Overview

Dataset statistics

Number of variables79
Number of observations6009192
Missing cells308479374
Missing cells (%)65.0%
Total size in memory3.5 GiB
Average record size in memory632.0 B

Variable types

Numeric55
Text23
Unsupported1

Alerts

annualeffectiverate_199L has 5731910 (95.4%) missing valuesMissing
annualeffectiverate_63L has 5893065 (98.1%) missing valuesMissing
contractsum_5085717L has 6009192 (100.0%) missing valuesMissing
credlmt_230A has 5598080 (93.2%) missing valuesMissing
credlmt_935A has 5513258 (91.7%) missing valuesMissing
dateofcredend_289D has 4990049 (83.0%) missing valuesMissing
dateofcredend_353D has 3053876 (50.8%) missing valuesMissing
dateofcredstart_181D has 3053870 (50.8%) missing valuesMissing
dateofcredstart_739D has 4990049 (83.0%) missing valuesMissing
dateofrealrepmt_138D has 3071560 (51.1%) missing valuesMissing
debtoutstand_525A has 5460006 (90.9%) missing valuesMissing
debtoverdue_47A has 5460006 (90.9%) missing valuesMissing
dpdmax_139P has 4996143 (83.1%) missing valuesMissing
dpdmax_757P has 3153903 (52.5%) missing valuesMissing
dpdmaxdatemonth_442T has 3153903 (52.5%) missing valuesMissing
dpdmaxdatemonth_89T has 4996143 (83.1%) missing valuesMissing
dpdmaxdateyear_596T has 4996143 (83.1%) missing valuesMissing
dpdmaxdateyear_896T has 3153903 (52.5%) missing valuesMissing
instlamount_768A has 5519523 (91.9%) missing valuesMissing
instlamount_852A has 5739349 (95.5%) missing valuesMissing
interestrate_508L has 5978342 (99.5%) missing valuesMissing
lastupdate_1112D has 4990049 (83.0%) missing valuesMissing
lastupdate_388D has 3053959 (50.8%) missing valuesMissing
monthlyinstlamount_332A has 4996500 (83.1%) missing valuesMissing
monthlyinstlamount_674A has 3269458 (54.4%) missing valuesMissing
nominalrate_281L has 5646001 (94.0%) missing valuesMissing
nominalrate_498L has 5050573 (84.0%) missing valuesMissing
numberofcontrsvalue_258L has 5502403 (91.6%) missing valuesMissing
numberofcontrsvalue_358L has 5513211 (91.7%) missing valuesMissing
numberofinstls_229L has 3466317 (57.7%) missing valuesMissing
numberofinstls_320L has 5486131 (91.3%) missing valuesMissing
numberofoutstandinstls_520L has 3464310 (57.7%) missing valuesMissing
numberofoutstandinstls_59L has 5486161 (91.3%) missing valuesMissing
numberofoverdueinstlmax_1039L has 4990049 (83.0%) missing valuesMissing
numberofoverdueinstlmax_1151L has 3053870 (50.8%) missing valuesMissing
numberofoverdueinstlmaxdat_148D has 5115502 (85.1%) missing valuesMissing
numberofoverdueinstlmaxdat_641D has 5752162 (95.7%) missing valuesMissing
numberofoverdueinstls_725L has 4996247 (83.1%) missing valuesMissing
numberofoverdueinstls_834L has 3057638 (50.9%) missing valuesMissing
outstandingamount_354A has 3463526 (57.6%) missing valuesMissing
outstandingamount_362A has 5486023 (91.3%) missing valuesMissing
overdueamount_31A has 3056888 (50.9%) missing valuesMissing
overdueamount_659A has 4996240 (83.1%) missing valuesMissing
overdueamountmax2_14A has 4990049 (83.0%) missing valuesMissing
overdueamountmax2_398A has 3053870 (50.8%) missing valuesMissing
overdueamountmax2date_1002D has 5124897 (85.3%) missing valuesMissing
overdueamountmax2date_1142D has 5749777 (95.7%) missing valuesMissing
overdueamountmax_155A has 4990049 (83.0%) missing valuesMissing
overdueamountmax_35A has 3150457 (52.4%) missing valuesMissing
overdueamountmaxdatemonth_284T has 3150457 (52.4%) missing valuesMissing
overdueamountmaxdatemonth_365T has 4990049 (83.0%) missing valuesMissing
overdueamountmaxdateyear_2T has 4990049 (83.0%) missing valuesMissing
overdueamountmaxdateyear_994T has 3150457 (52.4%) missing valuesMissing
periodicityofpmts_1102L has 3689232 (61.4%) missing valuesMissing
periodicityofpmts_837L has 5499190 (91.5%) missing valuesMissing
prolongationcount_1120L has 5829442 (97.0%) missing valuesMissing
prolongationcount_599L has 5998234 (99.8%) missing valuesMissing
refreshdate_3813885D has 1616565 (26.9%) missing valuesMissing
residualamount_488A has 5601681 (93.2%) missing valuesMissing
residualamount_856A has 5519517 (91.9%) missing valuesMissing
totalamount_6A has 3462700 (57.6%) missing valuesMissing
totalamount_996A has 5485983 (91.3%) missing valuesMissing
totaldebtoverduevalue_178A has 5502403 (91.6%) missing valuesMissing
totaldebtoverduevalue_718A has 5513211 (91.7%) missing valuesMissing
totaloutstanddebtvalue_39A has 5502403 (91.6%) missing valuesMissing
totaloutstanddebtvalue_668A has 5513211 (91.7%) missing valuesMissing
annualeffectiverate_63L is highly skewed (γ1 = 30.6315802)Skewed
credlmt_230A is highly skewed (γ1 = 232.8737795)Skewed
credlmt_935A is highly skewed (γ1 = 315.8247847)Skewed
debtoutstand_525A is highly skewed (γ1 = 67.39489857)Skewed
debtoverdue_47A is highly skewed (γ1 = 334.9811395)Skewed
dpdmax_757P is highly skewed (γ1 = 823.384431)Skewed
instlamount_852A is highly skewed (γ1 = 409.3522506)Skewed
interestrate_508L is highly skewed (γ1 = 75.87525084)Skewed
monthlyinstlamount_332A is highly skewed (γ1 = 269.5465008)Skewed
monthlyinstlamount_674A is highly skewed (γ1 = 171.2301518)Skewed
nominalrate_281L is highly skewed (γ1 = 107.7976601)Skewed
nominalrate_498L is highly skewed (γ1 = 48.90895507)Skewed
numberofoutstandinstls_520L is highly skewed (γ1 = 1595.25473)Skewed
numberofoverdueinstlmax_1039L is highly skewed (γ1 = 44.50470327)Skewed
numberofoverdueinstlmax_1151L is highly skewed (γ1 = 783.2679156)Skewed
numberofoverdueinstls_725L is highly skewed (γ1 = 21.49426442)Skewed
numberofoverdueinstls_834L is highly skewed (γ1 = 116.5091994)Skewed
outstandingamount_354A is highly skewed (γ1 = 389.8456022)Skewed
outstandingamount_362A is highly skewed (γ1 = 34.58409652)Skewed
overdueamount_31A is highly skewed (γ1 = 156.7964922)Skewed
overdueamount_659A is highly skewed (γ1 = 462.4873507)Skewed
overdueamountmax2_14A is highly skewed (γ1 = 301.7170903)Skewed
overdueamountmax2_398A is highly skewed (γ1 = 390.7553803)Skewed
overdueamountmax_155A is highly skewed (γ1 = 332.5359818)Skewed
overdueamountmax_35A is highly skewed (γ1 = 504.7531049)Skewed
periodicityofpmts_1102L is highly skewed (γ1 = 44.45102813)Skewed
periodicityofpmts_837L is highly skewed (γ1 = 21.94446409)Skewed
prolongationcount_1120L is highly skewed (γ1 = 111.7235505)Skewed
residualamount_488A is highly skewed (γ1 = 431.4920241)Skewed
residualamount_856A is highly skewed (γ1 = 645.4294369)Skewed
totalamount_6A is highly skewed (γ1 = 145.9647068)Skewed
totalamount_996A is highly skewed (γ1 = 46.16218305)Skewed
totaldebtoverduevalue_178A is highly skewed (γ1 = 321.9188515)Skewed
totaldebtoverduevalue_718A is highly skewed (γ1 = 64.71203358)Skewed
totaloutstanddebtvalue_39A is highly skewed (γ1 = 60.59200692)Skewed
totaloutstanddebtvalue_668A is highly skewed (γ1 = 163.562063)Skewed
contractsum_5085717L is an unsupported type, check if it needs cleaning or further analysisUnsupported
credlmt_230A has 152578 (2.5%) zerosZeros
credlmt_935A has 147289 (2.5%) zerosZeros
debtoutstand_525A has 117201 (2.0%) zerosZeros
debtoverdue_47A has 523826 (8.7%) zerosZeros
dpdmax_139P has 810296 (13.5%) zerosZeros
dpdmax_757P has 2018195 (33.6%) zerosZeros
instlamount_768A has 200269 (3.3%) zerosZeros
instlamount_852A has 172555 (2.9%) zerosZeros
monthlyinstlamount_332A has 201709 (3.4%) zerosZeros
monthlyinstlamount_674A has 1370292 (22.8%) zerosZeros
num_group1 has 549263 (9.1%) zerosZeros
numberofinstls_229L has 556053 (9.3%) zerosZeros
numberofoutstandinstls_520L has 2540854 (42.3%) zerosZeros
numberofoverdueinstlmax_1039L has 762113 (12.7%) zerosZeros
numberofoverdueinstlmax_1151L has 2061632 (34.3%) zerosZeros
numberofoverdueinstls_725L has 984018 (16.4%) zerosZeros
numberofoverdueinstls_834L has 2949589 (49.1%) zerosZeros
outstandingamount_354A has 2545024 (42.4%) zerosZeros
overdueamount_31A has 2951646 (49.1%) zerosZeros
overdueamount_659A has 984025 (16.4%) zerosZeros
overdueamountmax2_14A has 759728 (12.6%) zerosZeros
overdueamountmax2_398A has 2071027 (34.5%) zerosZeros
overdueamountmax_155A has 811733 (13.5%) zerosZeros
overdueamountmax_35A has 2014289 (33.5%) zerosZeros
prolongationcount_1120L has 138122 (2.3%) zerosZeros
residualamount_488A has 407505 (6.8%) zerosZeros
residualamount_856A has 199678 (3.3%) zerosZeros
totaldebtoverduevalue_178A has 481274 (8.0%) zerosZeros
totaldebtoverduevalue_718A has 495314 (8.2%) zerosZeros
totaloutstanddebtvalue_39A has 62924 (1.0%) zerosZeros
totaloutstanddebtvalue_668A has 495368 (8.2%) zerosZeros

Reproduction

Analysis started2024-02-13 19:39:29.699243
Analysis finished2024-02-13 19:40:13.496213
Duration43.8 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

case_id
Real number (ℝ)

Distinct549263
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1323252.596
Minimum19694
Maximum2651092
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:13.636161image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum19694
5-th percentile149346
Q1816067
median1489326
Q31623104.25
95-th percentile2624310
Maximum2651092
Range2631398
Interquartile range (IQR)807037.25

Descriptive statistics

Standard deviation685663.046
Coefficient of variation (CV)0.5181648977
Kurtosis-0.254239188
Mean1323252.596
Median Absolute Deviation (MAD)195026
Skewness0.06117987715
Sum7.951678913 × 1012
Variance4.701338127 × 1011
MonotonicityIncreasing
2024-02-13T20:40:13.811060image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1447318 244
 
< 0.1%
1621606 241
 
< 0.1%
749548 233
 
< 0.1%
1653560 201
 
< 0.1%
179043 182
 
< 0.1%
142741 181
 
< 0.1%
895818 166
 
< 0.1%
1589462 166
 
< 0.1%
1546440 164
 
< 0.1%
169356 164
 
< 0.1%
Other values (549253) 6007250
> 99.9%
ValueCountFrequency (%)
19694 11
 
< 0.1%
19697 11
 
< 0.1%
19926 29
< 0.1%
19980 11
 
< 0.1%
20018 11
 
< 0.1%
ValueCountFrequency (%)
2651092 10
< 0.1%
2651091 9
< 0.1%
2651090 9
< 0.1%
2651089 15
< 0.1%
2651088 9
< 0.1%

annualeffectiverate_199L
Real number (ℝ)

MISSING 

Distinct5683
Distinct (%)2.0%
Missing5731910
Missing (%)95.4%
Infinite0
Infinite (%)0.0%
Mean704.3338134
Minimum0
Maximum91250
Zeros8830
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:13.973061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12
Q19.13
median37.3
Q396.3
95-th percentile730
Maximum91250
Range91250
Interquartile range (IQR)87.17

Descriptive statistics

Standard deviation6031.664276
Coefficient of variation (CV)8.563644342
Kurtosis122.4425417
Mean704.3338134
Median Absolute Deviation (MAD)32.41
Skewness10.93421108
Sum195299088.4
Variance36380973.93
MonotonicityNot monotonic
2024-02-13T20:40:14.129066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96.3 32909
 
0.5%
0.12 19619
 
0.3%
730 12083
 
0.2%
98.55 11673
 
0.2%
365 9217
 
0.2%
0 8830
 
0.1%
38.3 8269
 
0.1%
438 5947
 
0.1%
7.3 5525
 
0.1%
55.62 4888
 
0.1%
Other values (5673) 158322
 
2.6%
(Missing) 5731910
95.4%
ValueCountFrequency (%)
0 8830
0.1%
0.01 2
 
< 0.1%
0.06 1
 
< 0.1%
0.08 12
 
< 0.1%
0.09 385
 
< 0.1%
ValueCountFrequency (%)
91250 64
 
< 0.1%
87600 1
 
< 0.1%
73000 706
< 0.1%
69350 852
< 0.1%
62415 2
 
< 0.1%

annualeffectiverate_63L
Real number (ℝ)

MISSING  SKEWED 

Distinct4759
Distinct (%)4.1%
Missing5893065
Missing (%)98.1%
Infinite0
Infinite (%)0.0%
Mean107.0004402
Minimum0
Maximum91250
Zeros3019
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:14.281065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.11
Q15.04
median22.42
Q338.3
95-th percentile52
Maximum91250
Range91250
Interquartile range (IQR)33.26

Descriptive statistics

Standard deviation2207.746296
Coefficient of variation (CV)20.63305806
Kurtosis977.383271
Mean107.0004402
Median Absolute Deviation (MAD)17.31
Skewness30.6315802
Sum12425640.12
Variance4874143.706
MonotonicityNot monotonic
2024-02-13T20:40:14.432698image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.12 15791
 
0.3%
0.11 3337
 
0.1%
0 3019
 
0.1%
5.11 1922
 
< 0.1%
96.3 1826
 
< 0.1%
26.8 1229
 
< 0.1%
0.3 961
 
< 0.1%
38.3 894
 
< 0.1%
48.02 736
 
< 0.1%
10.8 653
 
< 0.1%
Other values (4749) 85759
 
1.4%
(Missing) 5893065
98.1%
ValueCountFrequency (%)
0 3019
0.1%
0.01 7
 
< 0.1%
0.08 1
 
< 0.1%
0.09 19
 
< 0.1%
0.1 258
 
< 0.1%
ValueCountFrequency (%)
91250 4
 
< 0.1%
73000 44
< 0.1%
69350 45
< 0.1%
62415 3
 
< 0.1%
62050 1
 
< 0.1%
Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:14.620823image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters48073536
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowea6782cc
2nd rowea6782cc
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 4991058
83.1%
ea6782cc 889670
 
14.8%
01f63ac8 84626
 
1.4%
00135d9c 28654
 
0.5%
4408ff0f 13746
 
0.2%
be7b251d 703
 
< 0.1%
1cf4e481 437
 
< 0.1%
2c070815 200
 
< 0.1%
87bdbcba 63
 
< 0.1%
4a5a01e3 34
 
< 0.1%
2024-02-13T20:40:14.918255image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 15002766
31.2%
a 5965486
 
12.4%
7 5881694
 
12.2%
1 5106149
 
10.6%
4 5019458
 
10.4%
b 4992653
 
10.4%
c 1893320
 
3.9%
8 988744
 
2.1%
6 974296
 
2.0%
e 890844
 
1.9%
Other values (6) 1358126
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34175511
71.1%
Lowercase Letter 13898025
28.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 15002766
43.9%
7 5881694
 
17.2%
1 5106149
 
14.9%
4 5019458
 
14.7%
8 988744
 
2.9%
6 974296
 
2.9%
2 890574
 
2.6%
0 169861
 
0.5%
3 113314
 
0.3%
9 28655
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
a 5965486
42.9%
b 4992653
35.9%
c 1893320
 
13.6%
e 890844
 
6.4%
f 126301
 
0.9%
d 29421
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 34175511
71.1%
Latin 13898025
28.9%

Most frequent character per script

Common
ValueCountFrequency (%)
5 15002766
43.9%
7 5881694
 
17.2%
1 5106149
 
14.9%
4 5019458
 
14.7%
8 988744
 
2.9%
6 974296
 
2.9%
2 890574
 
2.6%
0 169861
 
0.5%
3 113314
 
0.3%
9 28655
 
0.1%
Latin
ValueCountFrequency (%)
a 5965486
42.9%
b 4992653
35.9%
c 1893320
 
13.6%
e 890844
 
6.4%
f 126301
 
0.9%
d 29421
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48073536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 15002766
31.2%
a 5965486
 
12.4%
7 5881694
 
12.2%
1 5106149
 
10.6%
4 5019458
 
10.4%
b 4992653
 
10.4%
c 1893320
 
3.9%
8 988744
 
2.1%
6 974296
 
2.0%
e 890844
 
1.9%
Other values (6) 1358126
 
2.8%
Distinct355
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:15.412602image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters48073536
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

