Overview

Dataset statistics

Number of variables79
Number of observations2079323
Missing cells101862954
Missing cells (%)62.0%
Total size in memory1.2 GiB
Average record size in memory632.0 B

Variable types

Numeric56
Text23

Alerts

residualamount_488A has constant value ""Constant
annualeffectiverate_199L has 1919447 (92.3%) missing valuesMissing
annualeffectiverate_63L has 2041314 (98.2%) missing valuesMissing
contractsum_5085717L has 1773699 (85.3%) missing valuesMissing
credlmt_230A has 1926765 (92.7%) missing valuesMissing
credlmt_935A has 1924976 (92.6%) missing valuesMissing
dateofcredend_289D has 1750967 (84.2%) missing valuesMissing
dateofcredend_353D has 812675 (39.1%) missing valuesMissing
dateofcredstart_181D has 812672 (39.1%) missing valuesMissing
dateofcredstart_739D has 1750967 (84.2%) missing valuesMissing
dateofrealrepmt_138D has 818708 (39.4%) missing valuesMissing
debtoutstand_525A has 1902656 (91.5%) missing valuesMissing
debtoverdue_47A has 1902656 (91.5%) missing valuesMissing
dpdmax_139P has 1752846 (84.3%) missing valuesMissing
dpdmax_757P has 852736 (41.0%) missing valuesMissing
dpdmaxdatemonth_442T has 852736 (41.0%) missing valuesMissing
dpdmaxdatemonth_89T has 1752846 (84.3%) missing valuesMissing
dpdmaxdateyear_596T has 1752846 (84.3%) missing valuesMissing
dpdmaxdateyear_896T has 852736 (41.0%) missing valuesMissing
instlamount_768A has 1926991 (92.7%) missing valuesMissing
instlamount_852A has 1977007 (95.1%) missing valuesMissing
interestrate_508L has 2068990 (99.5%) missing valuesMissing
lastupdate_1112D has 1750967 (84.2%) missing valuesMissing
lastupdate_388D has 812710 (39.1%) missing valuesMissing
monthlyinstlamount_332A has 1753025 (84.3%) missing valuesMissing
monthlyinstlamount_674A has 888003 (42.7%) missing valuesMissing
nominalrate_281L has 1953359 (93.9%) missing valuesMissing
nominalrate_498L has 1596430 (76.8%) missing valuesMissing
numberofcontrsvalue_258L has 1921187 (92.4%) missing valuesMissing
numberofcontrsvalue_358L has 1905185 (91.6%) missing valuesMissing
numberofinstls_229L has 965603 (46.4%) missing valuesMissing
numberofinstls_320L has 1905363 (91.6%) missing valuesMissing
numberofoutstandinstls_520L has 964922 (46.4%) missing valuesMissing
numberofoutstandinstls_59L has 1905355 (91.6%) missing valuesMissing
numberofoverdueinstlmax_1039L has 1750967 (84.2%) missing valuesMissing
numberofoverdueinstlmax_1151L has 812672 (39.1%) missing valuesMissing
numberofoverdueinstlmaxdat_148D has 1701590 (81.8%) missing valuesMissing
numberofoverdueinstlmaxdat_641D has 1990461 (95.7%) missing valuesMissing
numberofoverdueinstls_725L has 1752866 (84.3%) missing valuesMissing
numberofoverdueinstls_834L has 814326 (39.2%) missing valuesMissing
outstandingamount_354A has 964646 (46.4%) missing valuesMissing
outstandingamount_362A has 1905317 (91.6%) missing valuesMissing
overdueamount_31A has 814062 (39.2%) missing valuesMissing
overdueamount_659A has 1752866 (84.3%) missing valuesMissing
overdueamountmax2_14A has 1750967 (84.2%) missing valuesMissing
overdueamountmax2_398A has 812672 (39.1%) missing valuesMissing
overdueamountmax2date_1002D has 1705299 (82.0%) missing valuesMissing
overdueamountmax2date_1142D has 1989828 (95.7%) missing valuesMissing
overdueamountmax_155A has 1750968 (84.2%) missing valuesMissing
overdueamountmax_35A has 851225 (40.9%) missing valuesMissing
overdueamountmaxdatemonth_284T has 851225 (40.9%) missing valuesMissing
overdueamountmaxdatemonth_365T has 1750968 (84.2%) missing valuesMissing
overdueamountmaxdateyear_2T has 1750968 (84.2%) missing valuesMissing
overdueamountmaxdateyear_994T has 851225 (40.9%) missing valuesMissing
periodicityofpmts_1102L has 1089256 (52.4%) missing valuesMissing
periodicityofpmts_837L has 1908921 (91.8%) missing valuesMissing
prolongationcount_1120L has 1977663 (95.1%) missing valuesMissing
prolongationcount_599L has 2068341 (99.5%) missing valuesMissing
refreshdate_3813885D has 666459 (32.1%) missing valuesMissing
residualamount_488A has 1928438 (92.7%) missing valuesMissing
residualamount_856A has 1926906 (92.7%) missing valuesMissing
totalamount_6A has 964449 (46.4%) missing valuesMissing
totalamount_996A has 1905314 (91.6%) missing valuesMissing
totaldebtoverduevalue_178A has 1921187 (92.4%) missing valuesMissing
totaldebtoverduevalue_718A has 1905185 (91.6%) missing valuesMissing
totaloutstanddebtvalue_39A has 1921187 (92.4%) missing valuesMissing
totaloutstanddebtvalue_668A has 1905185 (91.6%) missing valuesMissing
annualeffectiverate_63L is highly skewed (γ1 = 25.47609767)Skewed
credlmt_230A is highly skewed (γ1 = 175.7236252)Skewed
credlmt_935A is highly skewed (γ1 = 159.332287)Skewed
debtoutstand_525A is highly skewed (γ1 = 156.7421448)Skewed
debtoverdue_47A is highly skewed (γ1 = 409.2294224)Skewed
dpdmax_139P is highly skewed (γ1 = 27.45490048)Skewed
dpdmax_757P is highly skewed (γ1 = 151.1815015)Skewed
instlamount_852A is highly skewed (γ1 = 31.69000589)Skewed
interestrate_508L is highly skewed (γ1 = 26.1670526)Skewed
monthlyinstlamount_332A is highly skewed (γ1 = 168.4209434)Skewed
monthlyinstlamount_674A is highly skewed (γ1 = 220.9307649)Skewed
nominalrate_281L is highly skewed (γ1 = 45.98914381)Skewed
nominalrate_498L is highly skewed (γ1 = 23.41495544)Skewed
numberofoutstandinstls_520L is highly skewed (γ1 = 95.13283139)Skewed
numberofoverdueinstlmax_1039L is highly skewed (γ1 = 124.4609752)Skewed
numberofoverdueinstlmax_1151L is highly skewed (γ1 = 235.4689954)Skewed
numberofoverdueinstls_725L is highly skewed (γ1 = 49.63039471)Skewed
numberofoverdueinstls_834L is highly skewed (γ1 = 279.913791)Skewed
outstandingamount_354A is highly skewed (γ1 = 431.1137107)Skewed
outstandingamount_362A is highly skewed (γ1 = 83.85920112)Skewed
overdueamount_31A is highly skewed (γ1 = 176.3038547)Skewed
overdueamount_659A is highly skewed (γ1 = 392.5318865)Skewed
overdueamountmax2_14A is highly skewed (γ1 = 392.8541134)Skewed
overdueamountmax2_398A is highly skewed (γ1 = 363.5508044)Skewed
overdueamountmax_155A is highly skewed (γ1 = 392.9331245)Skewed
overdueamountmax_35A is highly skewed (γ1 = 613.0158521)Skewed
periodicityofpmts_1102L is highly skewed (γ1 = 48.50365629)Skewed
periodicityofpmts_837L is highly skewed (γ1 = 28.31014469)Skewed
totalamount_6A is highly skewed (γ1 = 228.0579047)Skewed
totalamount_996A is highly skewed (γ1 = 182.363711)Skewed
totaldebtoverduevalue_178A is highly skewed (γ1 = 387.1727565)Skewed
totaldebtoverduevalue_718A is highly skewed (γ1 = 65.38393557)Skewed
totaloutstanddebtvalue_39A is highly skewed (γ1 = 152.4046712)Skewed
totaloutstanddebtvalue_668A is highly skewed (γ1 = 170.4689353)Skewed
contractsum_5085717L has 66218 (3.2%) zerosZeros
credlmt_230A has 57923 (2.8%) zerosZeros
credlmt_935A has 45620 (2.2%) zerosZeros
debtoutstand_525A has 38186 (1.8%) zerosZeros
debtoverdue_47A has 173753 (8.4%) zerosZeros
dpdmax_139P has 255094 (12.3%) zerosZeros
dpdmax_757P has 871213 (41.9%) zerosZeros
instlamount_768A has 67080 (3.2%) zerosZeros
instlamount_852A has 63075 (3.0%) zerosZeros
monthlyinstlamount_332A has 67678 (3.3%) zerosZeros
monthlyinstlamount_674A has 528046 (25.4%) zerosZeros
nominalrate_498L has 29774 (1.4%) zerosZeros
num_group1 has 176608 (8.5%) zerosZeros
numberofinstls_229L has 215858 (10.4%) zerosZeros
numberofoutstandinstls_520L has 1112968 (53.5%) zerosZeros
numberofoverdueinstlmax_1039L has 239494 (11.5%) zerosZeros
numberofoverdueinstlmax_1151L has 888918 (42.8%) zerosZeros
numberofoverdueinstls_725L has 323096 (15.5%) zerosZeros
numberofoverdueinstls_834L has 1264302 (60.8%) zerosZeros
outstandingamount_354A has 1114466 (53.6%) zerosZeros
overdueamount_31A has 1265044 (60.8%) zerosZeros
overdueamount_659A has 323097 (15.5%) zerosZeros
overdueamountmax2_14A has 238861 (11.5%) zerosZeros
overdueamountmax2_398A has 892627 (42.9%) zerosZeros
overdueamountmax_155A has 255514 (12.3%) zerosZeros
overdueamountmax_35A has 869327 (41.8%) zerosZeros
prolongationcount_1120L has 75910 (3.7%) zerosZeros
residualamount_488A has 150885 (7.3%) zerosZeros
residualamount_856A has 64487 (3.1%) zerosZeros
totaldebtoverduevalue_178A has 155204 (7.5%) zerosZeros
totaldebtoverduevalue_718A has 173922 (8.4%) zerosZeros
totaloutstanddebtvalue_668A has 173936 (8.4%) zerosZeros

Reproduction

Analysis started2024-02-13 19:42:24.715383
Analysis finished2024-02-13 19:42:44.525744
Duration19.81 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

case_id
Real number (ℝ)

Distinct176608
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1492726.545
Minimum51903
Maximum2703454
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:42:44.653739image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum51903
5-th percentile233518
Q1998550
median1880184
Q31923500
95-th percentile2694519
Maximum2703454
Range2651551
Interquartile range (IQR)924950

Descriptive statistics

Standard deviation793957.3997
Coefficient of variation (CV)0.5318840227
Kurtosis-0.9434466818
Mean1492726.545
Median Absolute Deviation (MAD)63416
Skewness-0.4588255656
Sum3.103860638 × 1012
Variance6.303683526 × 1011
MonotonicityIncreasing
2024-02-13T20:42:44.852640image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1863457 203
 
< 0.1%
245030 159
 
< 0.1%
252165 155
 
< 0.1%
1019972 150
 
< 0.1%
236981 146
 
< 0.1%
52198 128
 
< 0.1%
1887379 128
 
< 0.1%
1936653 122
 
< 0.1%
257052 121
 
< 0.1%
257343 119
 
< 0.1%
Other values (176598) 2077892
99.9%
ValueCountFrequency (%)
51903 9
< 0.1%
51904 9
< 0.1%
51906 10
< 0.1%
51911 9
< 0.1%
51913 9
< 0.1%
ValueCountFrequency (%)
2703454 10
< 0.1%
2703453 14
< 0.1%
2703452 9
< 0.1%
2703451 9
< 0.1%
2703450 12
< 0.1%

annualeffectiverate_199L
Real number (ℝ)

MISSING 

Distinct5390
Distinct (%)3.4%
Missing1919447
Missing (%)92.3%
Infinite0
Infinite (%)0.0%
Mean1526.065896
Minimum0
Maximum91250
Zeros5251
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:42:45.009531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12
Q111
median40
Q396.3
95-th percentile3370.1
Maximum91250
Range91250
Interquartile range (IQR)85.3

Descriptive statistics

Standard deviation8429.300114
Coefficient of variation (CV)5.523549237
Kurtosis53.58498927
Mean1526.065896
Median Absolute Deviation (MAD)35.98
Skewness7.183192984
Sum243981311.2
Variance71053100.42
MonotonicityNot monotonic
2024-02-13T20:42:45.202204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96.3 18845
 
0.9%
0.12 11770
 
0.6%
0 5251
 
0.3%
730 4435
 
0.2%
38.3 4372
 
0.2%
98.55 4296
 
0.2%
365 4054
 
0.2%
0.69 2793
 
0.1%
438 2681
 
0.1%
55.62 2144
 
0.1%
Other values (5380) 99235
 
4.8%
(Missing) 1919447
92.3%
ValueCountFrequency (%)
0 5251
0.3%
0.01 1
 
< 0.1%
0.03 1
 
< 0.1%
0.04 7
 
< 0.1%
0.05 23
 
< 0.1%
ValueCountFrequency (%)
91250 58
 
< 0.1%
87600 1
 
< 0.1%
73000 774
< 0.1%
69350 729
< 0.1%
69338.23 1
 
< 0.1%

annualeffectiverate_63L
Real number (ℝ)

MISSING  SKEWED 

Distinct4515
Distinct (%)11.9%
Missing2041314
Missing (%)98.2%
Infinite0
Infinite (%)0.0%
Mean161.6647947
Minimum0
Maximum73000
Zeros856
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:42:45.357411image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.11
Q15.12
median22.26
Q339.12
95-th percentile54.96
Maximum73000
Range73000
Interquartile range (IQR)34

Descriptive statistics

Standard deviation1992.341306
Coefficient of variation (CV)12.32390336
Kurtosis757.5887736
Mean161.6647947
Median Absolute Deviation (MAD)17.1
Skewness25.47609767
Sum6144717.18
Variance3969423.878
MonotonicityNot monotonic
2024-02-13T20:42:45.522408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.12 3433
 
0.2%
0.11 1049
 
0.1%
0 856
 
< 0.1%
5.11 749
 
< 0.1%
2333.9 564
 
< 0.1%
26.8 436
 
< 0.1%
54.96 322
 
< 0.1%
48.18 276
 
< 0.1%
0.33 268
 
< 0.1%
26.28 268
 
< 0.1%
Other values (4505) 29788
 
1.4%
(Missing) 2041314
98.2%
ValueCountFrequency (%)
0 856
< 0.1%
0.01 2
 
< 0.1%
0.03 1
 
< 0.1%
0.04 8
 
< 0.1%
0.05 28
 
< 0.1%
ValueCountFrequency (%)
73000 9
< 0.1%
69350 5
 
< 0.1%
43800 1
 
< 0.1%
37133.4 14
< 0.1%
36500 21
< 0.1%
Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.9 MiB
2024-02-13T20:42:45.702787image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters16634584
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 rowea6782cc
2nd rowea6782cc
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 1751284
84.2%
ea6782cc 281976
 
13.6%
01f63ac8 30399
 
1.5%
00135d9c 9851
 
0.5%
4408ff0f 5554
 
0.3%
be7b251d 125
 
< 0.1%
2c070815 56
 
< 0.1%
1cf4e481 44
 
< 0.1%
87bdbcba 24
 
< 0.1%
4a5a01e3 10
 
< 0.1%
2024-02-13T20:42:45.991828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 5263894
31.6%
a 2063703
 
12.4%
7 2033465
 
12.2%
1 1791813
 
10.8%
4 1762490
 
10.6%
b 1751606
 
10.5%
c 604326
 
3.6%
8 318053
 
1.9%
6 312375
 
1.9%
2 282157
 
1.7%
Other values (6) 450702
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11875689
71.4%
Lowercase Letter 4758895
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 5263894
44.3%
7 2033465
 
17.1%
1 1791813
 
15.1%
4 1762490
 
14.8%
8 318053
 
2.7%
6 312375
 
2.6%
2 282157
 
2.4%
0 61331
 
0.5%
3 40260
 
0.3%
9 9851
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
a 2063703
43.4%
b 1751606
36.8%
c 604326
 
12.7%
e 282155
 
5.9%
f 47105
 
1.0%
d 10000
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 11875689
71.4%
Latin 4758895
28.6%

Most frequent character per script

Common
ValueCountFrequency (%)
5 5263894
44.3%
7 2033465
 
17.1%
1 1791813
 
15.1%
4 1762490
 
14.8%
8 318053
 
2.7%
6 312375
 
2.6%
2 282157
 
2.4%
0 61331
 
0.5%
3 40260
 
0.3%
9 9851
 
0.1%
Latin
ValueCountFrequency (%)
a 2063703
43.4%
b 1751606
36.8%
c 604326
 
12.7%
e 282155
 
5.9%
f 47105
 
1.0%
d 10000
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16634584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 5263894
31.6%
a 2063703
 
12.4%
7 2033465
 
12.2%
1 1791813
 
10.8%
4 1762490
 
10.6%
b 1751606
 
10.5%
c 604326
 
3.6%
8 318053
 
1.9%
6 312375
 
1.9%
2 282157
 
1.7%
Other values (6) 450702
 
2.7%
Distinct286
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.9 MiB
2024-02-13T20:42:46.409028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters16634584
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)< 0.1%

