Overview

Dataset statistics

Number of variables79
Number of observations3743810
Missing cells187095066
Missing cells (%)63.3%
Total size in memory2.2 GiB
Average record size in memory632.0 B

Variable types

Numeric56
Text23

Alerts

annualeffectiverate_199L has 3491316 (93.3%) missing valuesMissing
annualeffectiverate_63L has 3659040 (97.7%) missing valuesMissing
contractsum_5085717L has 3730103 (99.6%) missing valuesMissing
credlmt_230A has 3475352 (92.8%) missing valuesMissing
credlmt_935A has 3437159 (91.8%) missing valuesMissing
dateofcredend_289D has 3090430 (82.5%) missing valuesMissing
dateofcredend_353D has 1670009 (44.6%) missing valuesMissing
dateofcredstart_181D has 1670007 (44.6%) missing valuesMissing
dateofcredstart_739D has 3090430 (82.5%) missing valuesMissing
dateofrealrepmt_138D has 1681312 (44.9%) missing valuesMissing
debtoutstand_525A has 3418641 (91.3%) missing valuesMissing
debtoverdue_47A has 3418641 (91.3%) missing valuesMissing
dpdmax_139P has 3094889 (82.7%) missing valuesMissing
dpdmax_757P has 1739084 (46.5%) missing valuesMissing
dpdmaxdatemonth_442T has 1739084 (46.5%) missing valuesMissing
dpdmaxdatemonth_89T has 3094889 (82.7%) missing valuesMissing
dpdmaxdateyear_596T has 3094889 (82.7%) missing valuesMissing
dpdmaxdateyear_896T has 1739084 (46.5%) missing valuesMissing
instlamount_768A has 3441756 (91.9%) missing valuesMissing
instlamount_852A has 3562859 (95.2%) missing valuesMissing
interestrate_508L has 3724410 (99.5%) missing valuesMissing
lastupdate_1112D has 3090430 (82.5%) missing valuesMissing
lastupdate_388D has 1670066 (44.6%) missing valuesMissing
monthlyinstlamount_332A has 3095447 (82.7%) missing valuesMissing
monthlyinstlamount_674A has 1803574 (48.2%) missing valuesMissing
nominalrate_281L has 3498044 (93.4%) missing valuesMissing
nominalrate_498L has 3014103 (80.5%) missing valuesMissing
numberofcontrsvalue_258L has 3441306 (91.9%) missing valuesMissing
numberofcontrsvalue_358L has 3436989 (91.8%) missing valuesMissing
numberofinstls_229L has 1939505 (51.8%) missing valuesMissing
numberofinstls_320L has 3397203 (90.7%) missing valuesMissing
numberofoutstandinstls_520L has 1938229 (51.8%) missing valuesMissing
numberofoutstandinstls_59L has 3397206 (90.7%) missing valuesMissing
numberofoverdueinstlmax_1039L has 3090430 (82.5%) missing valuesMissing
numberofoverdueinstlmax_1151L has 1670007 (44.6%) missing valuesMissing
numberofoverdueinstlmaxdat_148D has 3127695 (83.5%) missing valuesMissing
numberofoverdueinstlmaxdat_641D has 3577779 (95.6%) missing valuesMissing
numberofoverdueinstls_725L has 3094950 (82.7%) missing valuesMissing
numberofoverdueinstls_834L has 1672969 (44.7%) missing valuesMissing
outstandingamount_354A has 1937585 (51.8%) missing valuesMissing
outstandingamount_362A has 3397088 (90.7%) missing valuesMissing
overdueamount_31A has 1672509 (44.7%) missing valuesMissing
overdueamount_659A has 3094950 (82.7%) missing valuesMissing
overdueamountmax2_14A has 3090430 (82.5%) missing valuesMissing
overdueamountmax2_398A has 1670007 (44.6%) missing valuesMissing
overdueamountmax2date_1002D has 3132886 (83.7%) missing valuesMissing
overdueamountmax2date_1142D has 3576569 (95.5%) missing valuesMissing
overdueamountmax_155A has 3090430 (82.5%) missing valuesMissing
overdueamountmax_35A has 1736366 (46.4%) missing valuesMissing
overdueamountmaxdatemonth_284T has 1736366 (46.4%) missing valuesMissing
overdueamountmaxdatemonth_365T has 3090430 (82.5%) missing valuesMissing
overdueamountmaxdateyear_2T has 3090430 (82.5%) missing valuesMissing
overdueamountmaxdateyear_994T has 1736366 (46.4%) missing valuesMissing
periodicityofpmts_1102L has 2157320 (57.6%) missing valuesMissing
periodicityofpmts_837L has 3409479 (91.1%) missing valuesMissing
prolongationcount_1120L has 3570206 (95.4%) missing valuesMissing
prolongationcount_599L has 3732794 (99.7%) missing valuesMissing
refreshdate_3813885D has 1142858 (30.5%) missing valuesMissing
residualamount_488A has 3478174 (92.9%) missing valuesMissing
residualamount_856A has 3441750 (91.9%) missing valuesMissing
totalamount_6A has 1937086 (51.7%) missing valuesMissing
totalamount_996A has 3397081 (90.7%) missing valuesMissing
totaldebtoverduevalue_178A has 3441306 (91.9%) missing valuesMissing
totaldebtoverduevalue_718A has 3436989 (91.8%) missing valuesMissing
totaloutstanddebtvalue_39A has 3441306 (91.9%) missing valuesMissing
totaloutstanddebtvalue_668A has 3436989 (91.8%) missing valuesMissing
credlmt_230A is highly skewed (γ1 = 195.8570707)Skewed
credlmt_935A is highly skewed (γ1 = 260.4751764)Skewed
debtoutstand_525A is highly skewed (γ1 = 334.9546734)Skewed
debtoverdue_47A is highly skewed (γ1 = 549.9069855)Skewed
dpdmax_757P is highly skewed (γ1 = 132.6164523)Skewed
instlamount_852A is highly skewed (γ1 = 27.90462388)Skewed
interestrate_508L is highly skewed (γ1 = 70.86461209)Skewed
monthlyinstlamount_332A is highly skewed (γ1 = 378.6333081)Skewed
monthlyinstlamount_674A is highly skewed (γ1 = 290.4381206)Skewed
nominalrate_281L is highly skewed (γ1 = 59.76685496)Skewed
nominalrate_498L is highly skewed (γ1 = 44.0910622)Skewed
numberofoutstandinstls_520L is highly skewed (γ1 = 80.78584547)Skewed
numberofoverdueinstlmax_1151L is highly skewed (γ1 = 130.2042876)Skewed
numberofoverdueinstls_725L is highly skewed (γ1 = 21.97189701)Skewed
numberofoverdueinstls_834L is highly skewed (γ1 = 118.3759823)Skewed
outstandingamount_354A is highly skewed (γ1 = 612.635473)Skewed
outstandingamount_362A is highly skewed (γ1 = 198.3670412)Skewed
overdueamount_31A is highly skewed (γ1 = 149.954902)Skewed
overdueamount_659A is highly skewed (γ1 = 559.0095889)Skewed
overdueamountmax2_14A is highly skewed (γ1 = 508.7139687)Skewed
overdueamountmax2_398A is highly skewed (γ1 = 1119.625372)Skewed
overdueamountmax_155A is highly skewed (γ1 = 513.3546382)Skewed
overdueamountmax_35A is highly skewed (γ1 = 1226.615602)Skewed
periodicityofpmts_1102L is highly skewed (γ1 = 43.44900879)Skewed
periodicityofpmts_837L is highly skewed (γ1 = 21.81624579)Skewed
prolongationcount_1120L is highly skewed (γ1 = 31.43390254)Skewed
residualamount_488A is highly skewed (γ1 = 389.3798707)Skewed
residualamount_856A is highly skewed (γ1 = 257.5233858)Skewed
totalamount_6A is highly skewed (γ1 = 346.4477652)Skewed
totalamount_996A is highly skewed (γ1 = 157.6671532)Skewed
totaldebtoverduevalue_178A is highly skewed (γ1 = 530.4208004)Skewed
totaldebtoverduevalue_718A is highly skewed (γ1 = 57.69378788)Skewed
totaloutstanddebtvalue_39A is highly skewed (γ1 = 330.4401467)Skewed
totaloutstanddebtvalue_668A is highly skewed (γ1 = 194.5313543)Skewed
credlmt_230A has 101237 (2.7%) zerosZeros
credlmt_935A has 84193 (2.2%) zerosZeros
debtoutstand_525A has 61687 (1.6%) zerosZeros
debtoverdue_47A has 309064 (8.3%) zerosZeros
dpdmax_139P has 515645 (13.8%) zerosZeros
dpdmax_757P has 1426897 (38.1%) zerosZeros
instlamount_768A has 118911 (3.2%) zerosZeros
instlamount_852A has 113455 (3.0%) zerosZeros
monthlyinstlamount_332A has 120608 (3.2%) zerosZeros
monthlyinstlamount_674A has 921492 (24.6%) zerosZeros
num_group1 has 325127 (8.7%) zerosZeros
numberofinstls_229L has 357473 (9.5%) zerosZeros
numberofoutstandinstls_520L has 1802902 (48.2%) zerosZeros
numberofoverdueinstlmax_1039L has 487349 (13.0%) zerosZeros
numberofoverdueinstlmax_1151L has 1457688 (38.9%) zerosZeros
numberofoverdueinstls_725L has 629471 (16.8%) zerosZeros
numberofoverdueinstls_834L has 2069531 (55.3%) zerosZeros
outstandingamount_354A has 1805775 (48.2%) zerosZeros
overdueamount_31A has 2070863 (55.3%) zerosZeros
overdueamount_659A has 629471 (16.8%) zerosZeros
overdueamountmax2_14A has 486139 (13.0%) zerosZeros
overdueamountmax2_398A has 1462879 (39.1%) zerosZeros
overdueamountmax_155A has 517145 (13.8%) zerosZeros
overdueamountmax_35A has 1423079 (38.0%) zerosZeros
prolongationcount_1120L has 131043 (3.5%) zerosZeros
residualamount_488A has 265632 (7.1%) zerosZeros
residualamount_856A has 116565 (3.1%) zerosZeros
totaldebtoverduevalue_178A has 286308 (7.6%) zerosZeros
totaldebtoverduevalue_718A has 306384 (8.2%) zerosZeros
totaloutstanddebtvalue_668A has 306406 (8.2%) zerosZeros

Reproduction

Analysis started2024-02-13 19:40:59.751974
Analysis finished2024-02-13 19:41:44.111079
Duration44.36 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

case_id
Real number (ℝ)

Distinct325127
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1416355.353
Minimum40626
Maximum2683578
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:41:44.253080image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum40626
5-th percentile192734
Q1934632
median1732049.5
Q31811401
95-th percentile2667420.55
Maximum2683578
Range2642952
Interquartile range (IQR)876769

Descriptive statistics

Standard deviation735924.186
Coefficient of variation (CV)0.5195900763
Kurtosis-0.6698645074
Mean1416355.353
Median Absolute Deviation (MAD)118495.5
Skewness-0.3393176731
Sum5.302565335 × 1012
Variance5.415844075 × 1011
MonotonicityIncreasing
2024-02-13T20:41:44.436080image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
214097 529
 
< 0.1%
941237 361
 
< 0.1%
1782211 307
 
< 0.1%
973076 295
 
< 0.1%
188150 275
 
< 0.1%
220977 249
 
< 0.1%
1805200 210
 
< 0.1%
1716754 205
 
< 0.1%
2672738 182
 
< 0.1%
1856049 179
 
< 0.1%
Other values (325117) 3741018
99.9%
ValueCountFrequency (%)
40626 11
< 0.1%
40704 9
< 0.1%
40737 9
< 0.1%
40766 9
< 0.1%
40791 9
< 0.1%
ValueCountFrequency (%)
2683578 9
< 0.1%
2683577 9
< 0.1%
2683576 9
< 0.1%
2683575 9
< 0.1%
2683574 21
< 0.1%

annualeffectiverate_199L
Real number (ℝ)

MISSING 

Distinct5615
Distinct (%)2.2%
Missing3491316
Missing (%)93.3%
Infinite0
Infinite (%)0.0%
Mean1247.380637
Minimum0
Maximum91250
Zeros8939
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:41:44.600119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12
Q19.13
median38.3
Q396.3
95-th percentile730
Maximum91250
Range91250
Interquartile range (IQR)87.17

Descriptive statistics

Standard deviation8131.473525
Coefficient of variation (CV)6.518838986
Kurtosis63.03502926
Mean1247.380637
Median Absolute Deviation (MAD)36.47
Skewness7.861311114
Sum314956126.5
Variance66120861.69
MonotonicityNot monotonic
2024-02-13T20:41:44.776429image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96.3 36324
 
1.0%
0.12 16898
 
0.5%
38.3 10142
 
0.3%
730 9232
 
0.2%
0 8939
 
0.2%
98.55 8712
 
0.2%
365 7215
 
0.2%
0.69 5853
 
0.2%
438 4941
 
0.1%
7.3 4791
 
0.1%
Other values (5605) 139447
 
3.7%
(Missing) 3491316
93.3%
ValueCountFrequency (%)
0 8939
0.2%
0.01 6
 
< 0.1%
0.08 12
 
< 0.1%
0.09 354
 
< 0.1%
0.1 386
 
< 0.1%
ValueCountFrequency (%)
91250 115
 
< 0.1%
87600 9
 
< 0.1%
73000 1167
< 0.1%
69350 1213
< 0.1%
69338.23 1
 
< 0.1%

annualeffectiverate_63L
Real number (ℝ)

MISSING 

Distinct4588
Distinct (%)5.4%
Missing3659040
Missing (%)97.7%
Infinite0
Infinite (%)0.0%
Mean280.7132563
Minimum0
Maximum91250
Zeros2109
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:41:44.951512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.11
Q15.11
median23.38
Q341.29
95-th percentile55.43
Maximum91250
Range91250
Interquartile range (IQR)36.18

Descriptive statistics

Standard deviation3531.525747
Coefficient of variation (CV)12.58054498
Kurtosis323.6822344
Mean280.7132563
Median Absolute Deviation (MAD)18.2
Skewness17.39487578
Sum23796062.74
Variance12471674.1
MonotonicityNot monotonic
2024-02-13T20:41:45.103841image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.12 9995
 
0.3%
0.11 2279
 
0.1%
0 2109
 
0.1%
96.3 2053
 
0.1%
5.11 1313
 
< 0.1%
0.3 935
 
< 0.1%
26.8 926
 
< 0.1%
48.18 801
 
< 0.1%
26.28 745
 
< 0.1%
48.02 714
 
< 0.1%
Other values (4578) 62900
 
1.7%
(Missing) 3659040
97.7%
ValueCountFrequency (%)
0 2109
0.1%
0.01 2
 
< 0.1%
0.05 13
 
< 0.1%
0.08 2
 
< 0.1%
0.09 18
 
< 0.1%
ValueCountFrequency (%)
91250 3
 
< 0.1%
73000 70
< 0.1%
69350 60
< 0.1%
62415 7
 
< 0.1%
59140.9 26
 
< 0.1%
Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.6 MiB
2024-02-13T20:41:45.267497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters29950480
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 rowea6782cc
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 3091094
82.6%
ea6782cc 559955
 
15.0%
01f63ac8 61438
 
1.6%
00135d9c 20817
 
0.6%
4408ff0f 9445
 
0.3%
be7b251d 545
 
< 0.1%
1cf4e481 301
 
< 0.1%
2c070815 136
 
< 0.1%
4a5a01e3 55
 
< 0.1%
87bdbcba 23
 
< 0.1%
2024-02-13T20:41:45.559923image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 9294836
31.0%
a 3712621
 
12.4%
7 3651753
 
12.2%
1 3174687
 
10.6%
4 3110641
 
10.4%
b 3092253
 
10.3%
c 1202625
 
4.0%
8 631300
 
2.1%
6 621393
 
2.1%
e 560856
 
1.9%
Other values (6) 897515
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21270665
71.0%
Lowercase Letter 8679815
29.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 9294836
43.7%
7 3651753
 
17.2%
1 3174687
 
14.9%
4 3110641
 
14.6%
8 631300
 
3.0%
6 621393
 
2.9%
2 560637
 
2.6%
0 122290
 
0.6%
3 82310
 
0.4%
9 20818
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
a 3712621
42.8%
b 3092253
35.6%
c 1202625
 
13.9%
e 560856
 
6.5%
f 90074
 
1.0%
d 21386
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 21270665
71.0%
Latin 8679815
29.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 9294836
43.7%
7 3651753
 
17.2%
1 3174687
 
14.9%
4 3110641
 
14.6%
8 631300
 
3.0%
6 621393
 
2.9%
2 560637
 
2.6%
0 122290
 
0.6%
3 82310
 
0.4%
9 20818
 
0.1%
Latin
ValueCountFrequency (%)
a 3712621
42.8%
b 3092253
35.6%
c 1202625
 
13.9%
e 560856
 
6.5%
f 90074
 
1.0%
d 21386
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29950480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 9294836
31.0%
a 3712621
 
12.4%
7 3651753
 
12.2%
1 3174687
 
10.6%
4 3110641
 
10.4%
b 3092253
 
10.3%
c 1202625
 
4.0%
8 631300
 
2.1%
6 621393
 
2.1%
e 560856
 
1.9%
Other values (6) 897515
 
3.0%
Distinct322
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.6 MiB
2024-02-13T20:41:46.036621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters29950480
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

Unique51 ?
Unique (%)< 0.1%

Sample

1st rowea6782cc
2nd row01f63ac8
3rd row01f63ac8
4th row01f63ac8
5th row01f63ac8
ValueCountFrequency (%)
a55475b1 1673324
44.7%
ea6782cc 1241959
33.2%
01f63ac8 334895
 