Unique54 ?
Unique (%)< 0.1%

Sample

1st rowa55475b1
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 3059099
50.9%
ea6782cc 1812980
30.2%
01f63ac8 396364
 
6.6%
00135d9c 158100
 
2.6%
42a42e75 81577
 
1.4%
9158339f 50123
 
0.8%
4408ff0f 45255
 
0.8%
130920c8 41558
 
0.7%
f0a30139 32269
 
0.5%
e6e56e83 30686
 
0.5%
Other values (345) 301181
 
5.0%
2024-02-13T20:40:16.057567image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 9648339
20.1%
a 5490883
11.4%
7 5136664
10.7%
c 4325091
9.0%
1 3866733
8.0%
4 3523460
 
7.3%
b 3194092
 
6.6%
8 2517187
 
5.2%
6 2455307
 
5.1%
2 2291760
 
4.8%
Other values (6) 5624020
11.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31929548
66.4%
Lowercase Letter 16143988
33.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 9648339
30.2%
7 5136664
16.1%
1 3866733
12.1%
4 3523460
 
11.0%
8 2517187
 
7.9%
6 2455307
 
7.7%
2 2291760
 
7.2%
0 1025521
 
3.2%
3 955546
 
3.0%
9 509031
 
1.6%
Lowercase Letter
ValueCountFrequency (%)
a 5490883
34.0%
c 4325091
26.8%
b 3194092
19.8%
e 2149518
 
13.3%
f 659489
 
4.1%
d 324915
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31929548
66.4%
Latin 16143988
33.6%

Most frequent character per script

Common
ValueCountFrequency (%)
5 9648339
30.2%
7 5136664
16.1%
1 3866733
12.1%
4 3523460
 
11.0%
8 2517187
 
7.9%
6 2455307
 
7.7%
2 2291760
 
7.2%
0 1025521
 
3.2%
3 955546
 
3.0%
9 509031
 
1.6%
Latin
ValueCountFrequency (%)
a 5490883
34.0%
c 4325091
26.8%
b 3194092
19.8%
e 2149518
 
13.3%
f 659489
 
4.1%
d 324915
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48073536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 9648339
20.1%
a 5490883
11.4%
7 5136664
10.7%
c 4325091
9.0%
1 3866733
8.0%
4 3523460
 
7.3%
b 3194092
 
6.6%
8 2517187
 
5.2%
6 2455307
 
5.1%
2 2291760
 
4.8%
Other values (6) 5624020
11.7%
Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:16.249566image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters48073536
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

Unique2 ?
Unique (%)< 0.1%

Sample

1st row7241344e
2nd row7241344e
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 4993163
83.1%
7241344e 982551
 
16.4%
8f3a197f 9987
 
0.2%
0dc85f9d 8134
 
0.1%
a52d5641 3005
 
0.1%
dd67cff0 2692
 
< 0.1%
b919198c 2364
 
< 0.1%
885ce291 1333
 
< 0.1%
90d9e529 1211
 
< 0.1%
82a92878 1058
 
< 0.1%
Other values (27) 3694
 
0.1%
2024-02-13T20:40:16.542567image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 14996625
31.2%
4 7946277
16.5%
1 5996141
 
12.5%
7 5992255
 
12.5%
a 5007759
 
10.4%
b 4996442
 
10.4%
3 996841
 
2.1%
2 992036
 
2.1%
e 987706
 
2.1%
f 34521
 
0.1%
Other values (6) 126933
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37002960
77.0%
Lowercase Letter 11070576
 
23.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 14996625
40.5%
4 7946277
21.5%
1 5996141
 
16.2%
7 5992255
 
16.2%
3 996841
 
2.7%
2 992036
 
2.7%
9 33493
 
0.1%
8 27738
 
0.1%
0 13362
 
< 0.1%
6 8192
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 5007759
45.2%
b 4996442
45.1%
e 987706
 
8.9%
f 34521
 
0.3%
d 28328
 
0.3%
c 15820
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 37002960
77.0%
Latin 11070576
 
23.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 14996625
40.5%
4 7946277
21.5%
1 5996141
 
16.2%
7 5992255
 
16.2%
3 996841
 
2.7%
2 992036
 
2.7%
9 33493
 
0.1%
8 27738
 
0.1%
0 13362
 
< 0.1%
6 8192
 
< 0.1%
Latin
ValueCountFrequency (%)
a 5007759
45.2%
b 4996442
45.1%
e 987706
 
8.9%
f 34521
 
0.3%
d 28328
 
0.3%
c 15820
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48073536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 14996625
31.2%
4 7946277
16.5%
1 5996141
 
12.5%
7 5992255
 
12.5%
a 5007759
 
10.4%
b 4996442
 
10.4%
3 996841
 
2.1%
2 992036
 
2.1%
e 987706
 
2.1%
f 34521
 
0.1%
Other values (6) 126933
 
0.3%
Distinct245
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:17.019487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters48073536
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

Unique40 ?
Unique (%)< 0.1%

Sample

1st rowa55475b1
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 3054940
50.8%
7241344e 2756334
45.9%
8f3a197f 51130
 
0.9%
a3386307 28075
 
0.5%
8260bab9 20064
 
0.3%
d7416962 19028
 
0.3%
b83056f9 9546
 
0.2%
4476359f 8759
 
0.1%
3dc5f434 7694
 
0.1%
41694615 6283
 
0.1%
Other values (235) 47339
 
0.8%
2024-02-13T20:40:17.610415image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 11400886
23.7%
5 9219857
19.2%
7 5946352
12.4%
1 5919152
12.3%
a 3170723
 
6.6%
b 3131990
 
6.5%
3 2939849
 
6.1%
2 2810479
 
5.8%
e 2772219
 
5.8%
8 152257
 
0.3%
Other values (6) 609772
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38758395
80.6%
Lowercase Letter 9315141
 
19.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 11400886
29.4%
5 9219857
23.8%
7 5946352
15.3%
1 5919152
15.3%
3 2939849
 
7.6%
2 2810479
 
7.3%
8 152257
 
0.4%
6 150420
 
0.4%
9 145781
 
0.4%
0 73362
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
a 3170723
34.0%
b 3131990
33.6%
e 2772219
29.8%
f 152171
 
1.6%
d 50966
 
0.5%
c 37072
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 38758395
80.6%
Latin 9315141
 
19.4%

Most frequent character per script

Common
ValueCountFrequency (%)
4 11400886
29.4%
5 9219857
23.8%
7 5946352
15.3%
1 5919152
15.3%
3 2939849
 
7.6%
2 2810479
 
7.3%
8 152257
 
0.4%
6 150420
 
0.4%
9 145781
 
0.4%
0 73362
 
0.2%
Latin
ValueCountFrequency (%)
a 3170723
34.0%
b 3131990
33.6%
e 2772219
29.8%
f 152171
 
1.6%
d 50966
 
0.5%
c 37072
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48073536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 11400886
23.7%
5 9219857
19.2%
7 5946352
12.4%
1 5919152
12.3%
a 3170723
 
6.6%
b 3131990
 
6.5%
3 2939849
 
6.1%
2 2810479
 
5.8%
e 2772219
 
5.8%
8 152257
 
0.3%
Other values (6) 609772
 
1.3%

contractsum_5085717L
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6009192
Missing (%)100.0%
Memory size45.8 MiB

credlmt_230A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct25694
Distinct (%)6.2%
Missing5598080
Missing (%)93.2%
Infinite0
Infinite (%)0.0%
Mean32864.08706
Minimum0
Maximum139000000
Zeros152578
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:17.781391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12000
Q339000
95-th percentile93547.6
Maximum139000000
Range139000000
Interquartile range (IQR)39000

Descriptive statistics

Standard deviation356758.2315
Coefficient of variation (CV)10.85556495
Kurtosis75218.05505
Mean32864.08706
Median Absolute Deviation (MAD)12000
Skewness232.8737795
Sum1.351082056 × 1010
Variance1.272764358 × 1011
MonotonicityNot monotonic
2024-02-13T20:40:17.938428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 152578
 
2.5%
10000 28088
 
0.5%
20000 22756
 
0.4%
30000 10563
 
0.2%
40000 7645
 
0.1%
60000 5707
 
0.1%
58000 4505
 
0.1%
4000 4432
 
0.1%
50000 4207
 
0.1%
6000 4052
 
0.1%
Other values (25684) 166579
 
2.8%
(Missing) 5598080
93.2%
ValueCountFrequency (%)
0 152578
2.5%
0.042 1
 
< 0.1%
0.068 1
 
< 0.1%
0.086 1
 
< 0.1%
0.134 1
 
< 0.1%
ValueCountFrequency (%)
139000000 1
< 0.1%
100000000 1
< 0.1%
56885160 1
< 0.1%
52000000 1
< 0.1%
45000000 1
< 0.1%

credlmt_935A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct70886
Distinct (%)14.3%
Missing5513258
Missing (%)91.7%
Infinite0
Infinite (%)0.0%
Mean107324.3263
Minimum0
Maximum1848000100
Zeros147289
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:18.104354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20000
Q360900
95-th percentile240000
Maximum1848000100
Range1848000100
Interquartile range (IQR)60900

Descriptive statistics

Standard deviation4002494.696
Coefficient of variation (CV)37.29345277
Kurtosis127410.0476
Mean107324.3263
Median Absolute Deviation (MAD)20000
Skewness315.8247847
Sum5.322578245 × 1010
Variance1.601996379 × 1013
MonotonicityNot monotonic
2024-02-13T20:40:18.293357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 147289
 
2.5%
10000 49260
 
0.8%
20000 39590
 
0.7%
30000 20179
 
0.3%
200000 9384
 
0.2%
100000 8586
 
0.1%
40000 5769
 
0.1%
60000 2995
 
< 0.1%
80000 2884
 
< 0.1%
62000 2858
 
< 0.1%
Other values (70876) 207140
 
3.4%
(Missing) 5513258
91.7%
ValueCountFrequency (%)
0 147289
2.5%
0.2 14
 
< 0.1%
0.458 1
 
< 0.1%
0.6 1
 
< 0.1%
1.9180001 1
 
< 0.1%
ValueCountFrequency (%)
1848000100 1
< 0.1%
1425000100 1
< 0.1%
700000000 1
< 0.1%
595000000 1
< 0.1%
340000000 1
< 0.1%

dateofcredend_289D
Text

MISSING 

Distinct8829
Distinct (%)0.9%
Missing4990049
Missing (%)83.0%
Memory size45.8 MiB
2024-02-13T20:40:18.793555image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters10191430
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

Unique1984 ?
Unique (%)0.2%

Sample

1st row2020-09-29
2nd row2020-04-14
3rd row2019-11-06
4th row2022-07-03
5th row2020-09-22
ValueCountFrequency (%)
2021-11-14 4184
 
0.4%
2021-10-14 3939
 
0.4%
2021-05-14 3068
 
0.3%
2021-09-14 3058
 
0.3%
2022-03-14 2870
 
0.3%
2021-08-14 2336
 
0.2%
2022-01-14 2281
 
0.2%
2021-07-14 2195
 
0.2%
2021-06-29 2087
 
0.2%
2020-11-14 1993
 
0.2%
Other values (8819) 991132
97.3%
2024-02-13T20:40:19.371582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2680605
26.3%
2 2553592
25.1%
- 2038286
20.0%
1 1333631
13.1%
9 309477
 
3.0%
3 258346
 
2.5%
4 245274
 
2.4%
8 203023
 
2.0%
5 194193
 
1.9%
7 191330
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8153144
80.0%
Dash Punctuation 2038286
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2680605
32.9%
2 2553592
31.3%
1 1333631
16.4%
9 309477
 
3.8%
3 258346
 
3.2%
4 245274
 
3.0%
8 203023
 
2.5%
5 194193
 
2.4%
7 191330
 
2.3%
6 183673
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 2038286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10191430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2680605
26.3%
2 2553592
25.1%
- 2038286
20.0%
1 1333631
13.1%
9 309477
 
3.0%
3 258346
 
2.5%
4 245274
 
2.4%
8 203023
 
2.0%
5 194193
 
1.9%
7 191330
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10191430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2680605
26.3%
2 2553592
25.1%
- 2038286
20.0%
1 1333631
13.1%
9 309477
 
3.0%
3 258346
 
2.5%
4 245274
 
2.4%
8 203023
 
2.0%
5 194193
 
1.9%
7 191330
 
1.9%

dateofcredend_353D
Text

MISSING 

Distinct10171
Distinct (%)0.3%
Missing3053876
Missing (%)50.8%
Memory size45.8 MiB
2024-02-13T20:40:19.806590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters29553160
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

Unique1115 ?
Unique (%)< 0.1%

Sample

1st row2005-09-14
2nd row2009-12-28
3rd row2007-04-16
4th row2010-11-29
5th row2012-05-31
ValueCountFrequency (%)
2019-09-17 12115
 
0.4%
2019-09-16 3588
 
0.1%
2019-06-14 3482
 
0.1%
2019-07-15 3103
 
0.1%
2019-04-15 2686
 
0.1%
2019-06-24 2659
 
0.1%
2019-03-14 2614
 
0.1%
2018-12-14 2587
 
0.1%
2019-07-29 2580
 
0.1%
2013-08-29 2546
 
0.1%
Other values (10161) 2917356
98.7%
2024-02-13T20:40:20.280440image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7164902
24.2%
- 5910632
20.0%
1 5001268
16.9%
2 4956626
16.8%
9 1289517
 
4.4%
8 1114784
 
3.8%
7 944907
 
3.2%
5 821175
 
2.8%
6 808616
 
2.7%
3 777761
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23642528
80.0%
Dash Punctuation 5910632
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7164902
30.3%
1 5001268
21.2%
2 4956626
21.0%
9 1289517
 
5.5%
8 1114784
 
4.7%
7 944907
 
4.0%
5 821175
 
3.5%
6 808616
 
3.4%
3 777761
 
3.3%
4 762972
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 5910632
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29553160
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7164902
24.2%
- 5910632
20.0%
1 5001268
16.9%
2 4956626
16.8%
9 1289517
 
4.4%
8 1114784
 
3.8%
7 944907
 
3.2%
5 821175
 
2.8%
6 808616
 
2.7%
3 777761
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29553160
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7164902
24.2%
- 5910632
20.0%
1 5001268
16.9%
2 4956626
16.8%
9 1289517
 
4.4%
8 1114784
 
3.8%
7 944907
 
3.2%
5 821175
 
2.8%
6 808616
 
2.7%
3 777761
 
2.6%

dateofcredstart_181D
Text

MISSING 

Distinct6204
Distinct (%)0.2%
Missing3053870
Missing (%)50.8%
Memory size45.8 MiB
2024-02-13T20:40:20.749919image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters29553220
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

Unique170 ?
Unique (%)< 0.1%

Sample

1st row2004-09-20
2nd row2006-12-28
3rd row2006-04-22
4th row2007-11-19
5th row2009-10-01
ValueCountFrequency (%)
2018-01-13 2291
 
0.1%
2018-01-12 2281
 
0.1%
2017-12-08 2017
 
0.1%
2018-01-05 1964
 
0.1%
2017-12-29 1918
 
0.1%
2017-12-01 1889
 
0.1%
2018-01-08 1863
 
0.1%
2018-04-03 1857
 
0.1%
2018-05-04 1794
 
0.1%
2018-01-11 1789
 
0.1%
Other values (6194) 2935659
99.3%
2024-02-13T20:40:21.387300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7283656
24.6%
- 5910644
20.0%
1 5076099
17.2%
2 4873914
16.5%
8 1121779
 
3.8%
7 1114990
 
3.8%
6 926917
 
3.1%
3 904557
 
3.1%
5 791348
 
2.7%
9 778988
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23642576
80.0%
Dash Punctuation 5910644
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7283656
30.8%
1 5076099
21.5%
2 4873914
20.6%
8 1121779
 
4.7%
7 1114990
 
4.7%
6 926917
 
3.9%
3 904557
 
3.8%
5 791348
 
3.3%
9 778988
 
3.3%
4 770328
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 5910644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29553220
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7283656
24.6%
- 5910644
20.0%
1 5076099
17.2%
2 4873914
16.5%
8 1121779
 
3.8%
7 1114990
 
3.8%
6 926917
 
3.1%
3 904557
 
3.1%
5 791348
 
2.7%
9 778988
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29553220
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7283656
24.6%
- 5910644
20.0%
1 5076099
17.2%
2 4873914
16.5%
8 1121779
 
3.8%
7 1114990
 
3.8%
6 926917
 
3.1%
3 904557
 
3.1%
5 791348
 
2.7%
9 778988
 
2.6%

dateofcredstart_739D
Text

MISSING 

Distinct4798
Distinct (%)0.5%
Missing4990049
Missing (%)83.0%
Memory size45.8 MiB
2024-02-13T20:40:21.890933image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters10191430
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

Unique349 ?
Unique (%)< 0.1%

Sample

1st row2014-09-29
2nd row2016-04-14
3rd row2013-11-27
4th row2018-07-03
5th row2018-09-22
ValueCountFrequency (%)
2019-06-28 2988
 
0.3%
2019-07-01 2309
 
0.2%
2019-06-29 2228
 
0.2%
2019-06-24 2145
 
0.2%
2019-07-12 2130
 
0.2%
2019-07-26 2110
 
0.2%
2019-07-02 2063
 
0.2%
2019-07-03 2052
 
0.2%
2019-06-21 2044
 
0.2%
2019-07-23 2014
 
0.2%
Other values (4788) 997060
97.8%
2024-02-13T20:40:22.478952image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2301270
22.6%
- 2038286
20.0%
1 1861858
18.3%
2 1617264
15.9%
9 611476
 