Sample

1st rowa55475b1
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 814771
39.2%
ea6782cc 780129
37.5%
01f63ac8 197292
 
9.5%
00135d9c 84280
 
4.1%
42a42e75 27469
 
1.3%
9158339f 17559
 
0.8%
4408ff0f 14771
 
0.7%
130920c8 14517
 
0.7%
f0a30139 11745
 
0.6%
e2453741 11713
 
0.6%
Other values (276) 105077
 
5.1%
2024-02-13T20:42:46.963780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 2640133
15.9%
c 1892462
11.4%
a 1868148
11.2%
7 1691088
10.2%
1 1187043
7.1%
8 1082578
6.5%
6 1063225
6.4%
4 974751
 
5.9%
2 949155
 
5.7%
e 891989
 
5.4%
Other values (6) 2394012
14.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10691461
64.3%
Lowercase Letter 5943123
35.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2640133
24.7%
7 1691088
15.8%
1 1187043
11.1%
8 1082578
10.1%
6 1063225
9.9%
4 974751
 
9.1%
2 949155
 
8.9%
0 473546
 
4.4%
3 425231
 
4.0%
9 204711
 
1.9%
Lowercase Letter
ValueCountFrequency (%)
c 1892462
31.8%
a 1868148
31.4%
e 891989
15.0%
b 861778
14.5%
f 286604
 
4.8%
d 142142
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 10691461
64.3%
Latin 5943123
35.7%

Most frequent character per script

Common
ValueCountFrequency (%)
5 2640133
24.7%
7 1691088
15.8%
1 1187043
11.1%
8 1082578
10.1%
6 1063225
9.9%
4 974751
 
9.1%
2 949155
 
8.9%
0 473546
 
4.4%
3 425231
 
4.0%
9 204711
 
1.9%
Latin
ValueCountFrequency (%)
c 1892462
31.8%
a 1868148
31.4%
e 891989
15.0%
b 861778
14.5%
f 286604
 
4.8%
d 142142
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16634584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 2640133
15.9%
c 1892462
11.4%
a 1868148
11.2%
7 1691088
10.2%
1 1187043
7.1%
8 1082578
6.5%
6 1063225
6.4%
4 974751
 
5.9%
2 949155
 
5.7%
e 891989
 
5.4%
Other values (6) 2394012
14.4%
Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.9 MiB
2024-02-13T20:42:47.167975image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters16634584
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

Unique3 ?
Unique (%)< 0.1%

Sample

1st row7241344e
2nd row7241344e
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 1751666
84.2%
7241344e 322008
 
15.5%
0dc85f9d 1338
 
0.1%
8f3a197f 917
 
< 0.1%
7640edc3 862
 
< 0.1%
82a92878 570
 
< 0.1%
e2e7d341 560
 
< 0.1%
dd67cff0 361
 
< 0.1%
a52d5641 305
 
< 0.1%
885ce291 211
 
< 0.1%
Other values (18) 525
 
< 0.1%
2024-02-13T20:42:47.520436image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 5257251
31.6%
4 2719575
16.3%
7 2077263
 
12.5%
1 2076080
 
12.5%
a 1753553
 
10.5%
b 1751861
 
10.5%
3 324825
 
2.0%
2 324471
 
2.0%
e 324310
 
1.9%
d 5235
 
< 0.1%
Other values (6) 20160
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12792532
76.9%
Lowercase Letter 3842052
 
23.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 5257251
41.1%
4 2719575
21.3%
7 2077263
 
16.2%
1 2076080
 
16.2%
3 324825
 
2.5%
2 324471
 
2.5%
8 4716
 
< 0.1%
9 3841
 
< 0.1%
0 2725
 
< 0.1%
6 1785
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 1753553
45.6%
b 1751861
45.6%
e 324310
 
8.4%
d 5235
 
0.1%
f 4120
 
0.1%
c 2973
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 12792532
76.9%
Latin 3842052
 
23.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 5257251
41.1%
4 2719575
21.3%
7 2077263
 
16.2%
1 2076080
 
16.2%
3 324825
 
2.5%
2 324471
 
2.5%
8 4716
 
< 0.1%
9 3841
 
< 0.1%
0 2725
 
< 0.1%
6 1785
 
< 0.1%
Latin
ValueCountFrequency (%)
a 1753553
45.6%
b 1751861
45.6%
e 324310
 
8.4%
d 5235
 
0.1%
f 4120
 
0.1%
c 2973
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16634584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 5257251
31.6%
4 2719575
16.3%
7 2077263
 
12.5%
1 2076080
 
12.5%
a 1753553
 
10.5%
b 1751861
 
10.5%
3 324825
 
2.0%
2 324471
 
2.0%
e 324310
 
1.9%
d 5235
 
< 0.1%
Other values (6) 20160
 
0.1%
Distinct218
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.9 MiB
2024-02-13T20:42:47.932358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters16634584
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

Unique32 ?
Unique (%)< 0.1%

Sample

1st rowa55475b1
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
7241344e 1196161
57.5%
a55475b1 813354
39.1%
8f3a197f 17152
 
0.8%
a3386307 7608
 
0.4%
8260bab9 7457
 
0.4%
d7416962 6152
 
0.3%
b83056f9 3952
 
0.2%
4476359f 3446
 
0.2%
41694615 2498
 
0.1%
3dc5f434 2480
 
0.1%
Other values (208) 19063
 
0.9%
2024-02-13T20:42:48.500903image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 4430722
26.6%
5 2461832
14.8%
7 2054997
12.4%
1 2048368
12.3%
3 1254613
 
7.5%
2 1216583
 
7.3%
e 1201892
 
7.2%
a 852281
 
5.1%
b 843591
 
5.1%
f 54887
 
0.3%
Other values (6) 214818
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13649917
82.1%
Lowercase Letter 2984667
 
17.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 4430722
32.5%
5 2461832
18.0%
7 2054997
15.1%
1 2048368
15.0%
3 1254613
 
9.2%
2 1216583
 
8.9%
9 52299
 
0.4%
6 52056
 
0.4%
8 51509
 
0.4%
0 26938
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
e 1201892
40.3%
a 852281
28.6%
b 843591
28.3%
f 54887
 
1.8%
d 17200
 
0.6%
c 14816
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 13649917
82.1%
Latin 2984667
 
17.9%

Most frequent character per script

Common
ValueCountFrequency (%)
4 4430722
32.5%
5 2461832
18.0%
7 2054997
15.1%
1 2048368
15.0%
3 1254613
 
9.2%
2 1216583
 
8.9%
9 52299
 
0.4%
6 52056
 
0.4%
8 51509
 
0.4%
0 26938
 
0.2%
Latin
ValueCountFrequency (%)
e 1201892
40.3%
a 852281
28.6%
b 843591
28.3%
f 54887
 
1.8%
d 17200
 
0.6%
c 14816
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16634584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 4430722
26.6%
5 2461832
14.8%
7 2054997
12.4%
1 2048368
12.3%
3 1254613
 
7.5%
2 1216583
 
7.3%
e 1201892
 
7.2%
a 852281
 
5.1%
b 843591
 
5.1%
f 54887
 
0.3%
Other values (6) 214818
 
1.3%

contractsum_5085717L
Real number (ℝ)

MISSING  ZEROS 

Distinct221599
Distinct (%)72.5%
Missing1773699
Missing (%)85.3%
Infinite0
Infinite (%)0.0%
Mean316189.1354
Minimum0
Maximum30798821.4
Zeros66218
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:42:48.675729image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114081.9575
median97519.5
Q3348357.8825
95-th percentile1319057.421
Maximum30798821.4
Range30798821.4
Interquartile range (IQR)334275.925

Descriptive statistics

Standard deviation605939.3358
Coefficient of variation (CV)1.916382532
Kurtosis60.66856039
Mean316189.1354
Median Absolute Deviation (MAD)97519.5
Skewness5.070061002
Sum9.663498832 × 1010
Variance3.671624787 × 1011
MonotonicityNot monotonic
2024-02-13T20:42:48.835710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 66218
 
3.2%
1000000 117
 
< 0.1%
50000 99
 
< 0.1%
500000 97
 
< 0.1%
100000 91
 
< 0.1%
200000 79
 
< 0.1%
150000 79
 
< 0.1%
300000 74
 
< 0.1%
20000 70
 
< 0.1%
30000 69
 
< 0.1%
Other values (221589) 238631
 
11.5%
(Missing) 1773699
85.3%
ValueCountFrequency (%)
0 66218
3.2%
0.01 4
 
< 0.1%
0.02 1
 
< 0.1%
0.03 1
 
< 0.1%
0.04 1
 
< 0.1%
ValueCountFrequency (%)
30798821.4 1
< 0.1%
23190387.04 1
< 0.1%
18800000 1
< 0.1%
17388255.68 1
< 0.1%
15211519.07 1
< 0.1%

credlmt_230A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct13063
Distinct (%)8.6%
Missing1926765
Missing (%)92.7%
Infinite0
Infinite (%)0.0%
Mean34792.56554
Minimum0
Maximum100000000
Zeros57923
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:42:48.999322image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11998
Q339000
95-th percentile100000
Maximum100000000
Range100000000
Interquartile range (IQR)39000

Descriptive statistics

Standard deviation406900.8467
Coefficient of variation (CV)11.69505153
Kurtosis37185.4651
Mean34792.56554
Median Absolute Deviation (MAD)11998
Skewness175.7236252
Sum5307884213
Variance1.65568299 × 1011
MonotonicityNot monotonic
2024-02-13T20:42:49.162901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 57923
 
2.8%
10000 10342
 
0.5%
20000 8544
 
0.4%
30000 4234
 
0.2%
40000 2634
 
0.1%
60000 2122
 
0.1%
100000 1573
 
0.1%
50000 1485
 
0.1%
4000 1404
 
0.1%
58000 1381
 
0.1%
Other values (13053) 60916
 
2.9%
(Missing) 1926765
92.7%
ValueCountFrequency (%)
0 57923
2.8%
0.042 1
 
< 0.1%
0.2 166
 
< 0.1%
0.23599999 1
 
< 0.1%
0.69 1
 
< 0.1%
ValueCountFrequency (%)
100000000 1
< 0.1%
80000000 1
< 0.1%
60000000 1
< 0.1%
29400000 1
< 0.1%
28000000 1
< 0.1%

credlmt_935A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct27539
Distinct (%)17.8%
Missing1924976
Missing (%)92.6%
Infinite0
Infinite (%)0.0%
Mean108002.9422
Minimum0
Maximum600000000
Zeros45620
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:42:49.324574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20000
Q385796.4025
95-th percentile293527.18
Maximum600000000
Range600000000
Interquartile range (IQR)85796.4025

Descriptive statistics

Standard deviation2905770.732
Coefficient of variation (CV)26.9045516
Kurtosis29547.84264
Mean108002.9422
Median Absolute Deviation (MAD)20000
Skewness159.332287
Sum1.666993012 × 1010
Variance8.443503545 × 1012
MonotonicityNot monotonic
2024-02-13T20:42:49.521384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 45620
 
2.2%
10000 14226
 
0.7%
20000 11040
 
0.5%
30000 6457
 
0.3%
200000 3545
 
0.2%
100000 2688
 
0.1%
40000 1482
 
0.1%
80000 970
 
< 0.1%
60000 932
 
< 0.1%
120000 851
 
< 0.1%
Other values (27529) 66536
 
3.2%
(Missing) 1924976
92.6%
ValueCountFrequency (%)
0 45620
2.2%
0.2 13
 
< 0.1%
0.8 1
 
< 0.1%
1.2 1
 
< 0.1%
2.374 1
 
< 0.1%
ValueCountFrequency (%)
600000000 2
< 0.1%
470000030 1
< 0.1%
340000000 1
< 0.1%
196611410 1
< 0.1%
154285710 1
< 0.1%

dateofcredend_289D
Text

MISSING 

Distinct5969
Distinct (%)1.8%
Missing1750967
Missing (%)84.2%
Memory size15.9 MiB
2024-02-13T20:42:49.919553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3283560
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

Unique2004 ?
Unique (%)0.6%

Sample

1st row2021-11-30
2nd row2022-01-26
3rd row2021-11-16
4th row2026-02-26
5th row2020-07-28
ValueCountFrequency (%)
2024-10-14 1333
 
0.4%
2021-10-14 1285
 
0.4%
2021-09-14 1283
 
0.4%
2024-09-14 1277
 
0.4%
2024-07-14 1237
 
0.4%
2022-03-14 1070
 
0.3%
2021-05-14 1020
 
0.3%
2021-11-14 856
 
0.3%
2021-08-14 788
 
0.2%
2021-01-11 733
 
0.2%
Other values (5959) 317474
96.7%
2024-02-13T20:42:50.448442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 916156
27.9%
0 783381
23.9%
- 656712
20.0%
1 434297
13.2%
3 99305
 
3.0%
4 85254
 
2.6%
9 65732
 
2.0%
5 63561
 
1.9%
8 62417
 
1.9%
7 59639
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2626848
80.0%
Dash Punctuation 656712
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 916156
34.9%
0 783381
29.8%
1 434297
16.5%
3 99305
 
3.8%
4 85254
 
3.2%
9 65732
 
2.5%
5 63561
 
2.4%
8 62417
 
2.4%
7 59639
 
2.3%
6 57106
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 656712
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3283560
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 916156
27.9%
0 783381
23.9%
- 656712
20.0%
1 434297
13.2%
3 99305
 
3.0%
4 85254
 
2.6%
9 65732
 
2.0%
5 63561
 
1.9%
8 62417
 
1.9%
7 59639
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3283560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 916156
27.9%
0 783381
23.9%
- 656712
20.0%
1 434297
13.2%
3 99305
 
3.0%
4 85254
 
2.6%
9 65732
 
2.0%
5 63561
 
1.9%
8 62417
 
1.9%
7 59639
 
1.8%

dateofcredend_353D
Text

MISSING 

Distinct8974
Distinct (%)0.7%
Missing812675
Missing (%)39.1%
Memory size15.9 MiB
2024-02-13T20:42:50.783396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters12666480
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

Unique950 ?
Unique (%)0.1%

Sample

1st row2010-12-20
2nd row2012-03-28
3rd row2014-12-12
4th row2013-08-29
5th row2014-08-17
ValueCountFrequency (%)
2019-09-17 4840
 
0.4%
2019-09-16 1658
 
0.1%
2020-06-29 1610
 
0.1%
2019-11-15 1079
 
0.1%
2019-12-09 1071
 
0.1%
2020-01-08 1046
 
0.1%
2020-06-15 1037
 
0.1%
2019-06-14 1033
 
0.1%
2020-03-24 1030
 
0.1%
2020-01-03 1026
 
0.1%
Other values (8964) 1251218
98.8%
2024-02-13T20:42:51.231545image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3184846
25.1%
- 2533296
20.0%
2 2279710
18.0%
1 2003370
15.8%
9 541695
 
4.3%
8 437820
 
3.5%
7 380179
 
3.0%
5 332507
 
2.6%
3 332222
 
2.6%
6 327860
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10133184
80.0%
Dash Punctuation 2533296
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3184846
31.4%
2 2279710
22.5%
1 2003370
19.8%
9 541695
 
5.3%
8 437820
 
4.3%
7 380179
 
3.8%
5 332507
 
3.3%
3 332222
 
3.3%
6 327860
 
3.2%
4 312975
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 2533296
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12666480
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3184846
25.1%
- 2533296
20.0%
2 2279710
18.0%
1 2003370
15.8%
9 541695
 
4.3%
8 437820
 
3.5%
7 380179
 
3.0%
5 332507
 
2.6%
3 332222
 
2.6%
6 327860
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12666480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3184846
25.1%
- 2533296
20.0%
2 2279710
18.0%
1 2003370
15.8%
9 541695
 
4.3%
8 437820
 
3.5%
7 380179
 
3.0%
5 332507
 
2.6%
3 332222
 
2.6%
6 327860
 
2.6%

dateofcredstart_181D
Text

MISSING 

Distinct6274
Distinct (%)0.5%
Missing812672
Missing (%)39.1%
Memory size15.9 MiB
2024-02-13T20:42:51.682523image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters12666510
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

Unique198 ?
Unique (%)< 0.1%

Sample

1st row2008-02-06
2nd row2010-12-24
3rd row2012-11-27
4th row2012-12-31
5th row2014-01-17
ValueCountFrequency (%)
2019-01-02 813
 
0.1%
2018-12-07 805
 
0.1%
2018-07-27 791
 
0.1%
2018-08-27 784
 
0.1%
2018-07-30 780
 
0.1%
2018-08-24 777
 
0.1%
2018-05-28 776
 
0.1%
2018-09-03 776
 
0.1%
2018-12-28 774
 
0.1%
2018-06-04 774
 
0.1%
Other values (6264) 1258801
99.4%
2024-02-13T20:42:52.208935image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3119097
24.6%
- 2533302
20.0%
1 2150931
17.0%
2 2121199
16.7%
8 474113
 
3.7%
7 448904
 
3.5%
9 434050
 
3.4%
3 376951
 
3.0%
6 375645
 
3.0%
5 320480
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10133208
80.0%
Dash Punctuation 2533302
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3119097
30.8%
1 2150931
21.2%
2 2121199
20.9%
8 474113
 
4.7%
7 448904
 
4.4%
9 434050
 
4.3%
3 376951
 
3.7%
6 375645
 
3.7%
5 320480
 
3.2%
4 311838
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 2533302
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12666510
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3119097
24.6%
- 2533302
20.0%
1 2150931
17.0%
2 2121199
16.7%
8 474113
 
3.7%
7 448904
 
3.5%
9 434050
 
3.4%
3 376951
 
3.0%
6 375645
 
3.0%
5 320480
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12666510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3119097
24.6%
- 2533302
20.0%
1 2150931
17.0%
2 2121199
16.7%
8 474113
 