8.9%
00135d9c 135134
 
3.6%
42a42e75 50119
 
1.3%
9158339f 30715
 
0.8%
4408ff0f 28253
 
0.8%
130920c8 25779
 
0.7%
f0a30139 19608
 
0.5%
e2453741 18581
 
0.5%
Other values (312) 185443
 
5.0%
2024-02-13T20:41:46.646775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 5348203
17.9%
a 3386273
11.3%
7 3079909
10.3%
c 3043578
10.2%
1 2301304
7.7%
4 1959731
 
6.5%
8 1765413
 
5.9%
b 1757364
 
5.9%
6 1726104
 
5.8%
2 1536580
 
5.1%
Other values (6) 4046021
13.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19581696
65.4%
Lowercase Letter 10368784
34.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 5348203
27.3%
7 3079909
15.7%
1 2301304
11.8%
4 1959731
 
10.0%
8 1765413
 
9.0%
6 1726104
 
8.8%
2 1536580
 
7.8%
0 799227
 
4.1%
3 716097
 
3.7%
9 349128
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
a 3386273
32.7%
c 3043578
29.4%
b 1757364
16.9%
e 1445676
13.9%
f 497840
 
4.8%
d 238053
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 19581696
65.4%
Latin 10368784
34.6%

Most frequent character per script

Common
ValueCountFrequency (%)
5 5348203
27.3%
7 3079909
15.7%
1 2301304
11.8%
4 1959731
 
10.0%
8 1765413
 
9.0%
6 1726104
 
8.8%
2 1536580
 
7.8%
0 799227
 
4.1%
3 716097
 
3.7%
9 349128
 
1.8%
Latin
ValueCountFrequency (%)
a 3386273
32.7%
c 3043578
29.4%
b 1757364
16.9%
e 1445676
13.9%
f 497840
 
4.8%
d 238053
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29950480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 5348203
17.9%
a 3386273
11.3%
7 3079909
10.3%
c 3043578
10.2%
1 2301304
7.7%
4 1959731
 
6.5%
8 1765413
 
5.9%
b 1757364
 
5.9%
6 1726104
 
5.8%
2 1536580
 
5.1%
Other values (6) 4046021
13.5%
Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.6 MiB
2024-02-13T20:41:46.854990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters29950480
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

Unique5 ?
Unique (%)< 0.1%

Sample

1st row7241344e
2nd row7241344e
3rd row7241344e
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 3092437
82.6%
7241344e 629008
 
16.8%
8f3a197f 6641
 
0.2%
0dc85f9d 4824
 
0.1%
dd67cff0 2475
 
0.1%
a52d5641 2157
 
0.1%
b919198c 1440
 
< 0.1%
885ce291 980
 
< 0.1%
82a92878 876
 
< 0.1%
7640edc3 593
 
< 0.1%
Other values (28) 2379
 
0.1%
2024-02-13T20:41:47.182673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 9288068
31.0%
4 4983321
16.6%
1 3735386
12.5%
7 3733697
12.5%
a 3102252
 
10.4%
b 3094298
 
10.3%
3 638739
 
2.1%
2 635862
 
2.1%
e 632575
 
2.1%
f 23574
 
0.1%
Other values (6) 82708
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23068395
77.0%
Lowercase Letter 6882085
 
23.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 9288068
40.3%
4 4983321
21.6%
1 3735386
16.2%
7 3733697
16.2%
3 638739
 
2.8%
2 635862
 
2.8%
9 20571
 
0.1%
8 18324
 
0.1%
0 8475
 
< 0.1%
6 5952
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 3102252
45.1%
b 3094298
45.0%
e 632575
 
9.2%
f 23574
 
0.3%
d 18887
 
0.3%
c 10499
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 23068395
77.0%
Latin 6882085
 
23.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 9288068
40.3%
4 4983321
21.6%
1 3735386
16.2%
7 3733697
16.2%
3 638739
 
2.8%
2 635862
 
2.8%
9 20571
 
0.1%
8 18324
 
0.1%
0 8475
 
< 0.1%
6 5952
 
< 0.1%
Latin
ValueCountFrequency (%)
a 3102252
45.1%
b 3094298
45.0%
e 632575
 
9.2%
f 23574
 
0.3%
d 18887
 
0.3%
c 10499
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29950480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 9288068
31.0%
4 4983321
16.6%
1 3735386
12.5%
7 3733697
12.5%
a 3102252
 
10.4%
b 3094298
 
10.3%
3 638739
 
2.1%
2 635862
 
2.1%
e 632575
 
2.1%
f 23574
 
0.1%
Other values (6) 82708
 
0.3%
Distinct236
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.6 MiB
2024-02-13T20:41:47.588380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters29950480
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 row7241344e
2nd row7241344e
3rd row7241344e
4th row7241344e
5th row7241344e
ValueCountFrequency (%)
7241344e 1946842
52.0%
a55475b1 1671067
44.6%
8f3a197f 31166
 
0.8%
a3386307 16662
 
0.4%
8260bab9 12787
 
0.3%
d7416962 11717
 
0.3%
b83056f9 6306
 
0.2%
4476359f 5399
 
0.1%
3dc5f434 4431
 
0.1%
41694615 4033
 
0.1%
Other values (226) 33400
 
0.9%
2024-02-13T20:41:48.180790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 7560295
25.2%
5 5049786
16.9%
7 3702495
12.4%
1 3686930
12.3%
3 2058801
 
6.9%
2 1983088
 
6.6%
e 1957427
 
6.5%
a 1743406
 
5.8%
b 1722976
 
5.8%
f 96287
 
0.3%
Other values (6) 388989
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24372843
81.4%
Lowercase Letter 5577637
 
18.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 7560295
31.0%
5 5049786
20.7%
7 3702495
15.2%
1 3686930
15.1%
3 2058801
 
8.4%
2 1983088
 
8.1%
8 95936
 
0.4%
6 94978
 
0.4%
9 92412
 
0.4%
0 48122
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
e 1957427
35.1%
a 1743406
31.3%
b 1722976
30.9%
f 96287
 
1.7%
d 32347
 
0.6%
c 25194
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 24372843
81.4%
Latin 5577637
 
18.6%

Most frequent character per script

Common
ValueCountFrequency (%)
4 7560295
31.0%
5 5049786
20.7%
7 3702495
15.2%
1 3686930
15.1%
3 2058801
 
8.4%
2 1983088
 
8.1%
8 95936
 
0.4%
6 94978
 
0.4%
9 92412
 
0.4%
0 48122
 
0.2%
Latin
ValueCountFrequency (%)
e 1957427
35.1%
a 1743406
31.3%
b 1722976
30.9%
f 96287
 
1.7%
d 32347
 
0.6%
c 25194
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29950480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 7560295
25.2%
5 5049786
16.9%
7 3702495
12.4%
1 3686930
12.3%
3 2058801
 
6.9%
2 1983088
 
6.6%
e 1957427
 
6.5%
a 1743406
 
5.8%
b 1722976
 
5.8%
f 96287
 
0.3%
Other values (6) 388989
 
1.3%

contractsum_5085717L
Real number (ℝ)

MISSING 

Distinct10545
Distinct (%)76.9%
Missing3730103
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean314291.4646
Minimum0
Maximum8148887.53
Zeros2979
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:41:48.356792image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112553.755
median91659
Q3330109.285
95-th percentile1337451.443
Maximum8148887.53
Range8148887.53
Interquartile range (IQR)317555.53

Descriptive statistics

Standard deviation625214.1813
Coefficient of variation (CV)1.989281453
Kurtosis28.57858411
Mean314291.4646
Median Absolute Deviation (MAD)91659
Skewness4.556292735
Sum4307993106
Variance3.908927725 × 1011
MonotonicityNot monotonic
2024-02-13T20:41:48.519582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2979
 
0.1%
32500 8
 
< 0.1%
50000 7
 
< 0.1%
972801.3 5
 
< 0.1%
73258 4
 
< 0.1%
100000 4
 
< 0.1%
1000000 4
 
< 0.1%
150000 4
 
< 0.1%
1 4
 
< 0.1%
99990 3
 
< 0.1%
Other values (10535) 10685
 
0.3%
(Missing) 3730103
99.6%
ValueCountFrequency (%)
0 2979
0.1%
0.01 1
 
< 0.1%
1 4
 
< 0.1%
3.9 1
 
< 0.1%
4.01 1
 
< 0.1%
ValueCountFrequency (%)
8148887.53 1
< 0.1%
6845029.99 1
< 0.1%
6815609.74 1
< 0.1%
6718318.89 1
< 0.1%
6668858.16 1
< 0.1%

credlmt_230A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct19942
Distinct (%)7.4%
Missing3475352
Missing (%)92.8%
Infinite0
Infinite (%)0.0%
Mean38649.0104
Minimum0
Maximum244000000
Zeros101237
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:41:48.687666image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11998
Q339200
95-th percentile100000
Maximum244000000
Range244000000
Interquartile range (IQR)39200

Descriptive statistics

Standard deviation934300.0749
Coefficient of variation (CV)24.17397148
Kurtosis44482.10312
Mean38649.0104
Median Absolute Deviation (MAD)11998
Skewness195.8570707
Sum1.037563603 × 1010
Variance8.729166299 × 1011
MonotonicityNot monotonic
2024-02-13T20:41:49.031606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 101237
 
2.7%
10000 18117
 
0.5%
20000 15101
 
0.4%
30000 7128
 
0.2%
40000 4745
 
0.1%
60000 3650
 
0.1%
58000 2684
 
0.1%
50000 2680
 
0.1%
100000 2654
 
0.1%
4000 2577
 
0.1%
Other values (19932) 107885
 
2.9%
(Missing) 3475352
92.8%
ValueCountFrequency (%)
0 101237
2.7%
0.082 1
 
< 0.1%
0.2 230
 
< 0.1%
0.23599999 1
 
< 0.1%
0.458 1
 
< 0.1%
ValueCountFrequency (%)
244000000 1
< 0.1%
237093760 1
< 0.1%
203156590 1
< 0.1%
140000000 1
< 0.1%
96843400 1
< 0.1%

credlmt_935A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct51392
Distinct (%)16.8%
Missing3437159
Missing (%)91.8%
Infinite0
Infinite (%)0.0%
Mean154178.5816
Minimum0
Maximum2860000000
Zeros84193
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:41:49.214797image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20000
Q380000
95-th percentile290000
Maximum2860000000
Range2860000000
Interquartile range (IQR)80000

Descriptive statistics

Standard deviation7097211.156
Coefficient of variation (CV)46.03240658
Kurtosis91740.52274
Mean154178.5816
Median Absolute Deviation (MAD)20000
Skewness260.4751764
Sum4.727901624 × 1010
Variance5.03704062 × 1013
MonotonicityNot monotonic
2024-02-13T20:41:49.376676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 84193
 
2.2%
10000 29549
 
0.8%
20000 24042
 
0.6%
30000 13937
 
0.4%
200000 6278
 
0.2%
100000 5200
 
0.1%
40000 3421
 
0.1%
60000 1984
 
0.1%
80000 1762
 
< 0.1%
120000 1611
 
< 0.1%
Other values (51382) 134674
 
3.6%
(Missing) 3437159
91.8%
ValueCountFrequency (%)
0 84193
2.2%
0.2 13
 
< 0.1%
0.8 1
 
< 0.1%
9.8 1
 
< 0.1%
10.6 1
 
< 0.1%
ValueCountFrequency (%)
2860000000 1
< 0.1%
1040000000 1
< 0.1%
1023387600 1
< 0.1%
940000060 2
< 0.1%
718600000 1
< 0.1%

dateofcredend_289D
Text

MISSING 

Distinct7922
Distinct (%)1.2%
Missing3090430
Missing (%)82.5%
Memory size28.6 MiB
2024-02-13T20:41:49.798850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters6533800
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

Unique2134 ?
Unique (%)0.3%

Sample

1st row2022-03-31
2nd row2021-03-24
3rd row2021-03-24
4th row2021-05-14
5th row2020-10-18
ValueCountFrequency (%)
2024-07-14 2426
 
0.4%
2021-05-14 2408
 
0.4%
2021-10-14 2331
 
0.4%
2021-11-14 2231
 
0.3%
2022-03-14 1807
 
0.3%
2021-03-14 1707
 
0.3%
2024-09-14 1692
 
0.3%
2021-09-14 1581
 
0.2%
2022-01-14 1570
 
0.2%
2021-02-14 1364
 
0.2%
Other values (7912) 634263
97.1%
2024-02-13T20:41:50.366335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 1740494
26.6%
0 1685760
25.8%
- 1306760
20.0%
1 839614
12.9%
3 175239
 
2.7%
4 168007
 
2.6%
9 130903
 
2.0%
8 126043
 
1.9%
5 125186
 
1.9%
7 120649
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5227040
80.0%
Dash Punctuation 1306760
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1740494
33.3%
0 1685760
32.3%
1 839614
16.1%
3 175239
 
3.4%
4 168007
 
3.2%
9 130903
 
2.5%
8 126043
 
2.4%
5 125186
 
2.4%
7 120649
 
2.3%
6 115145
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 1306760
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6533800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1740494
26.6%
0 1685760
25.8%
- 1306760
20.0%
1 839614
12.9%
3 175239
 
2.7%
4 168007
 
2.6%
9 130903
 
2.0%
8 126043
 
1.9%
5 125186
 
1.9%
7 120649
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6533800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1740494
26.6%
0 1685760
25.8%
- 1306760
20.0%
1 839614
12.9%
3 175239
 
2.7%
4 168007
 
2.6%
9 130903
 
2.0%
8 126043
 
1.9%
5 125186
 
1.9%
7 120649
 
1.8%

dateofcredend_353D
Text

MISSING 

Distinct9683
Distinct (%)0.5%
Missing1670009
Missing (%)44.6%
Memory size28.6 MiB
2024-02-13T20:41:50.694138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters20738010
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

Unique1020 ?
Unique (%)< 0.1%

Sample

1st row2019-09-14
2nd row2019-10-04
3rd row2019-10-31
4th row2019-12-06
5th row2019-12-05
ValueCountFrequency (%)
2019-09-17 10691
 
0.5%
2019-09-16 3348
 
0.2%
2019-11-15 2218
 
0.1%
2019-12-09 2009
 
0.1%
2019-07-15 1974
 
0.1%
2019-06-14 1905
 
0.1%
2019-07-29 1897
 
0.1%
2019-11-08 1746
 
0.1%
2019-09-19 1733
 
0.1%
2019-12-17 1724
 
0.1%
Other values (9673) 2044556
98.6%
2024-02-13T20:41:51.141360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5044101
24.3%
- 4147602
20.0%
2 3591790
17.3%
1 3499230
16.9%
9 952355
 
4.6%
8 743155
 
3.6%
7 629225
 
3.0%
5 544874
 
2.6%
3 539345
 
2.6%
6 533737
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16590408
80.0%
Dash Punctuation 4147602
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5044101
30.4%
2 3591790
21.6%
1 3499230
21.1%
9 952355
 
5.7%
8 743155
 
4.5%
7 629225
 
3.8%
5 544874
 
3.3%
3 539345
 
3.3%
6 533737
 
3.2%
4 512596
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 4147602
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20738010
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5044101
24.3%
- 4147602
20.0%
2 3591790
17.3%
1 3499230
16.9%
9 952355
 
4.6%
8 743155
 
3.6%
7 629225
 
3.0%
5 544874
 
2.6%
3 539345
 
2.6%
6 533737
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20738010
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5044101
24.3%
- 4147602
20.0%
2 3591790
17.3%
1 3499230
16.9%
9 952355
 
4.6%
8 743155
 
3.6%
7 629225
 
3.0%
5 544874
 
2.6%
3 539345
 
2.6%
6 533737
 
2.6%

dateofcredstart_181D
Text

MISSING 

Distinct6312
Distinct (%)0.3%
Missing1670007
Missing (%)44.6%
Memory size28.6 MiB
2024-02-13T20:41:51.577274image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters20738030
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

Unique188 ?
Unique (%)< 0.1%

Sample

1st row2017-09-14
2nd row2019-09-04
3rd row2019-10-01
4th row2019-11-06
5th row2019-11-04
ValueCountFrequency (%)
2018-12-07 1498
 
0.1%
2018-01-12 1433
 
0.1%
2019-01-02 1389
 
0.1%
2018-01-03 1358
 
0.1%
2018-01-13 1349
 
0.1%
2018-04-09 1331
 
0.1%
2018-01-05 1326
 
0.1%
2018-01-08 1310
 
0.1%
2018-07-02 1301
 
0.1%
2018-05-28 1268
 
0.1%
Other values (6302) 2060240
99.3%
2024-02-13T20:41:52.102052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5067217
24.4%
- 4147606
20.0%
1 3588153
17.3%
2 3431741
16.5%
8 795157
 
3.8%
7 751518
 
3.6%
9 658468
 
3.2%
6 624045
 
3.0%
3 620727
 
3.0%
5 533188
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16590424
80.0%
Dash Punctuation 4147606
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5067217
30.5%
1 3588153
21.6%
2 3431741
20.7%
8 795157
 
4.8%
7 751518
 
4.5%
9 658468
 
4.0%
6 624045
 
3.8%
3 620727
 
3.7%
5 533188
 
3.2%
4 520210
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 4147606
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20738030
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5067217
24.4%
- 4147606
20.0%
1 3588153
17.3%
2 3431741
16.5%
8 795157
 
3.8%
7 751518
 
3.6%
9 658468
 
3.2%
6 624045
 
3.0%
3 620727
 
3.0%
5 533188
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20738030
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5067217
24.4%
- 4147606
20.0%
1 3588153
17.3%
2 3431741
16.5%
8 795157
 
3.8%
7 751518
 
3.6%
9 658468
 
3.2%
6 624045
 
3.0%
3 620727
 
3.0%
5 533188
 
2.6%

dateofcredstart_739D
Text

MISSING 

Distinct4747
Distinct (%)0.7%
Missing3090430
Missing (%)82.5%
Memory size28.6 MiB
2024-02-13T20:41:52.502046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters6533800
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