6.0%
8 477049
 
4.7%
7 319461
 
3.1%
3 268198
 
2.6%
6 247554
 
2.4%
5 229805
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8153144
80.0%
Dash Punctuation 2038286
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2301270
28.2%
1 1861858
22.8%
2 1617264
19.8%
9 611476
 
7.5%
8 477049
 
5.9%
7 319461
 
3.9%
3 268198
 
3.3%
6 247554
 
3.0%
5 229805
 
2.8%
4 219209
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 2038286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10191430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2301270
22.6%
- 2038286
20.0%
1 1861858
18.3%
2 1617264
15.9%
9 611476
 
6.0%
8 477049
 
4.7%
7 319461
 
3.1%
3 268198
 
2.6%
6 247554
 
2.4%
5 229805
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10191430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2301270
22.6%
- 2038286
20.0%
1 1861858
18.3%
2 1617264
15.9%
9 611476
 
6.0%
8 477049
 
4.7%
7 319461
 
3.1%
3 268198
 
2.6%
6 247554
 
2.4%
5 229805
 
2.3%

dateofrealrepmt_138D
Text

MISSING 

Distinct5922
Distinct (%)0.2%
Missing3071560
Missing (%)51.1%
Memory size45.8 MiB
2024-02-13T20:40:22.924965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters29376320
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

Unique356 ?
Unique (%)< 0.1%

Sample

1st row2005-09-15
2nd row2011-08-12
3rd row2007-04-17
4th row2012-03-14
5th row2012-05-31
ValueCountFrequency (%)
2018-08-10 29512
 
1.0%
2011-08-12 18722
 
0.6%
2019-09-17 12552
 
0.4%
2015-06-29 7734
 
0.3%
2012-11-15 6523
 
0.2%
2008-12-12 6096
 
0.2%
2015-02-23 5985
 
0.2%
2019-09-16 4821
 
0.2%
2019-09-11 4320
 
0.1%
2016-03-25 3794
 
0.1%
Other values (5912) 2837573
96.6%
2024-02-13T20:40:23.480961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7007435
23.9%
- 5875264
20.0%
1 5092611
17.3%
2 4838728
16.5%
9 1220415
 
4.2%
8 1217560
 
4.1%
7 1005297
 
3.4%
6 840848
 
2.9%
3 794780
 
2.7%
5 759532
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23501056
80.0%
Dash Punctuation 5875264
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7007435
29.8%
1 5092611
21.7%
2 4838728
20.6%
9 1220415
 
5.2%
8 1217560
 
5.2%
7 1005297
 
4.3%
6 840848
 
3.6%
3 794780
 
3.4%
5 759532
 
3.2%
4 723850
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 5875264
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29376320
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7007435
23.9%
- 5875264
20.0%
1 5092611
17.3%
2 4838728
16.5%
9 1220415
 
4.2%
8 1217560
 
4.1%
7 1005297
 
3.4%
6 840848
 
2.9%
3 794780
 
2.7%
5 759532
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29376320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7007435
23.9%
- 5875264
20.0%
1 5092611
17.3%
2 4838728
16.5%
9 1220415
 
4.2%
8 1217560
 
4.1%
7 1005297
 
3.4%
6 840848
 
2.9%
3 794780
 
2.7%
5 759532
 
2.6%

debtoutstand_525A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct412023
Distinct (%)75.0%
Missing5460006
Missing (%)90.9%
Infinite0
Infinite (%)0.0%
Mean192449.5305
Minimum0
Maximum147769090
Zeros117201
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:23.667957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15360.658225
median47273.875
Q3160239.685
95-th percentile772251.54
Maximum147769090
Range147769090
Interquartile range (IQR)154879.0268

Descriptive statistics

Standard deviation822789.0617
Coefficient of variation (CV)4.275349801
Kurtosis8494.049458
Mean192449.5305
Median Absolute Deviation (MAD)47273.875
Skewness67.39489857
Sum1.056905878 × 1011
Variance6.7698184 × 1011
MonotonicityNot monotonic
2024-02-13T20:40:23.903362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 117201
 
2.0%
200000 261
 
< 0.1%
100000 205
 
< 0.1%
20000 154
 
< 0.1%
40000 137
 
< 0.1%
30000 117
 
< 0.1%
60000 110
 
< 0.1%
10000 110
 
< 0.1%
12000 88
 
< 0.1%
80000 67
 
< 0.1%
Other values (412013) 430736
 
7.2%
(Missing) 5460006
90.9%
ValueCountFrequency (%)
0 117201
2.0%
0.002 8
 
< 0.1%
0.004 4
 
< 0.1%
0.008 1
 
< 0.1%
0.030000001 1
 
< 0.1%
ValueCountFrequency (%)
147769090 1
< 0.1%
145202620 1
< 0.1%
127456536 1
< 0.1%
126970330 1
< 0.1%
105809980 1
< 0.1%

debtoverdue_47A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct22210
Distinct (%)4.0%
Missing5460006
Missing (%)90.9%
Infinite0
Infinite (%)0.0%
Mean1984.232655
Minimum0
Maximum70695176
Zeros523826
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:24.087425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum70695176
Range70695176
Interquartile range (IQR)0

Descriptive statistics

Standard deviation159950.5662
Coefficient of variation (CV)80.61079217
Kurtosis136302.2168
Mean1984.232655
Median Absolute Deviation (MAD)0
Skewness334.9811395
Sum1089712795
Variance2.558418362 × 1010
MonotonicityNot monotonic
2024-02-13T20:40:24.265366image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 523826
 
8.7%
10 82
 
< 0.1%
14 58
 
< 0.1%
2 44
 
< 0.1%
8 41
 
< 0.1%
20 40
 
< 0.1%
1.2 33
 
< 0.1%
7000 32
 
< 0.1%
0.4 32
 
< 0.1%
6 31
 
< 0.1%
Other values (22200) 24967
 
0.4%
(Missing) 5460006
90.9%
ValueCountFrequency (%)
0 523826
8.7%
0.002 3
 
< 0.1%
0.004 2
 
< 0.1%
0.006 1
 
< 0.1%
0.008 3
 
< 0.1%
ValueCountFrequency (%)
70695176 1
< 0.1%
68885540 1
< 0.1%
26681114 1
< 0.1%
24471064 1
< 0.1%
21104634 1
< 0.1%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:24.470362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters48073536
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 5986788
99.6%
6da7c7ed 5379
 
0.1%
95decc86 3547
 
0.1%
0349102c 2642
 
< 0.1%
f8e51f8d 2405
 
< 0.1%
53179c19 2375
 
< 0.1%
1d89fa48 2321
 
< 0.1%
18e98e64 1921
 
< 0.1%
8a7423d5 900
 
< 0.1%
0cb4d552 606
 
< 0.1%
Other values (2) 308
 
< 0.1%
2024-02-13T20:40:24.806369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 17971111
37.4%
1 6000851
 
12.5%
7 6000821
 
12.5%
a 5995400
 
12.5%
4 5995178
 
12.5%
b 5987986
 
12.5%
d 20537
 
< 0.1%
c 18108
 
< 0.1%
8 18037
 
< 0.1%
9 15181
 
< 0.1%
Other values (6) 50326
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 36028301
74.9%
Lowercase Letter 12045235
 
25.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 17971111
49.9%
1 6000851
 
16.7%
7 6000821
 
16.7%
4 5995178
 
16.6%
8 18037
 
0.1%
9 15181
 
< 0.1%
6 10859
 
< 0.1%
0 6186
 
< 0.1%
3 5929
 
< 0.1%
2 4148
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 5995400
49.8%
b 5987986
49.7%
d 20537
 
0.2%
c 18108
 
0.2%
e 15173
 
0.1%
f 8031
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 36028301
74.9%
Latin 12045235
 
25.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 17971111
49.9%
1 6000851
 
16.7%
7 6000821
 
16.7%
4 5995178
 
16.6%
8 18037
 
0.1%
9 15181
 
< 0.1%
6 10859
 
< 0.1%
0 6186
 
< 0.1%
3 5929
 
< 0.1%
2 4148
 
< 0.1%
Latin
ValueCountFrequency (%)
a 5995400
49.8%
b 5987986
49.7%
d 20537
 
0.2%
c 18108
 
0.2%
e 15173
 
0.1%
f 8031
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48073536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 17971111
37.4%
1 6000851
 
12.5%
7 6000821
 
12.5%
a 5995400
 
12.5%
4 5995178
 
12.5%
b 5987986
 
12.5%
d 20537
 
< 0.1%
c 18108
 
< 0.1%
8 18037
 
< 0.1%
9 15181
 
< 0.1%
Other values (6) 50326
 
0.1%

dpdmax_139P
Real number (ℝ)

MISSING  ZEROS 

Distinct2674
Distinct (%)0.3%
Missing4996143
Missing (%)83.1%
Infinite0
Infinite (%)0.0%
Mean12.95368931
Minimum0
Maximum4871
Zeros810296
Zeros (%)13.5%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:25.034384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile21
Maximum4871
Range4871
Interquartile range (IQR)0

Descriptive statistics

Standard deviation132.2386756
Coefficient of variation (CV)10.20857243
Kurtosis397.2048693
Mean12.95368931
Median Absolute Deviation (MAD)0
Skewness18.022534
Sum13122722
Variance17487.06732
MonotonicityNot monotonic
2024-02-13T20:40:25.262361image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 810296
 
13.5%
1 44411
 
0.7%
2 14650
 
0.2%
3 12837
 
0.2%
4 11292
 
0.2%
7 7108
 
0.1%
5 6916
 
0.1%
6 5963
 
0.1%
8 5031
 
0.1%
9 4692
 
0.1%
Other values (2664) 89853
 
1.5%
(Missing) 4996143
83.1%
ValueCountFrequency (%)
0 810296
13.5%
1 44411
 
0.7%
2 14650
 
0.2%
3 12837
 
0.2%
4 11292
 
0.2%
ValueCountFrequency (%)
4871 1
< 0.1%
4841 1
< 0.1%
4808 1
< 0.1%
4797 1
< 0.1%
4772 1
< 0.1%

dpdmax_757P
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct3908
Distinct (%)0.1%
Missing3153903
Missing (%)52.5%
Infinite0
Infinite (%)0.0%
Mean46.083076
Minimum-12
Maximum657458
Zeros2018195
Zeros (%)33.6%
Negative449
Negative (%)< 0.1%
Memory size45.8 MiB
2024-02-13T20:40:25.457091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-12
5-th percentile0
Q10
median0
Q31
95-th percentile127
Maximum657458
Range657470
Interquartile range (IQR)1

Descriptive statistics

Standard deviation500.5157112
Coefficient of variation (CV)10.86116107
Kurtosis1047251.476
Mean46.083076
Median Absolute Deviation (MAD)0
Skewness823.384431
Sum131580500
Variance250515.9772
MonotonicityNot monotonic
2024-02-13T20:40:25.635091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2018195
33.6%
1 146490
 
2.4%
2 50413
 
0.8%
3 48195
 
0.8%
4 38666
 
0.6%
6 28538
 
0.5%
5 24870
 
0.4%
7 23966
 
0.4%
8 16889
 
0.3%
9 16802
 
0.3%
Other values (3898) 442265
 
7.4%
(Missing) 3153903
52.5%
ValueCountFrequency (%)
-12 2
< 0.1%
-11 1
 
< 0.1%
-9 1
 
< 0.1%
-8 3
< 0.1%
-7 1
 
< 0.1%
ValueCountFrequency (%)
657458 1
 
< 0.1%
84575 5
< 0.1%
84574 5
< 0.1%
84573 1
 
< 0.1%
84560 3
< 0.1%

dpdmaxdatemonth_442T
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing3153903
Missing (%)52.5%
Infinite0
Infinite (%)0.0%
Mean6.540715493
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:25.776091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.420602826
Coefficient of variation (CV)0.5229707406
Kurtosis-1.175906048
Mean6.540715493
Median Absolute Deviation (MAD)3
Skewness-0.06042856713
Sum18675633
Variance11.70052369
MonotonicityNot monotonic
2024-02-13T20:40:25.896091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 276927
 
4.6%
1 255302
 
4.2%
7 255013
 
4.2%
6 252651
 
4.2%
11 245224
 
4.1%
9 241839
 
4.0%
10 237179
 
3.9%
5 221604
 
3.7%
2 221211
 
3.7%
3 220477
 
3.7%
Other values (2) 427862
 
7.1%
(Missing) 3153903
52.5%
ValueCountFrequency (%)
1 255302
4.2%
2 221211
3.7%
3 220477
3.7%
4 211013
3.5%
5 221604
3.7%
ValueCountFrequency (%)
12 216849
3.6%
11 245224
4.1%
10 237179
3.9%
9 241839
4.0%
8 276927
4.6%

dpdmaxdatemonth_89T
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing4996143
Missing (%)83.1%
Infinite0
Infinite (%)0.0%
Mean6.997161046
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:26.035147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.501374906
Coefficient of variation (CV)0.5003993596
Kurtosis-1.085622281
Mean6.997161046
Median Absolute Deviation (MAD)3
Skewness-0.3378530238
Sum7088467
Variance12.25962623
MonotonicityNot monotonic
2024-02-13T20:40:26.163689image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 115065
 
1.9%
10 113734
 
1.9%
9 111805
 
1.9%
8 102472
 
1.7%
11 97308
 
1.6%
12 90560
 
1.5%
7 85595
 
1.4%
6 75197
 
1.3%
5 66443
 
1.1%
4 57996
 
1.0%
Other values (2) 96874
 
1.6%
(Missing) 4996143
83.1%
ValueCountFrequency (%)
1 115065
1.9%
2 52235
0.9%
3 44639
 
0.7%
4 57996
1.0%
5 66443
1.1%
ValueCountFrequency (%)
12 90560
1.5%
11 97308
1.6%
10 113734
1.9%
9 111805
1.9%
8 102472
1.7%

dpdmaxdateyear_596T
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)< 0.1%
Missing4996143
Missing (%)83.1%
Infinite0
Infinite (%)0.0%
Mean2018.366068
Minimum2009
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:26.285689image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2017
Q12018
median2019
Q32019
95-th percentile2019
Maximum2020
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7529273152
Coefficient of variation (CV)0.0003730380366
Kurtosis-0.8655936456
Mean2018.366068
Median Absolute Deviation (MAD)0
Skewness-0.7023578511
Sum2044703727
Variance0.5668995419
MonotonicityNot monotonic
2024-02-13T20:40:26.401706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2019 537055
 
8.9%
2018 306470
 
5.1%
2017 168394
 
2.8%
2020 1113
 
< 0.1%
2016 15
 
< 0.1%
2015 1
 
< 0.1%
2009 1
 
< 0.1%
(Missing) 4996143
83.1%
ValueCountFrequency (%)
2009 1
 
< 0.1%
2015 1
 
< 0.1%
2016 15
 
< 0.1%
2017 168394
2.8%
2018 306470
5.1%
ValueCountFrequency (%)
2020 1113
 
< 0.1%
2019 537055
8.9%
2018 306470
5.1%
2017 168394
 
2.8%
2016 15
 
< 0.1%

dpdmaxdateyear_896T
Real number (ℝ)

MISSING 

Distinct19
Distinct (%)< 0.1%
Missing3153903
Missing (%)52.5%
Infinite0
Infinite (%)0.0%
Mean2014.394582
Minimum2002
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:26.520689image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2002
5-th percentile2007
Q12012
median2015
Q32018
95-th percentile2019
Maximum2020
Range18
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.796577968
Coefficient of variation (CV)0.001884724077
Kurtosis-0.5578409648
Mean2014.394582
Median Absolute Deviation (MAD)3
Skewness-0.7391431543
Sum5751678691
Variance14.41400427
MonotonicityNot monotonic
2024-02-13T20:40:26.653689image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2018 509675
 
8.5%
2017 385178
 
6.4%
2016 265516
 
4.4%
2019 248467
 
4.1%
2015 236916
 
3.9%
2014 211711
 
3.5%
2013 182974
 
3.0%
2012 155508
 
2.6%
2007 140437
 
2.3%
2011 139733
 
2.3%
Other values (9) 379174
 
6.3%
(Missing) 3153903
52.5%
ValueCountFrequency (%)
2002 1
 
< 0.1%
2003 2
 
< 0.1%
2004 911
 
< 0.1%
2005 18792
 
0.3%
2006 71751
1.2%
ValueCountFrequency (%)
2020 168
 
< 0.1%
2019 248467
4.1%
2018 509675
8.5%
2017 385178
6.4%
2016 265516
4.4%
Distinct308
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:27.133689image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length9.017463746
Min length8

Characters and Unicode

Total characters54187671
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 3053870
44.0%
home 925362
 
13.3%
credit 925362
 
13.3%
p133_127_114 340311
 
4.9%
p150_136_157 278169
 
4.0%
b619fa46 215988
 
3.1%
p204_66_73 192194
 
2.8%
p40_52_135 159653
 
2.3%
d6a7d943 99668
 
1.4%
9a93e20f 95741
 
1.4%
Other values (299) 648236
 
9.3%
2024-02-13T20:40:28.071767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 10597542
19.6%
1 5856851
10.8%
4 4328670
 
8.0%
7 4135272
 
7.6%
a 3715669
 
6.9%
b 3675145
 
6.8%
e 2147742
 
4.0%
_ 2124044
 
3.9%
3 1770368
 
3.3%
d 1507746
 
2.8%
Other values (16) 14328622
26.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31666559
58.4%
Lowercase Letter 16558960
30.6%
Uppercase Letter 2912746
 
5.4%
Connector Punctuation 2124044
 
3.9%
Space Separator 925362
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3715669
22.4%
b 3675145
22.2%
e 2147742
13.0%
d 1507746
9.1%
r 925362
 
5.6%
m 925362
 
5.6%
i 925362
 
5.6%
t 925362
 
5.6%
o 925362
 
5.6%
f 513986
 
3.1%
Decimal Number
ValueCountFrequency (%)
5 10597542
33.5%
1 5856851
18.5%
4 4328670
13.7%
7 4135272
 