3.7%
7 448904
 
3.5%
9 434050
 
3.4%
3 376951
 
3.0%
6 375645
 
3.0%
5 320480
 
2.5%

dateofcredstart_739D
Text

MISSING 

Distinct4430
Distinct (%)1.3%
Missing1750967
Missing (%)84.2%
Memory size15.9 MiB
2024-02-13T20:42:52.625661image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3283560
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

Unique443 ?
Unique (%)0.1%

Sample

1st row2019-11-30
2nd row2020-01-26
3rd row2017-11-16
4th row2019-02-26
5th row2016-07-28
ValueCountFrequency (%)
2020-01-13 856
 
0.3%
2020-01-10 843
 
0.3%
2020-03-25 832
 
0.3%
2019-11-29 830
 
0.3%
2019-12-27 828
 
0.3%
2019-12-13 815
 
0.2%
2020-01-01 808
 
0.2%
2020-01-03 802
 
0.2%
2020-03-27 796
 
0.2%
2019-11-30 785
 
0.2%
Other values (4420) 320161
97.5%
2024-02-13T20:42:53.177372image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 820871
25.0%
- 656712
20.0%
2 618648
18.8%
1 526109
16.0%
9 183457
 
5.6%
8 106162
 
3.2%
3 92242
 
2.8%
7 83519
 
2.5%
6 71892
 
2.2%
4 63150
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2626848
80.0%
Dash Punctuation 656712
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 820871
31.2%
2 618648
23.6%
1 526109
20.0%
9 183457
 
7.0%
8 106162
 
4.0%
3 92242
 
3.5%
7 83519
 
3.2%
6 71892
 
2.7%
4 63150
 
2.4%
5 60798
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 656712
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3283560
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 820871
25.0%
- 656712
20.0%
2 618648
18.8%
1 526109
16.0%
9 183457
 
5.6%
8 106162
 
3.2%
3 92242
 
2.8%
7 83519
 
2.5%
6 71892
 
2.2%
4 63150
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3283560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 820871
25.0%
- 656712
20.0%
2 618648
18.8%
1 526109
16.0%
9 183457
 
5.6%
8 106162
 
3.2%
3 92242
 
2.8%
7 83519
 
2.5%
6 71892
 
2.2%
4 63150
 
1.9%

dateofrealrepmt_138D
Text

MISSING 

Distinct5853
Distinct (%)0.5%
Missing818708
Missing (%)39.4%
Memory size15.9 MiB
2024-02-13T20:42:53.549494image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters12606150
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique160 ?
Unique (%)< 0.1%

Sample

1st row2010-12-20
2nd row2012-03-28
3rd row2014-12-12
4th row2013-07-23
5th row2014-08-17
ValueCountFrequency (%)
2018-08-10 8069
 
0.6%
2011-08-12 7528
 
0.6%
2019-09-17 5081
 
0.4%
2015-06-29 2617
 
0.2%
2008-12-12 2514
 
0.2%
2019-09-16 2336
 
0.2%
2019-09-11 2287
 
0.2%
2012-11-15 2064
 
0.2%
2015-02-23 1999
 
0.2%
2020-06-29 1537
 
0.1%
Other values (5843) 1224583
97.1%
2024-02-13T20:42:54.088263image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3135100
24.9%
- 2521230
20.0%
2 2242586
17.8%
1 2030566
16.1%
9 528271
 
4.2%
8 468363
 
3.7%
7 398041
 
3.2%
6 341287
 
2.7%
3 339519
 
2.7%
5 305431
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10084920
80.0%
Dash Punctuation 2521230
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3135100
31.1%
2 2242586
22.2%
1 2030566
20.1%
9 528271
 
5.2%
8 468363
 
4.6%
7 398041
 
3.9%
6 341287
 
3.4%
3 339519
 
3.4%
5 305431
 
3.0%
4 295756
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 2521230
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12606150
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3135100
24.9%
- 2521230
20.0%
2 2242586
17.8%
1 2030566
16.1%
9 528271
 
4.2%
8 468363
 
3.7%
7 398041
 
3.2%
6 341287
 
2.7%
3 339519
 
2.7%
5 305431
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12606150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3135100
24.9%
- 2521230
20.0%
2 2242586
17.8%
1 2030566
16.1%
9 528271
 
4.2%
8 468363
 
3.7%
7 398041
 
3.2%
6 341287
 
2.7%
3 339519
 
2.7%
5 305431
 
2.4%

debtoutstand_525A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct135690
Distinct (%)76.8%
Missing1902656
Missing (%)91.5%
Infinite0
Infinite (%)0.0%
Mean200873.2273
Minimum0
Maximum322250800
Zeros38186
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:42:54.258080image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15788.7222
median55876.8
Q3178136.92
95-th percentile798171.43
Maximum322250800
Range322250800
Interquartile range (IQR)172348.1978

Descriptive statistics

Standard deviation1356680.774
Coefficient of variation (CV)6.753915353
Kurtosis31188.96299
Mean200873.2273
Median Absolute Deviation (MAD)55876.8
Skewness156.7421448
Sum3.548767045 × 1010
Variance1.840582722 × 1012
MonotonicityNot monotonic
2024-02-13T20:42:54.421072image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38186
 
1.8%
200000 39
 
< 0.1%
100000 33
 
< 0.1%
6000 18
 
< 0.1%
10000 18
 
< 0.1%
40000 17
 
< 0.1%
2000000 16
 
< 0.1%
20000 14
 
< 0.1%
30000 14
 
< 0.1%
4998 13
 
< 0.1%
Other values (135680) 138299
 
6.7%
(Missing) 1902656
91.5%
ValueCountFrequency (%)
0 38186
1.8%
0.002 1
 
< 0.1%
0.004 1
 
< 0.1%
0.076 1
 
< 0.1%
0.120000005 1
 
< 0.1%
ValueCountFrequency (%)
322250800 1
< 0.1%
249184380 1
< 0.1%
241102600 1
< 0.1%
127519784 1
< 0.1%
120040104 1
< 0.1%

debtoverdue_47A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct2381
Distinct (%)1.3%
Missing1902656
Missing (%)91.5%
Infinite0
Infinite (%)0.0%
Mean4165.619355
Minimum0
Maximum522296740
Zeros173753
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:42:54.582220image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum522296740
Range522296740
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1254644.722
Coefficient of variation (CV)301.1904389
Kurtosis170036.8241
Mean4165.619355
Median Absolute Deviation (MAD)0
Skewness409.2294224
Sum735927474.7
Variance1.574133379 × 1012
MonotonicityNot monotonic
2024-02-13T20:42:54.746289image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 173753
 
8.4%
0.2 46
 
< 0.1%
99.8 44
 
< 0.1%
10 23
 
< 0.1%
4 16
 
< 0.1%
0.8 10
 
< 0.1%
14 10
 
< 0.1%
20 9
 
< 0.1%
1 9
 
< 0.1%
0.4 9
 
< 0.1%
Other values (2371) 2738
 
0.1%
(Missing) 1902656
91.5%
ValueCountFrequency (%)
0 173753
8.4%
0.008 1
 
< 0.1%
0.014 1
 
< 0.1%
0.016 1
 
< 0.1%
0.018000001 3
 
< 0.1%
ValueCountFrequency (%)
522296740 1
< 0.1%
66728776 1
< 0.1%
18110382 1
< 0.1%
9986989 2
< 0.1%
9926879 1
< 0.1%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.9 MiB
2024-02-13T20:42:54.923324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters16634584
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 2070514
99.6%
6da7c7ed 2016
 
0.1%
95decc86 1646
 
0.1%
1d89fa48 987
 
< 0.1%
f8e51f8d 930
 
< 0.1%
53179c19 859
 
< 0.1%
18e98e64 841
 
< 0.1%
0349102c 583
 
< 0.1%
8a7423d5 476
 
< 0.1%
0cb4d552 346
 
< 0.1%
Other values (2) 125
 
< 0.1%
2024-02-13T20:42:55.216988image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 6216270
37.4%
7 2075881
 
12.5%
1 2075577
 
12.5%
a 2073995
 
12.5%
4 2073747
 
12.5%
b 2071106
 
12.5%
d 8417
 
0.1%
8 7761
 
< 0.1%
c 7098
 
< 0.1%
e 6274
 
< 0.1%
Other values (6) 18458
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12464476
74.9%
Lowercase Letter 4170108
 
25.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 6216270
49.9%
7 2075881
 
16.7%
1 2075577
 
16.7%
4 2073747
 
16.6%
8 7761
 
0.1%
9 5775
 
< 0.1%
6 4505
 
< 0.1%
3 1920
 
< 0.1%
0 1635
 
< 0.1%
2 1405
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 2073995
49.7%
b 2071106
49.7%
d 8417
 
0.2%
c 7098
 
0.2%
e 6274
 
0.2%
f 3218
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 12464476
74.9%
Latin 4170108
 
25.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 6216270
49.9%
7 2075881
 
16.7%
1 2075577
 
16.7%
4 2073747
 
16.6%
8 7761
 
0.1%
9 5775
 
< 0.1%
6 4505
 
< 0.1%
3 1920
 
< 0.1%
0 1635
 
< 0.1%
2 1405
 
< 0.1%
Latin
ValueCountFrequency (%)
a 2073995
49.7%
b 2071106
49.7%
d 8417
 
0.2%
c 7098
 
0.2%
e 6274
 
0.2%
f 3218
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16634584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 6216270
37.4%
7 2075881
 
12.5%
1 2075577
 
12.5%
a 2073995
 
12.5%
4 2073747
 
12.5%
b 2071106
 
12.5%
d 8417
 
0.1%
8 7761
 
< 0.1%
c 7098
 
< 0.1%
e 6274
 
< 0.1%
Other values (6) 18458
 
0.1%

dpdmax_139P
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct1086
Distinct (%)0.3%
Missing1752846
Missing (%)84.3%
Infinite0
Infinite (%)0.0%
Mean7.172195897
Minimum0
Maximum4520
Zeros255094
Zeros (%)12.3%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:42:55.376031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile21
Maximum4520
Range4520
Interquartile range (IQR)0

Descriptive statistics

Standard deviation82.68204757
Coefficient of variation (CV)11.5281357
Kurtosis936.1476207
Mean7.172195897
Median Absolute Deviation (MAD)0
Skewness27.45490048
Sum2341557
Variance6836.32099
MonotonicityNot monotonic
2024-02-13T20:42:55.536845image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 255094
 
12.3%
1 14313
 
0.7%
2 5728
 
0.3%
3 4182
 
0.2%
4 3819
 
0.2%
5 2465
 
0.1%
7 2364
 
0.1%
8 2353
 
0.1%
6 2124
 
0.1%
10 1933
 
0.1%
Other values (1076) 32102
 
1.5%
(Missing) 1752846
84.3%
ValueCountFrequency (%)
0 255094
12.3%
1 14313
 
0.7%
2 5728
 
0.3%
3 4182
 
0.2%
4 3819
 
0.2%
ValueCountFrequency (%)
4520 1
< 0.1%
4315 1
< 0.1%
4213 1
< 0.1%
4155 1
< 0.1%
4104 1
< 0.1%

dpdmax_757P
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct3624
Distinct (%)0.3%
Missing852736
Missing (%)41.0%
Infinite0
Infinite (%)0.0%
Mean47.34625754
Minimum-30
Maximum144000
Zeros871213
Zeros (%)41.9%
Negative176
Negative (%)< 0.1%
Memory size15.9 MiB
2024-02-13T20:42:55.885962image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-30
5-th percentile0
Q10
median0
Q31
95-th percentile134
Maximum144000
Range144030
Interquartile range (IQR)1

Descriptive statistics

Standard deviation350.0214765
Coefficient of variation (CV)7.392801346
Kurtosis45509.96406
Mean47.34625754
Median Absolute Deviation (MAD)0
Skewness151.1815015
Sum58074304
Variance122515.034
MonotonicityNot monotonic
2024-02-13T20:42:56.065136image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 871213
41.9%
1 60364
 
2.9%
2 20418
 
1.0%
3 19321
 
0.9%
4 15595
 
0.8%
6 11431
 
0.5%
5 10344
 
0.5%
7 10033
 
0.5%
8 7388
 
0.4%
9 7224
 
0.3%
Other values (3614) 193256
 
9.3%
(Missing) 852736
41.0%
ValueCountFrequency (%)
-30 1
< 0.1%
-15 1
< 0.1%
-10 1
< 0.1%
-8 1
< 0.1%
-7 1
< 0.1%
ValueCountFrequency (%)
144000 1
 
< 0.1%
84575 2
< 0.1%
84573 3
< 0.1%
84560 2
< 0.1%
84516 1
 
< 0.1%

dpdmaxdatemonth_442T
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing852736
Missing (%)41.0%
Infinite0
Infinite (%)0.0%
Mean6.563091733
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:42:56.203881image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation3.431591264
Coefficient of variation (CV)0.5228619991
Kurtosis-1.185767989
Mean6.563091733
Median Absolute Deviation (MAD)3
Skewness-0.06446138109
Sum8050203
Variance11.7758186
MonotonicityNot monotonic
2024-02-13T20:42:56.320866image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 118260
 
5.7%
7 108360
 
5.2%
1 107753
 
5.2%
9 105410
 
5.1%
11 104974
 
5.0%
6 104714
 
5.0%
10 103171
 
5.0%
2 97101
 
4.7%
12 96501
 
4.6%
5 94638
 
4.6%
Other values (2) 185705
 
8.9%
(Missing) 852736
41.0%
ValueCountFrequency (%)
1 107753
5.2%
2 97101
4.7%
3 93772
4.5%
4 91933
4.4%
5 94638
4.6%
ValueCountFrequency (%)
12 96501
4.6%
11 104974
5.0%
10 103171
5.0%
9 105410
5.1%
8 118260
5.7%

dpdmaxdatemonth_89T
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing1752846
Missing (%)84.3%
Infinite0
Infinite (%)0.0%
Mean7.116544198
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:42:56.442197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation3.354565669
Coefficient of variation (CV)0.4713756531
Kurtosis-1.101926276
Mean7.116544198
Median Absolute Deviation (MAD)3
Skewness-0.3543096679
Sum2323388
Variance11.25311083
MonotonicityNot monotonic
2024-02-13T20:42:56.558196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 48125
 
2.3%
8 35594
 
1.7%
9 34778
 
1.7%
11 31920
 
1.5%
7 25880
 
1.2%
2 23954
 
1.2%
4 23424
 
1.1%
12 23084
 
1.1%
5 21810
 
1.0%
1 21281
 
1.0%
Other values (2) 36627
 
1.8%
(Missing) 1752846
84.3%
ValueCountFrequency (%)
1 21281
1.0%
2 23954
1.2%
3 18867
0.9%
4 23424
1.1%
5 21810
1.0%
ValueCountFrequency (%)
12 23084
1.1%
11 31920
1.5%
10 48125
2.3%
9 34778
1.7%
8 35594
1.7%

dpdmaxdateyear_596T
Real number (ℝ)

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing1752846
Missing (%)84.3%
Infinite0
Infinite (%)0.0%
Mean2019.136512
Minimum2016
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:42:56.669201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2018
Q12018
median2019
Q32020
95-th percentile2020
Maximum2020
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.8028002384
Coefficient of variation (CV)0.0003975958206
Kurtosis-1.404482214
Mean2019.136512
Median Absolute Deviation (MAD)1
Skewness-0.2518820482
Sum659201631
Variance0.6444882228
MonotonicityNot monotonic
2024-02-13T20:42:56.793063image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
2020 130514
 
6.3%
2019 110032
 
5.3%
2018 85918
 
4.1%
2017 11
 
< 0.1%
2016 2
 
< 0.1%
(Missing) 1752846
84.3%
ValueCountFrequency (%)
2016 2
 
< 0.1%
2017 11
 
< 0.1%
2018 85918
4.1%
2019 110032
5.3%
2020 130514
6.3%
ValueCountFrequency (%)
2020 130514
6.3%
2019 110032
5.3%
2018 85918
4.1%
2017 11
 
< 0.1%
2016 2
 
< 0.1%

dpdmaxdateyear_896T
Real number (ℝ)

MISSING 

Distinct18
Distinct (%)< 0.1%
Missing852736
Missing (%)41.0%
Infinite0
Infinite (%)0.0%
Mean2015.211785
Minimum2003
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:42:56.926088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2003
5-th percentile2007
Q12013
median2017
Q32018
95-th percentile2020
Maximum2020
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.902971874
Coefficient of variation (CV)0.001936755185
Kurtosis-0.305407029
Mean2015.211785
Median Absolute Deviation (MAD)2
Skewness-0.8646412722
Sum2471832578
Variance15.23318945
MonotonicityNot monotonic
2024-02-13T20:42:57.058677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2018 207122
 
10.0%
2019 202933
 
9.8%
2017 135534
 
6.5%
2016 96205
 
4.6%
2015 85734
 
4.1%
2014 75846
 
3.6%
2020 67786
 
3.3%
2013 66058
 
3.2%
2012 55903
 
2.7%
2007 50301
 
2.4%
Other values (8) 183165
 
8.8%
(Missing) 852736
41.0%
ValueCountFrequency (%)
2003 2
 
< 0.1%
2004 287
 
< 0.1%
2005 6237
 
0.3%
2006 25858
1.2%
2007 50301
2.4%
ValueCountFrequency (%)
2020 67786
 
3.3%
2019 202933
9.8%
2018 207122
10.0%
2017 135534
6.5%
2016 96205
4.6%
Distinct260
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.9 MiB
2024-02-13T20:42:57.480839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length9.223959914
Min length8

