ?
Unique (%)0.3%

Sample

1st row2013-10-28
2nd row2018-07-12
3rd row2018-11-12
4th row2018-06-28
5th row2018-12-04
ValueCountFrequency (%)
2018-12-20 2424
 
1.4%
2018-11-20 2281
 
1.3%
2019-02-07 1900
 
1.1%
2019-03-26 1867
 
1.1%
2019-01-23 1555
 
0.9%
2019-01-07 1533
 
0.9%
2019-01-11 1518
 
0.9%
2019-01-24 1434
 
0.8%
2019-03-05 1434
 
0.8%
2018-12-15 1275
 
0.8%
Other values (2249) 152389
89.8%
2024-02-13T20:39:07.962535image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 380145
22.4%
- 339220
20.0%
1 321255
18.9%
2 278999
16.4%
8 91917
 
5.4%
9 67876
 
4.0%
7 48995
 
2.9%
6 47019
 
2.8%
5 42148
 
2.5%
3 39399
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1356880
80.0%
Dash Punctuation 339220
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 380145
28.0%
1 321255
23.7%
2 278999
20.6%
8 91917
 
6.8%
9 67876
 
5.0%
7 48995
 
3.6%
6 47019
 
3.5%
5 42148
 
3.1%
3 39399
 
2.9%
4 39127
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 339220
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1696100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 380145
22.4%
- 339220
20.0%
1 321255
18.9%
2 278999
16.4%
8 91917
 
5.4%
9 67876
 
4.0%
7 48995
 
2.9%
6 47019
 
2.8%
5 42148
 
2.5%
3 39399
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1696100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 380145
22.4%
- 339220
20.0%
1 321255
18.9%
2 278999
16.4%
8 91917
 
5.4%
9 67876
 
4.0%
7 48995
 
2.9%
6 47019
 
2.8%
5 42148
 
2.5%
3 39399
 
2.3%

overdueamountmax_155A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct111085
Distinct (%)16.9%
Missing3449815
Missing (%)84.0%
Infinite0
Infinite (%)0.0%
Mean2432.906219
Minimum0
Maximum49261336
Zeros521121
Zeros (%)12.7%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:08.144575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7153.918
Maximum49261336
Range49261336
Interquartile range (IQR)0

Descriptive statistics

Standard deviation91318.36174
Coefficient of variation (CV)37.5346822
Kurtosis161776.9999
Mean2432.906219
Median Absolute Deviation (MAD)0
Skewness350.8231859
Sum1601818156
Variance8339043191
MonotonicityNot monotonic
2024-02-13T20:39:08.315648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 521121
 
12.7%
1000 211
 
< 0.1%
800 173
 
< 0.1%
10 170
 
< 0.1%
0.4 162
 
< 0.1%
4 157
 
< 0.1%
2 154
 
< 0.1%
400 144
 
< 0.1%
3000 143
 
< 0.1%
2000 143
 
< 0.1%
Other values (111075) 135819
 
3.3%
(Missing) 3449815
84.0%
ValueCountFrequency (%)
0 521121
12.7%
0.002 14
 
< 0.1%
0.004 7
 
< 0.1%
0.006 11
 
< 0.1%
0.008 19
 
< 0.1%
ValueCountFrequency (%)
49261336 1
< 0.1%
33724950 1
< 0.1%
15845752 1
< 0.1%
15237162 1
< 0.1%
14662000 1
< 0.1%

overdueamountmax_35A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct93238
Distinct (%)21.2%
Missing3667444
Missing (%)89.3%
Infinite0
Infinite (%)0.0%
Mean4512.766137
Minimum0
Maximum38038588
Zeros311547
Zeros (%)7.6%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:08.468736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3464.4275
95-th percentile13853.75445
Maximum38038588
Range38038588
Interquartile range (IQR)464.4275

Descriptive statistics

Standard deviation91143.63386
Coefficient of variation (CV)20.19684404
Kurtosis85585.08525
Mean4512.766137
Median Absolute Deviation (MAD)0
Skewness248.4321213
Sum1989082905
Variance8307161992
MonotonicityNot monotonic
2024-02-13T20:39:08.624670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 311547
 
7.6%
0.2 579
 
< 0.1%
0.4 320
 
< 0.1%
0.8 218
 
< 0.1%
1.6 209
 
< 0.1%
2 206
 
< 0.1%
1 188
 
< 0.1%
0.6 180
 
< 0.1%
1.2 156
 
< 0.1%
4 148
 
< 0.1%
Other values (93228) 127017
 
3.1%
(Missing) 3667444
89.3%
ValueCountFrequency (%)
0 311547
7.6%
0.002 9
 
< 0.1%
0.004 9
 
< 0.1%
0.006 10
 
< 0.1%
0.008 6
 
< 0.1%
ValueCountFrequency (%)
38038588 1
< 0.1%
21444070 1
< 0.1%
20011292 1
< 0.1%
18579138 1
< 0.1%
8573098 1
< 0.1%

overdueamountmaxdatemonth_284T
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing3667444
Missing (%)89.3%
Infinite0
Infinite (%)0.0%
Mean6.536713192
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:08.757134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.467495122
Coefficient of variation (CV)0.5304646266
Kurtosis-1.211236032
Mean6.536713192
Median Absolute Deviation (MAD)3
Skewness-0.04163884392
Sum2881174
Variance12.02352242
MonotonicityNot monotonic
2024-02-13T20:39:08.869170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11 40773
 