Unique425 ?
Unique (%)0.1%

Sample

1st row2018-10-31
2nd row2019-03-24
3rd row2019-03-24
4th row2013-05-14
5th row2018-10-18
ValueCountFrequency (%)
2019-11-29 1747
 
0.3%
2019-12-13 1690
 
0.3%
2019-12-01 1597
 
0.2%
2019-06-28 1594
 
0.2%
2019-08-30 1567
 
0.2%
2019-09-27 1556
 
0.2%
2019-11-30 1551
 
0.2%
2019-12-02 1474
 
0.2%
2019-12-27 1455
 
0.2%
2019-10-11 1445
 
0.2%
Other values (4737) 637704
97.6%
2024-02-13T20:41:53.022768image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1493161
22.9%
- 1306760
20.0%
1 1211445
18.5%
2 1107788
17.0%
9 446327
 
6.8%
8 237305
 
3.6%
7 177189
 
2.7%
3 165310
 
2.5%
6 136990
 
2.1%
5 127417
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5227040
80.0%
Dash Punctuation 1306760
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1493161
28.6%
1 1211445
23.2%
2 1107788
21.2%
9 446327
 
8.5%
8 237305
 
4.5%
7 177189
 
3.4%
3 165310
 
3.2%
6 136990
 
2.6%
5 127417
 
2.4%
4 124108
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 1306760
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6533800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1493161
22.9%
- 1306760
20.0%
1 1211445
18.5%
2 1107788
17.0%
9 446327
 
6.8%
8 237305
 
3.6%
7 177189
 
2.7%
3 165310
 
2.5%
6 136990
 
2.1%
5 127417
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6533800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1493161
22.9%
- 1306760
20.0%
1 1211445
18.5%
2 1107788
17.0%
9 446327
 
6.8%
8 237305
 
3.6%
7 177189
 
2.7%
3 165310
 
2.5%
6 136990
 
2.1%
5 127417
 
2.0%

dateofrealrepmt_138D
Text

MISSING 

Distinct5928
Distinct (%)0.3%
Missing1681312
Missing (%)44.9%
Memory size28.6 MiB
2024-02-13T20:41:53.447495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters20624980
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

Unique256 ?
Unique (%)< 0.1%

Sample

1st row2018-12-19
2nd row2019-10-01
3rd row2019-10-30
4th row2019-12-06
5th row2019-12-04
ValueCountFrequency (%)
2018-08-10 17590
 
0.9%
2011-08-12 11973
 
0.6%
2019-09-17 11205
 
0.5%
2015-06-29 4783
 
0.2%
2019-09-16 4685
 
0.2%
2019-09-11 4217
 
0.2%
2008-12-12 4073
 
0.2%
2015-02-23 3894
 
0.2%
2012-11-15 3592
 
0.2%
2019-09-12 2555
 
0.1%
Other values (5918) 1993931
96.7%
2024-02-13T20:41:53.971181image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4918055
23.8%
- 4124996
20.0%
1 3595494
17.4%
2 3504183
17.0%
9 941881
 
4.6%
8 802801
 
3.9%
7 660880
 
3.2%
6 548912
 
2.7%
3 546956
 
2.7%
5 499864
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16499984
80.0%
Dash Punctuation 4124996
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4918055
29.8%
1 3595494
21.8%
2 3504183
21.2%
9 941881
 
5.7%
8 802801
 
4.9%
7 660880
 
4.0%
6 548912
 
3.3%
3 546956
 
3.3%
5 499864
 
3.0%
4 480958
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 4124996
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20624980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4918055
23.8%
- 4124996
20.0%
1 3595494
17.4%
2 3504183
17.0%
9 941881
 
4.6%
8 802801
 
3.9%
7 660880
 
3.2%
6 548912
 
2.7%
3 546956
 
2.7%
5 499864
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20624980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4918055
23.8%
- 4124996
20.0%
1 3595494
17.4%
2 3504183
17.0%
9 941881
 
4.6%
8 802801
 
3.9%
7 660880
 
3.2%
6 548912
 
2.7%
3 546956
 
2.7%
5 499864
 
2.4%

debtoutstand_525A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct254680
Distinct (%)78.3%
Missing3418641
Missing (%)91.3%
Infinite0
Infinite (%)0.0%
Mean223701.2966
Minimum0
Maximum948112900
Zeros61687
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:41:54.149685image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19454.906
median61882.07
Q3191902.67
95-th percentile878824.48
Maximum948112900
Range948112900
Interquartile range (IQR)182447.764

Descriptive statistics

Standard deviation2088065.945
Coefficient of variation (CV)9.334170058
Kurtosis139766.2167
Mean223701.2966
Median Absolute Deviation (MAD)61882.07
Skewness334.9546734
Sum7.274072691 × 1010
Variance4.360019389 × 1012
MonotonicityNot monotonic
2024-02-13T20:41:54.307726image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 61687
 
1.6%
200000 118
 
< 0.1%
100000 78
 
< 0.1%
40000 67
 
< 0.1%
20000 65
 
< 0.1%
10000 58
 
< 0.1%
30000 55
 
< 0.1%
60000 50
 
< 0.1%
12000 47
 
< 0.1%
11998 36
 
< 0.1%
Other values (254670) 262908
 
7.0%
(Missing) 3418641
91.3%
ValueCountFrequency (%)
0 61687
1.6%
0.002 1
 
< 0.1%
0.14600001 1
 
< 0.1%
0.2 2
 
< 0.1%
0.224 1
 
< 0.1%
ValueCountFrequency (%)
948112900 1
< 0.1%
468484580 1
< 0.1%
273658500 1
< 0.1%
228040830 1
< 0.1%
84338510 1
< 0.1%

debtoverdue_47A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct14120
Distinct (%)4.3%
Missing3418641
Missing (%)91.3%
Infinite0
Infinite (%)0.0%
Mean3546.802795
Minimum0
Maximum428557340
Zeros309064
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:41:54.470643image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum428557340
Range428557340
Interquartile range (IQR)0

Descriptive statistics

Standard deviation761016.0019
Coefficient of variation (CV)214.5639456
Kurtosis309296.8927
Mean3546.802795
Median Absolute Deviation (MAD)0
Skewness549.9069855
Sum1153310318
Variance5.791453551 × 1011
MonotonicityNot monotonic
2024-02-13T20:41:54.627575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 309064
 
8.3%
10 51
 
< 0.1%
14 43
 
< 0.1%
99.8 32
 
< 0.1%
6000 29
 
< 0.1%
1 26
 
< 0.1%
0.2 25
 
< 0.1%
4228 25
 
< 0.1%
2.4 20
 
< 0.1%
2 19
 
< 0.1%
Other values (14110) 15835
 
0.4%
(Missing) 3418641
91.3%
ValueCountFrequency (%)
0 309064
8.3%
0.002 2
 
< 0.1%
0.004 1
 
< 0.1%
0.006 3
 
< 0.1%
0.008 4
 
< 0.1%
ValueCountFrequency (%)
428557340 1
< 0.1%
32670790 1
< 0.1%
26681114 1
< 0.1%
23983900 1
< 0.1%
23045082 1
< 0.1%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.6 MiB
2024-02-13T20:41:54.810344image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters29950480
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 3730428
99.6%
6da7c7ed 3076
 
0.1%
95decc86 2294
 
0.1%
f8e51f8d 1482
 
< 0.1%
1d89fa48 1431
 
< 0.1%
53179c19 1383
 
< 0.1%
0349102c 1237
 
< 0.1%
18e98e64 1232
 
< 0.1%
8a7423d5 554
 
< 0.1%
0cb4d552 491
 
< 0.1%
Other values (2) 202
 
< 0.1%
2024-02-13T20:41:55.104485image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 11198181
37.4%
1 3738580
 
12.5%
7 3738517
 
12.5%
a 3735491
 
12.5%
4 3735373
 
12.5%
b 3731319
 
12.5%
d 12404
 
< 0.1%
8 11338
 
< 0.1%
c 10777
 
< 0.1%
e 9316
 
< 0.1%
Other values (6) 29184
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22446176
74.9%
Lowercase Letter 7504304
 
25.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 11198181
49.9%
1 3738580
 
16.7%
7 3738517
 
16.7%
4 3735373
 
16.6%
8 11338
 
0.1%
9 8960
 
< 0.1%
6 6604
 
< 0.1%
3 3176
 
< 0.1%
0 3165
 
< 0.1%
2 2282
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 3735491
49.8%
b 3731319
49.7%
d 12404
 
0.2%
c 10777
 
0.1%
e 9316
 
0.1%
f 4997
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 22446176
74.9%
Latin 7504304
 
25.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 11198181
49.9%
1 3738580
 
16.7%
7 3738517
 
16.7%
4 3735373
 
16.6%
8 11338
 
0.1%
9 8960
 
< 0.1%
6 6604
 
< 0.1%
3 3176
 
< 0.1%
0 3165
 
< 0.1%
2 2282
 
< 0.1%
Latin
ValueCountFrequency (%)
a 3735491
49.8%
b 3731319
49.7%
d 12404
 
0.2%
c 10777
 
0.1%
e 9316
 
0.1%
f 4997
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29950480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 11198181
37.4%
1 3738580
 
12.5%
7 3738517
 
12.5%
a 3735491
 
12.5%
4 3735373
 
12.5%
b 3731319
 
12.5%
d 12404
 
< 0.1%
8 11338
 
< 0.1%
c 10777
 
< 0.1%
e 9316
 
< 0.1%
Other values (6) 29184
 
0.1%

dpdmax_139P
Real number (ℝ)

MISSING  ZEROS 

Distinct2311
Distinct (%)0.4%
Missing3094889
Missing (%)82.7%
Infinite0
Infinite (%)0.0%
Mean13.62948649
Minimum0
Maximum4565
Zeros515645
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:41:55.258613image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile22
Maximum4565
Range4565
Interquartile range (IQR)0

Descriptive statistics

Standard deviation134.5387483
Coefficient of variation (CV)9.871153139
Kurtosis398.6913018
Mean13.62948649
Median Absolute Deviation (MAD)0
Skewness18.01247547
Sum8844460
Variance18100.6748
MonotonicityNot monotonic
2024-02-13T20:41:55.409934image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 515645
 
13.8%
1 28701
 
0.8%
2 10305
 
0.3%
3 8568
 
0.2%
4 7243
 
0.2%
5 4433
 
0.1%
7 4300
 
0.1%
6 3651
 
0.1%
8 3369
 
0.1%
9 3244
 
0.1%
Other values (2301) 59462
 
1.6%
(Missing) 3094889
82.7%
ValueCountFrequency (%)
0 515645
13.8%
1 28701
 
0.8%
2 10305
 
0.3%
3 8568
 
0.2%
4 7243
 
0.2%
ValueCountFrequency (%)
4565 1
< 0.1%
4552 1
< 0.1%
4537 1
< 0.1%
4534 1
< 0.1%
4521 1
< 0.1%

dpdmax_757P
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct3850
Distinct (%)0.2%
Missing1739084
Missing (%)46.5%
Infinite0
Infinite (%)0.0%
Mean47.1222157
Minimum-30
Maximum117000
Zeros1426897
Zeros (%)38.1%
Negative324
Negative (%)< 0.1%
Memory size28.6 MiB
2024-02-13T20:41:55.575348image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-30
5-th percentile0
Q10
median0
Q31
95-th percentile138
Maximum117000
Range117030
Interquartile range (IQR)1

Descriptive statistics

Standard deviation329.2494468
Coefficient of variation (CV)6.987138484
Kurtosis37597.17492
Mean47.1222157
Median Absolute Deviation (MAD)0
Skewness132.6164523
Sum94467131
Variance108405.1982
MonotonicityNot monotonic
2024-02-13T20:41:55.752028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1426897
38.1%
1 99898
 
2.7%
2 33729
 
0.9%
3 31944
 
0.9%
4 25749
 
0.7%
6 19009
 
0.5%
5 17080
 
0.5%
7 16177
 
0.4%
8 11844
 
0.3%
9 11590
 
0.3%
Other values (3840) 310809
 
8.3%
(Missing) 1739084
46.5%
ValueCountFrequency (%)
-30 1
< 0.1%
-14 1
< 0.1%
-13 1
< 0.1%
-10 1
< 0.1%
-9 1
< 0.1%
ValueCountFrequency (%)
117000 2
 
< 0.1%
84575 2
 
< 0.1%
84574 5
< 0.1%
84573 1
 
< 0.1%
84560 1
 
< 0.1%

dpdmaxdatemonth_442T
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing1739084
Missing (%)46.5%
Infinite0
Infinite (%)0.0%
Mean6.556746907
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:41:55.883996image/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 deviation3.457174453
Coefficient of variation (CV)0.527269773
Kurtosis-1.200904614
Mean6.556746907
Median Absolute Deviation (MAD)3
Skewness-0.06418845014
Sum13144481
Variance11.9520552
MonotonicityNot monotonic
2024-02-13T20:41:56.004595image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 188229
 
5.0%
1 183598
 
4.9%
11 175015
 
4.7%
7 174525
 
4.7%
6 171816
 
4.6%
10 170042
 
4.5%
9 169584
 
4.5%
12 160379
 
4.3%
2 159235
 
4.3%
3 153450
 
4.1%
Other values (2) 298853
 
8.0%
(Missing) 1739084
46.5%
ValueCountFrequency (%)
1 183598
4.9%
2 159235
4.3%
3 153450
4.1%
4 146994
3.9%
5 151859
4.1%
ValueCountFrequency (%)
12 160379
4.3%
11 175015
4.7%
10 170042
4.5%
9 169584
4.5%
8 188229
5.0%

dpdmaxdatemonth_89T
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing3094889
Missing (%)82.7%
Infinite0
Infinite (%)0.0%
Mean6.237276957
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:41:56.122484image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.672365552
Coefficient of variation (CV)0.5887770542
Kurtosis-1.368307807
Mean6.237276957
Median Absolute Deviation (MAD)3
Skewness0.1297861447
Sum4047500
Variance13.48626875
MonotonicityNot monotonic
2024-02-13T20:41:56.237223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 85699
 
2.3%
12 64213
 
1.7%
1 60204
 
1.6%
3 58316
 
1.6%
4 57219
 
1.5%
10 53396
 
1.4%
11 52589
 
1.4%
7 50120
 
1.3%
9 46929
 
1.3%
5 45302
 
1.2%
Other values (2) 74934
 
2.0%
(Missing) 3094889
82.7%
ValueCountFrequency (%)
1 60204
1.6%
2 85699
2.3%
3 58316
1.6%
4 57219
1.5%
5 45302
1.2%
ValueCountFrequency (%)
12 64213
1.7%
11 52589
1.4%
10 53396
1.4%
9 46929
1.3%
8 39882
1.1%

dpdmaxdateyear_596T
Real number (ℝ)

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing3094889
Missing (%)82.7%
Infinite0
Infinite (%)0.0%
Mean2018.833032
Minimum2016
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:41:56.347062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2018
Q12018
median2019
Q32019
95-th percentile2020
Maximum2020
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6771289788
Coefficient of variation (CV)0.0003354061322
Kurtosis-0.8363418763
Mean2018.833032
Median Absolute Deviation (MAD)0
Skewness0.2153334405
Sum1310063150
Variance0.4585036539
MonotonicityNot monotonic
2024-02-13T20:41:56.469526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
2019 333428
 
8.9%
2018 211872
 
5.7%
2020 103591
 
2.8%
2017 22
 
< 0.1%
2016 8
 
< 0.1%
(Missing) 3094889
82.7%
ValueCountFrequency (%)
2016 8
 
< 0.1%
2017 22
 
< 0.1%
2018 211872
5.7%
2019 333428
8.9%
2020 103591
 
2.8%
ValueCountFrequency (%)
2020 103591
 
2.8%
2019 333428
8.9%
2018 211872
5.7%
2017 22
 
< 0.1%
2016 8
 
< 0.1%

dpdmaxdateyear_896T
Real number (ℝ)

MISSING 

Distinct19
Distinct (%)< 0.1%
Missing1739084
Missing (%)46.5%
Infinite0
Infinite (%)0.0%
Mean2014.911306
Minimum2002
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:41:56.598544image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2002
5-th percentile2007
Q12012
median2016
Q32018
95-th percentile2019
Maximum2020
Range18
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.840487649
Coefficient of variation (CV)0.001906033104
Kurtosis-0.3681929433
Mean2014.911306
Median Absolute Deviation (MAD)2
Skewness-0.8415837739
Sum4039345082
Variance14.74934538
MonotonicityNot monotonic
2024-02-13T20:41:56.735525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2018 362424
 
9.7%
2019 296676
 
7.9%
2017 247541
 
6.6%
2016 168360
 
4.5%
2015 148507
 
4.0%
2014 132361
 
3.5%
2013 115236
 
3.1%
2012 96690
 
2.6%
2007 87385
 
2.3%
2011 86964
 
2.3%
Other values (9) 262582
 
7.0%
(Missing) 1739084
46.5%
ValueCountFrequency (%)
2002 1
 
< 0.1%
2003 3
 
< 0.1%
2004 600
 
< 0.1%
2005 11513
 
0.3%
2006 44197
1.2%
ValueCountFrequency (%)
2020 30745
 
0.8%
2019 296676
7.9%
2018 362424
9.7%
2017 247541
6.6%
2016 168360
4.5%
Distinct327
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.6 MiB
2024-02-13T20:41:57.143933image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length9.073842155
Min length8

Characters and Unicode

Total characters33970741
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

Unique34 ?
Unique (%)< 0.1%

Sample

1st rowP204_66_73
2nd row9a93e20f
3rd row9a93e20f
4th row9a93e20f
5th rowe9bfdb5c
ValueCountFrequency (%)
a55475b1 1670007
38.1%
home 639404
 
14.6%
credit 639404
 
14.6%
p133_127_114 236695
 
5.4%
b619fa46 219592
 
5.0%
p150_136_157 186865
 
4.3%
p40_52_135 99086
 
2.3%
9a93e20f 92237
 
2.1%
d6a7d943 84901
 
1.9%
p40_25_35 48177
 
1.1%
Other values (318) 466846
 
10.7%
2024-02-13T20:41:57.734351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 6022987
17.7%
1 3684483
 