Characters and Unicode

Total characters19179592
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

Unique36 ?
Unique (%)< 0.1%

Sample

1st rowa55475b1
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 812672
32.5%
home 418400
16.8%
credit 418400
16.8%
p133_127_114 150629
 
6.0%
b619fa46 141264
 
5.7%
p150_136_157 124842
 
5.0%
p40_52_135 58649
 
2.3%
d6a7d943 54295
 
2.2%
9a93e20f 46194
 
1.8%
50babcd4 30381
 
1.2%
Other values (251) 241997
 
9.7%
2024-02-13T20:42:58.093810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 3054314
15.9%
1 2108175
 
11.0%
4 1326733
 
6.9%
7 1200177
 
6.3%
a 1188918
 
6.2%
b 1123065
 
5.9%
e 932996
 
4.9%
_ 758818
 
4.0%
3 726825
 
3.8%
d 688402
 
3.6%
Other values (16) 6071169
31.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10306334
53.7%
Lowercase Letter 6479831
33.8%
Uppercase Letter 1216209
 
6.3%
Connector Punctuation 758818
 
4.0%
Space Separator 418400
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1188918
18.3%
b 1123065
17.3%
e 932996
14.4%
d 688402
10.6%
r 418400
 
6.5%
m 418400
 
6.5%
i 418400
 
6.5%
t 418400
 
6.5%
o 418400
 
6.5%
f 285335
 
4.4%
Decimal Number
ValueCountFrequency (%)
5 3054314
29.6%
1 2108175
20.5%
4 1326733
12.9%
7 1200177
 
11.6%
3 726825
 
7.1%
6 552132
 
5.4%
2 431933
 
4.2%
9 418519
 
4.1%
0 396282
 
3.8%
8 91244
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
C 418400
34.4%
H 418400
34.4%
P 379409
31.2%
Connector Punctuation
ValueCountFrequency (%)
_ 758818
100.0%
Space Separator
ValueCountFrequency (%)
418400
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11483552
59.9%
Latin 7696040
40.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1188918
15.4%
b 1123065
14.6%
e 932996
12.1%
d 688402
8.9%
C 418400
 
5.4%
r 418400
 
5.4%
m 418400
 
5.4%
i 418400
 
5.4%
t 418400
 
5.4%
o 418400
 
5.4%
Other values (4) 1252259
16.3%
Common
ValueCountFrequency (%)
5 3054314
26.6%
1 2108175
18.4%
4 1326733
11.6%
7 1200177
 
10.5%
_ 758818
 
6.6%
3 726825
 
6.3%
6 552132
 
4.8%
2 431933
 
3.8%
9 418519
 
3.6%
418400
 
3.6%
Other values (2) 487526
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19179592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 3054314
15.9%
1 2108175
 
11.0%
4 1326733
 
6.9%
7 1200177
 
6.3%
a 1188918
 
6.2%
b 1123065
 
5.9%
e 932996
 
4.9%
_ 758818
 
4.0%
3 726825
 
3.8%
d 688402
 
3.6%
Other values (16) 6071169
31.7%
Distinct152
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.9 MiB
2024-02-13T20:42:58.292880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.264762137
Min length8

Characters and Unicode

Total characters17185110
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

Unique27 ?
Unique (%)< 0.1%

Sample

1st rowb619fa46
2nd rowb619fa46
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 1750967
80.8%
b619fa46 117173
 
5.4%
home 88895
 
4.1%
credit 88895
 
4.1%
p133_127_114 33028
 
1.5%
p150_136_157 30987
 
1.4%
50babcd4 11660
 
0.5%
d6a7d943 9382
 
0.4%
p102_97_118 5579
 
0.3%
p51_123_23 5522
 
0.3%
Other values (143) 26130
 
1.2%
2024-02-13T20:42:58.624824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 5351591
31.1%
1 2136345
 
12.4%
4 1925805
 
11.2%
b 1906159
 
11.1%
a 1904574
 
11.1%
7 1834894
 
10.7%
6 290492
 
1.7%
e 181994
 
1.1%
_ 150232
 
0.9%
9 141654
 
0.8%
Other values (16) 1361370
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11966694
69.6%
Lowercase Letter 4726383
 
27.5%
Uppercase Letter 252906
 
1.5%
Connector Punctuation 150232
 
0.9%
Space Separator 88895
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 1906159
40.3%
a 1904574
40.3%
e 181994
 
3.9%
d 133668
 
2.8%
f 125645
 
2.7%
t 88895
 
1.9%
i 88895
 
1.9%
r 88895
 
1.9%
m 88895
 
1.9%
o 88895
 
1.9%
Decimal Number
ValueCountFrequency (%)
5 5351591
44.7%
1 2136345
 
17.9%
4 1925805
 
16.1%
7 1834894
 
15.3%
6 290492
 
2.4%
9 141654
 
1.2%
3 135172
 
1.1%
2 73952
 
0.6%
0 59977
 
0.5%
8 16812
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
C 88895
35.1%
H 88895
35.1%
P 75116
29.7%
Connector Punctuation
ValueCountFrequency (%)
_ 150232
100.0%
Space Separator
ValueCountFrequency (%)
88895
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12205821
71.0%
Latin 4979289
29.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 1906159
38.3%
a 1904574
38.2%
e 181994
 
3.7%
d 133668
 
2.7%
f 125645
 
2.5%
t 88895
 
1.8%
i 88895
 
1.8%
r 88895
 
1.8%
C 88895
 
1.8%
m 88895
 
1.8%
Other values (4) 282774
 
5.7%
Common
ValueCountFrequency (%)
5 5351591
43.8%
1 2136345
 
17.5%
4 1925805
 
15.8%
7 1834894
 
15.0%
6 290492
 
2.4%
_ 150232
 
1.2%
9 141654
 
1.2%
3 135172
 
1.1%
88895
 
0.7%
2 73952
 
0.6%
Other values (2) 76789
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17185110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 5351591
31.1%
1 2136345
 
12.4%
4 1925805
 
11.2%
b 1906159
 
11.1%
a 1904574
 
11.1%
7 1834894
 
10.7%
6 290492
 
1.7%
e 181994
 
1.1%
_ 150232
 
0.9%
9 141654
 
0.8%
Other values (16) 1361370
 
7.9%

instlamount_768A
Real number (ℝ)

MISSING  ZEROS 

Distinct50058
Distinct (%)32.9%
Missing1926991
Missing (%)92.7%
Infinite0
Infinite (%)0.0%
Mean3514.428461
Minimum0
Maximum394961.1
Zeros67080
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:42:58.787201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median999.86802
Q35305.0708
95-th percentile13915.374
Maximum394961.1
Range394961.1
Interquartile range (IQR)5305.0708

Descriptive statistics

Standard deviation5516.959352
Coefficient of variation (CV)1.569802718
Kurtosis182.0443699
Mean3514.428461
Median Absolute Deviation (MAD)999.86802
Skewness5.095698418
Sum535359916.4
Variance30436840.5
MonotonicityNot monotonic
2024-02-13T20:42:58.964044image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67080
 
3.2%
400 656
 
< 0.1%
9068 608
 
< 0.1%
600 497
 
< 0.1%
10230 337
 
< 0.1%
12772.4 305
 
< 0.1%
2389.6 305
 
< 0.1%
1000 267
 
< 0.1%
6386.4 200
 
< 0.1%
3127.8 188
 
< 0.1%
Other values (50048) 81889
 
3.9%
(Missing) 1926991
92.7%
ValueCountFrequency (%)
0 67080
3.2%
0.004 1
 
< 0.1%
0.008 1
 
< 0.1%
0.024 1
 
< 0.1%
0.076 1
 
< 0.1%
ValueCountFrequency (%)
394961.1 1
< 0.1%
130628.43 1
< 0.1%
103774.27 1
< 0.1%
86276.5 1
< 0.1%
83947.55 1
< 0.1%

instlamount_852A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct22791
Distinct (%)22.3%
Missing1977007
Missing (%)95.1%
Infinite0
Infinite (%)0.0%
Mean727.7284524
Minimum0
Maximum285583.62
Zeros63075
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:42:59.124526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3420
95-th percentile3937.60965
Maximum285583.62
Range285583.62
Interquartile range (IQR)420

Descriptive statistics

Standard deviation2331.102068
Coefficient of variation (CV)3.203258112
Kurtosis2801.847309
Mean727.7284524
Median Absolute Deviation (MAD)0
Skewness31.69000589
Sum74458264.33
Variance5434036.853
MonotonicityNot monotonic
2024-02-13T20:42:59.277525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63075
 
3.0%
400 4278
 
0.2%
1000 2166
 
0.1%
1200 599
 
< 0.1%
2000 594
 
< 0.1%
3000 412
 
< 0.1%
1540 403
 
< 0.1%
2260 362
 
< 0.1%
800 326
 
< 0.1%
3700 282
 
< 0.1%
Other values (22781) 29819
 
1.4%
(Missing) 1977007
95.1%
ValueCountFrequency (%)
0 63075
3.0%
0.002 5
 
< 0.1%
0.004 2
 
< 0.1%
0.006 8
 
< 0.1%
0.008 3
 
< 0.1%
ValueCountFrequency (%)
285583.62 1
< 0.1%
174457.98 1
< 0.1%
141503.19 1
< 0.1%
97266.734 2
< 0.1%
90555.96 1
< 0.1%

interestrate_508L
Real number (ℝ)

MISSING  SKEWED 

Distinct142
Distinct (%)1.4%
Missing2068990
Missing (%)99.5%
Infinite0
Infinite (%)0.0%
Mean94.12153876
Minimum0
Maximum53856
Zeros117
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:42:59.439524image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.5
Q118
median21
Q324
95-th percentile35
Maximum53856
Range53856
Interquartile range (IQR)6

Descriptive statistics

Standard deviation1162.872482
Coefficient of variation (CV)12.35500925
Kurtosis950.7793234
Mean94.12153876
Median Absolute Deviation (MAD)3
Skewness26.1670526
Sum972557.86
Variance1352272.409
MonotonicityNot monotonic
2024-02-13T20:42:59.600561image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 1858
 
0.1%
24 1354
 
0.1%
22 692
 
< 0.1%
20 621
 
< 0.1%
35 543
 
< 0.1%
18 519
 
< 0.1%
17 476
 
< 0.1%
16 401
 
< 0.1%
25 392
 
< 0.1%
26 371
 
< 0.1%
Other values (132) 3106
 
0.1%
(Missing) 2068990
99.5%
ValueCountFrequency (%)
0 117
< 0.1%
4 2
 
< 0.1%
5 7
 
< 0.1%
5.3 2
 
< 0.1%
5.5 6
 
< 0.1%
ValueCountFrequency (%)
53856 1
 
< 0.1%
52368 1
 
< 0.1%
25299 2
< 0.1%
19027 1
 
< 0.1%
18000 3
< 0.1%

lastupdate_1112D
Text

MISSING 

Distinct202
Distinct (%)0.1%
Missing1750967
Missing (%)84.2%
Memory size15.9 MiB
2024-02-13T20:42:59.941375image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3283560
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

Unique44 ?
Unique (%)< 0.1%

Sample

1st row2020-06-13
2nd row2020-06-17
3rd row2020-06-04
4th row2020-06-17
5th row2020-06-04
ValueCountFrequency (%)
2020-08-27 22820
 
6.9%
2020-09-09 18576
 
5.7%
2020-09-30 18034
 
5.5%
2020-09-12 17981
 
5.5%
2020-06-18 17074
 
5.2%
2020-09-24 16935
 
5.2%
2020-08-26 15457
 
4.7%
2020-06-17 13633
 
4.2%
2020-08-11 13159
 
4.0%
2020-07-01 10995
 
3.3%
Other values (192) 163692
49.9%
2024-02-13T20:43:00.465024image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1104888
33.6%
2 797641
24.3%
- 656712
20.0%
1 166611
 
5.1%
9 148321
 
4.5%
8 123181
 
3.8%
7 109659
 
3.3%
6 82049
 
2.5%
3 53810
 
1.6%
4 27282
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2626848
80.0%
Dash Punctuation 656712
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1104888
42.1%
2 797641
30.4%
1 166611
 
6.3%
9 148321
 
5.6%
8 123181
 
4.7%
7 109659
 
4.2%
6 82049
 
3.1%
3 53810
 
2.0%
4 27282
 
1.0%
5 13406
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 656712
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3283560
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1104888
33.6%
2 797641
24.3%
- 656712
20.0%
1 166611
 
5.1%
9 148321
 
4.5%
8 123181
 
3.8%
7 109659
 
3.3%
6 82049
 
2.5%
3 53810
 
1.6%
4 27282
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3283560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1104888
33.6%
2 797641
24.3%
- 656712
20.0%
1 166611
 
5.1%
9 148321
 
4.5%
8 123181
 
3.8%
7 109659
 
3.3%
6 82049
 
2.5%
3 53810
 
1.6%
4 27282
 
0.8%

lastupdate_388D
Text

MISSING 

Distinct4908
Distinct (%)0.4%
Missing812710
Missing (%)39.1%
Memory size15.9 MiB
2024-02-13T20:43:00.842970image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters12666130
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

Unique212 ?
Unique (%)< 0.1%

Sample

1st row2011-01-03
2nd row2012-04-13
3rd row2014-12-13
4th row2016-07-24
5th row2015-01-16
ValueCountFrequency (%)
2007-09-25 16436
 
1.3%
2008-06-13 9665
 
0.8%
2020-06-09 9251
 
0.7%
2015-04-10 8106
 
0.6%
2008-11-12 7566
 
0.6%
2013-03-07 6283
 
0.5%
2018-12-28 6244
 
0.5%
2013-10-02 5869
 
0.5%
2018-08-10 5129
 
0.4%
2018-12-31 4666
 
0.4%
Other values (4898) 1187398
93.7%
2024-02-13T20:43:01.341762image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3169050
25.0%
- 2533226
20.0%
2 2227408
17.6%
1 2053226
16.2%
9 513133
 
4.1%
8 458318
 
3.6%
7 384505
 
3.0%
6 367694
 
2.9%
3 345434
 
2.7%
5 324965
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10132904
80.0%
Dash Punctuation 2533226
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3169050
31.3%
2 2227408
22.0%
1 2053226
20.3%
9 513133
 
5.1%
8 458318
 
4.5%
7 384505
 
3.8%
6 367694
 
3.6%
3 345434
 
3.4%
5 324965
 
3.2%
4 289171
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 2533226
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12666130
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3169050
25.0%
- 2533226
20.0%
2 2227408
17.6%
1 2053226
16.2%
9 513133
 
4.1%
8 458318
 
3.6%
7 384505
 
3.0%
6 367694
 
2.9%
3 345434
 
2.7%
5 324965
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12666130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3169050
25.0%
- 2533226
20.0%
2 2227408
17.6%
1 2053226
16.2%
9 513133
 
4.1%
8 458318
 
3.6%
7 384505
 
3.0%
6 367694
 
2.9%
3 345434
 
2.7%
5 324965
 
2.6%

monthlyinstlamount_332A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct118374
Distinct (%)36.3%
Missing1753025
Missing (%)84.3%
Infinite0
Infinite (%)0.0%
Mean5648.897974
Minimum0
Maximum10406394
Zeros67678
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:01.527478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11012.2
median3335.4001
Q36757.15
95-th percentile15912.358
Maximum10406394
Range10406394
Interquartile range (IQR)5744.95

Descriptive statistics

Standard deviation46818.93197
Coefficient of variation (CV)8.288153228
Kurtosis34108.90373
Mean5648.897974
Median Absolute Deviation (MAD)2895.4002
Skewness168.4209434
Sum1843224111
Variance2192012391
MonotonicityNot monotonic
2024-02-13T20:43:01.693362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67678
 
3.3%
400 657
 
< 0.1%
9068 608
 
< 0.1%
600 501
 
< 0.1%
10230 337
 
< 0.1%
1000 320
 
< 0.1%
2389.6 311
 
< 0.1%
12772.4 305
 
< 0.1%
2100.246 304
 
< 0.1%
4200.492 291
 
< 0.1%
Other values (118364) 254986
 
12.3%
(Missing) 1753025
84.3%
ValueCountFrequency (%)
0 67678
3.3%
0.002 1
 
< 0.1%
0.004 2
 
< 0.1%
0.008 1
 
< 0.1%
0.024 1
 
< 0.1%
ValueCountFrequency (%)
10406394 1
 
< 0.1%
10166277 3
< 0.1%
9326755 1
 
< 0.1%
6082167 1
 
< 0.1%
3848977.8 1
 
< 0.1%

monthlyinstlamount_674A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct273080
Distinct (%)22.9%
Missing888003
Missing (%)42.7%
Infinite0
Infinite (%)0.0%
Mean7729.342044
Minimum0
Maximum58409576
Zeros528046
Zeros (%)25.4%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:01.849469image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1262.4
Q34400
95-th percentile24208.2057
Maximum58409576
Range58409576
Interquartile range (IQR)4400

Descriptive statistics

Standard deviation126166.4556
Coefficient of variation (CV)16.32305245
Kurtosis74229.66087
Mean7729.342044
Median Absolute Deviation (MAD)1262.4
Skewness220.9307649
Sum9208119764
Variance1.591797452 × 1010
MonotonicityNot monotonic
2024-02-13T20:43:02.008491image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 528046
25.4%
400 4288
 
0.2%
1000 2247
 
0.1%
4200 1185
 
0.1%
2100 1179
 
0.1%
3150 1109
 
0.1%
2114 1084
 
0.1%
2000 1043
 
0.1%
4228 1032
 
< 0.1%
4000 979
 
< 0.1%
Other values (273070) 649128
31.2%
(Missing) 888003
42.7%
ValueCountFrequency (%)
0 528046
25.4%
0.002 32
 
