Overview

Dataset statistics

Number of variables45
Number of observations85791
Missing cells1125130
Missing cells (%)29.1%
Total size in memory29.5 MiB
Average record size in memory360.0 B

Variable types

Numeric32
Text13

Alerts

amount_1115A has 42110 (49.1%) missing valuesMissing
contractdate_551D has 3892 (4.5%) missing valuesMissing
contractmaturitydate_151D has 4079 (4.8%) missing valuesMissing
credlmt_1052A has 58210 (67.9%) missing valuesMissing
credlmt_228A has 69661 (81.2%) missing valuesMissing
credlmt_3940954A has 47573 (55.5%) missing valuesMissing
credquantity_1099L has 32773 (38.2%) missing valuesMissing
credquantity_984L has 39563 (46.1%) missing valuesMissing
debtpastduevalue_732A has 4574 (5.3%) missing valuesMissing
debtvalue_227A has 42110 (49.1%) missing valuesMissing
dpd_550P has 32773 (38.2%) missing valuesMissing
dpd_733P has 39563 (46.1%) missing valuesMissing
dpdmax_851P has 4567 (5.3%) missing valuesMissing
dpdmaxdatemonth_804T has 4567 (5.3%) missing valuesMissing
dpdmaxdateyear_742T has 4567 (5.3%) missing valuesMissing
installmentamount_644A has 39563 (46.1%) missing valuesMissing
installmentamount_833A has 32773 (38.2%) missing valuesMissing
instlamount_892A has 42298 (49.3%) missing valuesMissing
interesteffectiverate_369L has 76285 (88.9%) missing valuesMissing
interestrateyearly_538L has 56966 (66.4%) missing valuesMissing
lastupdate_260D has 3892 (4.5%) missing valuesMissing
maxdebtpduevalodued_3940955A has 4567 (5.3%) missing valuesMissing
numberofinstls_810L has 42298 (49.3%) missing valuesMissing
overdueamountmax_950A has 4567 (5.3%) missing valuesMissing
overdueamountmaxdatemonth_494T has 4567 (5.3%) missing valuesMissing
overdueamountmaxdateyear_432T has 4567 (5.3%) missing valuesMissing
periodicityofpmts_997L has 83764 (97.6%) missing valuesMissing
periodicityofpmts_997M has 3272 (3.8%) missing valuesMissing
pmtdaysoverdue_1135P has 4574 (5.3%) missing valuesMissing
pmtnumpending_403L has 42111 (49.1%) missing valuesMissing
residualamount_1093A has 69666 (81.2%) missing valuesMissing
residualamount_127A has 58210 (67.9%) missing valuesMissing
residualamount_3940956A has 48272 (56.3%) missing valuesMissing
totalamount_503A has 32773 (38.2%) missing valuesMissing
totalamount_881A has 39563 (46.1%) missing valuesMissing
amount_1115A is highly skewed (γ1 = 27.97850839)Skewed
credlmt_1052A is highly skewed (γ1 = 130.7937608)Skewed
credlmt_3940954A is highly skewed (γ1 = 84.25533078)Skewed
debtpastduevalue_732A is highly skewed (γ1 = 227.1087697)Skewed
debtvalue_227A is highly skewed (γ1 = 20.95291114)Skewed
dpd_550P is highly skewed (γ1 = 192.5710284)Skewed
dpd_733P is highly skewed (γ1 = 214.6372448)Skewed
dpdmax_851P is highly skewed (γ1 = 157.2726863)Skewed
installmentamount_644A is highly skewed (γ1 = 204.4856852)Skewed
installmentamount_833A is highly skewed (γ1 = 48.18237574)Skewed
instlamount_892A is highly skewed (γ1 = 175.6797601)Skewed
interestrateyearly_538L is highly skewed (γ1 = 50.23514402)Skewed
maxdebtpduevalodued_3940955A is highly skewed (γ1 = 141.928446)Skewed
overdueamountmax_950A is highly skewed (γ1 = 141.6572117)Skewed
pmtdaysoverdue_1135P is highly skewed (γ1 = 141.7641911)Skewed
residualamount_1093A is highly skewed (γ1 = 126.984251)Skewed
totalamount_503A is highly skewed (γ1 = 171.5859115)Skewed
totalamount_881A is highly skewed (γ1 = 55.21379928)Skewed
credlmt_1052A has 7143 (8.3%) zerosZeros
credlmt_228A has 4484 (5.2%) zerosZeros
credlmt_3940954A has 9947 (11.6%) zerosZeros
debtpastduevalue_732A has 74968 (87.4%) zerosZeros
debtvalue_227A has 1090 (1.3%) zerosZeros
dpd_550P has 47788 (55.7%) zerosZeros
dpd_733P has 46181 (53.8%) zerosZeros
dpdmax_851P has 55895 (65.2%) zerosZeros
installmentamount_644A has 46141 (53.8%) zerosZeros
installmentamount_833A has 10054 (11.7%) zerosZeros
interestrateyearly_538L has 4116 (4.8%) zerosZeros
maxdebtpduevalodued_3940955A has 56144 (65.4%) zerosZeros
num_group1 has 36500 (42.5%) zerosZeros
overdueamountmax_950A has 56003 (65.3%) zerosZeros
pmtdaysoverdue_1135P has 75023 (87.4%) zerosZeros
pmtnumpending_403L has 1130 (1.3%) zerosZeros
residualamount_1093A has 16124 (18.8%) zerosZeros
residualamount_127A has 9632 (11.2%) zerosZeros
residualamount_3940956A has 14293 (16.7%) zerosZeros
totalamount_503A has 7143 (8.3%) zerosZeros
totalamount_881A has 4481 (5.2%) zerosZeros