10.8%
4 2539185
 
7.5%
7 2329498
 
6.9%
a 2272252
 
6.7%
b 2180640
 
6.4%
e 1474145
 
4.3%
_ 1290558
 
3.8%
3 1200837
 
3.5%
d 1096002
 
3.2%
Other values (16) 9880154
29.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19131077
56.3%
Lowercase Letter 10985615
32.3%
Uppercase Letter 1924087
 
5.7%
Connector Punctuation 1290558
 
3.8%
Space Separator 639404
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2272252
20.7%
b 2180640
19.8%
e 1474145
13.4%
d 1096002
10.0%
r 639404
 
5.8%
m 639404
 
5.8%
i 639404
 
5.8%
t 639404
 
5.8%
o 639404
 
5.8%
f 474109
 
4.3%
Decimal Number
ValueCountFrequency (%)
5 6022987
31.5%
1 3684483
19.3%
4 2539185
13.3%
7 2329498
 
12.2%
3 1200837
 
6.3%
6 941098
 
4.9%
2 790954
 
4.1%
9 740851
 
3.9%
0 694916
 
3.6%
8 186268
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
P 645279
33.5%
C 639404
33.2%
H 639404
33.2%
Connector Punctuation
ValueCountFrequency (%)
_ 1290558
100.0%
Space Separator
ValueCountFrequency (%)
639404
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21061039
62.0%
Latin 12909702
38.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2272252
17.6%
b 2180640
16.9%
e 1474145
11.4%
d 1096002
8.5%
P 645279
 
5.0%
C 639404
 
5.0%
r 639404
 
5.0%
m 639404
 
5.0%
i 639404
 
5.0%
t 639404
 
5.0%
Other values (4) 2044364
15.8%
Common
ValueCountFrequency (%)
5 6022987
28.6%
1 3684483
17.5%
4 2539185
12.1%
7 2329498
 
11.1%
_ 1290558
 
6.1%
3 1200837
 
5.7%
6 941098
 
4.5%
2 790954
 
3.8%
9 740851
 
3.5%
0 694916
 
3.3%
Other values (2) 825672
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33970741
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 6022987
17.7%
1 3684483
 
10.8%
4 2539185
 
7.5%
7 2329498
 
6.9%
a 2272252
 
6.7%
b 2180640
 
6.4%
e 1474145
 
4.3%
_ 1290558
 
3.8%
3 1200837
 
3.5%
d 1096002
 
3.2%
Other values (16) 9880154
29.1%
Distinct173
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.6 MiB
2024-02-13T20:41:57.975957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.323304334
Min length8

Characters and Unicode

Total characters31160870
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

Unique24 ?
Unique (%)< 0.1%

Sample

1st row0d39f5db
2nd rowP204_66_73
3rd rowP204_66_73
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 3090430
79.1%
home 164387
 
4.2%
credit 164387
 
4.2%
b619fa46 162567
 
4.2%
p204_66_73 81152
 
2.1%
p133_127_114 71264
 
1.8%
p150_136_157 52992
 
1.4%
50babcd4 21989
 
0.6%
d6a7d943 18098
 
0.5%
p102_97_118 12035
 
0.3%
Other values (164) 68896
 
1.8%
2024-02-13T20:41:58.331112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 9454927
30.3%
1 3781122
 
12.1%
4 3456027
 
11.1%
7 3339277
 
10.7%
b 3329487
 
10.7%
a 3325580
 
10.7%
6 589082
 
1.9%
_ 456682
 
1.5%
3 351872
 
1.1%
e 342029
 
1.1%
Other values (16) 2734785
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21660275
69.5%
Lowercase Letter 8322411
 
26.7%
Uppercase Letter 557115
 
1.8%
Connector Punctuation 456682
 
1.5%
Space Separator 164387
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 3329487
40.0%
a 3325580
40.0%
e 342029
 
4.1%
d 259472
 
3.1%
f 188967
 
2.3%
r 164387
 
2.0%
i 164387
 
2.0%
t 164387
 
2.0%
m 164387
 
2.0%
o 164387
 
2.0%
Decimal Number
ValueCountFrequency (%)
5 9454927
43.7%
1 3781122
 
17.5%
4 3456027
 
16.0%
7 3339277
 
15.4%
6 589082
 
2.7%
3 351872
 
1.6%
2 232576
 
1.1%
9 222552
 
1.0%
0 198623
 
0.9%
8 34217
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
P 228341
41.0%
C 164387
29.5%
H 164387
29.5%
Connector Punctuation
ValueCountFrequency (%)
_ 456682
100.0%
Space Separator
ValueCountFrequency (%)
164387
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22281344
71.5%
Latin 8879526
 
28.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 3329487
37.5%
a 3325580
37.5%
e 342029
 
3.9%
d 259472
 
2.9%
P 228341
 
2.6%
f 188967
 
2.1%
C 164387
 
1.9%
r 164387
 
1.9%
i 164387
 
1.9%
t 164387
 
1.9%
Other values (4) 548102
 
6.2%
Common
ValueCountFrequency (%)
5 9454927
42.4%
1 3781122
 
17.0%
4 3456027
 
15.5%
7 3339277
 
15.0%
6 589082
 
2.6%
_ 456682
 
2.0%
3 351872
 
1.6%
2 232576
 
1.0%
9 222552
 
1.0%
0 198623
 
0.9%
Other values (2) 198604
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31160870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 9454927
30.3%
1 3781122
 
12.1%
4 3456027
 
11.1%
7 3339277
 
10.7%
b 3329487
 
10.7%
a 3325580
 
10.7%
6 589082
 
1.9%
_ 456682
 
1.5%
3 351872
 
1.1%
e 342029
 
1.1%
Other values (16) 2734785
 
8.8%

instlamount_768A
Real number (ℝ)

MISSING  ZEROS 

Distinct84547
Distinct (%)28.0%
Missing3441756
Missing (%)91.9%
Infinite0
Infinite (%)0.0%
Mean3962.345219
Minimum0
Maximum149600.61
Zeros118911
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:41:58.483025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1518
Q35893.6
95-th percentile15354.81065
Maximum149600.61
Range149600.61
Interquartile range (IQR)5893.6

Descriptive statistics

Standard deviation5969.768784
Coefficient of variation (CV)1.506625105
Kurtosis20.34552549
Mean3962.345219
Median Absolute Deviation (MAD)1518
Skewness3.095025304
Sum1196842223
Variance35638139.33
MonotonicityNot monotonic
2024-02-13T20:41:58.643706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118911
 
3.2%
400 1406
 
< 0.1%
600 976
 
< 0.1%
9068 818
 
< 0.1%
2389.6 780
 
< 0.1%
1000 772
 
< 0.1%
10230 561
 
< 0.1%
12772.4 518
 
< 0.1%
4779 489
 
< 0.1%
9067.8 393
 
< 0.1%
Other values (84537) 176430
 
4.7%
(Missing) 3441756
91.9%
ValueCountFrequency (%)
0 118911
3.2%
0.002 1
 
< 0.1%
0.004 1
 
< 0.1%
0.006 1
 
< 0.1%
0.008 1
 
< 0.1%
ValueCountFrequency (%)
149600.61 1
< 0.1%
145851.48 1
< 0.1%
129783.2 1
< 0.1%
119895.2 1
< 0.1%
118080.6 1
< 0.1%

instlamount_852A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct36328
Distinct (%)20.1%
Missing3562859
Missing (%)95.2%
Infinite0
Infinite (%)0.0%
Mean682.8312387
Minimum0
Maximum280960.75
Zeros113455
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:41:58.802348image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3400
95-th percentile3779.104
Maximum280960.75
Range280960.75
Interquartile range (IQR)400

Descriptive statistics

Standard deviation2092.173971
Coefficient of variation (CV)3.063969328
Kurtosis2539.873173
Mean682.8312387
Median Absolute Deviation (MAD)0
Skewness27.90462388
Sum123558995.5
Variance4377191.926
MonotonicityNot monotonic
2024-02-13T20:41:58.959076image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 113455
 
3.0%
400 6795
 
0.2%
1000 3943
 
0.1%
1200 1165
 
< 0.1%
2000 982
 
< 0.1%
2260 721
 
< 0.1%
3000 691
 
< 0.1%
1540 686
 
< 0.1%
800 632
 
< 0.1%
3700 514
 
< 0.1%
Other values (36318) 51367
 
1.4%
(Missing) 3562859
95.2%
ValueCountFrequency (%)
0 113455
3.0%
0.002 15
 
< 0.1%
0.004 9
 
< 0.1%
0.006 21
 
< 0.1%
0.008 3
 
< 0.1%
ValueCountFrequency (%)
280960.75 1
< 0.1%
191104.88 1
< 0.1%
141503.19 1
< 0.1%
119361.914 1
< 0.1%
114776.1 1
< 0.1%

interestrate_508L
Real number (ℝ)

MISSING  SKEWED 

Distinct189
Distinct (%)1.0%
Missing3724410
Missing (%)99.5%
Infinite0
Infinite (%)0.0%
Mean105.1559485
Minimum0
Maximum227421
Zeros256
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:41:59.112138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q118
median20
Q324.5
95-th percentile35
Maximum227421
Range227421
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation2198.620749
Coefficient of variation (CV)20.90819189
Kurtosis6487.181475
Mean105.1559485
Median Absolute Deviation (MAD)4
Skewness70.86461209
Sum2040025.4
Variance4833933.197
MonotonicityNot monotonic
2024-02-13T20:41:59.262129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 3135
 
0.1%
24 2328
 
0.1%
18 1247
 
< 0.1%
20 1186
 
< 0.1%
22 1179
 
< 0.1%
35 1070
 
< 0.1%
17 998
 
< 0.1%
16 836
 
< 0.1%
25 782
 
< 0.1%
26 675
 
< 0.1%
Other values (179) 5964
 
0.2%
(Missing) 3724410
99.5%
ValueCountFrequency (%)
0 256
< 0.1%
3 3
 
< 0.1%
4 6
 
< 0.1%
4.5 2
 
< 0.1%
5 13
 
< 0.1%
ValueCountFrequency (%)
227421 1
< 0.1%
120013 1
< 0.1%
70942 1
< 0.1%
70795 1
< 0.1%
46048 1
< 0.1%

lastupdate_1112D
Text

MISSING 

Distinct309
Distinct (%)< 0.1%
Missing3090430
Missing (%)82.5%
Memory size28.6 MiB
2024-02-13T20:41:59.637336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters6533800
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

Unique56 ?
Unique (%)< 0.1%

Sample

1st row2019-12-13
2nd row2019-12-12
3rd row2019-12-11
4th row2019-12-12
5th row2019-12-11
ValueCountFrequency (%)
2020-01-02 41047
 
6.3%
2020-02-26 37840
 
5.8%
2020-01-08 34814
 
5.3%
2019-12-26 30760
 
4.7%
2020-02-11 27225
 
4.2%
2020-02-06 24297
 
3.7%
2020-01-23 23509
 
3.6%
2019-12-12 23226
 
3.6%
2020-03-16 22274
 
3.4%
2020-01-24 21511
 
3.3%
Other values (299) 366877
56.2%
2024-02-13T20:42:00.175976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2067447
31.6%
2 1774068
27.2%
- 1306760
20.0%
1 646746
 
9.9%
3 189522
 
2.9%
6 162717
 
2.5%
9 112244
 
1.7%
5 84554
 
1.3%
4 82529
 
1.3%
8 74769
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5227040
80.0%
Dash Punctuation 1306760
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2067447
39.6%
2 1774068
33.9%
1 646746
 
12.4%
3 189522
 
3.6%
6 162717
 
3.1%
9 112244
 
2.1%
5 84554
 
1.6%
4 82529
 
1.6%
8 74769
 
1.4%
7 32444
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 1306760
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6533800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2067447
31.6%
2 1774068
27.2%
- 1306760
20.0%
1 646746
 
9.9%
3 189522
 
2.9%
6 162717
 
2.5%
9 112244
 
1.7%
5 84554
 
1.3%
4 82529
 
1.3%
8 74769
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6533800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2067447
31.6%
2 1774068
27.2%
- 1306760
20.0%
1 646746
 
9.9%
3 189522
 
2.9%
6 162717
 
2.5%
9 112244
 
1.7%
5 84554
 
1.3%
4 82529
 
1.3%
8 74769
 
1.1%

lastupdate_388D
Text

MISSING 

Distinct4928
Distinct (%)0.2%
Missing1670066
Missing (%)44.6%
Memory size28.6 MiB
2024-02-13T20:42:00.538841image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters20737440
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

Unique182 ?
Unique (%)< 0.1%

Sample

1st row2018-12-29
2nd row2019-10-01
3rd row2019-10-30
4th row2019-12-06
5th row2019-12-04
ValueCountFrequency (%)
2007-09-25 28897
 
1.4%
2008-06-13 15900
 
0.8%
2008-11-12 12925
 
0.6%
2015-04-10 12911
 
0.6%
2013-03-07 11499
 
0.6%
2018-12-28 11248
 
0.5%
2018-08-10 10585
 
0.5%
2019-09-17 10368
 
0.5%
2013-10-02 8975
 
0.4%
2018-08-11 8621
 
0.4%
Other values (4918) 1941815
93.6%
2024-02-13T20:42:01.026408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4940633
23.8%
- 4147488
20.0%
1 3672687
17.7%
2 3479429
16.8%
9 928877
 
4.5%
8 785556
 
3.8%
7 635453
 
3.1%
6 569643
 
2.7%
3 568663
 
2.7%
5 540708
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16589952
80.0%
Dash Punctuation 4147488
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4940633
29.8%
1 3672687
22.1%
2 3479429
21.0%
9 928877
 
5.6%
8 785556
 
4.7%
7 635453
 
3.8%
6 569643
 
3.4%
3 568663
 
3.4%
5 540708
 
3.3%
4 468303
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 4147488
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20737440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4940633
23.8%
- 4147488
20.0%
1 3672687
17.7%
2 3479429
16.8%
9 928877
 
4.5%
8 785556
 
3.8%
7 635453
 
3.1%
6 569643
 
2.7%
3 568663
 
2.7%
5 540708
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20737440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4940633
23.8%
- 4147488
20.0%
1 3672687
17.7%
2 3479429
16.8%
9 928877
 
4.5%
8 785556
 
3.8%
7 635453
 
3.1%
6 569643
 
2.7%
3 568663
 
2.7%
5 540708
 
2.6%

monthlyinstlamount_332A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct184218
Distinct (%)28.4%
Missing3095447
Missing (%)82.7%
Infinite0
Infinite (%)0.0%
Mean6439.662765
Minimum0
Maximum75584216
Zeros120608
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:01.198424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11194.8
median3345.4001
Q37000
95-th percentile17585.34
Maximum75584216
Range75584216
Interquartile range (IQR)5805.2

Descriptive statistics

Standard deviation163848.3447
Coefficient of variation (CV)25.44362193
Kurtosis160446.9155
Mean6439.662765
Median Absolute Deviation (MAD)2763.9999
Skewness378.6333081
Sum4175239069
Variance2.684628007 × 1010
MonotonicityNot monotonic
2024-02-13T20:42:01.366164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 120608
 
3.2%
400 1417
 
< 0.1%
600 988
 
< 0.1%
1000 930
 
< 0.1%
9068 818
 
< 0.1%
2389.6 789
 
< 0.1%
2100.246 684
 
< 0.1%
4200.492 671
 
< 0.1%
2000 644
 
< 0.1%
10230 562
 
< 0.1%
Other values (184208) 520252
 
13.9%
(Missing) 3095447
82.7%
ValueCountFrequency (%)
0 120608
3.2%
0.002 2
 
< 0.1%
0.004 2
 
< 0.1%
0.006 1
 
< 0.1%
0.008 1
 
< 0.1%
ValueCountFrequency (%)
75584216 1
< 0.1%
72936856 1
< 0.1%
60478816 1
< 0.1%
20301918 1
< 0.1%
20000000 2
< 0.1%

monthlyinstlamount_674A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct389099
Distinct (%)20.1%
Missing1803574
Missing (%)48.2%
Infinite0
Infinite (%)0.0%
Mean7753.937445
Minimum0
Maximum87778840
Zeros921492
Zeros (%)24.6%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:01.539230image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median675.61402
Q34119.4
95-th percentile23157.984
Maximum87778840
Range87778840
Interquartile range (IQR)4119.4

Descriptive statistics

Standard deviation148322.2433
Coefficient of variation (CV)19.12863553
Kurtosis126391.067
Mean7753.937445
Median Absolute Deviation (MAD)675.61402
Skewness290.4381206
Sum1.504446857 × 1010
Variance2.199948787 × 1010
MonotonicityNot monotonic
2024-02-13T20:42:01.691707image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 921492
24.6%
400 6810
 
0.2%
1000 4017
 
0.1%
4200 1841
 
< 0.1%
2100 1807
 
< 0.1%
3150 1688
 
< 0.1%
2000 1492
 
< 0.1%
2114 1357
 
< 0.1%
0.1 1322
 
< 0.1%
4228 1282
 
< 0.1%
Other values (389089) 997128
26.6%
(Missing) 1803574
48.2%
ValueCountFrequency (%)
0 921492
24.6%
0.002 39
 
< 0.1%
0.004 31
 
< 0.1%
0.006 38
 
< 0.1%
0.008 40
 
< 0.1%
ValueCountFrequency (%)
87778840 1
< 0.1%
70400000 1
< 0.1%
64648000 1
< 0.1%
44216576 1
< 0.1%
40719430 1
< 0.1%

nominalrate_281L
Real number (ℝ)

MISSING  SKEWED 

Distinct860
Distinct (%)0.3%
Missing3498044
Missing (%)93.4%
Infinite0
Infinite (%)0.0%
Mean55.45875373
Minimum0
Maximum59140.9
Zeros37103
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:01.841752image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median39
Q342
95-th percentile45
Maximum59140.9
Range59140.9
Interquartile range (IQR)35