13.1%
3 1770368
 
5.6%
6 1354029
 
4.3%
2 1264543
 
4.0%
0 1078625
 
3.4%
9 931198
 
2.9%
8 349461
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
P 1062022
36.5%
C 925362
31.8%
H 925362
31.8%
Connector Punctuation
ValueCountFrequency (%)
_ 2124044
100.0%
Space Separator
ValueCountFrequency (%)
925362
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34715965
64.1%
Latin 19471706
35.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3715669
19.1%
b 3675145
18.9%
e 2147742
11.0%
d 1507746
7.7%
P 1062022
 
5.5%
C 925362
 
4.8%
r 925362
 
4.8%
m 925362
 
4.8%
i 925362
 
4.8%
t 925362
 
4.8%
Other values (4) 2736572
14.1%
Common
ValueCountFrequency (%)
5 10597542
30.5%
1 5856851
16.9%
4 4328670
12.5%
7 4135272
 
11.9%
_ 2124044
 
6.1%
3 1770368
 
5.1%
6 1354029
 
3.9%
2 1264543
 
3.6%
0 1078625
 
3.1%
9 931198
 
2.7%
Other values (2) 1274823
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54187671
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 10597542
19.6%
1 5856851
10.8%
4 4328670
 
8.0%
7 4135272
 
7.6%
a 3715669
 
6.9%
b 3675145
 
6.8%
e 2147742
 
4.0%
_ 2124044
 
3.9%
3 1770368
 
3.3%
d 1507746
 
2.8%
Other values (16) 14328622
26.4%
Distinct155
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:28.287477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.400395261
Min length8

Characters and Unicode

Total characters50479588
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

Unique22 ?
Unique (%)< 0.1%

Sample

1st rowP204_66_73
2nd rowP150_136_157
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 4990049
79.7%
p204_66_73 414874
 
6.6%
home 248273
 
4.0%
credit 248273
 
4.0%
p133_127_114 101022
 
1.6%
p150_136_157 83860
 
1.3%
50babcd4 29811
 
0.5%
d6a7d943 24892
 
0.4%
p102_97_118 20323
 
0.3%
0d39f5db 18925
 
0.3%
Other values (146) 77163
 
1.2%
2024-02-13T20:40:28.610473image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 15251035
30.2%
1 5762338
 
11.4%
7 5662001
 
11.2%
4 5583499
 
11.1%
b 5113855
 
10.1%
a 5078657
 
10.1%
_ 1271146
 
2.5%
6 959399
 
1.9%
3 803066
 
1.6%
P 635573
 
1.3%
Other values (16) 4359019
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35372087
70.1%
Lowercase Letter 12455963
 
24.7%
Connector Punctuation 1271146
 
2.5%
Uppercase Letter 1132119
 
2.2%
Space Separator 248273
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 5113855
41.1%
a 5078657
40.8%
e 526235
 
4.2%
d 395070
 
3.2%
r 248273
 
2.0%
t 248273
 
2.0%
i 248273
 
2.0%
o 248273
 
2.0%
m 248273
 
2.0%
f 51247
 
0.4%
Decimal Number
ValueCountFrequency (%)
5 15251035
43.1%
1 5762338
 
16.3%
7 5662001
 
16.0%
4 5583499
 
15.8%
6 959399
 
2.7%
3 803066
 
2.3%
0 611908
 
1.7%
2 608208
 
1.7%
9 93536
 
0.3%
8 37097
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
P 635573
56.1%
C 248273
 
21.9%
H 248273
 
21.9%
Connector Punctuation
ValueCountFrequency (%)
_ 1271146
100.0%
Space Separator
ValueCountFrequency (%)
248273
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36891506
73.1%
Latin 13588082
 
26.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 5113855
37.6%
a 5078657
37.4%
P 635573
 
4.7%
e 526235
 
3.9%
d 395070
 
2.9%
r 248273
 
1.8%
t 248273
 
1.8%
i 248273
 
1.8%
o 248273
 
1.8%
C 248273
 
1.8%
Other values (4) 597327
 
4.4%
Common
ValueCountFrequency (%)
5 15251035
41.3%
1 5762338
 
15.6%
7 5662001
 
15.3%
4 5583499
 
15.1%
_ 1271146
 
3.4%
6 959399
 
2.6%
3 803066
 
2.2%
0 611908
 
1.7%
2 608208
 
1.6%
248273
 
0.7%
Other values (2) 130633
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50479588
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 15251035
30.2%
1 5762338
 
11.4%
7 5662001
 
11.2%
4 5583499
 
11.1%
b 5113855
 
10.1%
a 5078657
 
10.1%
_ 1271146
 
2.5%
6 959399
 
1.9%
3 803066
 
1.6%
P 635573
 
1.3%
Other values (16) 4359019
 
8.6%

instlamount_768A
Real number (ℝ)

MISSING  ZEROS 

Distinct104349
Distinct (%)21.3%
Missing5519523
Missing (%)91.9%
Infinite0
Infinite (%)0.0%
Mean3520.906719
Minimum0
Maximum206222.2
Zeros200269
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:28.768602image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1237.4
Q35049.2
95-th percentile13995.76
Maximum206222.2
Range206222.2
Interquartile range (IQR)5049.2

Descriptive statistics

Standard deviation5480.087707
Coefficient of variation (CV)1.556442174
Kurtosis28.1743342
Mean3520.906719
Median Absolute Deviation (MAD)1237.4
Skewness3.369146058
Sum1724078872
Variance30031361.27
MonotonicityNot monotonic
2024-02-13T20:40:28.923613image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 200269
 
3.3%
400 2407
 
< 0.1%
600 1704
 
< 0.1%
1000 1328
 
< 0.1%
9067.8 1102
 
< 0.1%
2389.6 1004
 
< 0.1%
10230 907
 
< 0.1%
12772.4 772
 
< 0.1%
4779 653
 
< 0.1%
2000 612
 
< 0.1%
Other values (104339) 278911
 
4.6%
(Missing) 5519523
91.9%
ValueCountFrequency (%)
0 200269
3.3%
0.002 4
 
< 0.1%
0.006 3
 
< 0.1%
0.008 1
 
< 0.1%
0.010000001 1
 
< 0.1%
ValueCountFrequency (%)
206222.2 1
< 0.1%
164387.75 1
< 0.1%
160648.61 2
< 0.1%
159702.92 1
< 0.1%
145851.48 1
< 0.1%

instlamount_852A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct47766
Distinct (%)17.7%
Missing5739349
Missing (%)95.5%
Infinite0
Infinite (%)0.0%
Mean634.6283238
Minimum0
Maximum4063333.2
Zeros172555
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:29.074603image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3400
95-th percentile3508.86858
Maximum4063333.2
Range4063333.2
Interquartile range (IQR)400

Descriptive statistics

Standard deviation8646.470486
Coefficient of variation (CV)13.62446358
Kurtosis185238.8141
Mean634.6283238
Median Absolute Deviation (MAD)0
Skewness409.3522506
Sum171250010.8
Variance74761451.87
MonotonicityNot monotonic
2024-02-13T20:40:29.251729image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 172555
 
2.9%
400 9216
 
0.2%
1000 5624
 
0.1%
1200 1698
 
< 0.1%
2000 1490
 
< 0.1%
2260 1168
 
< 0.1%
800 1147
 
< 0.1%
1540 1146
 
< 0.1%
3000 1064
 
< 0.1%
3700 744
 
< 0.1%
Other values (47756) 73991
 
1.2%
(Missing) 5739349
95.5%
ValueCountFrequency (%)
0 172555
2.9%
0.002 16
 
< 0.1%
0.004 17
 
< 0.1%
0.006 21
 
< 0.1%
0.008 12
 
< 0.1%
ValueCountFrequency (%)
4063333.2 1
< 0.1%
1623111.2 1
< 0.1%
311258.2 1
< 0.1%
269403.6 1
< 0.1%
205175.16 1
< 0.1%

interestrate_508L
Real number (ℝ)

MISSING  SKEWED 

Distinct229
Distinct (%)0.7%
Missing5978342
Missing (%)99.5%
Infinite0
Infinite (%)0.0%
Mean74.38081232
Minimum0
Maximum151423
Zeros348
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:29.429767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.5
Q118
median20
Q324
95-th percentile35
Maximum151423
Range151423
Interquartile range (IQR)6

Descriptive statistics

Standard deviation1222.211177
Coefficient of variation (CV)16.43180733
Kurtosis8264.982577
Mean74.38081232
Median Absolute Deviation (MAD)4
Skewness75.87525084
Sum2294648.06
Variance1493800.162
MonotonicityNot monotonic
2024-02-13T20:40:29.595729image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 5107
 
0.1%
24 3710
 
0.1%
18 1900
 
< 0.1%
20 1854
 
< 0.1%
22 1851
 
< 0.1%
35 1752
 
< 0.1%
17 1507
 
< 0.1%
16 1410
 
< 0.1%
25 1276
 
< 0.1%
26 1027
 
< 0.1%
Other values (219) 9456
 
0.2%
(Missing) 5978342
99.5%
ValueCountFrequency (%)
0 348
< 0.1%
4 10
 
< 0.1%
4.5 3
 
< 0.1%
5 16
 
< 0.1%
5.3 7
 
< 0.1%
ValueCountFrequency (%)
151423 1
< 0.1%
71446 1
< 0.1%
61461 1
< 0.1%
32559 1
< 0.1%
27437 1
< 0.1%

lastupdate_1112D
Text

MISSING 

Distinct257
Distinct (%)< 0.1%
Missing4990049
Missing (%)83.0%
Memory size45.8 MiB
2024-02-13T20:40:29.993539image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters10191430
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

Unique33 ?
Unique (%)< 0.1%

Sample

1st row2019-06-27
2nd row2019-07-03
3rd row2019-06-27
4th row2019-06-30
5th row2019-06-27
ValueCountFrequency (%)
2019-08-07 50095
 
4.9%
2019-12-12 48460
 
4.8%
2019-11-30 35326
 
3.5%
2019-09-05 33070
 
3.2%
2019-08-22 31271
 
3.1%
2019-11-16 30403
 
3.0%
2019-10-08 29752
 
2.9%
2019-11-19 29410
 
2.9%
2019-09-21 27771
 
2.7%
2019-07-11 26879
 
2.6%
Other values (247) 676706
66.4%
2024-02-13T20:40:30.457314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2241635
22.0%
0 2118418
20.8%
- 2038286
20.0%
2 1569590
15.4%
9 1266541
12.4%
7 266817
 
2.6%
8 239656
 
2.4%
3 133256
 
1.3%
5 120132
 
1.2%
4 104159
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8153144
80.0%
Dash Punctuation 2038286
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2241635
27.5%
0 2118418
26.0%
2 1569590
19.3%
9 1266541
15.5%
7 266817
 
3.3%
8 239656
 
2.9%
3 133256
 
1.6%
5 120132
 
1.5%
4 104159
 
1.3%
6 92940
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 2038286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10191430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2241635
22.0%
0 2118418
20.8%
- 2038286
20.0%
2 1569590
15.4%
9 1266541
12.4%
7 266817
 
2.6%
8 239656
 
2.4%
3 133256
 
1.3%
5 120132
 
1.2%
4 104159
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10191430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2241635
22.0%
0 2118418
20.8%
- 2038286
20.0%
2 1569590
15.4%
9 1266541
12.4%
7 266817
 
2.6%
8 239656
 
2.4%
3 133256
 
1.3%
5 120132
 
1.2%
4 104159
 
1.0%

lastupdate_388D
Text

MISSING 

Distinct4831
Distinct (%)0.2%
Missing3053959
Missing (%)50.8%
Memory size45.8 MiB
2024-02-13T20:40:30.895838image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters29552330
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

Unique151 ?
Unique (%)< 0.1%

Sample

1st row2005-09-15
2nd row2011-10-29
3rd row2007-06-28
4th row2014-05-13
5th row2012-06-18
ValueCountFrequency (%)
2007-09-25 47367
 
1.6%
2008-06-13 25632
 
0.9%
2008-11-12 20316
 
0.7%
2015-04-10 20031
 
0.7%
2013-03-07 19305
 
0.7%
2018-12-28 17873
 
0.6%
2018-08-10 17407
 
0.6%
2018-08-11 14805
 
0.5%
2013-10-02 13939
 
0.5%
2018-12-31 13499
 
0.5%
Other values (4821) 2745059
92.9%
2024-02-13T20:40:31.460805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7010756
23.7%
- 5910466
20.0%
1 5254336
17.8%
2 4761808
16.1%
9 1231622
 
4.2%
8 1197956
 
4.1%
7 975092
 
3.3%
6 869142
 
2.9%
3 824668
 
2.8%
5 821937
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23641864
80.0%
Dash Punctuation 5910466
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7010756
29.7%
1 5254336
22.2%
2 4761808
20.1%
9 1231622
 
5.2%
8 1197956
 
5.1%
7 975092
 
4.1%
6 869142
 
3.7%
3 824668
 
3.5%
5 821937
 
3.5%
4 694547
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 5910466
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29552330
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7010756
23.7%
- 5910466
20.0%
1 5254336
17.8%
2 4761808
16.1%
9 1231622
 
4.2%
8 1197956
 
4.1%
7 975092
 
3.3%
6 869142
 
2.9%
3 824668
 
2.8%
5 821937
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29552330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7010756
23.7%
- 5910466
20.0%
1 5254336
17.8%
2 4761808
16.1%
9 1231622
 
4.2%
8 1197956
 
4.1%
7 975092
 
3.3%
6 869142
 
2.9%
3 824668
 
2.8%
5 821937
 
2.8%

monthlyinstlamount_332A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct242373
Distinct (%)23.9%
Missing4996500
Missing (%)83.1%
Infinite0
Infinite (%)0.0%
Mean5594.887054
Minimum0
Maximum20000000
Zeros201709
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:31.686842image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11027.996
median3150.3699
Q36516.803725
95-th percentile16249.61765
Maximum20000000
Range20000000
Interquartile range (IQR)5488.807725

Descriptive statistics

Standard deviation57772.92963
Coefficient of variation (CV)10.3260225
Kurtosis84963.33501
Mean5594.887054
Median Absolute Deviation (MAD)2730.3699
Skewness269.5465008
Sum5665897361
Variance3337711398
MonotonicityNot monotonic
2024-02-13T20:40:31.883802image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 201709
 
3.4%
400 2414
 
< 0.1%
600 1727
 
< 0.1%
1000 1453
 
< 0.1%
9067.8 1106
 
< 0.1%
2389.6 1021
 
< 0.1%
4200.492 1007
 
< 0.1%
2000 916
 
< 0.1%
10230 909
 
< 0.1%
2100.246 857
 
< 0.1%
Other values (242363) 799573
 
13.3%
(Missing) 4996500
83.1%
ValueCountFrequency (%)
0 201709
3.4%
0.002 4
 
< 0.1%
0.006 3
 
< 0.1%
0.008 1
 
< 0.1%
0.010000001 1
 
< 0.1%
ValueCountFrequency (%)
20000000 4
< 0.1%
18082384 1
 
< 0.1%
16000000 3
< 0.1%
10161606 1
 
< 0.1%
6742803.5 1
 
< 0.1%

monthlyinstlamount_674A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct482063
Distinct (%)17.6%
Missing3269458
Missing (%)54.4%
Infinite0
Infinite (%)0.0%
Mean6539.519352
Minimum0
Maximum50274444
Zeros1370292
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:32.060836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33588.051
95-th percentile18646.38175
Maximum50274444
Range50274444
Interquartile range (IQR)3588.051

Descriptive statistics

Standard deviation85896.17013
Coefficient of variation (CV)13.1349363
Kurtosis62040.01991
Mean6539.519352
Median Absolute Deviation (MAD)0
Skewness171.2301518
Sum1.791654351 × 1010
Variance7378152043
MonotonicityNot monotonic
2024-02-13T20:40:32.222837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1370292
22.8%
400 9232
 
0.2%
1000 5697
 
0.1%
4200 2623
 
< 0.1%
2100 2579
 
< 0.1%
3150 2293
 
< 0.1%
2000 2045
 
< 0.1%
0.1 1816
 
< 0.1%
1200 1777
 
< 0.1%
6300 1616
 
< 0.1%
Other values (482053) 1339764
22.3%
(Missing) 3269458
54.4%
ValueCountFrequency (%)
0 1370292
22.8%
0.002 36
 
< 0.1%
0.004 40
 
< 0.1%
0.006 36
 
< 0.1%
0.008 58
 
< 0.1%
ValueCountFrequency (%)
50274444 1
< 0.1%
30086070 1
< 0.1%
24654622 1
< 0.1%
21233590 1
< 0.1%
20139078 1
< 0.1%

nominalrate_281L
Real number (ℝ)

MISSING  SKEWED 

Distinct960
Distinct (%)0.3%
Missing5646001
Missing (%)94.0%
Infinite0
Infinite (%)0.0%
Mean32.29003604
Minimum0
Maximum30341.2
Zeros46636
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:32.370992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115
median39
Q343
95-th percentile45
Maximum30341.2
Range30341.2
Interquartile range (IQR)28

Descriptive statistics

Standard deviation135.0432076
Coefficient of variation (CV)4.18219439
Kurtosis18277.91913
Mean32.29003604
Median Absolute Deviation (MAD)6
Skewness107.7976601
Sum11727450.48
Variance18236.66791
MonotonicityNot monotonic
2024-02-13T20:40:32.525994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 56089
 