1.0%
1 40344
 
1.0%
6 39766
 
1.0%
7 37426
 
0.9%
8 37032
 
0.9%
9 36933
 
0.9%
10 36091
 
0.9%
2 35345
 
0.9%
12 35224
 
0.9%
5 34824
 
0.8%
Other values (2) 67010
 
1.6%
(Missing) 3667444
89.3%
ValueCountFrequency (%)
1 40344
1.0%
2 35345
0.9%
3 33352
0.8%
4 33658
0.8%
5 34824
0.8%
ValueCountFrequency (%)
12 35224
0.9%
11 40773
1.0%
10 36091
0.9%
9 36933
0.9%
8 37032
0.9%

overdueamountmaxdatemonth_365T
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing3449815
Missing (%)84.0%
Infinite0
Infinite (%)0.0%
Mean6.26020319
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:08.982171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.414489266
Coefficient of variation (CV)0.5454278658
Kurtosis-1.142484858
Mean6.26020319
Median Absolute Deviation (MAD)3
Skewness0.1044128972
Sum4121699
Variance11.65873695
MonotonicityNot monotonic
2024-02-13T20:39:09.355173image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 63956
 
1.6%
1 61658
 
1.5%
5 60303
 
1.5%
7 59992
 
1.5%
4 58327
 
1.4%
2 55159
 
1.3%
3 54766
 
1.3%
8 52365
 
1.3%
12 52045
 
1.3%
10 51586
 
1.3%
Other values (2) 88240
 
2.1%
(Missing) 3449815
84.0%
ValueCountFrequency (%)
1 61658
1.5%
2 55159
1.3%
3 54766
1.3%
4 58327
1.4%
5 60303
1.5%
ValueCountFrequency (%)
12 52045
1.3%
11 46721
1.1%
10 51586
1.3%
9 41519
1.0%
8 52365
1.3%

overdueamountmaxdateyear_2T
Real number (ℝ)

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing3449815
Missing (%)84.0%
Infinite0
Infinite (%)0.0%
Mean2017.976456
Minimum2015
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:09.465550image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2017
Q12017
median2018
Q32019
95-th percentile2019
Maximum2019
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.7359087522
Coefficient of variation (CV)0.0003646765798
Kurtosis-1.141239376
Mean2017.976456
Median Absolute Deviation (MAD)1
Skewness0.03325426477
Sum1328629645
Variance0.5415616916
MonotonicityNot monotonic
2024-02-13T20:39:09.589135image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
2018 301881
 
7.3%
2017 185839
 
4.5%
2019 170570
 
4.2%
2016 89
 
< 0.1%
2015 18
 
< 0.1%
(Missing) 3449815
84.0%
ValueCountFrequency (%)
2015 18
 
< 0.1%
2016 89
 
< 0.1%
2017 185839
4.5%
2018 301881
7.3%
2019 170570
4.2%
ValueCountFrequency (%)
2019 170570
4.2%
2018 301881
7.3%
2017 185839
4.5%
2016 89
 
< 0.1%
2015 18
 
< 0.1%

overdueamountmaxdateyear_994T
Real number (ℝ)

MISSING 

Distinct17
Distinct (%)< 0.1%
Missing3667444
Missing (%)89.3%
Infinite0
Infinite (%)0.0%
Mean2014.010942
Minimum2003
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:09.713118image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2003
5-th percentile2007
Q12011
median2015
Q32017
95-th percentile2018
Maximum2019
Range16
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.711946341
Coefficient of variation (CV)0.001843061655
Kurtosis-0.6463963288
Mean2014.010942
Median Absolute Deviation (MAD)3
Skewness-0.6728371949
Sum887711575
Variance13.77854564
MonotonicityNot monotonic
2024-02-13T20:39:09.841617image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2018 72373
 
1.8%
2017 62531
 
1.5%
2016 45628
 
1.1%
2015 40580
 
1.0%
2014 35967
 
0.9%
2013 31783
 
0.8%
2012 25249
 
0.6%
2011 21754
 
0.5%
2007 20067
 
0.5%
2010 18949
 
0.5%
Other values (7) 65887
 
1.6%
(Missing) 3667444
89.3%
ValueCountFrequency (%)
2003 1
 
< 0.1%
2004 147
 
< 0.1%
2005 3493
 
0.1%
2006 11999
0.3%
2007 20067
0.5%
ValueCountFrequency (%)
2019 15735
 
0.4%
2018 72373
1.8%
2017 62531
1.5%
2016 45628
1.1%
2015 40580
1.0%

periodicityofpmts_1102L
Real number (ℝ)