< 0.1%
0.004 11
 
< 0.1%
0.006 17
 
< 0.1%
0.008 19
 
< 0.1%
ValueCountFrequency (%)
58409576 1
< 0.1%
45180184 1
< 0.1%
40719430 1
< 0.1%
34831024 1
< 0.1%
30224384 1
< 0.1%

nominalrate_281L
Real number (ℝ)

MISSING  SKEWED 

Distinct544
Distinct (%)0.4%
Missing1953359
Missing (%)93.9%
Infinite0
Infinite (%)0.0%
Mean58.4855758
Minimum0
Maximum37133.4
Zeros17525
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:02.163333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118.1
median39
Q342
95-th percentile45
Maximum37133.4
Range37133.4
Interquartile range (IQR)23.9

Descriptive statistics

Standard deviation629.0540305
Coefficient of variation (CV)10.75571236
Kurtosis2415.270914
Mean58.4855758
Median Absolute Deviation (MAD)6
Skewness45.98914381
Sum7367077.07
Variance395708.9733
MonotonicityNot monotonic
2024-02-13T20:43:02.324520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 20758
 
1.0%
45 19982
 
1.0%
0 17525
 
0.8%
39 14447
 
0.7%
0.12 7599
 
0.4%
40 5700
 
0.3%
43 3501
 
0.2%
41.75 2073
 
0.1%
40.05 1516
 
0.1%
30 1434
 
0.1%
Other values (534) 31429
 
1.5%
(Missing) 1953359
93.9%
ValueCountFrequency (%)
0 17525
0.8%
0.01 26
 
< 0.1%
0.12 7599
0.4%
0.5 158
 
< 0.1%
0.61 1
 
< 0.1%
ValueCountFrequency (%)
37133.4 14
< 0.1%
30341.1 25
< 0.1%
11907.9 4
 
< 0.1%
9089.9 10
 
< 0.1%
6200 5
 
< 0.1%

nominalrate_498L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct1535
Distinct (%)0.3%
Missing1596430
Missing (%)76.8%
Infinite0
Infinite (%)0.0%
Mean193.1688011
Minimum0
Maximum59140.9
Zeros29774
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:02.519579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q125.64
median42.5
Q345
95-th percentile365
Maximum59140.9
Range59140.9
Interquartile range (IQR)19.36

Descriptive statistics

Standard deviation1762.551522
Coefficient of variation (CV)9.124410939
Kurtosis649.1796246
Mean193.1688011
Median Absolute Deviation (MAD)2.5
Skewness23.41495544
Sum93279861.86
Variance3106587.867
MonotonicityNot monotonic
2024-02-13T20:43:02.669425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 158909
 
7.6%
0.12 54561
 
2.6%
42 30233
 
1.5%
0 29774
 
1.4%
96.3 18845
 
0.9%
40 12485
 
0.6%
43.3 11714
 
0.6%
39 11638
 
0.6%
40.05 9323
 
0.4%
43 8407
 
0.4%
Other values (1525) 137004
 
6.6%
(Missing) 1596430
76.8%
ValueCountFrequency (%)
0 29774
1.4%
0.01 10
 
< 0.1%
0.1 24
 
< 0.1%
0.12 54561
2.6%
0.14 2
 
< 0.1%
ValueCountFrequency (%)
59140.9 175
< 0.1%
46334.2 1
 
< 0.1%
46334.1 52
 
< 0.1%
46333.9 2
 
< 0.1%
46333.8 1
 
< 0.1%

num_group1
Real number (ℝ)

ZEROS 

Distinct203
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.822535989
Minimum0
Maximum202
Zeros176608
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:02.827133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile19
Maximum202
Range202
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.293182639
Coefficient of variation (CV)1.068984121
Kurtosis39.54402562
Mean6.822535989
Median Absolute Deviation (MAD)3
Skewness4.232327133
Sum14186256
Variance53.19051301
MonotonicityNot monotonic
2024-02-13T20:43:02.989584image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 176608
8.5%
1 176608
8.5%
2 176608
8.5%
3 176608
8.5%
4 176608
8.5%
5 176608
8.5%
6 176608
8.5%
7 176608
8.5%
8 176608
8.5%
9 70499
 
3.4%
Other values (193) 419352
20.2%
ValueCountFrequency (%)
0 176608
8.5%
1 176608
8.5%
2 176608
8.5%
3 176608
8.5%
4 176608
8.5%
ValueCountFrequency (%)
202 1
< 0.1%
201 1
< 0.1%
200 1
< 0.1%
199 1
< 0.1%
198 1
< 0.1%

numberofcontrsvalue_258L
Real number (ℝ)

MISSING 

Distinct18
Distinct (%)< 0.1%
Missing1921187
Missing (%)92.4%
Infinite0
Infinite (%)0.0%
Mean2.064178935
Minimum0
Maximum99
Zeros371
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:03.133548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum99
Range99
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.103112318
Coefficient of variation (CV)0.5344073132
Kurtosis413.9707396
Mean2.064178935
Median Absolute Deviation (MAD)1
Skewness5.862795421
Sum326421
Variance1.216856787
MonotonicityNot monotonic
2024-02-13T20:43:03.265637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 58262
 
2.8%
2 51306
 
2.5%
3 31260
 
1.5%
4 13778
 
0.7%
5 2696
 
0.1%
0 371
 
< 0.1%
6 351
 
< 0.1%
7 57
 
< 0.1%
8 17
 
< 0.1%
9 13
 
< 0.1%
Other values (8) 25
 
< 0.1%
(Missing) 1921187
92.4%
ValueCountFrequency (%)
0 371
 
< 0.1%
1 58262
2.8%
2 51306
2.5%
3 31260
1.5%
4 13778
 
0.7%
ValueCountFrequency (%)
99 1
 
< 0.1%
56 1
 
< 0.1%
16 1
 
< 0.1%
14 1
 
< 0.1%
13 3
< 0.1%

numberofcontrsvalue_358L
Real number (ℝ)

MISSING 

Distinct113
Distinct (%)0.1%
Missing1905185
Missing (%)91.6%
Infinite0
Infinite (%)0.0%
Mean7.269516131
Minimum0
Maximum198
Zeros69
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:03.412610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q310
95-th percentile19
Maximum198
Range198
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.810659766
Coefficient of variation (CV)0.9368793801
Kurtosis25.00372252
Mean7.269516131
Median Absolute Deviation (MAD)3
Skewness3.181462851
Sum1265899
Variance46.38508645
MonotonicityNot monotonic
2024-02-13T20:43:03.593380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 22408
 
1.1%
2 18354
 
0.9%
3 16945
 
0.8%
4 15707
 
0.8%
5 13962
 
0.7%
6 12415
 
0.6%
7 11174
 
0.5%
8 9220
 
0.4%
9 8335
 
0.4%
10 7224
 
0.3%
Other values (103) 38394
 
1.8%
(Missing) 1905185
91.6%
ValueCountFrequency (%)
0 69
 
< 0.1%
1 22408
1.1%
2 18354
0.9%
3 16945
0.8%
4 15707
0.8%
ValueCountFrequency (%)
198 1
< 0.1%
153 1
< 0.1%
149 1
< 0.1%
142 1
< 0.1%
121 1
< 0.1%

numberofinstls_229L
Real number (ℝ)

MISSING  ZEROS 

Distinct320
Distinct (%)< 0.1%
Missing965603
Missing (%)46.4%
Infinite0
Infinite (%)0.0%
Mean11.38378318
Minimum0
Maximum600
Zeros215858
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:03.760162image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q312
95-th percentile36
Maximum600
Range600
Interquartile range (IQR)11

Descriptive statistics

Standard deviation17.88047601
Coefficient of variation (CV)1.570697169
Kurtosis86.28426755
Mean11.38378318
Median Absolute Deviation (MAD)6
Skewness6.971355652
Sum12678347
Variance319.7114225
MonotonicityNot monotonic
2024-02-13T20:43:03.916663image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 215858
 
10.4%
12 187514
 
9.0%
1 130500
 
6.3%
6 117648
 
5.7%
24 65546
 
3.2%
3 45391
 
2.2%
18 42735
 
2.1%
36 33800
 
1.6%
10 27797
 
1.3%
4 22307
 
1.1%
Other values (310) 224624
 
10.8%
(Missing) 965603
46.4%
ValueCountFrequency (%)
0 215858
10.4%
1 130500
6.3%
2 7607
 
0.4%
3 45391
 
2.2%
4 22307
 
1.1%
ValueCountFrequency (%)
600 1
 
< 0.1%
498 1
 
< 0.1%
488 1
 
< 0.1%
484 1
 
< 0.1%
482 5
< 0.1%

numberofinstls_320L
Real number (ℝ)

MISSING 

Distinct287
Distinct (%)0.2%
Missing1905363
Missing (%)91.6%
Infinite0
Infinite (%)0.0%
Mean32.4540584
Minimum0
Maximum362
Zeros30
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:04.071053image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q112
median24
Q339
95-th percentile75
Maximum362
Range362
Interquartile range (IQR)27

Descriptive statistics

Standard deviation33.6640834
Coefficient of variation (CV)1.037284243
Kurtosis20.42757174
Mean32.4540584
Median Absolute Deviation (MAD)12
Skewness3.876989753
Sum5645708
Variance1133.270511
MonotonicityNot monotonic
2024-02-13T20:43:04.230035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 34107
 
1.6%
24 25125
 
1.2%
36 15892
 
0.8%
48 11150
 
0.5%
18 10253
 
0.5%
60 8425
 
0.4%
6 6837
 
0.3%
16 5957
 
0.3%
30 4252
 
0.2%
1 2778
 
0.1%
Other values (277) 49184
 
2.4%
(Missing) 1905363
91.6%
ValueCountFrequency (%)
0 30
 
< 0.1%
1 2778
0.1%
2 10
 
< 0.1%
3 1548
0.1%
4 816
 
< 0.1%
ValueCountFrequency (%)
362 1
< 0.1%
354 1
< 0.1%
344 1
< 0.1%
338 1
< 0.1%
315 1
< 0.1%

numberofoutstandinstls_520L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct174
Distinct (%)< 0.1%
Missing964922
Missing (%)46.4%
Infinite0
Infinite (%)0.0%
Mean0.05953871183
Minimum0
Maximum525
Zeros1112968
Zeros (%)53.5%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:04.389016image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation3.202720264
Coefficient of variation (CV)53.79223309
Kurtosis11196.75792
Mean0.05953871183
Median Absolute Deviation (MAD)0
Skewness95.13283139
Sum66350
Variance10.25741709
MonotonicityNot monotonic
2024-02-13T20:43:04.562921image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1112968
53.5%
1 145
 
< 0.1%
2 85
 
< 0.1%
4 68
 
< 0.1%
20 46
 
< 0.1%
16 43
 
< 0.1%
12 43
 
< 0.1%
10 42
 
< 0.1%
8 41
 
< 0.1%
6 40
 
< 0.1%
Other values (164) 880
 
< 0.1%
(Missing) 964922
46.4%
ValueCountFrequency (%)
0 1112968
53.5%
1 145
 
< 0.1%
2 85
 
< 0.1%
3 26
 
< 0.1%
4 68
 
< 0.1%
ValueCountFrequency (%)
525 1
 
< 0.1%
512 1
 
< 0.1%
468 1
 
< 0.1%
466 2
< 0.1%
464 3
< 0.1%

numberofoutstandinstls_59L
Real number (ℝ)

MISSING 

Distinct290
Distinct (%)0.2%
Missing1905355
Missing (%)91.6%
Infinite0
Infinite (%)0.0%
Mean20.57792238
Minimum0
Maximum301
Zeros1860
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:04.740414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median12
Q325
95-th percentile59
Maximum301
Range301
Interquartile range (IQR)20

Descriptive statistics

Standard deviation29.12099854
Coefficient of variation (CV)1.415157372
Kurtosis27.54700772
Mean20.57792238
Median Absolute Deviation (MAD)8
Skewness4.465840128
Sum3579900
Variance848.0325561
MonotonicityNot monotonic
2024-02-13T20:43:04.895953image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 10959
 
0.5%
2 8519
 
0.4%
3 8316
 
0.4%
5 8316
 
0.4%
4 8244
 
0.4%
6 7813
 
0.4%
7 7023
 
0.3%
10 6343
 
0.3%
12 6002
 
0.3%
9 5907
 
0.3%
Other values (280) 96526
 
4.6%
(Missing) 1905355
91.6%
ValueCountFrequency (%)
0 1860
 
0.1%
1 10959
0.5%
2 8519
0.4%
3 8316
0.4%
4 8244
0.4%
ValueCountFrequency (%)
301 1
 
< 0.1%
300 21
< 0.1%
299 39
< 0.1%
298 28
< 0.1%
297 19
< 0.1%

numberofoverdueinstlmax_1039L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct1238
Distinct (%)0.4%
Missing1750967
Missing (%)84.2%
Infinite0
Infinite (%)0.0%
Mean10.33044013
Minimum0
Maximum40121
Zeros239494
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:05.051855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile30
Maximum40121
Range40121
Interquartile range (IQR)1

Descriptive statistics

Standard deviation120.9371059
Coefficient of variation (CV)11.70686866
Kurtosis37186.80835
Mean10.33044013
Median Absolute Deviation (MAD)0
Skewness124.4609752
Sum3392062
Variance14625.78357
MonotonicityNot monotonic
2024-02-13T20:43:05.220569image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 239494
 
11.5%
1 15816
 
0.8%
2 6450
 
0.3%
4 4455
 
0.2%
3 4384
 
0.2%
5 3817
 
0.2%
8 2576
 
0.1%
7 2249
 
0.1%
9 2243
 
0.1%
11 2131
 
0.1%
Other values (1228) 44741
 
2.2%
(Missing) 1750967
84.2%
ValueCountFrequency (%)
0 239494
11.5%
1 15816
 
0.8%
2 6450
 
0.3%
3 4384
 
0.2%
4 4455
 
0.2%
ValueCountFrequency (%)
40121 1
< 0.1%
5022 1
< 0.1%
4794 1
< 0.1%
4681 1
< 0.1%
4616 1
< 0.1%

numberofoverdueinstlmax_1151L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct3963
Distinct (%)0.3%
Missing812672
Missing (%)39.1%
Infinite0
Infinite (%)0.0%
Mean54.39403908
Minimum0
Maximum260000
Zeros888918
Zeros (%)42.8%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:05.389336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile159
Maximum260000
Range260000
Interquartile range (IQR)1

Descriptive statistics

Standard deviation461.8378486
Coefficient of variation (CV)8.490596698
Kurtosis102789.9843
Mean54.39403908
Median Absolute Deviation (MAD)0
Skewness235.4689954
Sum68898264
Variance213294.1984
MonotonicityNot monotonic
2024-02-13T20:43:05.556070image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 888918
42.8%
1 64846
 
3.1%
2 20132
 
1.0%
4 18338
 
0.9%
3 18234
 
0.9%
7 11855
 
0.6%
5 10689
 
0.5%
6 9006
 
0.4%
8 8428
 
0.4%
9 6597
 
0.3%
Other values (3953) 209608
 
10.1%
(Missing) 812672
39.1%
ValueCountFrequency (%)
0 888918
42.8%
1 64846
 
3.1%
2 20132
 
1.0%
3 18234
 
0.9%
4 18338
 
0.9%
ValueCountFrequency (%)
260000 1
 
< 0.1%
160000 1
 
< 0.1%
93972 2
< 0.1%
93971 2
< 0.1%
93970 3
< 0.1%
Distinct4670
Distinct (%)1.2%
Missing1701590
Missing (%)81.8%
Memory size15.9 MiB
2024-02-13T20:43:05.924234image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3777330
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

Unique330 ?
Unique (%)0.1%

Sample

1st row2009-04-30
2nd row2011-05-02
3rd row2014-06-21
4th row2019-05-09
5th row2015-06-26
ValueCountFrequency (%)
2007-07-31 13341
 
3.5%
2011-08-24 4016
 
1.1%
2007-07-05 2844
 
0.8%
2012-03-04 2540
 
0.7%
2008-10-15 2307
 
0.6%
2011-09-04 1929
 
0.5%
2010-01-07 1308
 
0.3%
2018-09-17 1301
 
0.3%
2019-12-12 1227
 
0.3%
2018-12-20 1208
 
0.3%
Other values (4660) 345712
91.5%
2024-02-13T20:43:06.653061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 933024
24.7%
- 755466
20.0%
1 638933
16.9%
2 623117
16.5%
7 136641
 
3.6%
9 133651
 
3.5%
8 132286
 
3.5%
5 108165
 
2.9%
3 107712
 
2.9%
4 104524
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3021864
80.0%
Dash Punctuation 755466
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 933024
30.9%
1 638933
21.1%
2 623117
20.6%
7 136641
 
4.5%
9 133651
 
4.4%
8 132286
 
4.4%
5 108165
 
3.6%
3 107712
 
3.6%
4 104524
 
3.5%
6 103811
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 755466
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3777330
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 933024
24.7%
- 755466
20.0%
1 638933
16.9%
2 623117
16.5%
7 136641
 
3.6%
9 133651
 
3.5%
8 132286
 
3.5%
5 108165
 
2.9%
3 107712
 
2.9%
4 104524
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3777330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 933024
24.7%
- 755466
20.0%
1 638933
16.9%
2 623117
16.5%
7 136641
 
3.6%
9 133651
 
3.5%
8 132286
 
3.5%
5 108165
 
2.9%
3 107712
 
2.9%
4 104524
 
2.8%
Distinct1619
Distinct (%)1.8%
Missing1990461
Missing (%)95.7%
Memory size15.9 MiB
2024-02-13T20:43:07.083294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters888620
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