0.9%
0 46636
 
0.8%
42 44227
 
0.7%
39 36776
 
0.6%
0.12 29343
 
0.5%
43.3 17839
 
0.3%
40 14672
 
0.2%
43 11085
 
0.2%
18.1 5642
 
0.1%
40.05 5328
 
0.1%
Other values (950) 95554
 
1.6%
(Missing) 5646001
94.0%
ValueCountFrequency (%)
0 46636
0.8%
0.01 7
 
< 0.1%
0.11 1
 
< 0.1%
0.12 29343
0.5%
0.16 1
 
< 0.1%
ValueCountFrequency (%)
30341.2 1
 
< 0.1%
30341.1 1
 
< 0.1%
19020.6 3
< 0.1%
11690.1 1
 
< 0.1%
9089.9 2
< 0.1%

nominalrate_498L
Real number (ℝ)

MISSING  SKEWED 

Distinct1759
Distinct (%)0.2%
Missing5050573
Missing (%)84.0%
Infinite0
Infinite (%)0.0%
Mean66.23276993
Minimum0
Maximum30341.2
Zeros33355
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:32.680238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12
Q120
median43
Q345
95-th percentile98.55
Maximum30341.2
Range30341.2
Interquartile range (IQR)25

Descriptive statistics

Standard deviation336.1849681
Coefficient of variation (CV)5.075810183
Kurtosis3793.782925
Mean66.23276993
Median Absolute Deviation (MAD)3
Skewness48.90895507
Sum63491991.68
Variance113020.3328
MonotonicityNot monotonic
2024-02-13T20:40:32.866650image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 338001
 
5.6%
0.12 151353
 
2.5%
42 34415
 
0.6%
0 33355
 
0.6%
96.3 32909
 
0.5%
43.3 24883
 
0.4%
40.05 17376
 
0.3%
39 16862
 
0.3%
40 16084
 
0.3%
35 15899
 
0.3%
Other values (1749) 277482
 
4.6%
(Missing) 5050573
84.0%
ValueCountFrequency (%)
0 33355
0.6%
0.01 2
 
< 0.1%
0.05 1
 
< 0.1%
0.06 2
 
< 0.1%
0.1 81
 
< 0.1%
ValueCountFrequency (%)
30341.2 1
 
< 0.1%
30341.1 50
< 0.1%
19020.6 15
 
< 0.1%
11690.1 23
< 0.1%
9891.4 3
 
< 0.1%

num_group1
Real number (ℝ)

ZEROS 

Distinct244
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.935895874
Minimum0
Maximum243
Zeros549263
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:33.036588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile16
Maximum243
Range243
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.232536646
Coefficient of variation (CV)1.049974052
Kurtosis103.1338
Mean5.935895874
Median Absolute Deviation (MAD)3
Skewness6.196251105
Sum35669938
Variance38.84451305
MonotonicityNot monotonic
2024-02-13T20:40:33.209289image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 549263
9.1%
1 549228
9.1%
2 549190
9.1%
3 549175
9.1%
4 549175
9.1%
5 549175
9.1%
6 549175
9.1%
7 549175
9.1%
8 549175
9.1%
9 189953
 
3.2%
Other values (234) 876508
14.6%
ValueCountFrequency (%)
0 549263
9.1%
1 549228
9.1%
2 549190
9.1%
3 549175
9.1%
4 549175
9.1%
ValueCountFrequency (%)
243 1
< 0.1%
242 1
< 0.1%
241 1
< 0.1%
240 2
< 0.1%
239 2
< 0.1%

numberofcontrsvalue_258L
Real number (ℝ)

MISSING 

Distinct24
Distinct (%)< 0.1%
Missing5502403
Missing (%)91.6%
Infinite0
Infinite (%)0.0%
Mean1.998587183
Minimum0
Maximum83
Zeros1436
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:33.350324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum83
Range83
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.111658653
Coefficient of variation (CV)0.5562222464
Kurtosis71.13826566
Mean1.998587183
Median Absolute Deviation (MAD)1
Skewness2.329365104
Sum1012862
Variance1.23578496
MonotonicityNot monotonic
2024-02-13T20:40:33.475669image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 204295
 
3.4%
2 164366
 
2.7%
3 87482
 
1.5%
4 34857
 
0.6%
5 10101
 
0.2%
6 3087
 
0.1%
0 1436
 
< 0.1%
7 837
 
< 0.1%
8 219
 
< 0.1%
9 54
 
< 0.1%
Other values (14) 55
 
< 0.1%
(Missing) 5502403
91.6%
ValueCountFrequency (%)
0 1436
 
< 0.1%
1 204295
3.4%
2 164366
2.7%
3 87482
1.5%
4 34857
 
0.6%
ValueCountFrequency (%)
83 1
< 0.1%
50 1
< 0.1%
46 1
< 0.1%
32 1
< 0.1%
24 1
< 0.1%

numberofcontrsvalue_358L
Real number (ℝ)

MISSING 

Distinct131
Distinct (%)< 0.1%
Missing5513211
Missing (%)91.7%
Infinite0
Infinite (%)0.0%
Mean6.000947617
Minimum0
Maximum237
Zeros94
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:33.615700image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q38
95-th percentile17
Maximum237
Range237
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.804850495
Coefficient of variation (CV)0.9673223074
Kurtosis40.09927965
Mean6.000947617
Median Absolute Deviation (MAD)3
Skewness3.568897713
Sum2976356
Variance33.69628928
MonotonicityNot monotonic
2024-02-13T20:40:33.779140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 86186
 
1.4%
2 65952
 
1.1%
3 55807
 
0.9%
4 47607
 
0.8%
5 40261
 
0.7%
6 33805
 
0.6%
7 28446
 
0.5%
8 23660
 
0.4%
9 19518
 
0.3%
10 16604
 
0.3%
Other values (121) 78135
 
1.3%
(Missing) 5513211
91.7%
ValueCountFrequency (%)
0 94
 
< 0.1%
1 86186
1.4%
2 65952
1.1%
3 55807
0.9%
4 47607
0.8%
ValueCountFrequency (%)
237 1
< 0.1%
215 1
< 0.1%
178 1
< 0.1%
164 1
< 0.1%
163 1
< 0.1%

numberofinstls_229L
Real number (ℝ)

MISSING  ZEROS 

Distinct384
Distinct (%)< 0.1%
Missing3466317
Missing (%)57.7%
Infinite0
Infinite (%)0.0%
Mean12.2203675
Minimum0
Maximum714
Zeros556053
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:33.946141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q313
95-th percentile36
Maximum714
Range714
Interquartile range (IQR)12

Descriptive statistics

Standard deviation20.70605313
Coefficient of variation (CV)1.694388743
Kurtosis71.16087694
Mean12.2203675
Median Absolute Deviation (MAD)6
Skewness6.650172547
Sum31074867
Variance428.7406361
MonotonicityNot monotonic
2024-02-13T20:40:34.104766image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 556053
 
9.3%
12 421852
 
7.0%
6 292275
 
4.9%
1 221575
 
3.7%
24 149841
 
2.5%
3 104440
 
1.7%
18 96178
 
1.6%
36 87562
 
1.5%
10 62379
 
1.0%
4 46893
 
0.8%
Other values (374) 503827
 
8.4%
(Missing) 3466317
57.7%
ValueCountFrequency (%)
0 556053
9.3%
1 221575
 
3.7%
2 14437
 
0.2%
3 104440
 
1.7%
4 46893
 
0.8%
ValueCountFrequency (%)
714 1
 
< 0.1%
602 1
 
< 0.1%
600 3
< 0.1%
576 1
 
< 0.1%
540 1
 
< 0.1%

numberofinstls_320L
Real number (ℝ)

MISSING 

Distinct331
Distinct (%)0.1%
Missing5486131
Missing (%)91.3%
Infinite0
Infinite (%)0.0%
Mean31.77488476
Minimum0
Maximum365
Zeros123
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:34.264935image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q112
median24
Q337
95-th percentile84
Maximum365
Range365
Interquartile range (IQR)25

Descriptive statistics

Standard deviation35.0344978
Coefficient of variation (CV)1.102584575
Kurtosis18.36133537
Mean31.77488476
Median Absolute Deviation (MAD)12
Skewness3.712640631
Sum16620203
Variance1227.416036
MonotonicityNot monotonic
2024-02-13T20:40:34.434896image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 107782
 
1.8%
24 72857
 
1.2%
36 43136
 
0.7%
48 36081
 
0.6%
6 28488
 
0.5%
60 27484
 
0.5%
18 27232
 
0.5%
16 27035
 
0.4%
30 12985
 
0.2%
1 10228
 
0.2%
Other values (321) 129753
 
2.2%
(Missing) 5486131
91.3%
ValueCountFrequency (%)
0 123
 
< 0.1%
1 10228
0.2%
2 60
 
< 0.1%
3 8633
0.1%
4 4070
 
0.1%
ValueCountFrequency (%)
365 1
 
< 0.1%
364 1
 
< 0.1%
362 2
 
< 0.1%
361 2
 
< 0.1%
360 8
< 0.1%

numberofoutstandinstls_520L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct255
Distinct (%)< 0.1%
Missing3464310
Missing (%)57.7%
Infinite0
Infinite (%)0.0%
Mean1.14516508
Minimum0
Maximum2700000
Zeros2540854
Zeros (%)42.3%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:34.612935image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2700000
Range2700000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1692.509781
Coefficient of variation (CV)1477.961396
Kurtosis2544852.432
Mean1.14516508
Median Absolute Deviation (MAD)0
Skewness1595.25473
Sum2914310
Variance2864589.358
MonotonicityNot monotonic
2024-02-13T20:40:34.828896image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2540854
42.3%
1 388
 
< 0.1%
2 195
 
< 0.1%
4 186
 
< 0.1%
6 136
 
< 0.1%
8 134
 
< 0.1%
10 134
 
< 0.1%
12 125
 
< 0.1%
14 104
 
< 0.1%
16 97
 
< 0.1%
Other values (245) 2529
 
< 0.1%
(Missing) 3464310
57.7%
ValueCountFrequency (%)
0 2540854
42.3%
1 388
 
< 0.1%
2 195
 
< 0.1%
3 51
 
< 0.1%
4 186
 
< 0.1%
ValueCountFrequency (%)
2700000 1
< 0.1%
699 2
< 0.1%
693 1
< 0.1%
690 1
< 0.1%
534 1
< 0.1%

numberofoutstandinstls_59L
Real number (ℝ)

MISSING 

Distinct301
Distinct (%)0.1%
Missing5486161
Missing (%)91.3%
Infinite0
Infinite (%)0.0%
Mean21.81110297
Minimum0
Maximum326
Zeros4910
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:35.032845image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q326
95-th percentile60
Maximum326
Range326
Interquartile range (IQR)20

Descriptive statistics

Standard deviation30.03119716
Coefficient of variation (CV)1.376876593
Kurtosis24.31773075
Mean21.81110297
Median Absolute Deviation (MAD)8
Skewness4.190885144
Sum11407883
Variance901.872803
MonotonicityNot monotonic
2024-02-13T20:40:35.201649image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 32924
 
0.5%
2 22281
 
0.4%
5 22086
 
0.4%
3 22030
 
0.4%
6 21774
 
0.4%
10 21638
 
0.4%
9 21501
 
0.4%
11 20152
 
0.3%
8 19977
 
0.3%
4 19858
 
0.3%
Other values (291) 298810
 
5.0%
(Missing) 5486161
91.3%
ValueCountFrequency (%)
0 4910
 
0.1%
1 32924
0.5%
2 22281
0.4%
3 22030
0.4%
4 19858
0.3%
ValueCountFrequency (%)
326 1
< 0.1%
307 1
< 0.1%
304 1
< 0.1%
302 2
< 0.1%
301 2
< 0.1%

numberofoverdueinstlmax_1039L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct2912
Distinct (%)0.3%
Missing4990049
Missing (%)83.0%
Infinite0
Infinite (%)0.0%
Mean16.68235665
Minimum0
Maximum40121
Zeros762113
Zeros (%)12.7%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:35.361326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile33
Maximum40121
Range40121
Interquartile range (IQR)1

Descriptive statistics

Standard deviation161.1306823
Coefficient of variation (CV)9.658748206
Kurtosis7808.901856
Mean16.68235665
Median Absolute Deviation (MAD)0
Skewness44.50470327
Sum17001707
Variance25963.09679
MonotonicityNot monotonic
2024-02-13T20:40:35.524197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 762113
 
12.7%
1 48766
 
0.8%
2 16236
 
0.3%
4 13781
 
0.2%
3 13736
 
0.2%
5 10664
 
0.2%
8 7656
 
0.1%
7 6058
 
0.1%
6 5923
 
0.1%
11 5491
 
0.1%
Other values (2902) 128719
 
2.1%
(Missing) 4990049
83.0%
ValueCountFrequency (%)
0 762113
12.7%
1 48766
 
0.8%
2 16236
 
0.3%
3 13736
 
0.2%
4 13781
 
0.2%
ValueCountFrequency (%)
40121 1
< 0.1%
40107 1
< 0.1%
5412 1
< 0.1%
5379 1
< 0.1%
5342 1
< 0.1%

numberofoverdueinstlmax_1151L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct4261
Distinct (%)0.1%
Missing3053870
Missing (%)50.8%
Infinite0
Infinite (%)0.0%
Mean52.86492098
Minimum0
Maximum730509
Zeros2061632
Zeros (%)34.3%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:35.686878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile152
Maximum730509
Range730509
Interquartile range (IQR)1

Descriptive statistics

Standard deviation559.7576371
Coefficient of variation (CV)10.58845122
Kurtosis986162.5518
Mean52.86492098
Median Absolute Deviation (MAD)0
Skewness783.2679156
Sum156232864
Variance313328.6123
MonotonicityNot monotonic
2024-02-13T20:40:35.876876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2061632
34.3%
1 158506
 
2.6%
2 49902
 
0.8%
4 45832
 
0.8%
3 45658
 
0.8%
7 29419
 
0.5%
5 26138
 
0.4%
6 21953
 
0.4%
8 19915
 
0.3%
9 15152
 
0.3%
Other values (4251) 481215
 
8.0%
(Missing) 3053870
50.8%
ValueCountFrequency (%)
0 2061632
34.3%
1 158506
 
2.6%
2 49902
 
0.8%
3 45658
 
0.8%
4 45832
 
0.8%
ValueCountFrequency (%)
730509 1
 
< 0.1%
93972 8
< 0.1%
93971 3
 
< 0.1%
93956 3
 
< 0.1%
93925 3
 
< 0.1%
Distinct4719
Distinct (%)0.5%
Missing5115502
Missing (%)85.1%
Memory size45.8 MiB
2024-02-13T20:40:36.242209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters8936900
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

Unique307 ?
Unique (%)< 0.1%

Sample

1st row2011-06-24
2nd row2009-07-16
3rd row2009-11-27
4th row2015-02-05
5th row2018-09-07
ValueCountFrequency (%)
2007-07-31 34902
 
3.9%
2011-08-24 9989
 
1.1%
2007-07-05 7993
 
0.9%
2012-03-04 7543
 
0.8%
2008-10-15 5826
 
0.7%
2011-09-04 4785
 
0.5%
2010-01-07 3364
 
0.4%
2018-08-02 3285
 
0.4%
2018-09-17 3172
 
0.4%
2015-06-26 3105
 
0.3%
Other values (4709) 809726
90.6%
2024-02-13T20:40:36.718237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2178743
24.4%
- 1787380
20.0%
1 1541544
17.2%
2 1415226
15.8%
7 351969
 
3.9%
8 326305
 
3.7%
5 273240
 
3.1%
9 269941
 
3.0%
4 266945
 
3.0%
3 264358
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7149520
80.0%
Dash Punctuation 1787380
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2178743
30.5%
1 1541544
21.6%
2 1415226
19.8%
7 351969
 
4.9%
8 326305
 
4.6%
5 273240
 
3.8%
9 269941
 
3.8%
4 266945
 
3.7%
3 264358
 
3.7%
6 261249
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 1787380
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8936900
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2178743
24.4%
- 1787380
20.0%
1 1541544
17.2%
2 1415226
15.8%
7 351969
 
3.9%
8 326305
 
3.7%
5 273240
 
3.1%
9 269941
 
3.0%
4 266945
 
3.0%
3 264358
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8936900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2178743
24.4%
- 1787380
20.0%
1 1541544
17.2%
2 1415226
15.8%
7 351969
 
3.9%
8 326305
 
3.7%
5 273240
 
3.1%
9 269941
 
3.0%
4 266945
 
3.0%
3 264358
 
3.0%
Distinct2210
Distinct (%)0.9%
Missing5752162
Missing (%)95.7%
Memory size45.8 MiB
2024-02-13T20:40:37.134194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2570300
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

Unique467 ?
Unique (%)0.2%

Sample

1st row2017-11-21
2nd row2018-03-08
3rd row2019-04-23
4th row2018-03-31
5th row2019-02-12
ValueCountFrequency (%)
2019-08-07 3550
 
1.4%
2019-07-11 2919
 
1.1%
2019-03-26 2697
 
1.0%
2019-07-10 2576
 
1.0%
2019-05-07 2556
 
1.0%
2019-07-24 2509
 
1.0%
2019-07-23 2172
 
0.8%
2019-04-10 2126
 
0.8%
2019-06-27 2035
 
0.8%
2019-06-25 2015
 
0.8%
Other values (2200) 231875
90.2%
2024-02-13T20:40:37.670878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 578765
22.5%
- 514060
20.0%
1 474921
18.5%
2 407897
15.9%
9 172698
 
6.7%
8 102917
 
4.0%
7 76652
 
3.0%
6 70525
 
2.7%
5 64110
 
2.5%
4 56533
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2056240
80.0%
Dash Punctuation 514060
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 578765
28.1%
1 474921
23.1%
2 407897
19.8%
9 172698
 