MISSING  SKEWED 

Distinct5
Distinct (%)< 0.1%
Missing3748546
Missing (%)91.2%
Infinite0
Infinite (%)0.0%
Mean30.11575462
Minimum1
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:09.967309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile30
Q130
median30
Q330
95-th percentile30
Maximum360
Range359
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.429628051
Coefficient of variation (CV)0.1802919475
Kurtosis2449.408091
Mean30.11575462
Median Absolute Deviation (MAD)0
Skewness46.13117812
Sum10831613
Variance29.48086077
MonotonicityNot monotonic
2024-02-13T20:39:10.080319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
30 359153
 
8.7%
1 233
 
< 0.1%
180 180
 
< 0.1%
360 57
 
< 0.1%
90 43
 
< 0.1%
(Missing) 3748546
91.2%
ValueCountFrequency (%)
1 233
 
< 0.1%
30 359153
8.7%
90 43
 
< 0.1%
180 180
 
< 0.1%
360 57
 
< 0.1%
ValueCountFrequency (%)
360 57
 
< 0.1%
180 180
 
< 0.1%
90 43
 
< 0.1%
30 359153
8.7%
1 233
 
< 0.1%

periodicityofpmts_837L
Real number (ℝ)

MISSING  SKEWED 

Distinct5
Distinct (%)< 0.1%
Missing3755452
Missing (%)91.4%
Infinite0
Infinite (%)0.0%
Mean30.31703991
Minimum1
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:10.192305image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile30
Q130
median30
Q330
95-th percentile30
Maximum360
Range359
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.829525219
Coefficient of variation (CV)0.225270186
Kurtosis614.9114899
Mean30.31703991
Median Absolute Deviation (MAD)0
Skewness23.45458419
Sum10694639
Variance46.64241472
MonotonicityNot monotonic
2024-02-13T20:39:10.322090image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
30 351861
 
8.6%
180 633
 
< 0.1%
90 224
 
< 0.1%
1 29
 
< 0.1%
360 13
 
< 0.1%
(Missing) 3755452
91.4%
ValueCountFrequency (%)
1 29
 
< 0.1%
30 351861
8.6%
90 224
 
< 0.1%
180 633
 
< 0.1%
360 13
 
< 0.1%
ValueCountFrequency (%)
360 13
 
< 0.1%
180 633
 
< 0.1%
90 224
 
< 0.1%
30 351861
8.6%
1 29
 
< 0.1%

prolongationcount_1120L
Real number (ℝ)

MISSING 

Distinct47
Distinct (%)0.2%
Missing4081757
Missing (%)99.4%
Infinite0
Infinite (%)0.0%
Mean0.5801549802
Minimum0
Maximum96
Zeros20649
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:10.483099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum96
Range96
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.257182628
Coefficient of variation (CV)3.890654576
Kurtosis329.3524278
Mean0.5801549802
Median Absolute Deviation (MAD)0
Skewness13.71077719
Sum15348
Variance5.094873417
MonotonicityNot monotonic
2024-02-13T20:39:10.636138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 20649
 
0.5%
1 3127
 
0.1%
2 1124
 
< 0.1%
3 538
 
< 0.1%
4 293
 
< 0.1%
5 202
 
< 0.1%
6 118
 
< 0.1%
7 84
 
< 0.1%
8 61
 
< 0.1%
9 40
 
< 0.1%
Other values (37) 219
 
< 0.1%
(Missing) 4081757
99.4%
ValueCountFrequency (%)
0 20649
0.5%
1 3127
 
0.1%
2 1124
 
< 0.1%
3 538
 
< 0.1%
4 293
 
< 0.1%
ValueCountFrequency (%)
96 1
< 0.1%
80 1
< 0.1%
66 1
< 0.1%
56 1
< 0.1%
55 1
< 0.1%

prolongationcount_599L
Real number (ℝ)

MISSING 

Distinct39
Distinct (%)0.7%
Missing4102220
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean1.027202937
Minimum0
Maximum63
Zeros3978
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:10.775228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum63
Range63
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.338884526
Coefficient of variation (CV)3.250462401
Kurtosis122.7831578
Mean1.027202937
Median Absolute Deviation (MAD)0
Skewness9.413827532
Sum6155
Variance11.14814988
MonotonicityNot monotonic
2024-02-13T20:39:10.920232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 3978
 