Unique442 ?
Unique (%)0.5%

Sample

1st row2019-08-07
2nd row2020-04-09
3rd row2019-04-23
4th row2020-05-27
5th row2020-02-26
ValueCountFrequency (%)
2020-05-08 1775
 
2.0%
2020-04-20 1517
 
1.7%
2020-05-20 1317
 
1.5%
2020-05-05 1226
 
1.4%
2020-08-11 1058
 
1.2%
2020-06-18 1047
 
1.2%
2020-06-01 972
 
1.1%
2020-05-13 933
 
1.0%
2020-07-23 926
 
1.0%
2020-02-26 911
 
1.0%
Other values (1609) 77180
86.9%
2024-02-13T20:43:07.594021image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 247178
27.8%
2 181519
20.4%
- 177724
20.0%
1 113401
12.8%
9 33207
 
3.7%
8 27048
 
3.0%
5 24021
 
2.7%
6 23933
 
2.7%
4 20697
 
2.3%
7 20612
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 710896
80.0%
Dash Punctuation 177724
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 247178
34.8%
2 181519
25.5%
1 113401
16.0%
9 33207
 
4.7%
8 27048
 
3.8%
5 24021
 
3.4%
6 23933
 
3.4%
4 20697
 
2.9%
7 20612
 
2.9%
3 19280
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 177724
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 888620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 247178
27.8%
2 181519
20.4%
- 177724
20.0%
1 113401
12.8%
9 33207
 
3.7%
8 27048
 
3.0%
5 24021
 
2.7%
6 23933
 
2.7%
4 20697
 
2.3%
7 20612
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 888620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 247178
27.8%
2 181519
20.4%
- 177724
20.0%
1 113401
12.8%
9 33207
 
3.7%
8 27048
 
3.0%
5 24021
 
2.7%
6 23933
 
2.7%
4 20697
 
2.3%
7 20612
 
2.3%

numberofoverdueinstls_725L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct604
Distinct (%)0.2%
Missing1752866
Missing (%)84.3%
Infinite0
Infinite (%)0.0%
Mean2.005489237
Minimum0
Maximum5022
Zeros323096
Zeros (%)15.5%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:07.766852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5022
Range5022
Interquartile range (IQR)0

Descriptive statistics

Standard deviation60.32858424
Coefficient of variation (CV)30.08172924
Kurtosis2955.204057
Mean2.005489237
Median Absolute Deviation (MAD)0
Skewness49.63039471
Sum654706
Variance3639.538076
MonotonicityNot monotonic
2024-02-13T20:43:07.942852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 323096
 
15.5%
1 364
 
< 0.1%
2 164
 
< 0.1%
4 119
 
< 0.1%
3 104
 
< 0.1%
8 100
 
< 0.1%
11 93
 
< 0.1%
5 93
 
< 0.1%
16 85
 
< 0.1%
9 83
 
< 0.1%
Other values (594) 2156
 
0.1%
(Missing) 1752866
84.3%
ValueCountFrequency (%)
0 323096
15.5%
1 364
 
< 0.1%
2 164
 
< 0.1%
3 104
 
< 0.1%
4 119
 
< 0.1%
ValueCountFrequency (%)
5022 1
< 0.1%
4794 1
< 0.1%
4681 1
< 0.1%
4616 1
< 0.1%
4560 1
< 0.1%

numberofoverdueinstls_834L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct118
Distinct (%)< 0.1%
Missing814326
Missing (%)39.2%
Infinite0
Infinite (%)0.0%
Mean0.03900088301
Minimum0
Maximum3071
Zeros1264302
Zeros (%)60.8%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:08.097709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3071
Range3071
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.872376506
Coefficient of variation (CV)124.9299024
Kurtosis137433.8503
Mean0.03900088301
Median Absolute Deviation (MAD)0
Skewness279.913791
Sum49336
Variance23.74005282
MonotonicityNot monotonic
2024-02-13T20:43:08.255211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1264302
60.8%
1 228
 
< 0.1%
2 120
 
< 0.1%
3 53
 
< 0.1%
4 48
 
< 0.1%
6 26
 
< 0.1%
5 25
 
< 0.1%
7 19
 
< 0.1%
10 8
 
< 0.1%
8 7
 
< 0.1%
Other values (108) 161
 
< 0.1%
(Missing) 814326
39.2%
ValueCountFrequency (%)
0 1264302
60.8%
1 228
 
< 0.1%
2 120
 
< 0.1%
3 53
 
< 0.1%
4 48
 
< 0.1%
ValueCountFrequency (%)
3071 1
< 0.1%
1337 1
< 0.1%
1161 1
< 0.1%
823 1
< 0.1%
704 1
< 0.1%

outstandingamount_354A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct153
Distinct (%)< 0.1%
Missing964646
Missing (%)46.4%
Infinite0
Infinite (%)0.0%
Mean2.864518945
Minimum0
Maximum445254
Zeros1114466
Zeros (%)53.6%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:08.413725image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum445254
Range445254
Interquartile range (IQR)0

Descriptive statistics

Standard deviation764.8788146
Coefficient of variation (CV)267.0182426
Kurtosis212260.0591
Mean2.864518945
Median Absolute Deviation (MAD)0
Skewness431.1137107
Sum3193013.384
Variance585039.6011
MonotonicityNot monotonic
2024-02-13T20:43:08.570792image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1114466
53.6%
0.2 12
 
< 0.1%
0.8 9
 
< 0.1%
0.6 8
 
< 0.1%
1 5
 
< 0.1%
0.4 5
 
< 0.1%
1.4 4
 
< 0.1%
2.2 3
 
< 0.1%
17.2 3
 
< 0.1%
4 3
 
< 0.1%
Other values (143) 159
 
< 0.1%
(Missing) 964646
46.4%
ValueCountFrequency (%)
0 1114466
53.6%
0.2 12
 
< 0.1%
0.4 5
 
< 0.1%
0.6 8
 
< 0.1%
0.8 9
 
< 0.1%
ValueCountFrequency (%)
445254 1
< 0.1%
390000 1
< 0.1%
342232.84 1
< 0.1%
234462 1
< 0.1%
145444.17 1
< 0.1%

outstandingamount_362A
Real number (ℝ)

MISSING  SKEWED 

Distinct165004
Distinct (%)94.8%
Missing1905317
Missing (%)91.6%
Infinite0
Infinite (%)0.0%
Mean161763.3627
Minimum0
Maximum131231420
Zeros1659
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:08.727402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3354.237
Q113987.7305
median39326.757
Q3113582.73
95-th percentile668300.05
Maximum131231420
Range131231420
Interquartile range (IQR)99594.9995

Descriptive statistics

Standard deviation755988.32
Coefficient of variation (CV)4.673421146
Kurtosis11868.91306
Mean161763.3627
Median Absolute Deviation (MAD)31456.5052
Skewness83.85920112
Sum2.81477957 × 1010
Variance5.7151834 × 1011
MonotonicityNot monotonic
2024-02-13T20:43:08.913479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1659
 
0.1%
100000 82
 
< 0.1%
60000 63
 
< 0.1%
200000 55
 
< 0.1%
40000 53
 
< 0.1%
5200 40
 
< 0.1%
10400 40
 
< 0.1%
20000 39
 
< 0.1%
2000000 37
 
< 0.1%
120000 36
 
< 0.1%
Other values (164994) 171902
 
8.3%
(Missing) 1905317
91.6%
ValueCountFrequency (%)
0 1659
0.1%
0.002 4
 
< 0.1%
0.004 1
 
< 0.1%
0.006 1
 
< 0.1%
0.034 1
 
< 0.1%
ValueCountFrequency (%)
131231420 1
< 0.1%
118736210 1
< 0.1%
106765980 1
< 0.1%
68851120 1
< 0.1%
55744710 1
< 0.1%

overdueamount_31A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct173
Distinct (%)< 0.1%
Missing814062
Missing (%)39.2%
Infinite0
Infinite (%)0.0%
Mean12.53579364
Minimum0
Maximum427110
Zeros1265044
Zeros (%)60.8%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:09.085303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum427110
Range427110
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1678.015364
Coefficient of variation (CV)133.8579281
Kurtosis35508.16513
Mean12.53579364
Median Absolute Deviation (MAD)0
Skewness176.3038547
Sum15861050.79
Variance2815735.561
MonotonicityNot monotonic
2024-02-13T20:43:09.239653image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1265044
60.8%
0.2 10
 
< 0.1%
0.8 7
 
< 0.1%
0.6 5
 
< 0.1%
1.4 4
 
< 0.1%
1 4
 
< 0.1%
17.2 3
 
< 0.1%
4 3
 
< 0.1%
1.2 3
 
< 0.1%
8 3
 
< 0.1%
Other values (163) 175
 
< 0.1%
(Missing) 814062
39.2%
ValueCountFrequency (%)
0 1265044
60.8%
0.2 10
 
< 0.1%
0.4 3
 
< 0.1%
0.6 5
 
< 0.1%
0.8 7
 
< 0.1%
ValueCountFrequency (%)
427110 1
< 0.1%
412360 1
< 0.1%
407650 1
< 0.1%
397750 1
< 0.1%
397050 1
< 0.1%

overdueamount_659A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct2695
Distinct (%)0.8%
Missing1752866
Missing (%)84.3%
Infinite0
Infinite (%)0.0%
Mean3708.823809
Minimum0
Maximum437339700
Zeros323097
Zeros (%)15.5%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:09.397654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum437339700
Range437339700
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1093778.332
Coefficient of variation (CV)294.9124542
Kurtosis156632.6579
Mean3708.823809
Median Absolute Deviation (MAD)0
Skewness392.5318865
Sum1210771494
Variance1.196351039 × 1012
MonotonicityNot monotonic
2024-02-13T20:43:09.556517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 323097
 
15.5%
0.2 48
 
< 0.1%
99.8 44
 
< 0.1%
10 23
 
< 0.1%
4 17
 
< 0.1%
14 11
 
< 0.1%
0.8 11
 
< 0.1%
6500 11
 
< 0.1%
13479.343 10
 
< 0.1%
1 9
 
< 0.1%
Other values (2685) 3176
 
0.2%
(Missing) 1752866
84.3%
ValueCountFrequency (%)
0 323097
15.5%
0.008 1
 
< 0.1%
0.014 1
 
< 0.1%
0.016 1
 
< 0.1%
0.018000001 3
 
< 0.1%
ValueCountFrequency (%)
437339700 2
< 0.1%
66728776 1
< 0.1%
31031926 2
< 0.1%
29950490 1
< 0.1%
9986989 2
< 0.1%

overdueamountmax2_14A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct75567
Distinct (%)23.0%
Missing1750967
Missing (%)84.2%
Infinite0
Infinite (%)0.0%
Mean5707.585946
Minimum0
Maximum437339700
Zeros238861
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:09.799439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q334
95-th percentile9403.58375
Maximum437339700
Range437339700
Interquartile range (IQR)34

Descriptive statistics

Standard deviation1091378.355
Coefficient of variation (CV)191.2154044
Kurtosis157098.4752
Mean5707.585946
Median Absolute Deviation (MAD)0
Skewness392.8541134
Sum1874120091
Variance1.191106713 × 1012
MonotonicityNot monotonic
2024-02-13T20:43:10.077433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 238861
 
11.5%
99.8 192
 
< 0.1%
10 127
 
< 0.1%
0.2 99
 
< 0.1%
0.4 90
 
< 0.1%
4 80
 
< 0.1%
400 73
 
< 0.1%
2 66
 
< 0.1%
0.8 64
 
< 0.1%
1 64
 
< 0.1%
Other values (75557) 88640
 
4.3%
(Missing) 1750967
84.2%
ValueCountFrequency (%)
0 238861
11.5%
0.002 5
 
< 0.1%
0.004 8
 
< 0.1%
0.006 3
 
< 0.1%
0.008 7
 
< 0.1%
ValueCountFrequency (%)
437339700 2
< 0.1%
66728776 1
 
< 0.1%
31031926 2
< 0.1%
29950490 1
 
< 0.1%
14662000 3
< 0.1%

overdueamountmax2_398A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct226964
Distinct (%)17.9%
Missing812672
Missing (%)39.1%
Infinite0
Infinite (%)0.0%
Mean5454.102794
Minimum0
Maximum120690770
Zeros892627
Zeros (%)42.9%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:10.250776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3615
95-th percentile15260.341
Maximum120690770
Range120690770
Interquartile range (IQR)615

Descriptive statistics

Standard deviation187387.5798
Coefficient of variation (CV)34.35717788
Kurtosis187478.036
Mean5454.102794
Median Absolute Deviation (MAD)0
Skewness363.5508044
Sum6908444758
Variance3.511410508 × 1010
MonotonicityNot monotonic
2024-02-13T20:43:10.424777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 892627
42.9%
0.2 1449
 
0.1%
0.4 850
 
< 0.1%
0.8 638
 
< 0.1%
1.6 582
 
< 0.1%
2 538
 
< 0.1%
0.6 516
 
< 0.1%
4 501
 
< 0.1%
1.2 500
 
< 0.1%
1 497
 
< 0.1%
Other values (226954) 367953
17.7%
(Missing) 812672
39.1%
ValueCountFrequency (%)
0 892627
42.9%
0.002 45
 
< 0.1%
0.004 29
 
< 0.1%
0.006 30
 
< 0.1%
0.008 22
 
< 0.1%
ValueCountFrequency (%)
120690770 1
< 0.1%
88297850 1
< 0.1%
51793576 1
< 0.1%
40498430 1
< 0.1%
34774812 1
< 0.1%
Distinct4537
Distinct (%)1.2%
Missing1705299
Missing (%)82.0%
Memory size15.9 MiB
2024-02-13T20:43:10.781826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3740240
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

Unique283 ?
Unique (%)0.1%

Sample

1st row2009-04-30
2nd row2011-05-02
3rd row2014-06-21
4th row2019-05-09
5th row2015-07-07
ValueCountFrequency (%)
2011-10-06 5150
 
1.4%
2008-10-15 2267
 
0.6%
2010-01-07 1622
 
0.4%
2015-07-07 1397
 
0.4%
2018-12-20 1188
 
0.3%
2019-04-11 1079
 
0.3%
2018-09-17 1064
 
0.3%
2018-11-20 1063
 
0.3%
2008-11-27 1040
 
0.3%
2019-08-07 1033
 
0.3%
Other values (4527) 357121
95.5%
2024-02-13T20:43:11.281929image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 918683
24.6%
- 748048
20.0%
1 649969
17.4%
2 620291
16.6%
8 134778
 
3.6%
9 131542
 
3.5%
7 114015
 
3.0%
6 111565
 
3.0%
5 109187
 
2.9%
3 101759
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2992192
80.0%
Dash Punctuation 748048
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 918683
30.7%
1 649969
21.7%
2 620291
20.7%
8 134778
 
4.5%
9 131542
 
4.4%
7 114015
 
3.8%
6 111565
 
3.7%
5 109187
 
3.6%
3 101759
 
3.4%
4 100403
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 748048
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3740240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 918683
24.6%
- 748048
20.0%
1 649969
17.4%
2 620291
16.6%
8 134778
 
3.6%
9 131542
 
3.5%
7 114015
 
3.0%
6 111565
 
3.0%
5 109187
 
2.9%
3 101759
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3740240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 918683
24.6%
- 748048
20.0%
1 649969
17.4%
2 620291
16.6%
8 134778
 
3.6%
9 131542
 
3.5%
7 114015
 
3.0%
6 111565
 
3.0%
5 109187
 
2.9%
3 101759
 
2.7%
Distinct1771
Distinct (%)2.0%
Missing1989828
Missing (%)95.7%
Memory size15.9 MiB
2024-02-13T20:43:11.690068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters894950
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

Unique445 ?
Unique (%)0.5%

Sample

1st row2018-02-17
2nd row2020-04-09
3rd row2019-02-12
4th row2019-08-06
5th row2017-09-26
ValueCountFrequency (%)
2020-05-08 1776
 
2.0%
2020-04-20 1456
 
1.6%
2020-05-05 1166
 
1.3%
2020-05-20 1023
 
1.1%
2020-08-11 980
 
1.1%
2020-06-01 965
 
1.1%
2020-06-18 922
 
1.0%
2019-12-12 895
 
1.0%
2020-02-11 881
 
1.0%
2020-07-23 874
 
1.0%
Other values (1761) 78557
87.8%
2024-02-13T20:43:12.207123image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 246903
27.6%
2 180529
20.2%
- 178990
20.0%
1 117834
13.2%
9 35289
 
3.9%
8 27709
 
3.1%
6 23231
 
2.6%
5 23057
 
2.6%
7 21213
 
2.4%
4 20360
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 715960
80.0%
Dash Punctuation 178990
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 246903
34.5%
2 180529
25.2%
1 117834
16.5%
9 35289
 
4.9%
8 27709
 
3.9%
6 23231
 
3.2%
5 23057
 
3.2%
7 21213
 
3.0%
4 20360
 
2.8%
3 19835
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 178990
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 894950
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 246903
27.6%
2 180529
20.2%
- 178990
20.0%
1 117834
13.2%
9 35289
 
3.9%
8 27709
 
3.1%
6 23231
 
2.6%
5 23057
 
2.6%
7 21213
 
2.4%
4 20360
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 894950
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 246903
27.6%
2 180529
20.2%
- 178990
20.0%
1 117834
13.2%
9 35289
 