Descriptive statistics

Standard deviation767.1776163
Coefficient of variation (CV)13.83330069
Kurtosis4214.628705
Mean55.45875373
Median Absolute Deviation (MAD)6
Skewness59.76685496
Sum13629876.07
Variance588561.495
MonotonicityNot monotonic
2024-02-13T20:42:01.999708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37103
 
1.0%
45 36065
 
1.0%
42 30849
 
0.8%
39 25293
 
0.7%
0.12 18622
 
0.5%
40 11897
 
0.3%
43 7251
 
0.2%
43.3 6180
 
0.2%
40.05 4080
 
0.1%
18.1 3047
 
0.1%
Other values (850) 65379
 
1.7%
(Missing) 3498044
93.4%
ValueCountFrequency (%)
0 37103
1.0%
0.01 4
 
< 0.1%
0.06 1
 
< 0.1%
0.07 1
 
< 0.1%
0.1 3
 
< 0.1%
ValueCountFrequency (%)
59140.9 26
< 0.1%
46334.1 3
 
< 0.1%
37133.4 10
 
< 0.1%
30341.1 8
 
< 0.1%
24079.4 1
 
< 0.1%

nominalrate_498L
Real number (ℝ)

MISSING  SKEWED 

Distinct1786
Distinct (%)0.2%
Missing3014103
Missing (%)80.5%
Infinite0
Infinite (%)0.0%
Mean106.3653105
Minimum0
Maximum59140.9
Zeros36121
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:02.175491image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12
Q123.56
median43
Q345
95-th percentile182.5
Maximum59140.9
Range59140.9
Interquartile range (IQR)21.44

Descriptive statistics

Standard deviation1054.717213
Coefficient of variation (CV)9.915988664
Kurtosis2296.615499
Mean106.3653105
Median Absolute Deviation (MAD)4
Skewness44.0910622
Sum77615511.65
Variance1112428.4
MonotonicityNot monotonic
2024-02-13T20:42:02.333559image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 235558
 
6.3%
0.12 95802
 
2.6%
96.3 36324
 
1.0%
0 36121
 
1.0%
42 32658
 
0.9%
43.3 20037
 
0.5%
39 15535
 
0.4%
40 15386
 
0.4%
40.05 13141
 
0.4%
43 10576
 
0.3%
Other values (1776) 218569
 
5.8%
(Missing) 3014103
80.5%
ValueCountFrequency (%)
0 36121
1.0%
0.01 12
 
< 0.1%
0.05 4
 
< 0.1%
0.06 4
 
< 0.1%
0.1 33
 
< 0.1%
ValueCountFrequency (%)
59140.9 153
< 0.1%
46334.1 24
 
< 0.1%
37133.5 1
 
< 0.1%
37133.4 36
 
< 0.1%
30341.1 30
 
< 0.1%

num_group1
Real number (ℝ)

ZEROS 

Distinct529
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.753277009
Minimum0
Maximum528
Zeros325127
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:02.487752image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile19
Maximum528
Range528
Interquartile range (IQR)6

Descriptive statistics

Standard deviation8.859530182
Coefficient of variation (CV)1.311886092
Kurtosis464.7873578
Mean6.753277009
Median Absolute Deviation (MAD)3
Skewness13.71045016
Sum25282986
Variance78.49127504
MonotonicityNot monotonic
2024-02-13T20:42:02.868509image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 325127
8.7%
1 325126
8.7%
2 325123
8.7%
3 325119
8.7%
4 325119
8.7%
5 325119
8.7%
6 325119
8.7%
7 325119
8.7%
8 325119
8.7%
9 117287
 
3.1%
Other values (519) 700433
18.7%
ValueCountFrequency (%)
0 325127
8.7%
1 325126
8.7%
2 325123
8.7%
3 325119
8.7%
4 325119
8.7%
ValueCountFrequency (%)
528 1
< 0.1%
527 1
< 0.1%
526 1
< 0.1%
525 1
< 0.1%
524 1
< 0.1%

numberofcontrsvalue_258L
Real number (ℝ)

MISSING 

Distinct25
Distinct (%)< 0.1%
Missing3441306
Missing (%)91.9%
Infinite0
Infinite (%)0.0%
Mean2.144735276
Minimum0
Maximum92
Zeros878
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:03.006063image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum92
Range92
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.201417517
Coefficient of variation (CV)0.5601705396
Kurtosis160.5038992
Mean2.144735276
Median Absolute Deviation (MAD)1
Skewness3.568698483
Sum648791
Variance1.44340405
MonotonicityNot monotonic
2024-02-13T20:42:03.134063image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 107664
 
2.9%
2 96103
 
2.6%
3 59507
 
1.6%
4 26904
 
0.7%
5 7917
 
0.2%
6 2487
 
0.1%
0 878
 
< 0.1%
7 705
 
< 0.1%
8 186
 
< 0.1%
9 62
 
< 0.1%
Other values (15) 91
 
< 0.1%
(Missing) 3441306
91.9%
ValueCountFrequency (%)
0 878
 
< 0.1%
1 107664
2.9%
2 96103
2.6%
3 59507
1.6%
4 26904
 
0.7%
ValueCountFrequency (%)
92 1
< 0.1%
71 1
< 0.1%
60 1
< 0.1%
28 1
< 0.1%
21 1
< 0.1%

numberofcontrsvalue_358L
Real number (ℝ)

MISSING 

Distinct130
Distinct (%)< 0.1%
Missing3436989
Missing (%)91.8%
Infinite0
Infinite (%)0.0%
Mean6.752428941
Minimum0
Maximum333
Zeros91
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:03.281025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q39
95-th percentile19
Maximum333
Range333
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.726849695
Coefficient of variation (CV)0.9962118453
Kurtosis77.30085422
Mean6.752428941
Median Absolute Deviation (MAD)3
Skewness4.477457388
Sum2071787
Variance45.25050682
MonotonicityNot monotonic
2024-02-13T20:42:03.443422image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 46410
 
1.2%
2 36846
 
1.0%
3 31956
 
0.9%
4 28164
 
0.8%
5 24504
 
0.7%
6 20989
 
0.6%
7 18525
 
0.5%
8 15328
 
0.4%
9 13337
 
0.4%
10 11319
 
0.3%
Other values (120) 59443
 
1.6%
(Missing) 3436989
91.8%
ValueCountFrequency (%)
0 91
 
< 0.1%
1 46410
1.2%
2 36846
1.0%
3 31956
0.9%
4 28164
0.8%
ValueCountFrequency (%)
333 1
< 0.1%
290 1
< 0.1%
283 1
< 0.1%
272 1
< 0.1%
232 1
< 0.1%

numberofinstls_229L
Real number (ℝ)

MISSING  ZEROS 

Distinct366
Distinct (%)< 0.1%
Missing1939505
Missing (%)51.8%
Infinite0
Infinite (%)0.0%
Mean11.61150415
Minimum0
Maximum1140
Zeros357473
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:03.603164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q312
95-th percentile36
Maximum1140
Range1140
Interquartile range (IQR)11

Descriptive statistics

Standard deviation19.66628886
Coefficient of variation (CV)1.693690034
Kurtosis87.45142758
Mean11.61150415
Median Absolute Deviation (MAD)6
Skewness7.055050245
Sum20950695
Variance386.7629175
MonotonicityNot monotonic
2024-02-13T20:42:03.762169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 357473
 
9.5%
12 291178
 
7.8%
1 223375
 
6.0%
6 194719
 
5.2%
24 99987
 
2.7%
3 76945
 
2.1%
18 65882
 
1.8%
36 56672
 
1.5%
10 43500
 
1.2%
4 35840
 
1.0%
Other values (356) 358734
 
9.6%
(Missing) 1939505
51.8%
ValueCountFrequency (%)
0 357473
9.5%
1 223375
6.0%
2 12841
 
0.3%
3 76945
 
2.1%
4 35840
 
1.0%
ValueCountFrequency (%)
1140 1
 
< 0.1%
720 1
 
< 0.1%
700 1
 
< 0.1%
600 4
< 0.1%
593 1
 
< 0.1%

numberofinstls_320L
Real number (ℝ)

MISSING 

Distinct321
Distinct (%)0.1%
Missing3397203
Missing (%)90.7%
Infinite0
Infinite (%)0.0%
Mean31.06570265
Minimum0
Maximum528
Zeros71
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:03.921752image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q112
median24
Q336
95-th percentile83
Maximum528
Range528
Interquartile range (IQR)24

Descriptive statistics

Standard deviation34.04347315
Coefficient of variation (CV)1.095853956
Kurtosis19.34720757
Mean31.06570265
Median Absolute Deviation (MAD)12
Skewness3.751756459
Sum10767590
Variance1158.958064
MonotonicityNot monotonic
2024-02-13T20:42:04.078964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 72780
 
1.9%
24 48794
 
1.3%
36 29702
 
0.8%
48 22285
 
0.6%
18 20098
 
0.5%
6 19605
 
0.5%
60 19094
 
0.5%
16 12972
 
0.3%
1 10369
 
0.3%
30 8643
 
0.2%
Other values (311) 82265
 
2.2%
(Missing) 3397203
90.7%
ValueCountFrequency (%)
0 71
 
< 0.1%
1 10369
0.3%
2 31
 
< 0.1%
3 5410
0.1%
4 2375
 
0.1%
ValueCountFrequency (%)
528 1
 
< 0.1%
377 1
 
< 0.1%
365 1
 
< 0.1%
360 5
< 0.1%
358 1
 
< 0.1%

numberofoutstandinstls_520L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct217
Distinct (%)< 0.1%
Missing1938229
Missing (%)51.8%
Infinite0
Infinite (%)0.0%
Mean0.07875359787
Minimum0
Maximum664
Zeros1802902
Zeros (%)48.2%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:04.259449image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum664
Range664
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.887045482
Coefficient of variation (CV)49.35705273
Kurtosis7855.543523
Mean0.07875359787
Median Absolute Deviation (MAD)0
Skewness80.78584547
Sum142196
Variance15.10912258
MonotonicityNot monotonic
2024-02-13T20:42:04.421285image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1802902
48.2%
1 279
 
< 0.1%
2 124
 
< 0.1%
6 108
 
< 0.1%
4 107
 
< 0.1%
12 94
 
< 0.1%
10 92
 
< 0.1%
8 90
 
< 0.1%
20 65
 
< 0.1%
22 63
 
< 0.1%
Other values (207) 1657
 
< 0.1%
(Missing) 1938229
51.8%
ValueCountFrequency (%)
0 1802902
48.2%
1 279
 
< 0.1%
2 124
 
< 0.1%
3 35
 
< 0.1%
4 107
 
< 0.1%
ValueCountFrequency (%)
664 1
< 0.1%
519 1
< 0.1%
474 1
< 0.1%
472 2
< 0.1%
470 1
< 0.1%

numberofoutstandinstls_59L
Real number (ℝ)

MISSING 

Distinct298
Distinct (%)0.1%
Missing3397206
Missing (%)90.7%
Infinite0
Infinite (%)0.0%
Mean21.11075175
Minimum0
Maximum468
Zeros3876
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:04.577441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q326
95-th percentile59
Maximum468
Range468
Interquartile range (IQR)20

Descriptive statistics

Standard deviation28.99147267
Coefficient of variation (CV)1.373303661
Kurtosis25.9173661
Mean21.11075175
Median Absolute Deviation (MAD)8
Skewness4.275757713
Sum7317071
Variance840.5054878
MonotonicityNot monotonic
2024-02-13T20:42:04.732839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 23815
 
0.6%
5 15054
 
0.4%
6 15012
 
0.4%
7 14314
 
0.4%
9 14231
 
0.4%
3 14224
 
0.4%
8 14193
 
0.4%
2 14191
 
0.4%
10 13809
 
0.4%
4 13669
 
0.4%
Other values (288) 194092
 
5.2%
(Missing) 3397206
90.7%
ValueCountFrequency (%)
0 3876
 
0.1%
1 23815
0.6%
2 14191
0.4%
3 14224
0.4%
4 13669
0.4%
ValueCountFrequency (%)
468 1
 
< 0.1%
304 1
 
< 0.1%
301 1
 
< 0.1%
300 61
< 0.1%
299 39
< 0.1%

numberofoverdueinstlmax_1039L
Real number (ℝ)

MISSING  ZEROS 

Distinct2502
Distinct (%)0.4%
Missing3090430
Missing (%)82.5%
Infinite0
Infinite (%)0.0%
Mean17.24620894
Minimum0
Maximum5072
Zeros487349
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:04.889650image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile36
Maximum5072
Range5072
Interquartile range (IQR)1

Descriptive statistics

Standard deviation153.0985337
Coefficient of variation (CV)8.877228278
Kurtosis369.1791979
Mean17.24620894
Median Absolute Deviation (MAD)0
Skewness17.29182876
Sum11268328
Variance23439.16103
MonotonicityNot monotonic
2024-02-13T20:42:05.048222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 487349
 
13.0%
1 31669
 
0.8%
2 11261
 
0.3%
3 8934
 
0.2%
4 8593
 
0.2%
5 6802
 
0.2%
8 4543
 
0.1%
7 3887
 
0.1%
6 3618
 
0.1%
11 3303
 
0.1%
Other values (2492) 83421
 
2.2%
(Missing) 3090430
82.5%
ValueCountFrequency (%)
0 487349
13.0%
1 31669
 
0.8%
2 11261
 
0.3%
3 8934
 
0.2%
4 8593
 
0.2%
ValueCountFrequency (%)
5072 1
< 0.1%
5058 1
< 0.1%
5041 1
< 0.1%
5038 1
< 0.1%
5023 1
< 0.1%

numberofoverdueinstlmax_1151L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct4209
Distinct (%)0.2%
Missing1670007
Missing (%)44.6%
Infinite0
Infinite (%)0.0%
Mean54.20721496
Minimum0
Maximum130000
Zeros1457688
Zeros (%)38.9%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:05.206880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile163
Maximum130000
Range130000
Interquartile range (IQR)1

Descriptive statistics

Standard deviation399.959299
Coefficient of variation (CV)7.378340674
Kurtosis32352.68595
Mean54.20721496
Median Absolute Deviation (MAD)0
Skewness130.2042876
Sum112415085
Variance159967.4408
MonotonicityNot monotonic
2024-02-13T20:42:05.371881image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1457688
38.9%
1 107875
 
2.9%
2 33382
 
0.9%
3 30259
 
0.8%
4 30198
 
0.8%
7 19372
 
0.5%
5 17755
 
0.5%
6 14950
 
0.4%
8 13641
 
0.4%
9 10647
 
0.3%
Other values (4199) 338036
 
9.0%
(Missing) 1670007
44.6%
ValueCountFrequency (%)
0 1457688
38.9%
1 107875
 
2.9%
2 33382
 
0.9%
3 30259
 
0.8%
4 30198
 
0.8%
ValueCountFrequency (%)
130000 2
 
< 0.1%
93972 8
< 0.1%
93971 1
 
< 0.1%
93970 2
 
< 0.1%
93956 1
 
< 0.1%
Distinct4736
Distinct (%)0.8%
Missing3127695
Missing (%)83.5%
Memory size28.6 MiB
2024-02-13T20:42:05.731563image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters6161150
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

Unique300 ?
Unique (%)< 0.1%

Sample

1st row2007-07-31
2nd row2012-12-11
3rd row2018-04-09
4th row2018-09-24
5th row2007-07-05
ValueCountFrequency (%)
2007-07-31 22029
 
3.6%
2011-08-24 6347
 
1.0%
2007-07-05 5018
 
0.8%
2012-03-04 4463
 
0.7%
2008-10-15 3999
 
0.6%
2011-09-04 3064
 
0.5%
2018-08-02 2304
 
0.4%
2018-09-17 2209
 
0.4%
2010-01-07 2198
 
0.4%
2019-04-11 2001
 
0.3%
Other values (4726) 562483
91.3%
2024-02-13T20:42:06.227195image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1494736
24.3%
- 1232230
20.0%
1 1071642
17.4%
2 984475
16.0%
7 232397
 
3.8%
8 222253
 
3.6%
9 218564
 
3.5%
5 179817
 
2.9%
3 176231
 
2.9%
4 176158
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4928920
80.0%
Dash Punctuation 1232230
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1494736
30.3%
1 1071642
21.7%
2 984475
20.0%
7 232397
 
4.7%
8 222253
 
4.5%
9 218564
 
4.4%
5 179817
 
3.6%
3 176231
 
3.6%
4 176158
 
3.6%
6 172647
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 1232230
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6161150
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1494736
24.3%
- 1232230
20.0%
1 1071642
17.4%
2 984475
16.0%
7 232397
 
3.8%
8 222253
 
3.6%
9 218564
 
3.5%
5 179817
 
2.9%
3 176231
 
2.9%
4 176158
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6161150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1494736
24.3%
- 1232230
20.0%
1 1071642
17.4%
2 984475
16.0%
7 232397
 
3.8%
8 222253
 
3.6%
9 218564
 
3.5%
5 179817
 
2.9%
3 176231
 
2.9%
4 176158
 
2.9%
Distinct2015
Distinct (%)1.2%
Missing3577779
Missing (%)95.6%
Memory size28.6 MiB
2024-02-13T20:42:06.629083image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1660310
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

Unique464 ?
Unique (%)0.3%

Sample

1st row2019-06-07
2nd row2016-09-20
3rd row2019-06-27
4th row2015-08-01
5th row2019-07-11
ValueCountFrequency (%)
2019-12-12 3443
 
2.1%
2020-02-11 2007
 
1.2%
2020-01-08 1987
 
1.2%
2020-01-02 1938
 
1.2%
2020-01-23 1810
 
1.1%
2020-02-26 1698
 
1.0%
2019-11-19 1655
 
1.0%
2019-08-07 1586
 
1.0%
2019-10-21 1554
 
0.9%
2019-12-11 1348
 
0.8%
Other values (2005) 147005
88.5%
2024-02-13T20:42:07.157881image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 394682
23.8%
- 332062
20.0%
2 308333
18.6%
1 297401
17.9%
9 97859
 