0.1%
1 1042
 
< 0.1%
2 350
 
< 0.1%
3 207
 
< 0.1%
4 124
 
< 0.1%
5 64
 
< 0.1%
7 37
 
< 0.1%
6 35
 
< 0.1%
9 25
 
< 0.1%
8 21
 
< 0.1%
Other values (29) 109
 
< 0.1%
(Missing) 4102220
99.9%
ValueCountFrequency (%)
0 3978
0.1%
1 1042
 
< 0.1%
2 350
 
< 0.1%
3 207
 
< 0.1%
4 124
 
< 0.1%
ValueCountFrequency (%)
63 1
< 0.1%
55 2
< 0.1%
54 2
< 0.1%
51 2
< 0.1%
50 2
< 0.1%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:11.092065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.00005842
Min length8

Characters and Unicode

Total characters32865936
Distinct characters18
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 row96a8fdfe
2nd row60c73645
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 3449815
84.0%
60c73645 504371
 
12.3%
96a8fdfe 147626
 
3.6%
e19fdece 3478
 
0.1%
9e302002 1668
 
< 0.1%
7a7d6960 642
 
< 0.1%
44164129 266
 
< 0.1%
e8f3b178 116
 
< 0.1%
28bfa260 102
 
< 0.1%
8193a6ce 62
 
< 0.1%
Other values (2) 66
 
< 0.1%
2024-02-13T20:39:11.407627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 10853828
33.0%
7 3955586
 
12.0%
4 3954984
 
12.0%
a 3598247
 
10.9%
1 3454243
 
10.5%
b 3450039
 
10.5%
6 1158148
 
3.5%
0 510125
 
1.6%
c 507917
 
1.5%
3 506217
 
1.5%
Other values (8) 916602
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24698953
75.2%
Lowercase Letter 8166803
 
24.8%
Connector Punctuation 120
 
< 0.1%
Uppercase Letter 60
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 10853828
43.9%
7 3955586
 
16.0%
4 3954984
 
16.0%
1 3454243
 
14.0%
6 1158148
 
4.7%
0 510125
 
2.1%
3 506217
 
2.0%
9 153742
 
0.6%
8 148148
 
0.6%
2 3932
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 3598247
44.1%
b 3450039
42.2%
c 507917
 
6.2%
f 298948
 
3.7%
e 159906
 
2.0%
d 151746
 
1.9%
Connector Punctuation
ValueCountFrequency (%)
_ 120
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 60
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24699073
75.2%
Latin 8166863
 
24.8%

Most frequent character per script

Common
ValueCountFrequency (%)
5 10853828
43.9%
7 3955586
 
16.0%
4 3954984
 
16.0%
1 3454243
 
14.0%
6 1158148
 
4.7%
0 510125
 
2.1%
3 506217
 
2.0%
9 153742
 
0.6%
8 148148
 
0.6%
2 3932
 
< 0.1%
Latin
ValueCountFrequency (%)
a 3598247
44.1%
b 3450039
42.2%
c 507917
 
6.2%
f 298948
 
3.7%
e 159906
 
2.0%
d 151746
 
1.9%
P 60
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32865936
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 10853828
33.0%
7 3955586
 
12.0%
4 3954984
 
12.0%
a 3598247
 
10.9%
1 3454243
 
10.5%
b 3450039
 
10.5%
6 1158148
 
3.5%
0 510125
 
1.6%
c 507917
 
1.5%
3 506217
 
1.5%
Other values (8) 916602
 
2.8%
Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:11.572204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.00030865
Min length8

Characters and Unicode

Total characters32866964
Distinct characters18
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 3651541
88.9%
60c73645 208624
 
5.1%
5065c2b8 125894
 
3.1%
96a8fdfe 101951
 
2.5%
e19fdece 8652
 
0.2%
d9ae1a0e 5635
 
0.1%
27b6de28 2021
 
< 0.1%
5d1b0cdd 1088
 
< 0.1%
d11871e7 736
 
< 0.1%
89ccf2a3 665
 
< 0.1%
Other values (8) 1405
 
< 0.1%
2024-02-13T20:39:11.887086image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 11416123
34.7%
7 3864808
 
11.8%
4 3861050
 
11.7%
b 3781070
 
11.5%
a 3765800
 
11.5%
1 3671218
 
11.2%
6 648464
 
2.0%
c 345619
 
1.1%
0 341583
 
1.0%
8 232425
 
0.7%
Other values (8) 938804
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24494036
74.5%
Lowercase Letter 8371977
 
25.5%
Connector Punctuation 634
 
< 0.1%
Uppercase Letter 317
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 11416123
46.6%
7 3864808
 
15.8%
4 3861050
 
15.8%
1 3671218
 
15.0%
6 648464
 
2.6%
0 341583
 
1.4%
8 232425
 
0.9%
3 209416
 
0.9%
2 131484
 
0.5%
9 117465
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
b 3781070
45.2%
a 3765800
45.0%
c 345619
 
4.1%
f 213593
 
2.6%
e 142904
 
1.7%
d 122991
 
1.5%
Connector Punctuation
ValueCountFrequency (%)
_ 634
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 317
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24494670
74.5%
Latin 8372294
 