3.9%
8 27709
 
3.1%
6 23231
 
2.6%
5 23057
 
2.6%
7 21213
 
2.4%
4 20360
 
2.3%

overdueamountmax_155A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct61696
Distinct (%)18.8%
Missing1750968
Missing (%)84.2%
Infinite0
Infinite (%)0.0%
Mean5171.160496
Minimum0
Maximum437339700
Zeros255514
Zeros (%)12.3%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:12.406111image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7584.02652
Maximum437339700
Range437339700
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1091307.313
Coefficient of variation (CV)211.0372157
Kurtosis157140.6365
Mean5171.160496
Median Absolute Deviation (MAD)0
Skewness392.9331245
Sum1697976405
Variance1.190951652 × 1012
MonotonicityNot monotonic
2024-02-13T20:43:12.594368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 255514
 
12.3%
99.8 195
 
< 0.1%
10 131
 
< 0.1%
4 82
 
< 0.1%
0.4 74
 
< 0.1%
0.2 73
 
< 0.1%
400 66
 
< 0.1%
2 63
 
< 0.1%
20 62
 
< 0.1%
600 61
 
< 0.1%
Other values (61686) 72034
 
3.5%
(Missing) 1750968
84.2%
ValueCountFrequency (%)
0 255514
12.3%
0.002 5
 
< 0.1%
0.004 7
 
< 0.1%
0.006 4
 
< 0.1%
0.008 7
 
< 0.1%
ValueCountFrequency (%)
437339700 2
< 0.1%
66728776 1
 
< 0.1%
31031926 2
< 0.1%
29950490 1
 
< 0.1%
14662000 3
< 0.1%

overdueamountmax_35A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct216532
Distinct (%)17.6%
Missing851225
Missing (%)40.9%
Infinite0
Infinite (%)0.0%
Mean4452.040695
Minimum0
Maximum120690770
Zeros869327
Zeros (%)41.8%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:12.820368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3360.5595
95-th percentile14042.48515
Maximum120690770
Range120690770
Interquartile range (IQR)360.5595

Descriptive statistics

Standard deviation138305.0135
Coefficient of variation (CV)31.06553219
Kurtosis492842.8079
Mean4452.040695
Median Absolute Deviation (MAD)0
Skewness613.0158521
Sum5467542274
Variance1.912827677 × 1010
MonotonicityNot monotonic
2024-02-13T20:43:13.020655image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 869327
41.8%
0.2 1364
 
0.1%
0.4 814
 
< 0.1%
0.8 633
 
< 0.1%
1.6 590
 
< 0.1%
2 544
 
< 0.1%
0.6 505
 
< 0.1%
4 502
 
< 0.1%
1.2 487
 
< 0.1%
1 479
 
< 0.1%
Other values (216522) 352853
17.0%
(Missing) 851225
40.9%
ValueCountFrequency (%)
0 869327
41.8%
0.002 50
 
< 0.1%
0.004 29
 
< 0.1%
0.006 31
 
< 0.1%
0.008 21
 
< 0.1%
ValueCountFrequency (%)
120690770 1
< 0.1%
51793576 1
< 0.1%
28935590 1
< 0.1%
27888398 1
< 0.1%
23032604 1
< 0.1%

overdueamountmaxdatemonth_284T
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing851225
Missing (%)40.9%
Infinite0
Infinite (%)0.0%
Mean6.593110648
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:13.196345image/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 deviation3.452843202
Coefficient of variation (CV)0.5237047255
Kurtosis-1.203664368
Mean6.593110648
Median Absolute Deviation (MAD)3
Skewness-0.06781125261
Sum8096986
Variance11.92212618
MonotonicityNot monotonic
2024-02-13T20:43:13.342487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11 111494
 
5.4%
8 108838
 
5.2%
1 107568
 
5.2%
9 107440
 
5.2%
6 106674
 
5.1%
10 104685
 
5.0%
7 103941
 
5.0%
12 98945
 
4.8%
2 97958
 
4.7%
5 95516
 
4.6%
Other values (2) 185039
 
8.9%
(Missing) 851225
40.9%
ValueCountFrequency (%)
1 107568
5.2%
2 97958
4.7%
3 90153
4.3%
4 94886
4.6%
5 95516
4.6%
ValueCountFrequency (%)
12 98945
4.8%
11 111494
5.4%
10 104685
5.0%
9 107440
5.2%
8 108838
5.2%

overdueamountmaxdatemonth_365T
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing1750968
Missing (%)84.2%
Infinite0
Infinite (%)0.0%
Mean7.123223341
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:13.466486image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation3.365738005
Coefficient of variation (CV)0.4725021025
Kurtosis-1.108885312
Mean7.123223341
Median Absolute Deviation (MAD)3
Skewness-0.3543717004
Sum2338946
Variance11.32819232
MonotonicityNot monotonic
2024-02-13T20:43:13.589405image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 48483
 
2.3%
8 36165
 
1.7%
9 34509
 
1.7%
11 32405
 
1.6%
7 25423
 
1.2%
4 24354
 
1.2%
2 23901
 
1.1%
12 23737
 
1.1%
5 21692
 
1.0%
1 21683
 
1.0%
Other values (2) 36003
 
1.7%
(Missing) 1750968
84.2%
ValueCountFrequency (%)
1 21683
1.0%
2 23901
1.1%
3 18808
0.9%
4 24354
1.2%
5 21692
1.0%
ValueCountFrequency (%)
12 23737
1.1%
11 32405
1.6%
10 48483
2.3%
9 34509
1.7%
8 36165
1.7%

overdueamountmaxdateyear_2T
Real number (ℝ)

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing1750968
Missing (%)84.2%
Infinite0
Infinite (%)0.0%
Mean2019.129302
Minimum2016
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:13.705431image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2018
Q12018
median2019
Q32020
95-th percentile2020
Maximum2020
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.8028627044
Coefficient of variation (CV)0.0003976281774
Kurtosis-1.408842094
Mean2019.129302
Median Absolute Deviation (MAD)1
Skewness-0.2382221176
Sum662991202
Variance0.6445885221
MonotonicityNot monotonic
2024-02-13T20:43:13.829449image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
2020 129782
 
6.2%
2019 111263
 
5.4%
2018 87298
 
4.2%
2017 9
 
< 0.1%
2016 3
 
< 0.1%
(Missing) 1750968
84.2%
ValueCountFrequency (%)
2016 3
 
< 0.1%
2017 9
 
< 0.1%
2018 87298
4.2%
2019 111263
5.4%
2020 129782
6.2%
ValueCountFrequency (%)
2020 129782
6.2%
2019 111263
5.4%
2018 87298
4.2%
2017 9
 
< 0.1%
2016 3
 
< 0.1%

overdueamountmaxdateyear_994T
Real number (ℝ)

MISSING 

Distinct18
Distinct (%)< 0.1%
Missing851225
Missing (%)40.9%
Infinite0
Infinite (%)0.0%
Mean2015.160614
Minimum2003
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:13.961888image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2003
5-th percentile2007
Q12013
median2016
Q32018
95-th percentile2020
Maximum2020
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.882181475
Coefficient of variation (CV)0.001926487371
Kurtosis-0.3297601305
Mean2015.160614
Median Absolute Deviation (MAD)3
Skewness-0.8475661873
Sum2474814720
Variance15.071333
MonotonicityNot monotonic
2024-02-13T20:43:14.090907image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2018 208073
 
10.0%
2019 195428
 
9.4%
2017 137984
 
6.6%
2016 98848
 
4.8%
2015 87561
 
4.2%
2014 77856
 
3.7%
2013 68611
 
3.3%
2020 62584
 
3.0%
2012 54833
 
2.6%
2011 46077
 
2.2%
Other values (8) 190243
 
9.1%
(Missing) 851225
40.9%
ValueCountFrequency (%)
2003 2
 
< 0.1%
2004 286
 
< 0.1%
2005 6929
 
0.3%
2006 26126
1.3%
2007 42531
2.0%
ValueCountFrequency (%)
2020 62584
 
3.0%
2019 195428
9.4%
2018 208073
10.0%
2017 137984
6.6%
2016 98848
4.8%

periodicityofpmts_1102L
Real number (ℝ)

MISSING  SKEWED 

Distinct5
Distinct (%)< 0.1%
Missing1089256
Missing (%)52.4%
Infinite0
Infinite (%)0.0%
Mean30.09023531
Minimum1
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:14.218907image/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 deviation4.560327927
Coefficient of variation (CV)0.1515550769
Kurtosis2828.788551
Mean30.09023531
Median Absolute Deviation (MAD)0
Skewness48.50365629
Sum29791349
Variance20.7965908
MonotonicityNot monotonic
2024-02-13T20:43:14.336906image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
30 988768
47.6%
1 599
 
< 0.1%
180 473
 
< 0.1%
90 145
 
< 0.1%
360 82
 
< 0.1%
(Missing) 1089256
52.4%
ValueCountFrequency (%)
1 599
 
< 0.1%
30 988768
47.6%
90 145
 
< 0.1%
180 473
 
< 0.1%
360 82
 
< 0.1%
ValueCountFrequency (%)
360 82
 
< 0.1%
180 473
 
< 0.1%
90 145
 
< 0.1%
30 988768
47.6%
1 599
 
< 0.1%

periodicityofpmts_837L
Real number (ℝ)

MISSING  SKEWED 

Distinct5
Distinct (%)< 0.1%
Missing1908921
Missing (%)91.8%
Infinite0
Infinite (%)0.0%
Mean30.21251511
Minimum1
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:14.457376image/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 deviation5.823223943
Coefficient of variation (CV)0.1927421111
Kurtosis918.3747232
Mean30.21251511
Median Absolute Deviation (MAD)0
Skewness28.31014469
Sum5148273
Variance33.90993709
MonotonicityNot monotonic
2024-02-13T20:43:14.587655image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
30 170056
 
8.2%
180 216
 
< 0.1%
1 63
 
< 0.1%
90 61
 
< 0.1%
360 6
 
< 0.1%
(Missing) 1908921
91.8%
ValueCountFrequency (%)
1 63
 
< 0.1%
30 170056
8.2%
90 61
 
< 0.1%
180 216
 
< 0.1%
360 6
 
< 0.1%
ValueCountFrequency (%)
360 6
 
< 0.1%
180 216
 
< 0.1%
90 61
 
< 0.1%
30 170056
8.2%
1 63
 
< 0.1%

prolongationcount_1120L
Real number (ℝ)

MISSING  ZEROS 

Distinct54
Distinct (%)0.1%
Missing1977663
Missing (%)95.1%
Infinite0
Infinite (%)0.0%
Mean0.5990261657
Minimum0
Maximum78
Zeros75910
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:14.734277image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum78
Range78
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.002615136
Coefficient of variation (CV)3.343117965
Kurtosis221.4088283
Mean0.5990261657
Median Absolute Deviation (MAD)0
Skewness11.19818034
Sum60897
Variance4.010467382
MonotonicityNot monotonic
2024-02-13T20:43:14.899242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 75910
 
3.7%
1 14647
 
0.7%
2 5036
 
0.2%
3 2289
 
0.1%
4 1142
 
0.1%
5 808
 
< 0.1%
6 430
 
< 0.1%
7 258
 
< 0.1%
8 212
 
< 0.1%
9 158
 
< 0.1%
Other values (44) 770
 
< 0.1%
(Missing) 1977663
95.1%
ValueCountFrequency (%)
0 75910
3.7%
1 14647
 
0.7%
2 5036
 
0.2%
3 2289
 
0.1%
4 1142
 
0.1%
ValueCountFrequency (%)
78 1
 
< 0.1%
68 3
< 0.1%
67 2
< 0.1%
60 2
< 0.1%
59 2
< 0.1%

prolongationcount_599L
Real number (ℝ)

MISSING 

Distinct27
Distinct (%)0.2%
Missing2068341
Missing (%)99.5%
Infinite0
Infinite (%)0.0%
Mean0.9331633582
Minimum0
Maximum60
Zeros3075
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:15.087240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum60
Range60
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.689517867
Coefficient of variation (CV)1.810527441
Kurtosis490.8341655
Mean0.9331633582
Median Absolute Deviation (MAD)0
Skewness18.03978875
Sum10248
Variance2.854470622
MonotonicityNot monotonic
2024-02-13T20:43:15.234300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 6987
 
0.3%
0 3075
 
0.1%
2 590
 
< 0.1%
3 161
 
< 0.1%
4 53
 
< 0.1%
5 33
 
< 0.1%
9 12
 
< 0.1%
13 10
 
< 0.1%
7 10
 
< 0.1%
10 9
 
< 0.1%
Other values (17) 42
 
< 0.1%
(Missing) 2068341
99.5%
ValueCountFrequency (%)
0 3075
0.1%
1 6987
0.3%
2 590
 
< 0.1%
3 161
 
< 0.1%
4 53
 
< 0.1%
ValueCountFrequency (%)
60 1
< 0.1%
58 1
< 0.1%
53 2
< 0.1%
36 1
< 0.1%
34 1
< 0.1%
Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:15.438699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.000075024
Min length8

Characters and Unicode

Total characters16634740
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row60c73645
2nd row60c73645
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 1750967
84.2%
60c73645 240296
 
11.6%
96a8fdfe 80818
 
3.9%
164ee705 3667
 
0.2%
e19fdece 1323
 
0.1%
6ec903ee 792
 
< 0.1%
9e302002 740
 
< 0.1%
7a7d6960 217
 
< 0.1%
44164129 186
 
< 0.1%
2162d1a4 84
 
< 0.1%
Other values (7) 233
 
< 0.1%
2024-02-13T20:43:15.767065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 5496975
33.0%
4 1995605
 
12.0%
7 1995395
 
12.0%
a 1832166
 
11.0%
1 1756740
 
10.6%
b 1751122
 
10.5%
6 566692
 
3.4%
0 247349
 
1.5%
c 242472
 
1.5%
3 241898
 
1.5%
Other values (8) 508326
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12467984
75.0%
Lowercase Letter 4166639
 
25.0%
Connector Punctuation 78
 
< 0.1%
Uppercase Letter 39
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 5496975
44.1%
4 1995605
 
16.0%
7 1995395
 
16.0%
1 1756740
 
14.1%
6 566692
 
4.5%
0 247349
 
2.0%
3 241898
 
1.9%
9 84143
 
0.7%
8 81066
 
0.7%
2 2121
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 1832166
44.0%
b 1751122
42.0%
c 242472
 
5.8%
f 163097
 
3.9%
e 95274
 
2.3%
d 82508
 
2.0%
Connector Punctuation
ValueCountFrequency (%)
_ 78
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12468062
75.0%
Latin 4166678
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 5496975
44.1%
4 1995605
 
16.0%
7 1995395
 
16.0%
1 1756740
 
14.1%
6 566692
 
4.5%
0 247349
 
2.0%
3 241898
 
1.9%
9 84143
 
0.7%
8 81066
 
0.7%
2 2121
 
< 0.1%
Latin
ValueCountFrequency (%)
a 1832166
44.0%
b 1751122
42.0%
c 242472
 
5.8%
f 163097
 
3.9%
e 95274
 
2.3%
d 82508
 
2.0%
P 39
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16634740
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 5496975
33.0%
4 1995605
 
12.0%
7 1995395
 
12.0%
a 1832166
 
11.0%
1 1756740
 
10.6%
b 1751122
 
10.5%
6 566692
 
3.4%
0 247349
 
1.5%
c 242472
 
1.5%
3 241898
 
1.5%
Other values (8) 508326
 
3.1%
Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:15.951710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.001404303
Min length8

Characters and Unicode

Total characters16637504
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowa55475b1
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 812744
39.1%
60c73645 594077
28.6%
96a8fdfe 353933
17.0%
5065c2b8 269170
 
12.9%
e19fdece 24043
 
1.2%
d9ae1a0e 12405
 
0.6%
27b6de28 3326
 
0.2%
5d1b0cdd 2214
 
0.1%
164ee705 1393
 
0.1%
89ccf2a3 1332
 
0.1%
Other values (14) 4686
 
0.2%
2024-02-13T20:43:16.327533image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 3574296
21.5%
6 1819383
10.9%
7 1415935
 
8.5%
4 1411634
 
8.5%
a 1193703
 
7.2%
b 1088498
 
6.5%
c 892279
 
5.4%
0 879963
 
5.3%
1 862066
 
5.2%
f 734064
 
4.4%
Other values (8) 2765683
16.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11863658
71.3%
Lowercase Letter 4771656
28.7%
Connector Punctuation 1460
 
< 0.1%
Uppercase Letter 730
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 3574296
30.1%
6 1819383
15.3%
7 1415935
 
11.9%
4 1411634
 
11.9%
0 879963
 
7.4%
1 862066
 
7.3%
8 631343
 
5.3%
3 595710
 
5.0%
9 393283
 
3.3%
2 280045
 
2.4%
Lowercase Letter
ValueCountFrequency (%)
a 1193703
25.0%
b 1088498
22.8%
c 892279
18.7%
f 734064
15.4%
e 460040
 
9.6%
d 403072
 
8.4%
Connector Punctuation
ValueCountFrequency (%)
_ 1460
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 730
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11865118
71.3%
Latin 4772386
28.7%

Most frequent character per script

Common
ValueCountFrequency (%)
5 3574296
30.1%
6 1819383
15.3%
7 1415935
 
11.9%
4 1411634
 
11.9%
0 879963
 
7.4%
1 862066
 
7.3%
8 631343
 
5.3%
3 595710
 
5.0%
9 393283
 
3.3%
2 280045
 
2.4%
Latin
ValueCountFrequency (%)
a 1193703
25.0%
b 1088498
22.8%
c 892279
18.7%
f 734064
15.4%
e 460040
 
9.6%
d 403072
 
8.4%
P 730
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16637504
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 3574296
21.5%
6 1819383
10.9%
7 1415935
 