5.9%
8 51322
 
3.1%
6 38522
 
2.3%
7 36618
 
2.2%
5 35925
 
2.2%
3 34347
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1328248
80.0%
Dash Punctuation 332062
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 394682
29.7%
2 308333
23.2%
1 297401
22.4%
9 97859
 
7.4%
8 51322
 
3.9%
6 38522
 
2.9%
7 36618
 
2.8%
5 35925
 
2.7%
3 34347
 
2.6%
4 33239
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 332062
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1660310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 394682
23.8%
- 332062
20.0%
2 308333
18.6%
1 297401
17.9%
9 97859
 
5.9%
8 51322
 
3.1%
6 38522
 
2.3%
7 36618
 
2.2%
5 35925
 
2.2%
3 34347
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1660310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 394682
23.8%
- 332062
20.0%
2 308333
18.6%
1 297401
17.9%
9 97859
 
5.9%
8 51322
 
3.1%
6 38522
 
2.3%
7 36618
 
2.2%
5 35925
 
2.2%
3 34347
 
2.1%

numberofoverdueinstls_725L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct2103
Distinct (%)0.3%
Missing3094950
Missing (%)82.7%
Infinite0
Infinite (%)0.0%
Mean8.894000247
Minimum0
Maximum5072
Zeros629471
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:07.327204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5072
Range5072
Interquartile range (IQR)0

Descriptive statistics

Standard deviation127.9804742
Coefficient of variation (CV)14.38952897
Kurtosis585.228444
Mean8.894000247
Median Absolute Deviation (MAD)0
Skewness21.97189701
Sum5770961
Variance16379.00178
MonotonicityNot monotonic
2024-02-13T20:42:07.495196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 629471
 
16.8%
1 2997
 
0.1%
2 1101
 
< 0.1%
3 810
 
< 0.1%
4 787
 
< 0.1%
5 599
 
< 0.1%
8 373
 
< 0.1%
9 364
 
< 0.1%
6 362
 
< 0.1%
7 361
 
< 0.1%
Other values (2093) 11635
 
0.3%
(Missing) 3094950
82.7%
ValueCountFrequency (%)
0 629471
16.8%
1 2997
 
0.1%
2 1101
 
< 0.1%
3 810
 
< 0.1%
4 787
 
< 0.1%
ValueCountFrequency (%)
5072 1
< 0.1%
5058 1
< 0.1%
5041 1
< 0.1%
5038 1
< 0.1%
5023 1
< 0.1%

numberofoverdueinstls_834L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct181
Distinct (%)< 0.1%
Missing1672969
Missing (%)44.7%
Infinite0
Infinite (%)0.0%
Mean0.04905736365
Minimum0
Maximum1433
Zeros2069531
Zeros (%)55.3%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:07.653214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1433
Range1433
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.713146826
Coefficient of variation (CV)96.07419712
Kurtosis17323.84468
Mean0.04905736365
Median Absolute Deviation (MAD)0
Skewness118.3759823
Sum101590
Variance22.213753
MonotonicityNot monotonic
2024-02-13T20:42:07.809358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2069531
55.3%
1 443
 
< 0.1%
2 225
 
< 0.1%
3 78
 
< 0.1%
5 64
 
< 0.1%
4 57
 
< 0.1%
6 40
 
< 0.1%
7 35
 
< 0.1%
8 31
 
< 0.1%
11 21
 
< 0.1%
Other values (171) 316
 
< 0.1%
(Missing) 1672969
44.7%
ValueCountFrequency (%)
0 2069531
55.3%
1 443
 
< 0.1%
2 225
 
< 0.1%
3 78
 
< 0.1%
4 57
 
< 0.1%
ValueCountFrequency (%)
1433 1
 
< 0.1%
1208 1
 
< 0.1%
864 3
< 0.1%
790 1
 
< 0.1%
762 1
 
< 0.1%

outstandingamount_354A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct295
Distinct (%)< 0.1%
Missing1937585
Missing (%)51.8%
Infinite0
Infinite (%)0.0%
Mean8.659404149
Minimum0
Maximum2396708.8
Zeros1805775
Zeros (%)48.2%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:07.973817image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2396708.8
Range2396708.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2511.178055
Coefficient of variation (CV)289.9943242
Kurtosis499736.1813
Mean8.659404149
Median Absolute Deviation (MAD)0
Skewness612.635473
Sum15640832.26
Variance6306015.222
MonotonicityNot monotonic
2024-02-13T20:42:08.136155image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1805775
48.2%
0.2 25
 
< 0.1%
0.4 21
 
< 0.1%
0.6 18
 
< 0.1%
1 14
 
< 0.1%
0.8 11
 
< 0.1%
1.2 9
 
< 0.1%
1.6 5
 
< 0.1%
16.2 4
 
< 0.1%
4 4
 
< 0.1%
Other values (285) 339
 
< 0.1%
(Missing) 1937585
51.8%
ValueCountFrequency (%)
0 1805775
48.2%
0.2 25
 
< 0.1%
0.4 21
 
< 0.1%
0.6 18
 
< 0.1%
0.792 2
 
< 0.1%
ValueCountFrequency (%)
2396708.8 1
< 0.1%
1025000 1
< 0.1%
852877.94 1
< 0.1%
793499.6 1
< 0.1%
700000 1
< 0.1%

outstandingamount_362A
Real number (ℝ)

MISSING  SKEWED 

Distinct320359
Distinct (%)92.4%
Missing3397088
Missing (%)90.7%
Infinite0
Infinite (%)0.0%
Mean170305.6277
Minimum0
Maximum363346780
Zeros4750
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:08.287541image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3291.5271
Q114319.318
median38295.8275
Q3115313.185
95-th percentile697502.8
Maximum363346780
Range363346780
Interquartile range (IQR)100993.867

Descriptive statistics

Standard deviation1176301.45
Coefficient of variation (CV)6.907002817
Kurtosis54626.43314
Mean170305.6277
Median Absolute Deviation (MAD)30192.3755
Skewness198.3670412
Sum5.904870785 × 1010
Variance1.383685102 × 1012
MonotonicityNot monotonic
2024-02-13T20:42:08.449625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4750
 
0.1%
100000 137
 
< 0.1%
12000 118
 
< 0.1%
40000 116
 
< 0.1%
200000 112
 
< 0.1%
60000 109
 
< 0.1%
10000 106
 
< 0.1%
20000 94
 
< 0.1%
8000 79
 
< 0.1%
30000 77
 
< 0.1%
Other values (320349) 341024
 
9.1%
(Missing) 3397088
90.7%
ValueCountFrequency (%)
0 4750
0.1%
0.002 13
 
< 0.1%
0.004 5
 
< 0.1%
0.012 1
 
< 0.1%
0.028 2
 
< 0.1%
ValueCountFrequency (%)
363346780 1
< 0.1%
350143200 1
< 0.1%
234526580 1
< 0.1%
138702800 1
< 0.1%
124329656 1
< 0.1%

overdueamount_31A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct326
Distinct (%)< 0.1%
Missing1672509
Missing (%)44.7%
Infinite0
Infinite (%)0.0%
Mean18.40370842
Minimum0
Maximum480810
Zeros2070863
Zeros (%)55.3%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:08.634386image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum480810
Range480810
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2162.851115
Coefficient of variation (CV)117.5225702
Kurtosis25189.7733
Mean18.40370842
Median Absolute Deviation (MAD)0
Skewness149.954902
Sum38119619.65
Variance4677924.947
MonotonicityNot monotonic
2024-02-13T20:42:08.793385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2070863
55.3%
0.2 17
 
< 0.1%
0.4 16
 
< 0.1%
0.6 15
 
< 0.1%
1 12
 
< 0.1%
0.8 9
 
< 0.1%
1.2 7
 
< 0.1%
1.6 5
 
< 0.1%
16.2 4
 
< 0.1%
5.4 4
 
< 0.1%
Other values (316) 349
 
< 0.1%
(Missing) 1672509
44.7%
ValueCountFrequency (%)
0 2070863
55.3%
0.072000004 1
 
< 0.1%
0.2 17
 
< 0.1%
0.4 16
 
< 0.1%
0.6 15
 
< 0.1%
ValueCountFrequency (%)
480810 1
< 0.1%
433195 1
< 0.1%
429100 1
< 0.1%
426325 1
< 0.1%
421675 1
< 0.1%

overdueamount_659A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct16374
Distinct (%)2.5%
Missing3094950
Missing (%)82.7%
Infinite0
Infinite (%)0.0%
Mean2449.49268
Minimum0
Maximum401980320
Zeros629471
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:08.949853image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum401980320
Range401980320
Interquartile range (IQR)0

Descriptive statistics

Standard deviation710291.5566
Coefficient of variation (CV)289.9749659
Kurtosis316194.5438
Mean2449.49268
Median Absolute Deviation (MAD)0
Skewness559.0095889
Sum1589377821
Variance5.045140954 × 1011
MonotonicityNot monotonic
2024-02-13T20:42:09.119747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 629471
 
16.8%
10 56
 
< 0.1%
14 50
 
< 0.1%
6000 45
 
< 0.1%
4228 42
 
< 0.1%
6342 36
 
< 0.1%
2114 34
 
< 0.1%
99.8 33
 
< 0.1%
8000 29
 
< 0.1%
1 27
 
< 0.1%
Other values (16364) 19037
 
0.5%
(Missing) 3094950
82.7%
ValueCountFrequency (%)
0 629471
16.8%
0.002 3
 
< 0.1%
0.004 1
 
< 0.1%
0.006 3
 
< 0.1%
0.008 4
 
< 0.1%
ValueCountFrequency (%)
401980320 2
< 0.1%
28325886 1
< 0.1%
23045082 2
< 0.1%
17402200 1
< 0.1%
17362792 1
< 0.1%

overdueamountmax2_14A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct134722
Distinct (%)20.6%
Missing3090430
Missing (%)82.5%
Infinite0
Infinite (%)0.0%
Mean5291.559337
Minimum0
Maximum401980320
Zeros486139
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:09.288622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34.5465
95-th percentile10205.03915
Maximum401980320
Range401980320
Interquartile range (IQR)4.5465

Descriptive statistics

Standard deviation737758.6052
Coefficient of variation (CV)139.4217769
Kurtosis272410.4478
Mean5291.559337
Median Absolute Deviation (MAD)0
Skewness508.7139687
Sum3457399039
Variance5.442877596 × 1011
MonotonicityNot monotonic
2024-02-13T20:42:09.462609image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 486139
 
13.0%
10 226
 
< 0.1%
0.2 155
 
< 0.1%
400 144
 
< 0.1%
0.4 140
 
< 0.1%
2 137
 
< 0.1%
0.8 124
 
< 0.1%
2000 116
 
< 0.1%
1 116
 
< 0.1%
800 113
 
< 0.1%
Other values (134712) 165970
 
4.4%
(Missing) 3090430
82.5%
ValueCountFrequency (%)
0 486139
13.0%
0.002 13
 
< 0.1%
0.004 11
 
< 0.1%
0.006 10
 
< 0.1%
0.008 15
 
< 0.1%
ValueCountFrequency (%)
401980320 2
< 0.1%
149930380 1
< 0.1%
32219968 1
< 0.1%
31818938 1
< 0.1%
28325886 1
< 0.1%

overdueamountmax2_398A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct347856
Distinct (%)16.8%
Missing1670007
Missing (%)44.6%
Infinite0
Infinite (%)0.0%
Mean6182.288253
Minimum0
Maximum593465000
Zeros1462879
Zeros (%)39.1%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:09.635575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3703.31
95-th percentile16219.4197
Maximum593465000
Range593465000
Interquartile range (IQR)703.31

Descriptive statistics

Standard deviation450623.7578
Coefficient of variation (CV)72.88947706
Kurtosis1453586.663
Mean6182.288253
Median Absolute Deviation (MAD)0
Skewness1119.625372
Sum1.282084793 × 1010
Variance2.030617711 × 1011
MonotonicityNot monotonic
2024-02-13T20:42:09.800593image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1462879
39.1%
0.2 2498
 
0.1%
0.4 1369
 
< 0.1%
0.8 954
 
< 0.1%
1.6 835
 
< 0.1%
2 832
 
< 0.1%
0.6 779
 
< 0.1%
1 766
 
< 0.1%
1.2 745
 
< 0.1%
4 740
 
< 0.1%
Other values (347846) 601406
 
16.1%
(Missing) 1670007
44.6%
ValueCountFrequency (%)
0 1462879
39.1%
0.002 75
 
< 0.1%
0.004 45
 
< 0.1%
0.006 27
 
< 0.1%
0.008 38
 
< 0.1%
ValueCountFrequency (%)
593465000 1
< 0.1%
107822220 1
< 0.1%
90406290 1
< 0.1%
52067108 1
< 0.1%
50906470 1
< 0.1%
Distinct4612
Distinct (%)0.8%
Missing3132886
Missing (%)83.7%
Memory size28.6 MiB
2024-02-13T20:42:10.127901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters6109240
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

Unique290 ?
Unique (%)< 0.1%

Sample

1st row2009-02-13
2nd row2012-12-11
3rd row2018-04-09
4th row2018-09-24
5th row2017-08-12
ValueCountFrequency (%)
2011-10-06 8332
 
1.4%
2008-10-15 3981
 
0.7%
2010-01-07 2602
 
0.4%
2015-07-07 2453
 
0.4%
2018-12-20 1999
 
0.3%
2018-09-17 1865
 
0.3%
2019-04-11 1784
 
0.3%
2018-11-20 1748
 
0.3%
2018-08-02 1718
 
0.3%
2007-07-31 1716
 
0.3%
Other values (4602) 582726
95.4%
2024-02-13T20:42:10.571297image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1478473
24.2%
- 1221848
20.0%
1 1085687
17.8%
2 987054
16.2%
8 225758
 
3.7%
9 212689
 
3.5%
7 194360
 
3.2%
6 184897
 
3.0%
5 182008
 
3.0%
4 168450
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4887392
80.0%
Dash Punctuation 1221848
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1478473
30.3%
1 1085687
22.2%
2 987054
20.2%
8 225758
 
4.6%
9 212689
 
4.4%
7 194360
 
4.0%
6 184897
 
3.8%
5 182008
 
3.7%
4 168450
 
3.4%
3 168016
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 1221848
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6109240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1478473
24.2%
- 1221848
20.0%
1 1085687
17.8%
2 987054
16.2%
8 225758
 
3.7%
9 212689
 
3.5%
7 194360
 
3.2%
6 184897
 
3.0%
5 182008
 
3.0%
4 168450
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6109240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1478473
24.2%
- 1221848
20.0%
1 1085687
17.8%
2 987054
16.2%
8 225758
 
3.7%
9 212689
 
3.5%
7 194360
 
3.2%
6 184897
 
3.0%
5 182008
 
3.0%
4 168450
 
2.8%
Distinct2249
Distinct (%)1.3%
Missing3576569
Missing (%)95.5%
Memory size28.6 MiB
2024-02-13T20:42:10.966830image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1672410
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique505 ?
Unique (%)0.3%

Sample

1st row2019-05-21
2nd row2019-07-24
3rd row2018-11-20
4th row2015-08-01
5th row2019-07-11
ValueCountFrequency (%)
2019-12-12 3292
 
2.0%
2020-01-02 2018
 
1.2%
2020-02-11 1818
 
1.1%
2020-01-08 1802
 
1.1%
2019-11-19 1669
 
1.0%
2020-01-23 1552
 
0.9%
2019-08-07 1533
 
0.9%
2019-10-21 1430
 
0.9%
2019-09-05 1413
 
0.8%
2019-12-11 1386
 
0.8%
Other values (2239) 149328
89.3%
2024-02-13T20:42:11.475830image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 395384
23.6%
- 334482
20.0%
1 303395
18.1%
2 303094
18.1%
9 101818
 
6.1%
8 53958
 
3.2%
7 38696
 
2.3%
6 38455
 
2.3%
5 35619
 
2.1%
3 34430
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1337928
80.0%
Dash Punctuation 334482
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 395384
29.6%
1 303395
22.7%
2 303094
22.7%
9 101818
 
7.6%
8 53958
 
4.0%
7 38696
 
2.9%
6 38455
 
2.9%
5 35619
 
2.7%
3 34430
 
2.6%
4 33079
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 334482
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1672410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 395384
23.6%
- 334482
20.0%
1 303395
18.1%
2 303094
18.1%
9 101818
 
6.1%
8 53958
 
3.2%
7 38696
 
2.3%
6 38455
 
2.3%
5 35619
 
2.1%
3 34430
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1672410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 395384
23.6%
- 334482
20.0%
1 303395
18.1%
2 303094
18.1%
9 101818
 
6.1%
8 53958
 
3.2%
7 38696
 
2.3%
6 38455
 
2.3%
5 35619
 
2.1%
3 34430
 
2.1%

overdueamountmax_155A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct110718
Distinct (%)16.9%
Missing3090430
Missing (%)82.5%
Infinite0
Infinite (%)0.0%
Mean4472.248715
Minimum0
Maximum401980320
Zeros517145
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:11.655201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8266.8305
Maximum401980320
Range401980320
Interquartile range (IQR)0

Descriptive statistics

Standard deviation735463.5484
Coefficient of variation (CV)164.4505025
Kurtosis275825.0087
Mean4472.248715
Median Absolute Deviation (MAD)0
Skewness513.3546382
Sum2922077866
Variance5.40906631 × 1011
MonotonicityNot monotonic
2024-02-13T20:42:11.832217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 517145
 