25.5%

Most frequent character per script

Common
ValueCountFrequency (%)
5 11416123
46.6%
7 3864808
 
15.8%
4 3861050
 
15.8%
1 3671218
 
15.0%
6 648464
 
2.6%
0 341583
 
1.4%
8 232425
 
0.9%
3 209416
 
0.9%
2 131484
 
0.5%
9 117465
 
0.5%
Latin
ValueCountFrequency (%)
b 3781070
45.2%
a 3765800
45.0%
c 345619
 
4.1%
f 213593
 
2.6%
e 142904
 
1.7%
d 122991
 
1.5%
P 317
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32866964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 11416123
34.7%
7 3864808
 
11.8%
4 3861050
 
11.7%
b 3781070
 
11.5%
a 3765800
 
11.5%
1 3671218
 
11.2%
6 648464
 
2.0%
c 345619
 
1.1%
0 341583
 
1.0%
8 232425
 
0.7%
Other values (8) 938804
 
2.9%

refreshdate_3813885D
Text

MISSING 

Distinct181
Distinct (%)< 0.1%
Missing1429958
Missing (%)34.8%
Memory size31.3 MiB
2024-02-13T20:39:12.262911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters26782540
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2019-01-28
2nd row2019-01-28
3rd row2019-01-28
4th row2019-01-03
5th row2019-01-24
ValueCountFrequency (%)
2019-01-03 334789
 
12.5%
2019-02-06 299723
 
11.2%
2019-02-03 246648
 
9.2%
2019-02-04 162667
 
6.1%
2019-05-04 128743
 
4.8%
2019-05-03 76402
 
2.9%
2018-11-02 50900
 
1.9%
2018-12-19 50204
 
1.9%
2019-06-26 31009
 
1.2%
2019-03-29 29197
 
1.1%
Other values (171) 1267972
47.3%
2024-02-13T20:39:12.779896image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7075418
26.4%
- 5356508
20.0%
2 4260513
15.9%
1 3886247
14.5%
9 2775292
 
10.4%
3 1092120
 
4.1%
6 739965
 
2.8%
4 625238
 
2.3%
5 534120
 
2.0%
8 241869
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21426032
80.0%
Dash Punctuation 5356508
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7075418
33.0%
2 4260513
19.9%
1 3886247
18.1%
9 2775292
 
13.0%
3 1092120
 
5.1%
6 739965
 
3.5%
4 625238
 
2.9%
5 534120
 
2.5%
8 241869
 
1.1%
7 195250
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 5356508
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26782540
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7075418
26.4%
- 5356508
20.0%
2 4260513
15.9%
1 3886247
14.5%
9 2775292
 
10.4%
3 1092120
 
4.1%
6 739965
 
2.8%
4 625238
 
2.3%
5 534120
 
2.0%
8 241869
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26782540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7075418
26.4%
- 5356508
20.0%
2 4260513
15.9%
1 3886247
14.5%
9 2775292
 
10.4%
3 1092120
 
4.1%
6 739965
 
2.8%
4 625238
 
2.3%
5 534120
 
2.0%
8 241869
 
0.9%

residualamount_488A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing4044452
Missing (%)98.4%
Infinite0
Infinite (%)0.0%
Mean0.8399551443
Minimum0
Maximum53555.54
Zeros63759
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:12.915500image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum53555.54
Range53555.54
Interquartile range (IQR)0

Descriptive statistics

Standard deviation212.0949111
Coefficient of variation (CV)252.5074256
Kurtosis63760
Mean0.8399551443
Median Absolute Deviation (MAD)0
Skewness252.5074256
Sum53555.54
Variance44984.25133
MonotonicityNot monotonic
2024-02-13T20:39:13.027124image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 63759
 
1.6%
53555.54 1
 
< 0.1%
(Missing) 4044452
98.4%
ValueCountFrequency (%)
0 63759
1.6%
53555.54 1
 
< 0.1%
ValueCountFrequency (%)
53555.54 1
 
< 0.1%
0 63759
1.6%

residualamount_856A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct143229
Distinct (%)48.5%
Missing3813073
Missing (%)92.8%
Infinite0
Infinite (%)0.0%
Mean29451.45127
Minimum0
Maximum40000000
Zeros122406
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:13.165834image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5040
Q328315.669
95-th percentile153570.047
Maximum40000000
Range40000000
Interquartile range (IQR)28315.669

Descriptive statistics

Standard deviation97608.64742
Coefficient of variation (CV)3.314221989
Kurtosis95301.54389
Mean29451.45127
Median Absolute Deviation (MAD)5040
Skewness234.7795756
Sum8692271876
Variance9527448051
MonotonicityNot monotonic
2024-02-13T20:39:13.332798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 122406
 