8.5%
4 1411634
 
8.5%
a 1193703
 
7.2%
b 1088498
 
6.5%
c 892279
 
5.4%
0 879963
 
5.3%
1 862066
 
5.2%
f 734064
 
4.4%
Other values (8) 2765683
16.6%

refreshdate_3813885D
Text

MISSING 

Distinct131
Distinct (%)< 0.1%
Missing666459
Missing (%)32.1%
Memory size15.9 MiB
2024-02-13T20:43:16.716213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters14128640
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 row2020-06-25
2nd row2020-06-25
3rd row2020-06-25
4th row2019-01-03
5th row2020-06-17
ValueCountFrequency (%)
2019-01-03 176608
 
12.5%
2019-11-03 176608
 
12.5%
2020-04-16 176608
 
12.5%
2020-05-06 118931
 
8.4%
2020-08-17 60131
 
4.3%
2020-07-30 33753
 
2.4%
2020-09-11 30222
 
2.1%
2020-06-26 28506
 
2.0%
2020-09-07 23953
 
1.7%
2020-09-24 21870
 
1.5%
Other values (121) 565674
40.0%
2024-02-13T20:43:17.197465image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4481688
31.7%
- 2825728
20.0%
2 2722306
19.3%
1 1569031
 
11.1%
9 671406
 
4.8%
3 470932
 
3.3%
6 452489
 
3.2%
7 294826
 
2.1%
4 245500
 
1.7%
8 219563
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11302912
80.0%
Dash Punctuation 2825728
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4481688
39.7%
2 2722306
24.1%
1 1569031
 
13.9%
9 671406
 
5.9%
3 470932
 
4.2%
6 452489
 
4.0%
7 294826
 
2.6%
4 245500
 
2.2%
8 219563
 
1.9%
5 175171
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 2825728
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14128640
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4481688
31.7%
- 2825728
20.0%
2 2722306
19.3%
1 1569031
 
11.1%
9 671406
 
4.8%
3 470932
 
3.3%
6 452489
 
3.2%
7 294826
 
2.1%
4 245500
 
1.7%
8 219563
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14128640
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4481688
31.7%
- 2825728
20.0%
2 2722306
19.3%
1 1569031
 
11.1%
9 671406
 
4.8%
3 470932
 
3.3%
6 452489
 
3.2%
7 294826
 
2.1%
4 245500
 
1.7%
8 219563
 
1.6%

residualamount_488A
Real number (ℝ)

CONSTANT  MISSING  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing1928438
Missing (%)92.7%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros150885
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:17.331980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2024-02-13T20:43:17.435804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 150885
 
7.3%
(Missing) 1928438
92.7%
ValueCountFrequency (%)
0 150885
7.3%
ValueCountFrequency (%)
0 150885
7.3%

residualamount_856A
Real number (ℝ)

MISSING  ZEROS 

Distinct79474
Distinct (%)52.1%
Missing1926906
Missing (%)92.7%
Infinite0
Infinite (%)0.0%
Mean48078.8717
Minimum0
Maximum2590000
Zeros64487
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:17.598670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5863.4
Q347363.375
95-th percentile215162.74
Maximum2590000
Range2590000
Interquartile range (IQR)47363.375

Descriptive statistics

Standard deviation104694.8731
Coefficient of variation (CV)2.177565102
Kurtosis38.77833566
Mean48078.8717
Median Absolute Deviation (MAD)5863.4
Skewness4.928940699
Sum7328037387
Variance1.096101646 × 1010
MonotonicityNot monotonic
2024-02-13T20:43:17.761673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 64487
 
3.1%
200000 91
 
< 0.1%
10000 76
 
< 0.1%
20000 61
 
< 0.1%
30000 53
 
< 0.1%
4000 50
 
< 0.1%
6000 45
 
< 0.1%
100000 42
 
< 0.1%
40000 37
 
< 0.1%
2000 37
 
< 0.1%
Other values (79464) 87438
 
4.2%
(Missing) 1926906
92.7%
ValueCountFrequency (%)
0 64487
3.1%
0.002 1
 
< 0.1%
0.008 1
 
< 0.1%
0.030000001 1
 
< 0.1%
0.034 1
 
< 0.1%
ValueCountFrequency (%)
2590000 1
< 0.1%
2115215.5 1
< 0.1%
1925747 1
< 0.1%
1798054.4 1
< 0.1%
1787136 1
< 0.1%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:17.939470image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.000247196
Min length8

Characters and Unicode

Total characters16635098
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowab3c25cf
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 1921187
92.4%
ab3c25cf 153016
 
7.4%
be4fd70b 2615
 
0.1%
daf49a8a 1817
 
0.1%
p28_48_88 514
 
< 0.1%
15f04f45 174
 
< 0.1%
2024-02-13T20:43:18.223892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 5916925
35.6%
a 2079654
 
12.5%
b 2079433
 
12.5%
4 1926481
 
11.6%
7 1923802
 
11.6%
1 1921361
 
11.6%
c 306032
 
1.8%
f 157796
 
0.9%
2 153530
 
0.9%
3 153016
 
0.9%
Other values (7) 17068
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12003594
72.2%
Lowercase Letter 4629962
 
27.8%
Connector Punctuation 1028
 
< 0.1%
Uppercase Letter 514
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 5916925
49.3%
4 1926481
 
16.0%
7 1923802
 
16.0%
1 1921361
 
16.0%
2 153530
 
1.3%
3 153016
 
1.3%
8 3873
 
< 0.1%
0 2789
 
< 0.1%
9 1817
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 2079654
44.9%
b 2079433
44.9%
c 306032
 
6.6%
f 157796
 
3.4%
d 4432
 
0.1%
e 2615
 
0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 1028
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 514
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12004622
72.2%
Latin 4630476
 
27.8%

Most frequent character per script

Common
ValueCountFrequency (%)
5 5916925
49.3%
4 1926481
 
16.0%
7 1923802
 
16.0%
1 1921361
 
16.0%
2 153530
 
1.3%
3 153016
 
1.3%
8 3873
 
< 0.1%
0 2789
 
< 0.1%
9 1817
 
< 0.1%
_ 1028
 
< 0.1%
Latin
ValueCountFrequency (%)
a 2079654
44.9%
b 2079433
44.9%
c 306032
 
6.6%
f 157796
 
3.4%
d 4432
 
0.1%
e 2615
 
0.1%
P 514
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16635098
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 5916925
35.6%
a 2079654
 
12.5%
b 2079433
 
12.5%
4 1926481
 
11.6%
7 1923802
 
11.6%
1 1921361
 
11.6%
c 306032
 
1.8%
f 157796
 
0.9%
2 153530
 
0.9%
3 153016
 
0.9%
Other values (7) 17068
 
0.1%
Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:18.385011image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.000000481
Min length8

Characters and Unicode

Total characters16634585
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 rowa55475b1
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 1905185
91.6%
ab3c25cf 166861
 
8.0%
be4fd70b 3920
 
0.2%
daf49a8a 2497
 
0.1%
15f04f45 836
 
< 0.1%
71ddaa88 20
 
< 0.1%
0c42a10e 3
 
< 0.1%
p28_48_88 1
 
< 0.1%
2024-02-13T20:43:18.688549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 5884088
35.4%
b 2079886
 
12.5%
a 2079580
 
12.5%
4 1913278
 
11.5%
7 1909125
 
11.5%
1 1906044
 
11.5%
c 333725
 
2.0%
f 174950
 
1.1%
2 166865
 
1.0%
3 166861
 
1.0%
Other values (7) 20183
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11956061
71.9%
Lowercase Letter 4678521
 
28.1%
Connector Punctuation 2
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 5884088
49.2%
4 1913278
 
16.0%
7 1909125
 
16.0%
1 1906044
 
15.9%
2 166865
 
1.4%
3 166861
 
1.4%
0 4762
 
< 0.1%
8 2541
 
< 0.1%
9 2497
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
b 2079886
44.5%
a 2079580
44.4%
c 333725
 
7.1%
f 174950
 
3.7%
d 6457
 
0.1%
e 3923
 
0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11956063
71.9%
Latin 4678522
 
28.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 5884088
49.2%
4 1913278
 
16.0%
7 1909125
 
16.0%
1 1906044
 
15.9%
2 166865
 
1.4%
3 166861
 
1.4%
0 4762
 
< 0.1%
8 2541
 
< 0.1%
9 2497
 
< 0.1%
_ 2
 
< 0.1%
Latin
ValueCountFrequency (%)
b 2079886
44.5%
a 2079580
44.4%
c 333725
 
7.1%
f 174950
 
3.7%
d 6457
 
0.1%
e 3923
 
0.1%
P 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16634585
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 5884088
35.4%
b 2079886
 
12.5%
a 2079580
 
12.5%
4 1913278
 
11.5%
7 1909125
 
11.5%
1 1906044
 
11.5%
c 333725
 
2.0%
f 174950
 
1.1%
2 166865
 
1.0%
3 166861
 
1.0%
Other values (7) 20183
 
0.1%

totalamount_6A
Real number (ℝ)

MISSING  SKEWED 

Distinct193528
Distinct (%)17.4%
Missing964449
Missing (%)46.4%
Infinite0
Infinite (%)0.0%
Mean71393.57092
Minimum0
Maximum249922850
Zeros1714
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:18.842574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4000
Q112735.601
median28247.7
Q360000
95-th percentile220000
Maximum249922850
Range249922850
Interquartile range (IQR)47264.399

Descriptive statistics

Standard deviation545743.3044
Coefficient of variation (CV)7.64415195
Kurtosis79364.37564
Mean71393.57092
Median Absolute Deviation (MAD)18249.4
Skewness228.0579047
Sum7.959483598 × 1010
Variance2.978357543 × 1011
MonotonicityNot monotonic
2024-02-13T20:43:19.003432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000 23208
 
1.1%
30000 23134
 
1.1%
40000 21326
 
1.0%
60000 18434
 
0.9%
100000 15515
 
0.7%
4000 15005
 
0.7%
10000 14531
 
0.7%
6000 12987
 
0.6%
2000 12899
 
0.6%
3000 11762
 
0.6%
Other values (193518) 946073
45.5%
(Missing) 964449
46.4%
ValueCountFrequency (%)
0 1714
0.1%
0.002 2
 
< 0.1%
0.2 1
 
< 0.1%
7.6 1
 
< 0.1%
8.226001 1
 
< 0.1%
ValueCountFrequency (%)
249922850 1
< 0.1%
215200000 1
< 0.1%
159142640 1
< 0.1%
140000000 1
< 0.1%
120000000 1
< 0.1%

totalamount_996A
Real number (ℝ)

MISSING  SKEWED 

Distinct62184
Distinct (%)35.7%
Missing1905314
Missing (%)91.6%
Infinite0
Infinite (%)0.0%
Mean215174.5893
Minimum54.488003
Maximum378982270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:19.406507image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum54.488003
5-th percentile12396.4
Q132097
median70000
Q3168000
95-th percentile823901.92
Maximum378982270
Range378982215.5
Interquartile range (IQR)135903

Descriptive statistics

Standard deviation1257578.119
Coefficient of variation (CV)5.844454605
Kurtosis49566.43334
Mean215174.5893
Median Absolute Deviation (MAD)47544
Skewness182.363711
Sum3.74423151 × 1010
Variance1.581502726 × 1012
MonotonicityNot monotonic
2024-02-13T20:43:19.566131image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 4577
 
0.2%
200000 2845
 
0.1%
60000 2416
 
0.1%
40000 2209
 
0.1%
20000 1597
 
0.1%
30000 1580
 
0.1%
150000 1419
 
0.1%
80000 1276
 
0.1%
120000 1170
 
0.1%
400000 1117
 
0.1%
Other values (62174) 153803
 
7.4%
(Missing) 1905314
91.6%
ValueCountFrequency (%)
54.488003 1
 
< 0.1%
800 1
 
< 0.1%
895 1
 
< 0.1%
1000 27
< 0.1%
1103 1
 
< 0.1%
ValueCountFrequency (%)
378982270 1
< 0.1%
139386000 1
< 0.1%
134010000 1
< 0.1%
118657100 1
< 0.1%
72792664 1
< 0.1%

totaldebtoverduevalue_178A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct2395
Distinct (%)1.5%
Missing1921187
Missing (%)92.4%
Infinite0
Infinite (%)0.0%
Mean4654.307979
Minimum0
Maximum522296740
Zeros155204
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:19.762896image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum522296740
Range522296740
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1326120.189
Coefficient of variation (CV)284.9231711
Kurtosis152201.4075
Mean4654.307979
Median Absolute Deviation (MAD)0
Skewness387.1727565
Sum736013646.5
Variance1.758594755 × 1012
MonotonicityNot monotonic
2024-02-13T20:43:19.923983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 155204
 
7.5%
0.2 46
 
< 0.1%
99.8 44
 
< 0.1%
10 23
 
< 0.1%
4 16
 
< 0.1%
0.8 10
 
< 0.1%
14 10
 
< 0.1%
20 9
 
< 0.1%
1 9
 
< 0.1%
0.4 9
 
< 0.1%
Other values (2385) 2756
 
0.1%
(Missing) 1921187
92.4%
ValueCountFrequency (%)
0 155204
7.5%
0.008 1
 
< 0.1%
0.014 1
 
< 0.1%
0.016 1
 
< 0.1%
0.018000001 3
 
< 0.1%
ValueCountFrequency (%)
522296740 1
< 0.1%
66728776 1
< 0.1%
18110382 1
< 0.1%
9986989 2
< 0.1%
9926879 1
< 0.1%

totaldebtoverduevalue_718A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct172
Distinct (%)0.1%
Missing1905185
Missing (%)91.6%
Infinite0
Infinite (%)0.0%
Mean91.08322591
Minimum0
Maximum427110
Zeros173922
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:20.079947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum427110
Range427110
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4522.47276
Coefficient of variation (CV)49.65209252
Kurtosis4882.880522
Mean91.08322591
Median Absolute Deviation (MAD)0
Skewness65.38393557
Sum15861050.79
Variance20452759.86
MonotonicityNot monotonic
2024-02-13T20:43:20.233910image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 173922
 
8.4%
0.2 10
 
< 0.1%
0.8 7
 
< 0.1%
0.6 5
 
< 0.1%
1.4 4
 
< 0.1%
1 4
 
< 0.1%
17.2 3
 
< 0.1%
4 3
 
< 0.1%
1.2 3
 
< 0.1%
8 3
 
< 0.1%
Other values (162) 174
 
< 0.1%
(Missing) 1905185
91.6%
ValueCountFrequency (%)
0 173922
8.4%
0.2 10
 
< 0.1%
0.4 3
 
< 0.1%
0.6 5
 
< 0.1%
0.8 7
 
< 0.1%
ValueCountFrequency (%)
427110 1
< 0.1%
412360 1
< 0.1%
407650 1
< 0.1%
397750 1
< 0.1%
397050 1
< 0.1%

totaloutstanddebtvalue_39A
Real number (ℝ)

MISSING  SKEWED 

Distinct139099
Distinct (%)88.0%
Missing1921187
Missing (%)92.4%
Infinite0
Infinite (%)0.0%
Mean224319.6384
Minimum0
Maximum318299360
Zeros16029
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:20.375811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q119829.79
median77338.467
Q3203469.32
95-th percentile871481.65
Maximum318299360
Range318299360
Interquartile range (IQR)183639.53

Descriptive statistics

Standard deviation1390060.732
Coefficient of variation (CV)6.196785719
Kurtosis29425.61864
Mean224319.6384
Median Absolute Deviation (MAD)68818.467
Skewness152.4046712
Sum3.547301034 × 1010
Variance1.932268838 × 1012
MonotonicityNot monotonic
2024-02-13T20:43:20.532031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16029
 
0.8%
200000 45
 
< 0.1%
100000 34
 
< 0.1%
2000000 25
 
< 0.1%
40000 20
 
< 0.1%
10000 19
 
< 0.1%
6000 19
 
< 0.1%
30000 15
 
< 0.1%
20000 14
 
< 0.1%
4998 13
 
< 0.1%
Other values (139089) 141903
 
6.8%
(Missing) 1921187
92.4%
ValueCountFrequency (%)
0 16029
0.8%
0.002 1
 
< 0.1%
0.004 1
 
< 0.1%
0.076 1
 
< 0.1%
0.120000005 1
 
< 0.1%
ValueCountFrequency (%)
318299360 1
< 0.1%
245728940 1
< 0.1%
234672670 1
< 0.1%
118736210 1
< 0.1%
106975440 1
< 0.1%

totaloutstanddebtvalue_668A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct145
Distinct (%)0.1%
Missing1905185
Missing (%)91.6%
Infinite0
Infinite (%)0.0%
Mean18.14398675
Minimum0
Maximum445254
Zeros173936
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-02-13T20:43:20.696168image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum445254
Range445254
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1934.822311
Coefficient of variation (CV)106.63711
Kurtosis33177.29801
Mean18.14398675
Median Absolute Deviation (MAD)0
Skewness170.4689353
Sum3159557.565
Variance3743537.376
MonotonicityNot monotonic
2024-02-13T20:43:20.849166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 173936
 
8.4%
0.2 12
 
< 0.1%
0.8 9
 
< 0.1%
0.6 8
 
< 0.1%
0.4 5
 
< 0.1%
1 5
 
< 0.1%
1.4 4
 
< 0.1%
8 3
 
< 0.1%
4 3
 
< 0.1%
17.2 3
 
< 0.1%
Other values (135) 150
 
< 0.1%
(Missing) 1905185
91.6%
ValueCountFrequency (%)
0 173936
8.4%
0.2 12
 
< 0.1%
0.4 5
 
< 0.1%
0.6 8
 
< 0.1%
0.8 9
 
< 0.1%
ValueCountFrequency (%)
445254 1
< 0.1%
390000 1
< 0.1%
342232.84 1
< 0.1%
234462 1
< 0.1%
145444.17 1
< 0.1%