8.4%
8 102917
 
5.0%
7 76652
 
3.7%
6 70525
 
3.4%
5 64110
 
3.1%
4 56533
 
2.7%
3 51222
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 514060
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2570300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 578765
22.5%
- 514060
20.0%
1 474921
18.5%
2 407897
15.9%
9 172698
 
6.7%
8 102917
 
4.0%
7 76652
 
3.0%
6 70525
 
2.7%
5 64110
 
2.5%
4 56533
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2570300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 578765
22.5%
- 514060
20.0%
1 474921
18.5%
2 407897
15.9%
9 172698
 
6.7%
8 102917
 
4.0%
7 76652
 
3.0%
6 70525
 
2.7%
5 64110
 
2.5%
4 56533
 
2.2%

numberofoverdueinstls_725L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct2592
Distinct (%)0.3%
Missing4996247
Missing (%)83.1%
Infinite0
Infinite (%)0.0%
Mean8.813187291
Minimum0
Maximum5412
Zeros984018
Zeros (%)16.4%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:37.843858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5412
Range5412
Interquartile range (IQR)0

Descriptive statistics

Standard deviation129.2048515
Coefficient of variation (CV)14.66040006
Kurtosis556.1088097
Mean8.813187291
Median Absolute Deviation (MAD)0
Skewness21.49426442
Sum8927274
Variance16693.89365
MonotonicityNot monotonic
2024-02-13T20:40:38.014377image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 984018
 
16.4%
1 4291
 
0.1%
2 1526
 
< 0.1%
3 1457
 
< 0.1%
4 1291
 
< 0.1%
8 802
 
< 0.1%
5 770
 
< 0.1%
7 692
 
< 0.1%
6 678
 
< 0.1%
11 586
 
< 0.1%
Other values (2582) 16834
 
0.3%
(Missing) 4996247
83.1%
ValueCountFrequency (%)
0 984018
16.4%
1 4291
 
0.1%
2 1526
 
< 0.1%
3 1457
 
< 0.1%
4 1291
 
< 0.1%
ValueCountFrequency (%)
5412 1
< 0.1%
5379 1
< 0.1%
5342 1
< 0.1%
5330 1
< 0.1%
5302 1
< 0.1%

numberofoverdueinstls_834L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct221
Distinct (%)< 0.1%
Missing3057638
Missing (%)50.9%
Infinite0
Infinite (%)0.0%
Mean0.04916562597
Minimum0
Maximum1300
Zeros2949589
Zeros (%)49.1%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:38.463413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1300
Range1300
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.644498331
Coefficient of variation (CV)94.46637237
Kurtosis16609.29612
Mean0.04916562597
Median Absolute Deviation (MAD)0
Skewness116.5091994
Sum145115
Variance21.57136475
MonotonicityNot monotonic
2024-02-13T20:40:38.630374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2949589
49.1%
1 638
 
< 0.1%
2 307
 
< 0.1%
3 163
 
< 0.1%
4 122
 
< 0.1%
5 86
 
< 0.1%
6 67
 
< 0.1%
7 38
 
< 0.1%
11 29
 
< 0.1%
10 29
 
< 0.1%
Other values (211) 486
 
< 0.1%
(Missing) 3057638
50.9%
ValueCountFrequency (%)
0 2949589
49.1%
1 638
 
< 0.1%
2 307
 
< 0.1%
3 163
 
< 0.1%
4 122
 
< 0.1%
ValueCountFrequency (%)
1300 1
< 0.1%
1195 1
< 0.1%
1178 1
< 0.1%
1144 1
< 0.1%
882 1
< 0.1%

outstandingamount_354A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct374
Distinct (%)< 0.1%
Missing3463526
Missing (%)57.6%
Infinite0
Infinite (%)0.0%
Mean3.35444537
Minimum0
Maximum443392.3
Zeros2545024
Zeros (%)42.4%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:38.793221image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum443392.3
Range443392.3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation740.455286
Coefficient of variation (CV)220.7385139
Kurtosis181311.4163
Mean3.35444537
Median Absolute Deviation (MAD)0
Skewness389.8456022
Sum8539297.528
Variance548274.0305
MonotonicityNot monotonic
2024-02-13T20:40:38.944109image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2545024
42.4%
0.2 43
 
< 0.1%
0.4 32
 
< 0.1%
0.6 30
 
< 0.1%
1.2 14
 
< 0.1%
0.8 14
 
< 0.1%
1 14
 
< 0.1%
4 11
 
< 0.1%
1.8000001 10
 
< 0.1%
1.4 9
 
< 0.1%
Other values (364) 465
 
< 0.1%
(Missing) 3463526
57.6%
ValueCountFrequency (%)
0 2545024
42.4%
0.2 43
 
< 0.1%
0.4 32
 
< 0.1%
0.6 30
 
< 0.1%
0.8 14
 
< 0.1%
ValueCountFrequency (%)
443392.3 1
< 0.1%
392057.16 1
< 0.1%
390000 2
< 0.1%
300000 1
< 0.1%
274440 1
< 0.1%

outstandingamount_362A
Real number (ℝ)

MISSING  SKEWED 

Distinct481793
Distinct (%)92.1%
Missing5486023
Missing (%)91.3%
Infinite0
Infinite (%)0.0%
Mean169818.5725
Minimum0
Maximum73109624
Zeros5570
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:39.103129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3325.9808
Q114422.372
median38828.22
Q3120118.4
95-th percentile703690.06
Maximum73109624
Range73109624
Interquartile range (IQR)105696.028

Descriptive statistics

Standard deviation612226.718
Coefficient of variation (CV)3.605181158
Kurtosis2561.207821
Mean169818.5725
Median Absolute Deviation (MAD)30851.3762
Skewness34.58409652
Sum8.884381278 × 1010
Variance3.748215543 × 1011
MonotonicityNot monotonic
2024-02-13T20:40:39.274405image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5570
 
0.1%
4228 283
 
< 0.1%
6342 247
 
< 0.1%
20000 225
 
< 0.1%
60000 225
 
< 0.1%
2114 210
 
< 0.1%
40000 206
 
< 0.1%
100000 200
 
< 0.1%
10000 162
 
< 0.1%
30000 148
 
< 0.1%
Other values (481783) 515693
 
8.6%
(Missing) 5486023
91.3%
ValueCountFrequency (%)
0 5570
0.1%
0.002 18
 
< 0.1%
0.004 8
 
< 0.1%
0.006 1
 
< 0.1%
0.012 1
 
< 0.1%
ValueCountFrequency (%)
73109624 1
< 0.1%
66130624 1
< 0.1%
66000000 1
< 0.1%
62687850 1
< 0.1%
58697600 1
< 0.1%

overdueamount_31A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct446
Distinct (%)< 0.1%
Missing3056888
Missing (%)50.9%
Infinite0
Infinite (%)0.0%
Mean16.77646951
Minimum0
Maximum496510
Zeros2951646
Zeros (%)49.1%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:39.467788image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum496510
Range496510
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1976.527388
Coefficient of variation (CV)117.815455
Kurtosis28272.19037
Mean16.77646951
Median Absolute Deviation (MAD)0
Skewness156.7964922
Sum49529238.03
Variance3906660.515
MonotonicityNot monotonic
2024-02-13T20:40:39.633788image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2951646
49.1%
0.2 26
 
< 0.1%
0.6 24
 
< 0.1%
0.4 21
 
< 0.1%
1.2 13
 
< 0.1%
0.8 12
 
< 0.1%
4 11
 
< 0.1%
1 11
 
< 0.1%
1.8000001 9
 
< 0.1%
1.4 9
 
< 0.1%
Other values (436) 522
 
< 0.1%
(Missing) 3056888
50.9%
ValueCountFrequency (%)
0 2951646
49.1%
0.2 26
 
< 0.1%
0.4 21
 
< 0.1%
0.6 24
 
< 0.1%
0.8 12
 
< 0.1%
ValueCountFrequency (%)
496510 1
< 0.1%
486075 1
< 0.1%
433225 1
< 0.1%
429100 2
< 0.1%
425800 2
< 0.1%

overdueamount_659A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct24837
Distinct (%)2.5%
Missing4996240
Missing (%)83.1%
Infinite0
Infinite (%)0.0%
Mean1081.671286
Minimum0
Maximum70695176
Zeros984025
Zeros (%)16.4%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:39.796235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum70695176
Range70695176
Interquartile range (IQR)0

Descriptive statistics

Standard deviation103765.3929
Coefficient of variation (CV)95.93061616
Kurtosis272880.4703
Mean1081.671286
Median Absolute Deviation (MAD)0
Skewness462.4873507
Sum1095681092
Variance1.076725677 × 1010
MonotonicityNot monotonic
2024-02-13T20:40:39.991235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 984025
 
16.4%
10 92
 
< 0.1%
14 68
 
< 0.1%
2 49
 
< 0.1%
20 45
 
< 0.1%
7000 43
 
< 0.1%
8 41
 
< 0.1%
0.2 40
 
< 0.1%
6000 38
 
< 0.1%
1.2 38
 
< 0.1%
Other values (24827) 28473
 
0.5%
(Missing) 4996240
83.1%
ValueCountFrequency (%)
0 984025
16.4%
0.002 4
 
< 0.1%
0.004 2
 
< 0.1%
0.006 1
 
< 0.1%
0.008 3
 
< 0.1%
ValueCountFrequency (%)
70695176 1
< 0.1%
50082108 1
< 0.1%
24141158 1
< 0.1%
21104634 1
< 0.1%
17402200 1
< 0.1%

overdueamountmax2_14A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct198271
Distinct (%)19.5%
Missing4990049
Missing (%)83.0%
Infinite0
Infinite (%)0.0%
Mean3466.317557
Minimum0
Maximum70698930
Zeros759728
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:40.165234image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.94799995
95-th percentile9471.5314
Maximum70698930
Range70698930
Interquartile range (IQR)2.94799995

Descriptive statistics

Standard deviation132925.3761
Coefficient of variation (CV)38.34772028
Kurtosis124514.1774
Mean3466.317557
Median Absolute Deviation (MAD)0
Skewness301.7170903
Sum3532673274
Variance1.76691556 × 1010
MonotonicityNot monotonic
2024-02-13T20:40:40.331235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 759728
 
12.6%
10 312
 
< 0.1%
0.2 259
 
< 0.1%
2 225
 
< 0.1%
0.4 219
 
< 0.1%
800 215
 
< 0.1%
400 211
 
< 0.1%
0.8 204
 
< 0.1%
4 182
 
< 0.1%
2000 172
 
< 0.1%
Other values (198261) 257416
 
4.3%
(Missing) 4990049
83.0%
ValueCountFrequency (%)
0 759728
12.6%
0.002 23
 
< 0.1%
0.004 18
 
< 0.1%
0.006 14
 
< 0.1%
0.008 12
 
< 0.1%
ValueCountFrequency (%)
70698930 1
< 0.1%
50082108 1
< 0.1%
48865336 1
< 0.1%
31050000 1
< 0.1%
24141158 1
< 0.1%

overdueamountmax2_398A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct468092
Distinct (%)15.8%
Missing3053870
Missing (%)50.8%
Infinite0
Infinite (%)0.0%
Mean6189.370993
Minimum0
Maximum229478910
Zeros2071027
Zeros (%)34.5%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:40.504243image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3805.86703
95-th percentile15172.33775
Maximum229478910
Range229478910
Interquartile range (IQR)805.86703

Descriptive statistics

Standard deviation301629.9601
Coefficient of variation (CV)48.7335402
Kurtosis213013.8948
Mean6189.370993
Median Absolute Deviation (MAD)0
Skewness390.7553803
Sum1.829158426 × 1010
Variance9.098063284 × 1010
MonotonicityNot monotonic
2024-02-13T20:40:40.671248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2071027
34.5%
0.2 3906
 
0.1%
0.4 2082
 
< 0.1%
0.8 1460
 
< 0.1%
2 1274
 
< 0.1%
0.6 1213
 
< 0.1%
1.6 1179
 
< 0.1%
1 1126
 
< 0.1%
1.2 1103
 
< 0.1%
4 1052
 
< 0.1%
Other values (468082) 869900
 
14.5%
(Missing) 3053870
50.8%
ValueCountFrequency (%)
0 2071027
34.5%
0.002 113
 
< 0.1%
0.004 61
 
< 0.1%
0.006 57
 
< 0.1%
0.008 62
 
< 0.1%
ValueCountFrequency (%)
229478910 1
< 0.1%
153404140 1
< 0.1%
147928240 2
< 0.1%
147251890 1
< 0.1%
124524430 1
< 0.1%
Distinct4540
Distinct (%)0.5%
Missing5124897
Missing (%)85.3%
Memory size45.8 MiB
2024-02-13T20:40:41.010811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters8842950
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

Unique293 ?
Unique (%)< 0.1%

Sample

1st row2011-06-24
2nd row2009-07-16
3rd row2010-11-17
4th row2015-02-05
5th row2018-09-07
ValueCountFrequency (%)
2011-10-06 13023
 
1.5%
2008-10-15 5811
 
0.7%
2010-01-07 3825
 
0.4%
2015-07-07 3769
 
0.4%
2018-12-20 2863
 
0.3%
2018-09-17 2706
 
0.3%
2007-07-31 2581
 
0.3%
2008-11-27 2578
 
0.3%
2018-05-16 2553
 
0.3%
2018-11-20 2493
 
0.3%
Other values (4530) 842093
95.2%
2024-02-13T20:40:41.518844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2148185
24.3%
- 1768590
20.0%
1 1565164
17.7%
2 1423704
16.1%
8 327643
 
3.7%
7 288392
 
3.3%
6 278326
 
3.1%
5 275605
 
3.1%
9 264041
 
3.0%
4 253423
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7074360
80.0%
Dash Punctuation 1768590
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2148185
30.4%
1 1565164
22.1%
2 1423704
20.1%
8 327643
 
4.6%
7 288392
 
4.1%
6 278326
 
3.9%
5 275605
 
3.9%
9 264041
 
3.7%
4 253423
 
3.6%
3 249877
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 1768590
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8842950
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2148185
24.3%
- 1768590
20.0%
1 1565164
17.7%
2 1423704
16.1%
8 327643
 
3.7%
7 288392
 
3.3%
6 278326
 
3.1%
5 275605
 
3.1%
9 264041
 
3.0%
4 253423
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8842950
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2148185
24.3%
- 1768590
20.0%
1 1565164
17.7%
2 1423704
16.1%
8 327643
 
3.7%
7 288392
 
3.3%
6 278326
 
3.1%
5 275605
 
3.1%
9 264041
 
3.0%
4 253423
 
2.9%
Distinct2491
Distinct (%)1.0%
Missing5749777
Missing (%)95.7%
Memory size45.8 MiB
2024-02-13T20:40:41.945300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2594150
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

Unique489 ?
Unique (%)0.2%

Sample

1st row2018-03-31
2nd row2017-01-19
3rd row2019-04-23
4th row2018-06-15
5th row2019-02-12
ValueCountFrequency (%)
2019-08-07 3249
 
1.3%
2019-05-07 2903
 
1.1%
2019-03-26 2789
 
1.1%
2019-07-10 2570
 
1.0%
2019-04-10 2482
 
1.0%
2019-07-24 2436
 
0.9%
2019-07-23 2075
 
0.8%
2018-11-20 1986
 
0.8%
2019-06-10 1960
 
0.8%
2019-04-11 1899
 
0.7%
Other values (2481) 235066
90.6%
2024-02-13T20:40:42.484660image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 587775
22.7%
- 518830
20.0%
1 477927
18.4%
2 411032
15.8%
9 168097
 
6.5%
8 106615
 
4.1%
7 78874
 
3.0%
6 70759
 
2.7%
5 63550
 
2.4%
4 56616
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2075320
80.0%
Dash Punctuation 518830
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 587775
28.3%
1 477927
23.0%
2 411032
19.8%
9 168097
 
8.1%
8 106615
 
5.1%
7 78874
 
3.8%
6 70759
 
3.4%
5 63550
 
3.1%
4 56616
 
2.7%
3 54075
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 518830
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2594150
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 587775
22.7%
- 518830
20.0%
1 477927
18.4%
2 411032
15.8%
9 168097
 
6.5%
8 106615
 
4.1%
7 78874
 
3.0%
6 70759
 
2.7%
5 63550
 
2.4%
4 56616
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2594150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 587775
22.7%
- 518830
20.0%
1 477927
18.4%
2 411032
15.8%
9 168097
 
6.5%
8 106615
 
4.1%
7 78874
 
3.0%
6 70759
 
2.7%
5 63550
 
2.4%
4 56616
 
2.2%

overdueamountmax_155A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct161750
Distinct (%)15.9%
Missing4990049
Missing (%)83.0%
Infinite0
Infinite (%)0.0%
Mean2738.434173
Minimum0
Maximum70698930
Zeros811733
Zeros (%)13.5%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:42.670686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7424.63243
Maximum70698930
Range70698930
Interquartile range (IQR)0

Descriptive statistics

Standard deviation127843.8103
Coefficient of variation (CV)46.68500401
Kurtosis144862.7465
Mean2738.434173
Median Absolute Deviation (MAD)0
Skewness332.5359818
Sum2790856018
Variance1.634403984 × 1010
MonotonicityNot monotonic
2024-02-13T20:40:42.838676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 811733
 
13.5%
10 310
 
< 0.1%
1000 253
 
< 0.1%
2 215
 
< 0.1%
400 207
 
< 0.1%
0.2 200
 
< 0.1%
0.4 194
 
< 0.1%
2000 191
 
< 0.1%
0.8 180
 
< 0.1%
800 174
 
< 0.1%
Other values (161740) 205486
 
3.4%
(Missing) 4990049
83.0%
ValueCountFrequency (%)
0 811733
13.5%
0.002 20
 
< 0.1%
0.004 15
 
< 0.1%
0.006 12
 
< 0.1%
0.008 10
 
< 0.1%
ValueCountFrequency (%)
70698930 1
< 0.1%
50082108 1
< 0.1%
48865336 1
< 0.1%
31050000 1
< 0.1%
24141158 1
< 0.1%

overdueamountmax_35A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct442677
Distinct (%)15.5%
Missing3150457
Missing (%)52.4%
Infinite0
Infinite (%)0.0%
Mean5144.05513
Minimum0
Maximum229478910
Zeros2014289
Zeros (%)33.5%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:43.006667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3596.865
95-th percentile14006.7118
Maximum229478910
Range229478910
Interquartile range (IQR)596.865