3.0%
200000 294
 
< 0.1%
20000 274
 
< 0.1%
10000 264
 
< 0.1%
40000 224
 
< 0.1%
100000 203
 
< 0.1%
30000 181
 
< 0.1%
62000 142
 
< 0.1%
4000 135
 
< 0.1%
6000 121
 
< 0.1%
Other values (143219) 170895
 
4.2%
(Missing) 3813073
92.8%
ValueCountFrequency (%)
0 122406
3.0%
0.002 4
 
< 0.1%
0.010000001 2
 
< 0.1%
0.012 1
 
< 0.1%
0.018000001 3
 
< 0.1%
ValueCountFrequency (%)
40000000 1
< 0.1%
3591242.8 1
< 0.1%
2439628.2 1
< 0.1%
2113132.8 1
< 0.1%
2093794 1
< 0.1%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:13.510801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.000244875
Min length8

Characters and Unicode

Total characters32866702
Distinct characters17
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 rowab3c25cf
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 3770638
91.8%
ab3c25cf 323758
 
7.9%
be4fd70b 8394
 
0.2%
daf49a8a 4087
 
0.1%
p28_48_88 1006
 
< 0.1%
15f04f45 328
 
< 0.1%
0c42a10e 1
 
< 0.1%
2024-02-13T20:39:13.799596image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 11636328
35.4%
b 4111184
 
12.5%
a 4106658
 
12.5%
4 3784782
 
11.5%
7 3779032
 
11.5%
1 3770967
 
11.5%
c 647517
 
2.0%
f 336895
 
1.0%
2 324765
 
1.0%
3 323758
 
1.0%
Other values (7) 44816
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23640554
71.9%
Lowercase Letter 9223130
 
28.1%
Connector Punctuation 2012
 
< 0.1%
Uppercase Letter 1006
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 11636328
49.2%
4 3784782
 
16.0%
7 3779032
 
16.0%
1 3770967
 
16.0%
2 324765
 
1.4%
3 323758
 
1.4%
0 8724
 
< 0.1%
8 8111
 
< 0.1%
9 4087
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
b 4111184
44.6%
a 4106658
44.5%
c 647517
 
7.0%
f 336895
 
3.7%
d 12481
 
0.1%
e 8395
 
0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 2012
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 1006
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23642566
71.9%
Latin 9224136
 
28.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 11636328
49.2%
4 3784782
 
16.0%
7 3779032
 
16.0%
1 3770967
 
15.9%
2 324765
 
1.4%
3 323758
 
1.4%
0 8724
 
< 0.1%
8 8111
 
< 0.1%
9 4087
 
< 0.1%
_ 2012
 
< 0.1%
Latin
ValueCountFrequency (%)
b 4111184
44.6%
a 4106658
44.5%
c 647517
 
7.0%
f 336895
 
3.7%
d 12481
 
0.1%
e 8395
 
0.1%
P 1006
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32866702
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 11636328
35.4%
b 4111184
 
12.5%
a 4106658
 
12.5%
4 3784782
 
11.5%
7 3779032
 
11.5%
1 3770967
 
11.5%
c 647517
 
2.0%
f 336895
 
1.0%
2 324765
 
1.0%
3 323758
 
1.0%
Other values (7) 44816
 
0.1%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:13.972976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters32865696
Distinct characters15
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 rowab3c25cf
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 3809610
92.7%
ab3c25cf 288101
 
7.0%
be4fd70b 4601
 
0.1%
daf49a8a 4340
 
0.1%
15f04f45 1533
 
< 0.1%
71ddaa88 24
 
< 0.1%
0c42a10e 3
 
< 0.1%
2024-02-13T20:39:14.246876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 11719997
35.7%
a 4110782
 
12.5%
b 4106913
 
12.5%
4 3821620
 
11.6%
7 3814235
 
11.6%
1 3811170
 
11.6%
c 576205
 
1.8%
f 300108
 
0.9%
2 288104
 
0.9%
3 288101
 
0.9%
Other values (5) 28461
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23758095
72.3%
Lowercase Letter 9107601
 
27.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 11719997
49.3%
4 3821620
 
16.1%
7 3814235
 
16.1%
1 3811170
 
16.0%
2 288104
 
1.2%
3 288101
 
1.2%
0 6140
 
< 0.1%
8 4388
 
< 0.1%
9 4340
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 4110782
45.1%
b 4106913
45.1%
c 576205
 
6.3%
f 300108
 
3.3%
d 8989
 
0.1%
e 4604
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 23758095
72.3%
Latin 9107601
 
27.7%

Most frequent character per script

Common
ValueCountFrequency (%)
5 11719997
49.3%
4 3821620
 
16.1%
7 3814235
 
16.1%
1 3811170
 
16.0%
2 288104
 
1.2%
3 288101
 
1.2%
0 6140
 
< 0.1%
8 4388
 
< 0.1%
9 4340
 
< 0.1%
Latin
ValueCountFrequency (%)
a 4110782
45.1%
b 4106913
45.1%
c 576205
 
6.3%
f 300108
 
3.3%
d 8989
 
0.1%
e 4604
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32865696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 11719997
35.7%
a 4110782
 