13.8%
10 230
 
< 0.1%
1000 147
 
< 0.1%
400 139
 
< 0.1%
2 125
 
< 0.1%
0.4 121
 
< 0.1%
0.2 113
 
< 0.1%
0.8 113
 
< 0.1%
2000 110
 
< 0.1%
20 109
 
< 0.1%
Other values (110708) 135028
 
3.6%
(Missing) 3090430
82.5%
ValueCountFrequency (%)
0 517145
13.8%
0.002 12
 
< 0.1%
0.004 10
 
< 0.1%
0.006 10
 
< 0.1%
0.008 15
 
< 0.1%
ValueCountFrequency (%)
401980320 2
< 0.1%
149930380 1
< 0.1%
32219968 1
< 0.1%
31818938 1
< 0.1%
28325886 1
< 0.1%

overdueamountmax_35A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct330297
Distinct (%)16.5%
Missing1736366
Missing (%)46.4%
Infinite0
Infinite (%)0.0%
Mean5268.003303
Minimum0
Maximum593465000
Zeros1423079
Zeros (%)38.0%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:12.006757image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3452.447005
95-th percentile14997.45195
Maximum593465000
Range593465000
Interquartile range (IQR)452.447005

Descriptive statistics

Standard deviation440902.2626
Coefficient of variation (CV)83.69437855
Kurtosis1637337.819
Mean5268.003303
Median Absolute Deviation (MAD)0
Skewness1226.615602
Sum1.057522162 × 1010
Variance1.943948052 × 1011
MonotonicityNot monotonic
2024-02-13T20:42:12.162597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1423079
38.0%
0.2 2339
 
0.1%
0.4 1270
 
< 0.1%
0.8 919
 
< 0.1%
1.6 835
 
< 0.1%
2 823
 
< 0.1%
0.6 760
 
< 0.1%
1 746
 
< 0.1%
1.2 739
 
< 0.1%
4 731
 
< 0.1%
Other values (330287) 575203
 
15.4%
(Missing) 1736366
46.4%
ValueCountFrequency (%)
0 1423079
38.0%
0.002 72
 
< 0.1%
0.004 41
 
< 0.1%
0.006 27
 
< 0.1%
0.008 36
 
< 0.1%
ValueCountFrequency (%)
593465000 1
< 0.1%
107822220 1
< 0.1%
52067108 1
< 0.1%
50906470 1
< 0.1%
48927640 1
< 0.1%

overdueamountmaxdatemonth_284T
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing1736366
Missing (%)46.4%
Infinite0
Infinite (%)0.0%
Mean6.58494085
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:12.292632image/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 deviation3.476550897
Coefficient of variation (CV)0.5279547646
Kurtosis-1.217321381
Mean6.58494085
Median Absolute Deviation (MAD)3
Skewness-0.06703187612
Sum13218900
Variance12.08640614
MonotonicityNot monotonic
2024-02-13T20:42:12.412865image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11 185655
 
5.0%
1 182758
 
4.9%
6 175525
 
4.7%
8 173268
 
4.6%
9 172450
 
4.6%
10 172169
 
4.6%
7 167843
 
4.5%
12 163952
 
4.4%
2 161527
 
4.3%
5 153712
 
4.1%
Other values (2) 298585
 
8.0%
(Missing) 1736366
46.4%
ValueCountFrequency (%)
1 182758
4.9%
2 161527
4.3%
3 147376
3.9%
4 151209
4.0%
5 153712
4.1%
ValueCountFrequency (%)
12 163952
4.4%
11 185655
5.0%
10 172169
4.6%
9 172450
4.6%
8 173268
4.6%

overdueamountmaxdatemonth_365T
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing3090430
Missing (%)82.5%
Infinite0
Infinite (%)0.0%
Mean6.27580734
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:12.750868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.673567674
Coefficient of variation (CV)0.5853537999
Kurtosis-1.37049223
Mean6.27580734
Median Absolute Deviation (MAD)3
Skewness0.1161481383
Sum4100487
Variance13.49509945
MonotonicityNot monotonic
2024-02-13T20:42:12.882038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 85283
 
2.3%
12 65924
 
1.8%
1 59747
 
1.6%
4 57923
 
1.5%
3 57174
 
1.5%
10 54349
 
1.5%
11 53501
 
1.4%
7 50342
 
1.3%
9 47004
 
1.3%
5 45873
 
1.2%
Other values (2) 76260
 
2.0%
(Missing) 3090430
82.5%
ValueCountFrequency (%)
1 59747
1.6%
2 85283
2.3%
3 57174
1.5%
4 57923
1.5%
5 45873
1.2%
ValueCountFrequency (%)
12 65924
1.8%
11 53501
1.4%
10 54349
1.5%
9 47004
1.3%
8 40758
1.1%

overdueamountmaxdateyear_2T
Real number (ℝ)

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing3090430
Missing (%)82.5%
Infinite0
Infinite (%)0.0%
Mean2018.81744
Minimum2016
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:12.996358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2018
Q12018
median2019
Q32019
95-th percentile2020
Maximum2020
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6709601463
Coefficient of variation (CV)0.0003323530563
Kurtosis-0.8027024151
Mean2018.81744
Median Absolute Deviation (MAD)0
Skewness0.2288566572
Sum1319054939
Variance0.4501875179
MonotonicityNot monotonic
2024-02-13T20:42:13.119046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
2019 337713
 
9.0%
2018 217378
 
5.8%
2020 98230
 
2.6%
2017 44
 
< 0.1%
2016 15
 
< 0.1%
(Missing) 3090430
82.5%
ValueCountFrequency (%)
2016 15
 
< 0.1%
2017 44
 
< 0.1%
2018 217378
5.8%
2019 337713
9.0%
2020 98230
 
2.6%
ValueCountFrequency (%)
2020 98230
 
2.6%
2019 337713
9.0%
2018 217378
5.8%
2017 44
 
< 0.1%
2016 15
 
< 0.1%

overdueamountmaxdateyear_994T
Real number (ℝ)

MISSING 

Distinct19
Distinct (%)< 0.1%
Missing1736366
Missing (%)46.4%
Infinite0
Infinite (%)0.0%
Mean2014.860846
Minimum2002
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:13.244350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2002
5-th percentile2007
Q12012
median2016
Q32018
95-th percentile2019
Maximum2020
Range18
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.82049035
Coefficient of variation (CV)0.001896155934
Kurtosis-0.3894147126
Mean2014.860846
Median Absolute Deviation (MAD)2
Skewness-0.8251359258
Sum4044720316
Variance14.59614651
MonotonicityNot monotonic
2024-02-13T20:42:13.380349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2018 360036
 
9.6%
2019 281799
 
7.5%
2017 251790
 
6.7%
2016 172693
 
4.6%
2015 152023
 
4.1%
2014 135290
 
3.6%
2013 120144
 
3.2%
2012 94891
 
2.5%
2011 80863
 
2.2%
2007 75081
 
2.0%
Other values (9) 282834
 
7.6%
(Missing) 1736366
46.4%
ValueCountFrequency (%)
2002 1
 
< 0.1%
2003 3
 
< 0.1%
2004 597
 
< 0.1%
2005 12682
 
0.3%
2006 44835
1.2%
ValueCountFrequency (%)
2020 27484
 
0.7%
2019 281799
7.5%
2018 360036
9.6%
2017 251790
6.7%
2016 172693
4.6%

periodicityofpmts_1102L
Real number (ℝ)

MISSING  SKEWED 

Distinct5
Distinct (%)< 0.1%
Missing2157320
Missing (%)57.6%
Infinite0
Infinite (%)0.0%
Mean30.1138299
Minimum1
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:13.496331image/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 deviation5.171502902
Coefficient of variation (CV)0.1717318228
Kurtosis2258.789885
Mean30.1138299
Median Absolute Deviation (MAD)0
Skewness43.44900879
Sum47775290
Variance26.74444226
MonotonicityNot monotonic
2024-02-13T20:42:13.609025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
30 1583883
42.3%
1 1220
 
< 0.1%
180 947
 
< 0.1%
90 264
 
< 0.1%
360 176
 
< 0.1%
(Missing) 2157320
57.6%
ValueCountFrequency (%)
1 1220
 
< 0.1%
30 1583883
42.3%
90 264
 
< 0.1%
180 947
 
< 0.1%
360 176
 
< 0.1%
ValueCountFrequency (%)
360 176
 
< 0.1%
180 947
 
< 0.1%
90 264
 
< 0.1%
30 1583883
42.3%
1 1220
 
< 0.1%

periodicityofpmts_837L
Real number (ℝ)

MISSING  SKEWED 

Distinct5
Distinct (%)< 0.1%
Missing3409479
Missing (%)91.1%
Infinite0
Infinite (%)0.0%
Mean30.36255388
Minimum1
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:13.722026image/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 deviation7.350584826
Coefficient of variation (CV)0.2420937598
Kurtosis529.7191307
Mean30.36255388
Median Absolute Deviation (MAD)0
Skewness21.81624579
Sum10151143
Variance54.03109729
MonotonicityNot monotonic
2024-02-13T20:42:13.845025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
30 333363
 
8.9%
180 702
 
< 0.1%
90 209
 
< 0.1%
1 43
 
< 0.1%
360 14
 
< 0.1%
(Missing) 3409479
91.1%
ValueCountFrequency (%)
1 43
 
< 0.1%
30 333363
8.9%
90 209
 
< 0.1%
180 702
 
< 0.1%
360 14
 
< 0.1%
ValueCountFrequency (%)
360 14
 
< 0.1%
180 702
 
< 0.1%
90 209
 
< 0.1%
30 333363
8.9%
1 43
 
< 0.1%

prolongationcount_1120L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct67
Distinct (%)< 0.1%
Missing3570206
Missing (%)95.4%
Infinite0
Infinite (%)0.0%
Mean0.6044503583
Minimum0
Maximum343
Zeros131043
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:14.001027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum343
Range343
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.203694574
Coefficient of variation (CV)3.645782559
Kurtosis3577.045998
Mean0.6044503583
Median Absolute Deviation (MAD)0
Skewness31.43390254
Sum104935
Variance4.856269774
MonotonicityNot monotonic
2024-02-13T20:42:14.167035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 131043
 
3.5%
1 23071
 
0.6%
2 8738
 
0.2%
3 3828
 
0.1%
4 2171
 
0.1%
5 1472
 
< 0.1%
6 757
 
< 0.1%
7 558
 
< 0.1%
8 393
 
< 0.1%
9 238
 
< 0.1%
Other values (57) 1335
 
< 0.1%
(Missing) 3570206
95.4%
ValueCountFrequency (%)
0 131043
3.5%
1 23071
 
0.6%
2 8738
 
0.2%
3 3828
 
0.1%
4 2171
 
0.1%
ValueCountFrequency (%)
343 1
< 0.1%
106 1
< 0.1%
101 1
< 0.1%
80 2
< 0.1%
76 1
< 0.1%

prolongationcount_599L
Real number (ℝ)

MISSING 

Distinct44
Distinct (%)0.4%
Missing3732794
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean0.9440813362
Minimum0
Maximum96
Zeros6960
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:14.328035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum96
Range96
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.089430448
Coefficient of variation (CV)3.272419789
Kurtosis236.5074791
Mean0.9440813362
Median Absolute Deviation (MAD)0
Skewness12.14990494
Sum10400
Variance9.544580491
MonotonicityNot monotonic
2024-02-13T20:42:14.482020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 6960
 
0.2%
1 2426
 
0.1%
2 750
 
< 0.1%
3 273
 
< 0.1%
4 167
 
< 0.1%
5 78
 
< 0.1%
7 69
 
< 0.1%
6 61
 
< 0.1%
8 44
 
< 0.1%
9 35
 
< 0.1%
Other values (34) 153
 
< 0.1%
(Missing) 3732794
99.7%
ValueCountFrequency (%)
0 6960
0.2%
1 2426
 
0.1%
2 750
 
< 0.1%
3 273
 
< 0.1%
4 167
 
< 0.1%
ValueCountFrequency (%)
96 1
< 0.1%
81 1
< 0.1%
75 1
< 0.1%
64 2
< 0.1%
56 1
< 0.1%
Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:14.651822image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.000185907
Min length8

Characters and Unicode

Total characters29951176
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 row96a8fdfe
2nd row60c73645
3rd row60c73645
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 3090430
82.5%
60c73645 481706
 
12.9%
96a8fdfe 162468
 
4.3%
164ee705 3067
 
0.1%
e19fdece 2464
 
0.1%
9e302002 1427
 
< 0.1%
6ec903ee 747
 
< 0.1%
7a7d6960 541
 
< 0.1%
44164129 405
 
< 0.1%
p188_162_121 174
 
< 0.1%
Other values (7) 381
 
< 0.1%
2024-02-13T20:42:14.984488image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 9756252
32.6%
4 3576450
 
11.9%
7 3576373
 
11.9%
a 3253603
 
10.9%
1 3097688
 
10.3%
b 3090742
 
10.3%
6 1131520
 
3.8%
0 490598
 
1.6%
c 485024
 
1.6%
3 484037
 
1.6%
Other values (8) 1008889
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22448267
74.9%
Lowercase Letter 7502387
 
25.0%
Connector Punctuation 348
 
< 0.1%
Uppercase Letter 174
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 9756252
43.5%
4 3576450
 
15.9%
7 3576373
 
15.9%
1 3097688
 
13.8%
6 1131520
 
5.0%
0 490598
 
2.2%
3 484037
 
2.2%
9 168180
 
0.7%
8 163216
 
0.7%
2 3953
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 3253603
43.4%
b 3090742
41.2%
c 485024
 
6.5%
f 327647
 
4.4%
e 179787
 
2.4%
d 165584
 
2.2%
Connector Punctuation
ValueCountFrequency (%)
_ 348
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 174
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22448615
75.0%
Latin 7502561
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 9756252
43.5%
4 3576450
 
15.9%
7 3576373
 
15.9%
1 3097688
 
13.8%
6 1131520
 
5.0%
0 490598
 
2.2%
3 484037
 
2.2%
9 168180
 
0.7%
8 163216
 
0.7%
2 3953
 
< 0.1%
Latin
ValueCountFrequency (%)
a 3253603
43.4%
b 3090742
41.2%
c 485024
 
6.5%
f 327647
 
4.4%
e 179787
 
2.4%
d 165584
 
2.2%
P 174
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29951176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 9756252
32.6%
4 3576450
 
11.9%
7 3576373
 
11.9%
a 3253603
 
10.9%
1 3097688
 
10.3%
b 3090742
 
10.3%
6 1131520
 
3.8%
0 490598
 
1.6%
c 485024
 
1.6%
3 484037
 
1.6%
Other values (8) 1008889
 
3.4%
Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:15.180684image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.001958433
Min length8

Characters and Unicode

Total characters29957812
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 row96a8fdfe
4th row96a8fdfe
5th row96a8fdfe
ValueCountFrequency (%)
a55475b1 1670161
44.6%
60c73645 952838
25.5%
96a8fdfe 568579
 
15.2%
5065c2b8 467026
 
12.5%
e19fdece 37832
 
1.0%
d9ae1a0e 23162
 
0.6%
27b6de28 6868
 
0.2%
5d1b0cdd 4835
 
0.1%
89ccf2a3 2539
 
0.1%
d11871e7 2441
 
0.1%
Other values (13) 7529
 
0.2%
2024-02-13T20:42:15.520029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 6902742
23.0%
6 2955561
9.9%
7 2640013
 
8.8%
4 2629250
 
8.8%
a 2289181
 
7.6%
b 2151066
 
7.2%
1 1755767
 
5.9%
c 1467735
 
4.9%
0 1449902
 
4.8%
f 1179207
 
3.9%
Other values (8) 4537388
15.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21466163
71.7%
Lowercase Letter 8486150
 
28.3%
Connector Punctuation 3666
 
< 0.1%
Uppercase Letter 1833
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 6902742
32.2%
6 2955561
13.8%
7 2640013
 
12.3%
4 2629250
 
12.2%
1 1755767
 
8.2%
0 1449902
 
6.8%
8 1053185
 
4.9%
3 955881
 
4.5%
9 635108
 
3.0%
2 488754
 
2.3%
Lowercase Letter
ValueCountFrequency (%)
a 2289181
27.0%
b 2151066
25.3%
c 1467735
17.3%
f 1179207
13.9%
e 742559
 
8.8%
d 656402
 
7.7%
Connector Punctuation
ValueCountFrequency (%)
_ 3666
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 1833
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21469829
71.7%
Latin 8487983
 
28.3%

Most frequent character per script

Common
ValueCountFrequency (%)
5 6902742
32.2%
6 2955561
13.8%
7 2640013
 
12.3%
4 2629250
 
12.2%
1 1755767
 
8.2%
0 1449902
 
6.8%
8 1053185
 
4.9%
3 955881
 
4.5%
9 635108
 
3.0%
2 488754
 
2.3%
Latin
ValueCountFrequency (%)
a 2289181
27.0%
b 2151066
25.3%
c 1467735
17.3%
f 1179207
13.9%
e 742559
 
8.7%
d 656402
 
7.7%
P 1833
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29957812
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 6902742
23.0%
6 2955561
9.9%
7 2640013
 
8.8%
4 2629250
 
8.8%
a 2289181
 
7.6%
b 2151066
 
7.2%
1 1755767
 
5.9%
c 1467735
 
4.9%
0 1449902
 
4.8%
f 1179207
 
3.9%
Other values (8) 4537388
15.1%

refreshdate_3813885D
Text

MISSING 

Distinct179
Distinct (%)< 0.1%
Missing1142858
Missing (%)30.5%
Memory size28.6 MiB
2024-02-13T20:42:15.864633image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters26009520
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 row2020-01-01
2nd row2019-12-31
3rd row2020-01-01
4th row2019-01-03
5th row2019-12-27
ValueCountFrequency (%)
2019-11-03 444741
17.1%
2019-01-03 325119
 
12.5%
2019-12-13 283810
 
10.9%
2020-02-03 194739
 
7.5%
2020-03-01 145424
 
5.6%
2020-02-08 63580
 
2.4%
2020-04-16 44173
 
1.7%
2020-01-10 40985
 
1.6%
2020-01-13 39624
 
1.5%
2020-03-17 36221
 
1.4%
Other values (169) 982536
37.8%
2024-02-13T20:42:16.364035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7533731
29.0%
2 5360555
20.6%
- 5201904
20.0%
1 4035850
15.5%
3 1804276
 