Descriptive statistics

Standard deviation273629.8705
Coefficient of variation (CV)53.19341718
Kurtosis318754.1476
Mean5144.05513
Median Absolute Deviation (MAD)0
Skewness504.7531049
Sum1.470549044 × 1010
Variance7.487330604 × 1010
MonotonicityNot monotonic
2024-02-13T20:40:43.173674image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2014289
33.5%
0.2 3647
 
0.1%
0.4 1970
 
< 0.1%
0.8 1427
 
< 0.1%
2 1260
 
< 0.1%
0.6 1181
 
< 0.1%
1.6 1179
 
< 0.1%
1 1106
 
< 0.1%
1.2 1089
 
< 0.1%
4 1022
 
< 0.1%
Other values (442667) 830565
 
13.8%
(Missing) 3150457
52.4%
ValueCountFrequency (%)
0 2014289
33.5%
0.002 109
 
< 0.1%
0.004 62
 
< 0.1%
0.006 59
 
< 0.1%
0.008 61
 
< 0.1%
ValueCountFrequency (%)
229478910 1
< 0.1%
153404140 1
< 0.1%
147928240 2
< 0.1%
147251890 1
< 0.1%
124524430 1
< 0.1%

overdueamountmaxdatemonth_284T
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing3150457
Missing (%)52.4%
Infinite0
Infinite (%)0.0%
Mean6.571696572
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:43.308954image/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 deviation3.446101235
Coefficient of variation (CV)0.5243853238
Kurtosis-1.195983767
Mean6.571696572
Median Absolute Deviation (MAD)3
Skewness-0.06299174456
Sum18786739
Variance11.87561372
MonotonicityNot monotonic
2024-02-13T20:40:43.429948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11 261656
 
4.4%
6 257641
 
4.3%
1 255327
 
4.2%
8 253063
 
4.2%
9 245477
 
4.1%
7 244642
 
4.1%
10 240396
 
4.0%
2 225019
 
3.7%
5 224416
 
3.7%
12 223893
 
3.7%
Other values (2) 427205
 
7.1%
(Missing) 3150457
52.4%
ValueCountFrequency (%)
1 255327
4.2%
2 225019
3.7%
3 210555
3.5%
4 216650
3.6%
5 224416
3.7%
ValueCountFrequency (%)
12 223893
3.7%
11 261656
4.4%
10 240396
4.0%
9 245477
4.1%
8 253063
4.2%

overdueamountmaxdatemonth_365T
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing4990049
Missing (%)83.0%
Infinite0
Infinite (%)0.0%
Mean6.993357164
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:43.553527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.508968266
Coefficient of variation (CV)0.5017573369
Kurtosis-1.092743608
Mean6.993357164
Median Absolute Deviation (MAD)3
Skewness-0.3320765647
Sum7127231
Variance12.31285829
MonotonicityNot monotonic
2024-02-13T20:40:43.710012image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 116461
 
1.9%
10 114211
 
1.9%
9 111150
 
1.8%
8 103298
 
1.7%
11 97268
 
1.6%
12 92814
 
1.5%
7 84165
 
1.4%
6 76225
 
1.3%
5 66584
 
1.1%
4 60244
 
1.0%
Other values (2) 96723
 
1.6%
(Missing) 4990049
83.0%
ValueCountFrequency (%)
1 116461
1.9%
2 52360
0.9%
3 44363
 
0.7%
4 60244
1.0%
5 66584
1.1%
ValueCountFrequency (%)
12 92814
1.5%
11 97268
1.6%
10 114211
1.9%
9 111150
1.8%
8 103298
1.7%

overdueamountmaxdateyear_2T
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)< 0.1%
Missing4990049
Missing (%)83.0%
Infinite0
Infinite (%)0.0%
Mean2018.357355
Minimum2009
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:43.836009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2017
Q12018
median2019
Q32019
95-th percentile2019
Maximum2020
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.754067765
Coefficient of variation (CV)0.0003736046855
Kurtosis-0.8912350512
Mean2018.357355
Median Absolute Deviation (MAD)0
Skewness-0.6853889533
Sum2056994770
Variance0.5686181942
MonotonicityNot monotonic
2024-02-13T20:40:43.958008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2019 534285
 
8.9%
2018 312254
 
5.2%
2017 171718
 
2.9%
2020 853
 
< 0.1%
2016 30
 
< 0.1%
2015 1
 
< 0.1%
2013 1
 
< 0.1%
2009 1
 
< 0.1%
(Missing) 4990049
83.0%
ValueCountFrequency (%)
2009 1
 
< 0.1%
2013 1
 
< 0.1%
2015 1
 
< 0.1%
2016 30
 
< 0.1%
2017 171718
2.9%
ValueCountFrequency (%)
2020 853
 
< 0.1%
2019 534285
8.9%
2018 312254
5.2%
2017 171718
 
2.9%
2016 30
 
< 0.1%

overdueamountmaxdateyear_994T
Real number (ℝ)

MISSING 

Distinct19
Distinct (%)< 0.1%
Missing3150457
Missing (%)52.4%
Infinite0
Infinite (%)0.0%
Mean2014.345151
Minimum2002
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:44.085043image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2002
5-th percentile2007
Q12012
median2015
Q32018
95-th percentile2019
Maximum2020
Range18
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.77402838
Coefficient of variation (CV)0.001873575826
Kurtosis-0.5711652853
Mean2014.345151
Median Absolute Deviation (MAD)3
Skewness-0.7242163846
Sum5758478984
Variance14.24329021
MonotonicityNot monotonic
2024-02-13T20:40:44.226008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2018 498578
 
8.3%
2017 388501
 
6.5%
2016 272953
 
4.5%
2015 242598
 
4.0%
2019 229605
 
3.8%
2014 215988
 
3.6%
2013 190636
 
3.2%
2012 152508
 
2.5%
2011 130459
 
2.2%
2007 121513
 
2.0%
Other values (9) 415396
 
6.9%
(Missing) 3150457
52.4%
ValueCountFrequency (%)
2002 1
 
< 0.1%
2003 2
 
< 0.1%
2004 906
 
< 0.1%
2005 20615
 
0.3%
2006 72823
1.2%
ValueCountFrequency (%)
2020 154
 
< 0.1%
2019 229605
3.8%
2018 498578
8.3%
2017 388501
6.5%
2016 272953
4.5%

periodicityofpmts_1102L
Real number (ℝ)

MISSING  SKEWED 

Distinct5
Distinct (%)< 0.1%
Missing3689232
Missing (%)61.4%
Infinite0
Infinite (%)0.0%
Mean30.11027087
Minimum1
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:44.358013image/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 deviation5.098239628
Coefficient of variation (CV)0.169318956
Kurtosis2352.045994
Mean30.11027087
Median Absolute Deviation (MAD)0
Skewness44.45102813
Sum69854624
Variance25.9920473
MonotonicityNot monotonic
2024-02-13T20:40:44.480014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
30 2316430
38.5%
1 1634
 
< 0.1%
180 1343
 
< 0.1%
90 299
 
< 0.1%
360 254
 
< 0.1%
(Missing) 3689232
61.4%
ValueCountFrequency (%)
1 1634
 
< 0.1%
30 2316430
38.5%
90 299
 
< 0.1%
180 1343
 
< 0.1%
360 254
 
< 0.1%
ValueCountFrequency (%)
360 254
 
< 0.1%
180 1343
 
< 0.1%
90 299
 
< 0.1%
30 2316430
38.5%
1 1634
 
< 0.1%

periodicityofpmts_837L
Real number (ℝ)

MISSING  SKEWED 

Distinct5
Distinct (%)< 0.1%
Missing5499190
Missing (%)91.5%
Infinite0
Infinite (%)0.0%
Mean30.36903777
Minimum1
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:44.600008image/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 deviation7.449534517
Coefficient of variation (CV)0.2453003145
Kurtosis540.903782
Mean30.36903777
Median Absolute Deviation (MAD)0
Skewness21.94446409
Sum15488270
Variance55.49556452
MonotonicityNot monotonic
2024-02-13T20:40:44.721033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
30 508534
 
8.5%
180 1084
 
< 0.1%
90 308
 
< 0.1%
1 50
 
< 0.1%
360 26
 
< 0.1%
(Missing) 5499190
91.5%
ValueCountFrequency (%)
1 50
 
< 0.1%
30 508534
8.5%
90 308
 
< 0.1%
180 1084
 
< 0.1%
360 26
 
< 0.1%
ValueCountFrequency (%)
360 26
 
< 0.1%
180 1084
 
< 0.1%
90 308
 
< 0.1%
30 508534
8.5%
1 50
 
< 0.1%

prolongationcount_1120L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct64
Distinct (%)< 0.1%
Missing5829442
Missing (%)97.0%
Infinite0
Infinite (%)0.0%
Mean0.5652183588
Minimum0
Maximum682
Zeros138122
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:44.870046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum682
Range682
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.652265916
Coefficient of variation (CV)4.692462435
Kurtosis25902.92843
Mean0.5652183588
Median Absolute Deviation (MAD)0
Skewness111.7235505
Sum101598
Variance7.034514491
MonotonicityNot monotonic
2024-02-13T20:40:45.038045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 138122
 
2.3%
1 22795
 
0.4%
2 8399
 
0.1%
3 3784
 
0.1%
4 2102
 
< 0.1%
5 1445
 
< 0.1%
6 753
 
< 0.1%
7 520
 
< 0.1%
8 350
 
< 0.1%
9 262
 
< 0.1%
Other values (54) 1218
 
< 0.1%
(Missing) 5829442
97.0%
ValueCountFrequency (%)
0 138122
2.3%
1 22795
 
0.4%
2 8399
 
0.1%
3 3784
 
0.1%
4 2102
 
< 0.1%
ValueCountFrequency (%)
682 1
< 0.1%
343 1
< 0.1%
120 1
< 0.1%
101 1
< 0.1%
100 1
< 0.1%

prolongationcount_599L
Real number (ℝ)

MISSING 

Distinct39
Distinct (%)0.4%
Missing5998234
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean0.8608322687
Minimum0
Maximum88
Zeros7128
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:45.212044image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum88
Range88
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.632745148
Coefficient of variation (CV)3.058371815
Kurtosis199.2550991
Mean0.8608322687
Median Absolute Deviation (MAD)0
Skewness10.55634786
Sum9433
Variance6.931347012
MonotonicityNot monotonic
2024-02-13T20:40:45.364585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 7128
 
0.1%
1 2286
 
< 0.1%
2 704
 
< 0.1%
3 285
 
< 0.1%
4 159
 
< 0.1%
5 94
 
< 0.1%
6 59
 
< 0.1%
7 37
 
< 0.1%
9 27
 
< 0.1%
8 23
 
< 0.1%
Other values (29) 156
 
< 0.1%
(Missing) 5998234
99.8%
ValueCountFrequency (%)
0 7128
0.1%
1 2286
 
< 0.1%
2 704
 
< 0.1%
3 285
 
< 0.1%
4 159
 
< 0.1%
ValueCountFrequency (%)
88 1
< 0.1%
60 1
< 0.1%
54 1
< 0.1%
43 1
< 0.1%
40 1
< 0.1%
Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:45.525927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.000096519
Min length8

Characters and Unicode

Total characters48074116
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st row60c73645
2nd row96a8fdfe
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 4990049
83.0%
60c73645 777839
 
12.9%
96a8fdfe 232497
 
3.9%
e19fdece 4323
 
0.1%
9e302002 2542
 
< 0.1%
7a7d6960 951
 
< 0.1%
44164129 451
 
< 0.1%
28bfa260 156
 
< 0.1%
e8f3b178 156
 
< 0.1%
p188_162_121 145
 
< 0.1%
Other values (3) 83
 
< 0.1%
2024-02-13T20:40:45.855799image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 15747990
32.8%
7 5769946
 
12.0%
4 5769241
 
12.0%
a 5223735
 
10.9%
1 4996091
 
10.4%
b 4990363
 
10.4%
6 1790911
 
3.7%
0 786575
 
1.6%
c 782244
 
1.6%
3 780617
 
1.6%
Other values (8) 1436403
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 36121692
75.1%
Lowercase Letter 11951989
 
24.9%
Connector Punctuation 290
 
< 0.1%
Uppercase Letter 145
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 15747990
43.6%
7 5769946
 
16.0%
4 5769241
 
16.0%
1 4996091
 
13.8%
6 1790911
 
5.0%
0 786575
 
2.2%
3 780617
 
2.2%
9 240845
 
0.7%
8 233337
 
0.6%
2 6139
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 5223735
43.7%
b 4990363
41.8%
c 782244
 
6.5%
f 469629
 
3.9%
e 248246
 
2.1%
d 237772
 
2.0%
Connector Punctuation
ValueCountFrequency (%)
_ 290
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 145
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36121982
75.1%
Latin 11952134
 
24.9%

Most frequent character per script

Common
ValueCountFrequency (%)
5 15747990
43.6%
7 5769946
 
16.0%
4 5769241
 
16.0%
1 4996091
 
13.8%
6 1790911
 
5.0%
0 786575
 
2.2%
3 780617
 
2.2%
9 240845
 
0.7%
8 233337
 
0.6%
2 6139
 
< 0.1%
Latin
ValueCountFrequency (%)
a 5223735
43.7%
b 4990363
41.8%
c 782244
 
6.5%
f 469629
 
3.9%
e 248246
 
2.1%
d 237772
 
2.0%
P 145
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48074116
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 15747990
32.8%
7 5769946
 
12.0%
4 5769241
 
12.0%
a 5223735
 
10.9%
1 4996091
 
10.4%
b 4990363
 
10.4%
6 1790911
 
3.7%
0 786575
 
1.6%
c 782244
 
1.6%
3 780617
 
1.6%
Other values (8) 1436403
 
3.0%
Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:46.044548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.00149704
Min length8

Characters and Unicode

Total characters48082532
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 3054058
50.8%
60c73645 1370567
22.8%
5065c2b8 758824
 
12.6%
96a8fdfe 699129
 
11.6%
e19fdece 56354
 
0.9%
d9ae1a0e 34956
 
0.6%
27b6de28 11572
 
0.2%
5d1b0cdd 6387
 
0.1%
d11871e7 4154
 
0.1%
89ccf2a3 4000
 
0.1%
Other values (9) 9191
 
0.2%
2024-02-13T20:40:46.381967image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 12056776
25.1%
7 4451584
 
9.3%
4 4430868
 
9.2%
6 4219510
 
8.8%
b 3834162
 
8.0%
a 3829373
 
8.0%
1 3178661
 
6.6%
c 2200308
 
4.6%
0 2172838
 
4.5%
8 1485416
 
3.1%
Other values (8) 6223036
12.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34961338
72.7%
Lowercase Letter 13114447
 
27.3%
Connector Punctuation 4498
 
< 0.1%
Uppercase Letter 2249
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 12056776
34.5%
7 4451584
 
12.7%
4 4430868
 
12.7%
6 4219510
 
12.1%
1 3178661
 
9.1%
0 2172838
 
6.2%
8 1485416
 
4.2%
3 1375372
 
3.9%
9 798060
 
2.3%
2 792253
 
2.3%
Lowercase Letter
ValueCountFrequency (%)
b 3834162
29.2%
a 3829373
29.2%
c 2200308
16.8%
f 1461155
 
11.1%
e 959623
 
7.3%
d 829826
 
6.3%
Connector Punctuation
ValueCountFrequency (%)
_ 4498
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 2249
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34965836
72.7%
Latin 13116696
 
27.3%

Most frequent character per script

Common
ValueCountFrequency (%)
5 12056776
34.5%
7 4451584
 
12.7%
4 4430868
 
12.7%
6 4219510
 
12.1%
1 3178661
 
9.1%
0 2172838
 
6.2%
8 1485416
 
4.2%
3 1375372
 
3.9%
9 798060
 
2.3%
2 792253
 
2.3%
Latin
ValueCountFrequency (%)
b 3834162
29.2%
a 3829373
29.2%
c 2200308
16.8%
f 1461155
 
11.1%
e 959623
 
7.3%
d 829826
 
6.3%
P 2249
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48082532
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 12056776
25.1%
7 4451584
 
9.3%
4 4430868
 
9.2%
6 4219510
 
8.8%
b 3834162
 
8.0%
a 3829373
 
8.0%
1 3178661
 
6.6%
c 2200308
 
4.6%
0 2172838
 
4.5%
8 1485416
 
3.1%
Other values (8) 6223036
12.9%

refreshdate_3813885D
Text

MISSING 

Distinct188
Distinct (%)< 0.1%
Missing1616565
Missing (%)26.9%
Memory size45.8 MiB
2024-02-13T20:40:46.718563image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters43926270
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

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-07-10
2nd row2019-07-10
3rd row2019-07-10
4th row2019-01-03
5th row2019-07-01
ValueCountFrequency (%)
2019-01-03 549175
 
12.5%
2019-08-07 402856
 
9.2%
2019-08-04 292421
 
6.7%
2019-02-06 291842
 
6.6%
2019-11-03 273694
 
6.2%
2019-11-05 102187
 
2.3%
2019-11-13 98553
 
2.2%
2019-12-13 89447
 
2.0%
2019-05-03 77149
 
1.8%
2019-05-04 77147
 
1.8%
Other values (178) 2138156
48.7%
2024-02-13T20:40:47.213175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 10417481
23.7%
- 8785254
20.0%
1 8634261
19.7%
2 6080971
13.8%
9 4974994
11.3%
3 1453067
 