12.5%
b 4106913
 
12.5%
4 3821620
 
11.6%
7 3814235
 
11.6%
1 3811170
 
11.6%
c 576205
 
1.8%
f 300108
 
0.9%
2 288104
 
0.9%
3 288101
 
0.9%
Other values (5) 28461
 
0.1%

totalamount_6A
Real number (ℝ)

MISSING  SKEWED 

Distinct107779
Distinct (%)27.4%
Missing3715421
Missing (%)90.4%
Infinite0
Infinite (%)0.0%
Mean79191.42813
Minimum0
Maximum159574000
Zeros658
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:14.396323image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4000
Q112639.601
median27547.201
Q360000
95-th percentile272594.91
Maximum159574000
Range159574000
Interquartile range (IQR)47360.399

Descriptive statistics

Standard deviation599747.9128
Coefficient of variation (CV)7.57339433
Kurtosis33803.64992
Mean79191.42813
Median Absolute Deviation (MAD)17749.201
Skewness157.8880663
Sum3.110568025 × 1010
Variance3.59697559 × 1011
MonotonicityNot monotonic
2024-02-13T20:39:14.562276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30000 7404
 
0.2%
20000 6931
 
0.2%
40000 6578
 
0.2%
60000 5734
 
0.1%
100000 4640
 
0.1%
4000 3932
 
0.1%
10000 3821
 
0.1%
200000 3796
 
0.1%
2000 3694
 
0.1%
3000 3542
 
0.1%
Other values (107769) 342719
 
8.3%
(Missing) 3715421
90.4%
ValueCountFrequency (%)
0 658
< 0.1%
50.114002 1
 
< 0.1%
52 1
 
< 0.1%
60 2
 
< 0.1%
64 1
 
< 0.1%
ValueCountFrequency (%)
159574000 1
< 0.1%
144081460 1
< 0.1%
135394800 1
< 0.1%
95710220 1
< 0.1%
94540000 2
< 0.1%

totalamount_996A
Real number (ℝ)

MISSING  SKEWED 

Distinct103553
Distinct (%)28.7%
Missing3747868
Missing (%)91.2%
Infinite0
Infinite (%)0.0%
Mean205977.3162
Minimum5
Maximum1391240100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:14.743277image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10505.901
Q128213.15075
median61076
Q3154000
95-th percentile786082.074
Maximum1391240100
Range1391240095
Interquartile range (IQR)125786.8492

Descriptive statistics

Standard deviation2558844.276
Coefficient of variation (CV)12.42294212
Kurtosis244612.0883
Mean205977.3162
Median Absolute Deviation (MAD)41093.4995
Skewness462.3917867
Sum7.422269003 × 1010
Variance6.547684031 × 1012
MonotonicityNot monotonic
2024-02-13T20:39:14.901328image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 8868
 
0.2%
200000 5590
 
0.1%
40000 5100
 
0.1%
60000 4661
 
0.1%
30000 4484
 
0.1%
20000 4001
 
0.1%
80000 2899
 
0.1%
300000 2626
 
0.1%
120000 2561
 
0.1%
103980 2528
 
0.1%
Other values (103543) 317026
 
7.7%
(Missing) 3747868
91.2%
ValueCountFrequency (%)
5 1
< 0.1%
128.6 1
< 0.1%
189.8 1
< 0.1%
320 1
< 0.1%
400 1
< 0.1%
ValueCountFrequency (%)
1391240100 1
< 0.1%
364000000 1
< 0.1%
360301570 1
< 0.1%
138027230 1
< 0.1%
62000000 1
< 0.1%

totaldebtoverduevalue_178A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct15325
Distinct (%)4.5%
Missing3770638
Missing (%)91.8%
Infinite0
Infinite (%)0.0%
Mean1807.512258
Minimum0
Maximum49005736
Zeros320411
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:15.060925image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.296100035
Maximum49005736
Range49005736
Interquartile range (IQR)0

Descriptive statistics

Standard deviation108044.733
Coefficient of variation (CV)59.77538048
Kurtosis130822.452
Mean1807.512258
Median Absolute Deviation (MAD)0
Skewness317.0916596
Sum610169143
Variance1.167366432 × 1010
MonotonicityNot monotonic
2024-02-13T20:39:15.220934image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 320411
 