6.9%
9 1180608
 
4.5%
6 223126
 
0.9%
5 188344
 
0.7%
4 181756
 
0.7%
7 165625
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20807616
80.0%
Dash Punctuation 5201904
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7533731
36.2%
2 5360555
25.8%
1 4035850
19.4%
3 1804276
 
8.7%
9 1180608
 
5.7%
6 223126
 
1.1%
5 188344
 
0.9%
4 181756
 
0.9%
7 165625
 
0.8%
8 133745
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 5201904
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26009520
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7533731
29.0%
2 5360555
20.6%
- 5201904
20.0%
1 4035850
15.5%
3 1804276
 
6.9%
9 1180608
 
4.5%
6 223126
 
0.9%
5 188344
 
0.7%
4 181756
 
0.7%
7 165625
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26009520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7533731
29.0%
2 5360555
20.6%
- 5201904
20.0%
1 4035850
15.5%
3 1804276
 
6.9%
9 1180608
 
4.5%
6 223126
 
0.9%
5 188344
 
0.7%
4 181756
 
0.7%
7 165625
 
0.6%

residualamount_488A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct5
Distinct (%)< 0.1%
Missing3478174
Missing (%)92.9%
Infinite0
Infinite (%)0.0%
Mean1.575526058
Minimum0
Maximum239912.92
Zeros265632
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:16.507046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum239912.92
Range239912.92
Interquartile range (IQR)0

Descriptive statistics

Standard deviation547.4277149
Coefficient of variation (CV)347.4570998
Kurtosis158227.2889
Mean1.575526058
Median Absolute Deviation (MAD)0
Skewness389.3798707
Sum418516.44
Variance299677.103
MonotonicityNot monotonic
2024-02-13T20:42:16.618043image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
0 265632
 
7.1%
21028.402 1
 
< 0.1%
11001.038 1
 
< 0.1%
146574.08 1
 
< 0.1%
239912.92 1
 
< 0.1%
(Missing) 3478174
92.9%
ValueCountFrequency (%)
0 265632
7.1%
11001.038 1
 
< 0.1%
21028.402 1
 
< 0.1%
146574.08 1
 
< 0.1%
239912.92 1
 
< 0.1%
ValueCountFrequency (%)
239912.92 1
 
< 0.1%
146574.08 1
 
< 0.1%
21028.402 1
 
< 0.1%
11001.038 1
 
< 0.1%
0 265632
7.1%

residualamount_856A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct153166
Distinct (%)50.7%
Missing3441750
Missing (%)91.9%
Infinite0
Infinite (%)0.0%
Mean45687.78671
Minimum0
Maximum66000000
Zeros116565
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:16.754550image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7493.37
Q346510.4425
95-th percentile208385.6965
Maximum66000000
Range66000000
Interquartile range (IQR)46510.4425

Descriptive statistics

Standard deviation155227.4807
Coefficient of variation (CV)3.397570595
Kurtosis108118.5086
Mean45687.78671
Median Absolute Deviation (MAD)7493.37
Skewness257.5233858
Sum1.380045285 × 1010
Variance2.409557076 × 1010
MonotonicityNot monotonic
2024-02-13T20:42:16.914514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 116565
 
3.1%
200000 322
 
< 0.1%
10000 266
 
< 0.1%
20000 245
 
< 0.1%
30000 205
 
< 0.1%
40000 203
 
< 0.1%
100000 199
 
< 0.1%
4000 152
 
< 0.1%
6000 105
 
< 0.1%
60000 105
 
< 0.1%
Other values (153156) 183693
 
4.9%
(Missing) 3441750
91.9%
ValueCountFrequency (%)
0 116565
3.1%
0.002 1
 
< 0.1%
0.006 1
 
< 0.1%
0.030000001 1
 
< 0.1%
0.042 1
 
< 0.1%
ValueCountFrequency (%)
66000000 1
< 0.1%
14000000 1
< 0.1%
2508491.8 1
< 0.1%
2495764.8 1
< 0.1%
2022909.2 1
< 0.1%
Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:17.087523image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.000228644
Min length8

Characters and Unicode

Total characters29951336
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowab3c25cf
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 3441306
91.9%
ab3c25cf 291967
 
7.8%
be4fd70b 5316
 
0.1%
daf49a8a 3854
 
0.1%
p28_48_88 856
 
< 0.1%
15f04f45 507
 
< 0.1%
0c42a10e 3
 
< 0.1%
71ddaa88 1
 
< 0.1%
2024-02-13T20:42:17.399334image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 10616899
35.4%
a 3744840
 
12.5%
b 3743905
 
12.5%
4 3452349
 
11.5%
7 3446623
 
11.5%
1 3441817
 
11.5%
c 583937
 
1.9%
f 302151
 
1.0%
2 292826
 
1.0%
3 291967
 
1.0%
Other values (7) 34022
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21559444
72.0%
Lowercase Letter 8389324
 
28.0%
Connector Punctuation 1712
 
< 0.1%
Uppercase Letter 856
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 10616899
49.2%
4 3452349
 
16.0%
7 3446623
 
16.0%
1 3441817
 
16.0%
2 292826
 
1.4%
3 291967
 
1.4%
8 7280
 
< 0.1%
0 5829
 
< 0.1%
9 3854
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 3744840
44.6%
b 3743905
44.6%
c 583937
 
7.0%
f 302151
 
3.6%
d 9172
 
0.1%
e 5319
 
0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 1712
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 856
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21561156
72.0%
Latin 8390180
 
28.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 10616899
49.2%
4 3452349
 
16.0%
7 3446623
 
16.0%
1 3441817
 
16.0%
2 292826
 
1.4%
3 291967
 
1.4%
8 7280
 
< 0.1%
0 5829
 
< 0.1%
9 3854
 
< 0.1%
_ 1712
 
< 0.1%
Latin
ValueCountFrequency (%)
a 3744840
44.6%
b 3743905
44.6%
c 583937
 
7.0%
f 302151
 
3.6%
d 9172
 
0.1%
e 5319
 
0.1%
P 856
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29951336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 10616899
35.4%
a 3744840
 
12.5%
b 3743905
 
12.5%
4 3452349
 
11.5%
7 3446623
 
11.5%
1 3441817
 
11.5%
c 583937
 
1.9%
f 302151
 
1.0%
2 292826
 
1.0%
3 291967
 
1.0%
Other values (7) 34022
 
0.1%
Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:17.563761image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.000000534
Min length8

Characters and Unicode

Total characters29950482
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 3436989
91.8%
ab3c25cf 294527
 
7.9%
be4fd70b 6081
 
0.2%
daf49a8a 4726
 
0.1%
15f04f45 1445
 
< 0.1%
71ddaa88 35
 
< 0.1%
0c42a10e 4
 
< 0.1%
p28_48_88 2
 
< 0.1%
652d52e3 1
 
< 0.1%
2024-02-13T20:42:17.850589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 10608386
35.4%
a 3745768
 
12.5%
b 3743678
 
12.5%
4 3450692
 
11.5%
7 3443105
 
11.5%
1 3438473
 
11.5%
c 589058
 
2.0%
f 308224
 
1.0%
2 294535
 
1.0%
3 294528
 
1.0%
Other values (8) 34035
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21546784
71.9%
Lowercase Letter 8403692
 
28.1%
Connector Punctuation 4
 
< 0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 10608386
49.2%
4 3450692
 
16.0%
7 3443105
 
16.0%
1 3438473
 
16.0%
2 294535
 
1.4%
3 294528
 
1.4%
0 7534
 
< 0.1%
8 4804
 
< 0.1%
9 4726
 
< 0.1%
6 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 3745768
44.6%
b 3743678
44.5%
c 589058
 
7.0%
f 308224
 
3.7%
d 10878
 
0.1%
e 6086
 
0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21546788
71.9%
Latin 8403694
 
28.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 10608386
49.2%
4 3450692
 
16.0%
7 3443105
 
16.0%
1 3438473
 
16.0%
2 294535
 
1.4%
3 294528
 
1.4%
0 7534
 
< 0.1%
8 4804
 
< 0.1%
9 4726
 
< 0.1%
_ 4
 
< 0.1%
Latin
ValueCountFrequency (%)
a 3745768
44.6%
b 3743678
44.5%
c 589058
 
7.0%
f 308224
 
3.7%
d 10878
 
0.1%
e 6086
 
0.1%
P 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29950482
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 10608386
35.4%
a 3745768
 
12.5%
b 3743678
 
12.5%
4 3450692
 
11.5%
7 3443105
 
11.5%
1 3438473
 
11.5%
c 589058
 
2.0%
f 308224
 
1.0%
2 294535
 
1.0%
3 294528
 
1.0%
Other values (8) 34035
 
0.1%

totalamount_6A
Real number (ℝ)

MISSING  SKEWED 

Distinct258750
Distinct (%)14.3%
Missing1937086
Missing (%)51.7%
Infinite0
Infinite (%)0.0%
Mean82442.50455
Minimum0
Maximum635000000
Zeros2629
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:18.006235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3800
Q112000
median27275.9
Q360000
95-th percentile260000
Maximum635000000
Range635000000
Interquartile range (IQR)48000

Descriptive statistics

Standard deviation901351.1778
Coefficient of variation (CV)10.93308825
Kurtosis186395.0143
Mean82442.50455
Median Absolute Deviation (MAD)18020.299
Skewness346.4477652
Sum1.489508516 × 1011
Variance8.124339458 × 1011
MonotonicityNot monotonic
2024-02-13T20:42:18.167230image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000 36065
 
1.0%
30000 35854
 
1.0%
40000 33608
 
0.9%
60000 29951
 
0.8%
10000 24942
 
0.7%
4000 24672
 
0.7%
100000 24474
 
0.7%
6000 21510
 
0.6%
2000 20914
 
0.6%
3000 19551
 
0.5%
Other values (258740) 1535183
41.0%
(Missing) 1937086
51.7%
ValueCountFrequency (%)
0 2629
0.1%
0.002 1
 
< 0.1%
0.4 1
 
< 0.1%
4 2
 
< 0.1%
4.2000003 1
 
< 0.1%
ValueCountFrequency (%)
635000000 1
< 0.1%
360353860 1
< 0.1%
358034020 1
< 0.1%
347134700 1
< 0.1%
244000000 1
< 0.1%

totalamount_996A
Real number (ℝ)

MISSING  SKEWED 

Distinct108139
Distinct (%)31.2%
Missing3397081
Missing (%)90.7%
Infinite0
Infinite (%)0.0%
Mean225792.6409
Minimum37.4
Maximum378982270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:18.331758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum37.4
5-th percentile10360
Q127733.201
median62992
Q3170137
95-th percentile886093.12
Maximum378982270
Range378982232.6
Interquartile range (IQR)142403.799

Descriptive statistics

Standard deviation1472321.497
Coefficient of variation (CV)6.52067973
Kurtosis34927.08339
Mean225792.6409
Median Absolute Deviation (MAD)44008
Skewness157.6671532
Sum7.828885659 × 1010
Variance2.16773059 × 1012
MonotonicityNot monotonic
2024-02-13T20:42:18.499542image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 7644
 
0.2%
200000 5310
 
0.1%
60000 4330
 
0.1%
40000 4162
 
0.1%
20000 3523
 
0.1%
30000 3181
 
0.1%
400000 2445
 
0.1%
80000 2393
 
0.1%
300000 2231
 
0.1%
150000 2139
 
0.1%
Other values (108129) 309371
 
8.3%
(Missing) 3397081
90.7%
ValueCountFrequency (%)
37.4 1
< 0.1%
320 1
< 0.1%
400 1
< 0.1%
600 1
< 0.1%
646 1
< 0.1%
ValueCountFrequency (%)
378982270 1
< 0.1%
360353860 1
< 0.1%
347134700 1
< 0.1%
232511470 1
< 0.1%
146862500 1
< 0.1%

totaldebtoverduevalue_178A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct14201
Distinct (%)4.7%
Missing3441306
Missing (%)91.9%
Infinite0
Infinite (%)0.0%
Mean3805.408681
Minimum0
Maximum428557340
Zeros286308
Zeros (%)7.6%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:18.660893image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11.5417
Maximum428557340
Range428557340
Interquartile range (IQR)0

Descriptive statistics

Standard deviation788997.8709
Coefficient of variation (CV)207.3359098
Kurtosis287756.2305
Mean3805.408681
Median Absolute Deviation (MAD)0
Skewness530.4208004
Sum1151151348
Variance6.225176403 × 1011
MonotonicityNot monotonic
2024-02-13T20:42:18.816278image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 286308
 
7.6%
10 51
 
< 0.1%
14 43
 
< 0.1%
99.8 32
 
< 0.1%
6000 29
 
< 0.1%
1 26
 
< 0.1%
0.2 25
 
< 0.1%
4228 25
 
< 0.1%
2.4 20
 
< 0.1%
2 19
 
< 0.1%
Other values (14191) 15926
 
0.4%
(Missing) 3441306
91.9%
ValueCountFrequency (%)
0 286308
7.6%
0.002 3
 
< 0.1%
0.004 1
 
< 0.1%
0.006 3
 
< 0.1%
0.008 4
 
< 0.1%
ValueCountFrequency (%)
428557340 1
< 0.1%
32670790 1
< 0.1%
26681114 1
< 0.1%
23983900 1
< 0.1%
23045082 1
< 0.1%

totaldebtoverduevalue_718A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct325
Distinct (%)0.1%
Missing3436989
Missing (%)91.8%
Infinite0
Infinite (%)0.0%
Mean124.2405821
Minimum0
Maximum480810
Zeros306384
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:18.971403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum480810
Range480810
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5618.443278
Coefficient of variation (CV)45.22228712
Kurtosis3727.602795
Mean124.2405821
Median Absolute Deviation (MAD)0
Skewness57.69378788
Sum38119619.65
Variance31566904.86
MonotonicityNot monotonic
2024-02-13T20:42:19.136921image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 306384
 
8.2%
0.2 17
 
< 0.1%
0.4 16
 
< 0.1%
0.6 15
 
< 0.1%
1 12
 
< 0.1%
0.8 9
 
< 0.1%
1.2 7
 
< 0.1%
1.6 5
 
< 0.1%
4 4
 
< 0.1%
5.4 4
 
< 0.1%
Other values (315) 348
 
< 0.1%
(Missing) 3436989
91.8%
ValueCountFrequency (%)
0 306384
8.2%
0.072000004 1
 
< 0.1%
0.2 17
 
< 0.1%
0.4 16
 
< 0.1%
0.6 15
 
< 0.1%
ValueCountFrequency (%)
480810 1
< 0.1%
433195 1
< 0.1%
429100 1
< 0.1%
426325 1
< 0.1%
421675 1
< 0.1%

totaloutstanddebtvalue_39A
Real number (ℝ)

MISSING  SKEWED 

Distinct261674
Distinct (%)86.5%
Missing3441306
Missing (%)91.9%
Infinite0
Infinite (%)0.0%
Mean240404.7107
Minimum0
Maximum948016600
Zeros31326
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:19.316512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120149.888
median78556.645
Q3211022.7125
95-th percentile927773.874
Maximum948016600
Range948016600
Interquartile range (IQR)190872.8245

Descriptive statistics

Standard deviation2144488.265
Coefficient of variation (CV)8.920325475
Kurtosis134644.6721
Mean240404.7107
Median Absolute Deviation (MAD)70268.7625
Skewness330.4401467
Sum7.272338659 × 1010
Variance4.598829918 × 1012
MonotonicityNot monotonic
2024-02-13T20:42:19.480937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31326
 
0.8%
200000 129
 
< 0.1%
100000 84
 
< 0.1%
40000 70
 
< 0.1%
20000 66
 
< 0.1%
10000 61
 
< 0.1%
30000 56
 
< 0.1%
60000 52
 
< 0.1%
12000 47
 
< 0.1%
400000 38
 
< 0.1%
Other values (261664) 270575
 
7.2%
(Missing) 3441306
91.9%
ValueCountFrequency (%)
0 31326
0.8%
0.002 1
 
< 0.1%
0.028 1
 
< 0.1%
0.14600001 1
 
< 0.1%
0.2 2
 
< 0.1%
ValueCountFrequency (%)
948016600 1
< 0.1%
465430940 1
< 0.1%
267138050 1
< 0.1%
221618380 1
< 0.1%
84338510 1
< 0.1%

totaloutstanddebtvalue_668A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct261
Distinct (%)0.1%
Missing3436989
Missing (%)91.8%
Infinite0
Infinite (%)0.0%
Mean32.03262567
Minimum0
Maximum1077763.1
Zeros306406
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size28.6 MiB
2024-02-13T20:42:19.639287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1077763.1
Range1077763.1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3536.641246
Coefficient of variation (CV)110.4074728
Kurtosis46176.56361
Mean32.03262567
Median Absolute Deviation (MAD)0
Skewness194.5313543
Sum9828282.242
Variance12507831.31
MonotonicityNot monotonic
2024-02-13T20:42:19.803891image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 306406
 
8.2%
0.2 25
 
< 0.1%
0.4 21
 
< 0.1%
0.6 18
 
< 0.1%
1 14
 
< 0.1%
0.8 11
 
< 0.1%
1.2 9
 
< 0.1%
1.6 5
 
< 0.1%
5.4 4
 
< 0.1%
4 4
 
< 0.1%
Other values (251) 304
 
< 0.1%
(Missing) 3436989
91.8%
ValueCountFrequency (%)
0 306406
8.2%
0.2 25
 
< 0.1%
0.4 21
 
< 0.1%
0.6 18
 
< 0.1%
0.792 2
 
< 0.1%
ValueCountFrequency (%)
1077763.1 1
< 0.1%
793499.6 1
< 0.1%
700000 1
< 0.1%
577977.2 1
< 0.1%
439192.25 1
< 0.1%