3.3%
8 1264262
 
2.9%
7 838255
 
1.9%
4 576265
 
1.3%
6 491057
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35141016
80.0%
Dash Punctuation 8785254
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10417481
29.6%
1 8634261
24.6%
2 6080971
17.3%
9 4974994
14.2%
3 1453067
 
4.1%
8 1264262
 
3.6%
7 838255
 
2.4%
4 576265
 
1.6%
6 491057
 
1.4%
5 410403
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 8785254
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43926270
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10417481
23.7%
- 8785254
20.0%
1 8634261
19.7%
2 6080971
13.8%
9 4974994
11.3%
3 1453067
 
3.3%
8 1264262
 
2.9%
7 838255
 
1.9%
4 576265
 
1.3%
6 491057
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43926270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10417481
23.7%
- 8785254
20.0%
1 8634261
19.7%
2 6080971
13.8%
9 4974994
11.3%
3 1453067
 
3.3%
8 1264262
 
2.9%
7 838255
 
1.9%
4 576265
 
1.3%
6 491057
 
1.1%

residualamount_488A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing5601681
Missing (%)93.2%
Infinite0
Infinite (%)0.0%
Mean1.510894204
Minimum0
Maximum260000
Zeros407505
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:47.351659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum260000
Range260000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation564.2469674
Coefficient of variation (CV)373.4523343
Kurtosis191065.2929
Mean1.510894204
Median Absolute Deviation (MAD)0
Skewness431.4920241
Sum615706.008
Variance318374.6403
MonotonicityNot monotonic
2024-02-13T20:40:47.465660image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 407505
 
6.8%
29154.219 2
 
< 0.1%
239912.92 1
 
< 0.1%
260000 1
 
< 0.1%
3929.11 1
 
< 0.1%
53555.54 1
 
< 0.1%
(Missing) 5601681
93.2%
ValueCountFrequency (%)
0 407505
6.8%
3929.11 1
 
< 0.1%
29154.219 2
 
< 0.1%
53555.54 1
 
< 0.1%
239912.92 1
 
< 0.1%
ValueCountFrequency (%)
260000 1
< 0.1%
239912.92 1
< 0.1%
53555.54 1
< 0.1%
29154.219 2
< 0.1%
3929.11 1
< 0.1%

residualamount_856A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct224519
Distinct (%)45.9%
Missing5519517
Missing (%)91.9%
Infinite0
Infinite (%)0.0%
Mean35364.81488
Minimum0
Maximum340000000
Zeros199678
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:47.603356image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5520.6
Q331734.2825
95-th percentile180000
Maximum340000000
Range340000000
Interquartile range (IQR)31734.2825

Descriptive statistics

Standard deviation500031.4367
Coefficient of variation (CV)14.13923524
Kurtosis436780.4648
Mean35364.81488
Median Absolute Deviation (MAD)5520.6
Skewness645.4294369
Sum1.731726573 × 1010
Variance2.500314377 × 1011
MonotonicityNot monotonic
2024-02-13T20:40:47.768384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 199678
 
3.3%
200000 702
 
< 0.1%
100000 521
 
< 0.1%
20000 495
 
< 0.1%
10000 436
 
< 0.1%
40000 408
 
< 0.1%
30000 374
 
< 0.1%
6000 211
 
< 0.1%
4000 208
 
< 0.1%
60000 183
 
< 0.1%
Other values (224509) 286459
 
4.8%
(Missing) 5519517
91.9%
ValueCountFrequency (%)
0 199678
3.3%
0.002 2
 
< 0.1%
0.004 1
 
< 0.1%
0.008 1
 
< 0.1%
0.014 1
 
< 0.1%
ValueCountFrequency (%)
340000000 1
< 0.1%
60000000 1
< 0.1%
14000000 1
< 0.1%
12082400 1
< 0.1%
5185148.5 1
< 0.1%
Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:47.973102image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.000252114
Min length8

Characters and Unicode

Total characters48075051
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowab3c25cf
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 5502403
91.6%
ab3c25cf 489835
 
8.2%
be4fd70b 8423
 
0.1%
daf49a8a 6475
 
0.1%
p28_48_88 1515
 
< 0.1%
15f04f45 536
 
< 0.1%
652d52e3 2
 
< 0.1%
0c42a10e 2
 
< 0.1%
71ddaa88 1
 
< 0.1%
2024-02-13T20:40:48.276837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 16998120
35.4%
a 6011667
 
12.5%
b 6009084
 
12.5%
4 5519890
 
11.5%
7 5510827
 
11.5%
1 5502942
 
11.4%
c 979672
 
2.0%
f 505805
 
1.1%
2 491356
 
1.0%
3 489837
 
1.0%
Other values (8) 55851
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34540949
71.8%
Lowercase Letter 13529557
 
28.1%
Connector Punctuation 3030
 
< 0.1%
Uppercase Letter 1515
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 16998120
49.2%
4 5519890
 
16.0%
7 5510827
 
16.0%
1 5502942
 
15.9%
2 491356
 
1.4%
3 489837
 
1.4%
8 12537
 
< 0.1%
0 8963
 
< 0.1%
9 6475
 
< 0.1%
6 2
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 6011667
44.4%
b 6009084
44.4%
c 979672
 
7.2%
f 505805
 
3.7%
d 14902
 
0.1%
e 8427
 
0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 3030
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 1515
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34543979
71.9%
Latin 13531072
 
28.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 16998120
49.2%
4 5519890
 
16.0%
7 5510827
 
16.0%
1 5502942
 
15.9%
2 491356
 
1.4%
3 489837
 
1.4%
8 12537
 
< 0.1%
0 8963
 
< 0.1%
9 6475
 
< 0.1%
_ 3030
 
< 0.1%
Latin
ValueCountFrequency (%)
a 6011667
44.4%
b 6009084
44.4%
c 979672
 
7.2%
f 505805
 
3.7%
d 14902
 
0.1%
e 8427
 
0.1%
P 1515
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48075051
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 16998120
35.4%
a 6011667
 
12.5%
b 6009084
 
12.5%
4 5519890
 
11.5%
7 5510827
 
11.5%
1 5502942
 
11.4%
c 979672
 
2.0%
f 505805
 
1.1%
2 491356
 
1.0%
3 489837
 
1.0%
Other values (8) 55851
 
0.1%
Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:48.449272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.000000333
Min length8

Characters and Unicode

Total characters48073538
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowab3c25cf
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 5513211
91.7%
ab3c25cf 478242
 
8.0%
be4fd70b 8036
 
0.1%
daf49a8a 7247
 
0.1%
15f04f45 2403
 
< 0.1%
71ddaa88 44
 
< 0.1%
0c42a10e 6
 
< 0.1%
p28_48_88 2
 
< 0.1%
652d52e3 1
 
< 0.1%
2024-02-13T20:40:48.751783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 17022683
35.4%
a 6013288
 
12.5%
b 6007525
 
12.5%
4 5533308
 
11.5%
7 5521291
 
11.5%
1 5515664
 
11.5%
c 956490
 
2.0%
f 498331
 
1.0%
2 478252
 
1.0%
3 478243
 
1.0%
Other values (8) 48463
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34574483
71.9%
Lowercase Letter 13499049
 
28.1%
Connector Punctuation 4
 
< 0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 17022683
49.2%
4 5533308
 
16.0%
7 5521291
 
16.0%
1 5515664
 
16.0%
2 478252
 
1.4%
3 478243
 
1.4%
0 10451
 
< 0.1%
8 7343
 
< 0.1%
9 7247
 
< 0.1%
6 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 6013288
44.5%
b 6007525
44.5%
c 956490
 
7.1%
f 498331
 
3.7%
d 15372
 
0.1%
e 8043
 
0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34574487
71.9%
Latin 13499051
 
28.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 17022683
49.2%
4 5533308
 
16.0%
7 5521291
 
16.0%
1 5515664
 
16.0%
2 478252
 
1.4%
3 478243
 
1.4%
0 10451
 
< 0.1%
8 7343
 
< 0.1%
9 7247
 
< 0.1%
_ 4
 
< 0.1%
Latin
ValueCountFrequency (%)
a 6013288
44.5%
b 6007525
44.5%
c 956490
 
7.1%
f 498331
 
3.7%
d 15372
 
0.1%
e 8043
 
0.1%
P 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48073538
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 17022683
35.4%
a 6013288
 
12.5%
b 6007525
 
12.5%
4 5533308
 
11.5%
7 5521291
 
11.5%
1 5515664
 
11.5%
c 956490
 
2.0%
f 498331
 
1.0%
2 478252
 
1.0%
3 478243
 
1.0%
Other values (8) 48463
 
0.1%

totalamount_6A
Real number (ℝ)

MISSING  SKEWED 

Distinct310495
Distinct (%)12.2%
Missing3462700
Missing (%)57.6%
Infinite0
Infinite (%)0.0%
Mean81135.13699
Minimum0
Maximum240916600
Zeros3928
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:48.906948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4000
Q112736
median27717
Q360000
95-th percentile266000
Maximum240916600
Range240916600
Interquartile range (IQR)47264

Descriptive statistics

Standard deviation593050.1602
Coefficient of variation (CV)7.3094122
Kurtosis36570.17049
Mean81135.13699
Median Absolute Deviation (MAD)17820
Skewness145.9647068
Sum2.066099773 × 1011
Variance3.517084925 × 1011
MonotonicityNot monotonic
2024-02-13T20:40:49.073905image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30000 48395
 
0.8%
20000 47045
 
0.8%
40000 44321
 
0.7%
60000 39121
 
0.7%
100000 31528
 
0.5%
10000 26795
 
0.4%
4000 26621
 
0.4%
2000 24028
 
0.4%
200000 23537
 
0.4%
3000 22717
 
0.4%
Other values (310485) 2212384
36.8%
(Missing) 3462700
57.6%
ValueCountFrequency (%)
0 3928
0.1%
0.2 2
 
< 0.1%
1 2
 
< 0.1%
1.8000001 1
 
< 0.1%
2 1
 
< 0.1%
ValueCountFrequency (%)
240916600 1
 
< 0.1%
215200000 1
 
< 0.1%
180220720 1
 
< 0.1%
152781840 1
 
< 0.1%
145400000 4
< 0.1%

totalamount_996A
Real number (ℝ)

MISSING  SKEWED 

Distinct143936
Distinct (%)27.5%
Missing5485983
Missing (%)91.3%
Infinite0
Infinite (%)0.0%
Mean221622.5214
Minimum3.8
Maximum113469390
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:49.244010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile10586.6
Q128022.4
median63240
Q3175524.61
95-th percentile895926.34
Maximum113469390
Range113469386.2
Interquartile range (IQR)147502.21

Descriptive statistics

Standard deviation798481.4643
Coefficient of variation (CV)3.60288954
Kurtosis4187.828824
Mean221622.5214
Median Absolute Deviation (MAD)43847
Skewness46.16218305
Sum1.159548978 × 1011
Variance6.375726489 × 1011
MonotonicityNot monotonic
2024-02-13T20:40:49.405009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 11593
 
0.2%
200000 7782
 
0.1%
40000 6531
 
0.1%
60000 5935
 
0.1%
30000 5279
 
0.1%
20000 5072
 
0.1%
400000 3709
 
0.1%
300000 3630
 
0.1%
80000 3594
 
0.1%
120000 3274
 
0.1%
Other values (143926) 466810
 
7.8%
(Missing) 5485983
91.3%
ValueCountFrequency (%)
3.8 1
< 0.1%
5.1 1
< 0.1%
37.4 1
< 0.1%
48.546 1
< 0.1%
52 1
< 0.1%
ValueCountFrequency (%)
113469390 1
< 0.1%
100273980 2
< 0.1%
90000000 1
< 0.1%
89670510 2
< 0.1%
81802600 1
< 0.1%

totaldebtoverduevalue_178A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct22351
Distinct (%)4.4%
Missing5502403
Missing (%)91.6%
Infinite0
Infinite (%)0.0%
Mean2151.331523
Minimum0
Maximum70695176
Zeros481274
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:49.560005image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.4
Maximum70695176
Range70695176
Interquartile range (IQR)0

Descriptive statistics

Standard deviation166483.1712
Coefficient of variation (CV)77.3861069
Kurtosis125849.536
Mean2151.331523
Median Absolute Deviation (MAD)0
Skewness321.9188515
Sum1090271151
Variance2.771664629 × 1010
MonotonicityNot monotonic
2024-02-13T20:40:49.719382image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 481274
 
8.0%
10 82
 
< 0.1%
14 58
 
< 0.1%
2 44
 
< 0.1%
8 41
 
< 0.1%
20 40
 
< 0.1%
1.2 33
 
< 0.1%
0.4 32
 
< 0.1%
7000 32
 
< 0.1%
6 31
 
< 0.1%
Other values (22341) 25122
 
0.4%
(Missing) 5502403
91.6%
ValueCountFrequency (%)
0 481274
8.0%
0.002 3
 
< 0.1%
0.004 2
 
< 0.1%
0.006 1
 
< 0.1%
0.008 3
 
< 0.1%
ValueCountFrequency (%)
70695176 1
< 0.1%
68885540 1
< 0.1%
26681114 1
< 0.1%
24471064 1
< 0.1%
21104634 1
< 0.1%

totaldebtoverduevalue_718A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct454
Distinct (%)0.1%
Missing5513211
Missing (%)91.7%
Infinite0
Infinite (%)0.0%
Mean103.4532525
Minimum0
Maximum616558.5
Zeros495314
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:49.879589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum616558.5
Range616558.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4973.151542
Coefficient of variation (CV)48.07148563
Kurtosis4862.575398
Mean103.4532525
Median Absolute Deviation (MAD)0
Skewness64.71203358
Sum51310847.63
Variance24732236.26
MonotonicityNot monotonic
2024-02-13T20:40:50.347477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 495314
 
8.2%
0.2 26
 
< 0.1%
0.6 24
 
< 0.1%
0.4 21
 
< 0.1%
1.2 13
 
< 0.1%
4 12
 
< 0.1%
0.8 12
 
< 0.1%
1 11
 
< 0.1%
1.4 9
 
< 0.1%
1.8000001 9
 
< 0.1%
Other values (444) 530
 
< 0.1%
(Missing) 5513211
91.7%
ValueCountFrequency (%)
0 495314
8.2%
0.2 26
 
< 0.1%
0.4 21
 
< 0.1%
0.6 24
 
< 0.1%
0.8 12
 
< 0.1%
ValueCountFrequency (%)
616558.5 1
< 0.1%
496510 1
< 0.1%
486075 1
< 0.1%
433225 1
< 0.1%
429100 2
< 0.1%

totaloutstanddebtvalue_39A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct422215
Distinct (%)83.3%
Missing5502403
Missing (%)91.6%
Infinite0
Infinite (%)0.0%
Mean208544.3457
Minimum0
Maximum147065070
Zeros62924
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:50.512486image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114612.159
median62080.25
Q3180595.22
95-th percentile824243.02
Maximum147065070
Range147065070
Interquartile range (IQR)165983.061

Descriptive statistics

Standard deviation812797.6202
Coefficient of variation (CV)3.897480977
Kurtosis7156.791203
Mean208544.3457
Median Absolute Deviation (MAD)57798.182
Skewness60.59200692
Sum1.056879804 × 1011
Variance6.606399714 × 1011
MonotonicityNot monotonic
2024-02-13T20:40:50.700474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 62924
 
1.0%
200000 284
 
< 0.1%
100000 213
 
< 0.1%
20000 164
 
< 0.1%
40000 142
 
< 0.1%
30000 121
 
< 0.1%
60000 116
 
< 0.1%
10000 113
 
< 0.1%
12000 90
 
< 0.1%
6000 73
 
< 0.1%
Other values (422205) 442549
 
7.4%
(Missing) 5502403
91.6%
ValueCountFrequency (%)
0 62924
1.0%
0.002 8
 
< 0.1%
0.004 4
 
< 0.1%
0.006 1
 
< 0.1%
0.008 1
 
< 0.1%
ValueCountFrequency (%)
147065070 1
< 0.1%
144741020 1
< 0.1%
105720930 1
< 0.1%
91659870 1
< 0.1%
91366536 1
< 0.1%

totaloutstanddebtvalue_668A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct344
Distinct (%)0.1%
Missing5513211
Missing (%)91.7%
Infinite0
Infinite (%)0.0%
Mean18.02715917
Minimum0
Maximum443392.3
Zeros495368
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size45.8 MiB
2024-02-13T20:40:50.889845image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum443392.3
Range443392.3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1747.659411
Coefficient of variation (CV)96.94591337
Kurtosis31645.68046
Mean18.02715917
Median Absolute Deviation (MAD)0
Skewness163.562063
Sum8941128.433
Variance3054313.418
MonotonicityNot monotonic
2024-02-13T20:40:51.072826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 495368
 
8.2%
0.2 43
 
< 0.1%
0.4 32
 
< 0.1%
0.6 30
 
< 0.1%
0.8 14
 
< 0.1%
1 14
 
< 0.1%
1.2 14
 
< 0.1%
4 12
 
< 0.1%
1.8000001 10
 
< 0.1%
1.4 9
 
< 0.1%
Other values (334) 435
 
< 0.1%
(Missing) 5513211
91.7%
ValueCountFrequency (%)
0 495368
8.2%
0.2 43
 
< 0.1%
0.4 32
 
< 0.1%
0.6 30
 
< 0.1%
0.8 14
 
< 0.1%
ValueCountFrequency (%)
443392.3 1
< 0.1%
392057.16 1
< 0.1%
390000 2
< 0.1%
300000 1
< 0.1%
274440 1
< 0.1%