7.8%
10 36
 
< 0.1%
2 36
 
< 0.1%
4 35
 
< 0.1%
0.8 31
 
< 0.1%
0.4 31
 
< 0.1%
1.2 27
 
< 0.1%
14 27
 
< 0.1%
0.2 25
 
< 0.1%
1.6 23
 
< 0.1%
Other values (15315) 16892
 
0.4%
(Missing) 3770638
91.8%
ValueCountFrequency (%)
0 320411
7.8%
0.002 4
 
< 0.1%
0.004 3
 
< 0.1%
0.006 2
 
< 0.1%
0.008 1
 
< 0.1%
ValueCountFrequency (%)
49005736 1
< 0.1%
17978344 1
< 0.1%
15792015 1
< 0.1%
15233466 1
< 0.1%
11154340 1
< 0.1%

totaldebtoverduevalue_718A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct288
Distinct (%)0.1%
Missing3809610
Missing (%)92.7%
Infinite0
Infinite (%)0.0%
Mean104.7931191
Minimum0
Maximum433225
Zeros298208
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:15.370661image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum433225
Range433225
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5031.547024
Coefficient of variation (CV)48.01409739
Kurtosis4392.306227
Mean104.7931191
Median Absolute Deviation (MAD)0
Skewness62.28840764
Sum31291434.94
Variance25316465.46
MonotonicityNot monotonic
2024-02-13T20:39:15.519726image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 298208
 
7.3%
0.6 15
 
< 0.1%
0.4 15
 
< 0.1%
0.2 11
 
< 0.1%
1 11
 
< 0.1%
0.8 10
 
< 0.1%
1.2 10
 
< 0.1%
2.2 4
 
< 0.1%
1.6 4
 
< 0.1%
4 4
 
< 0.1%
Other values (278) 310
 
< 0.1%
(Missing) 3809610
92.7%
ValueCountFrequency (%)
0 298208
7.3%
0.128 2
 
< 0.1%
0.2 11
 
< 0.1%
0.4 15
 
< 0.1%
0.6 15
 
< 0.1%
ValueCountFrequency (%)
433225 1
< 0.1%
421656.6 1
< 0.1%
417631 1
< 0.1%
415876 1
< 0.1%
413830 1
< 0.1%

totaloutstanddebtvalue_39A
Real number (ℝ)

MISSING  SKEWED 

Distinct288730
Distinct (%)85.5%
Missing3770638
Missing (%)91.8%
Infinite0
Infinite (%)0.0%
Mean196536.969
Minimum0
Maximum1688617600
Zeros39062
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:15.673776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114662.651
median57493.942
Q3161288.8875
95-th percentile720540.676
Maximum1688617600
Range1688617600
Interquartile range (IQR)146626.2365

Descriptive statistics

Standard deviation3379200.543
Coefficient of variation (CV)17.19371455
Kurtosis191928.6093
Mean196536.969
Median Absolute Deviation (MAD)52163.15365
Skewness409.4556995
Sum6.634577078 × 1010
Variance1.141899631 × 1013
MonotonicityNot monotonic
2024-02-13T20:39:15.839722image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 39062
 
1.0%
200000 157
 
< 0.1%
100000 136
 
< 0.1%
20000 108
 
< 0.1%
40000 101
 
< 0.1%
10000 95
 
< 0.1%
30000 72
 
< 0.1%
4000 67
 
< 0.1%
60000 66
 
< 0.1%
80000 53
 
< 0.1%
Other values (288720) 297657
 
7.2%
(Missing) 3770638
91.8%
ValueCountFrequency (%)
0 39062
1.0%
0.002 4
 
< 0.1%
0.004 3
 
< 0.1%
0.006 2
 
< 0.1%
0.018000001 2
 
< 0.1%
ValueCountFrequency (%)
1688617600 1
< 0.1%
701584060 1
< 0.1%
497909340 1
< 0.1%
365137500 1
< 0.1%
88925784 1
< 0.1%

totaloutstanddebtvalue_668A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct277
Distinct (%)0.1%
Missing3809610
Missing (%)92.7%
Infinite0
Infinite (%)0.0%
Mean107.904553
Minimum0
Maximum16952312
Zeros298175
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size31.3 MiB
2024-02-13T20:39:15.998463image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum16952312
Range16952312
Interquartile range (IQR)0

Descriptive statistics

Standard deviation32373.83679
Coefficient of variation (CV)300.0228989
Kurtosis252453.4039
Mean107.904553
Median Absolute Deviation (MAD)0
Skewness487.9555286
Sum32220515.33
Variance1048065309
MonotonicityNot monotonic
2024-02-13T20:39:16.158005image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 298175
 
7.3%
0.2 22
 
< 0.1%
0.4 19
 
< 0.1%
0.6 16
 
< 0.1%
1 13
 
< 0.1%
1.2 11
 
< 0.1%
0.8 10
 
< 0.1%
2.2 4
 
< 0.1%
1.6 4
 
< 0.1%
4 4
 
< 0.1%
Other values (267) 324
 
< 0.1%
(Missing) 3809610
92.7%
ValueCountFrequency (%)
0 298175
7.3%
0.2 22
 
< 0.1%
0.4 19
 
< 0.1%
0.6 16
 
< 0.1%
0.8 10
 
< 0.1%
ValueCountFrequency (%)
16952312 1
< 0.1%
3296000 1
< 0.1%
3009001.5 1
< 0.1%
2030793.2 1
< 0.1%
612921.75 1
< 0.1%