Reproduction

Analysis started2024-02-13 19:52:58.472378
Analysis finished2024-02-13 19:53:01.167110
Duration2.69 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

case_id
Real number (ℝ)

Distinct36500
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1218998.116
Minimum467
Maximum2703436
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:01.342107image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum467
5-th percentile127597
Q1727201
median1413976
Q31778253
95-th percentile1938955
Maximum2703436
Range2702969
Interquartile range (IQR)1051052

Descriptive statistics

Standard deviation686332.0161
Coefficient of variation (CV)0.5630295956
Kurtosis-0.9351035525
Mean1218998.116
Median Absolute Deviation (MAD)462031
Skewness-0.2682179975
Sum1.045790673 × 1011
Variance4.710516364 × 1011
MonotonicityIncreasing
2024-02-13T20:53:01.512147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1858096 21
 
< 0.1%
1640901 17
 
< 0.1%
1394674 14
 
< 0.1%
1923954 14
 
< 0.1%
248503 13
 
< 0.1%
179913 13
 
< 0.1%
1945145 12
 
< 0.1%
1019053 12
 
< 0.1%
1642444 11
 
< 0.1%
1828337 11
 
< 0.1%
Other values (36490) 85653
99.8%
ValueCountFrequency (%)
467 3
< 0.1%
1445 5
< 0.1%
1934 3
< 0.1%
3159 1
 
< 0.1%
3208 2
 
< 0.1%
ValueCountFrequency (%)
2703436 2
< 0.1%
2703377 1
< 0.1%
2703357 2
< 0.1%
2702661 1
< 0.1%
2701996 1
< 0.1%

amount_1115A
Real number (ℝ)

MISSING  SKEWED 

Distinct20526
Distinct (%)47.0%
Missing42110
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean214110.4892
Minimum0.2
Maximum54833332
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:01.663171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile9586.8
Q125998
median60000
Q3160000
95-th percentile880000
Maximum54833332
Range54833331.8
Interquartile range (IQR)134002

Descriptive statistics

Standard deviation691019.6024
Coefficient of variation (CV)3.227397243
Kurtosis1617.398746
Mean214110.4892
Median Absolute Deviation (MAD)41960
Skewness27.97850839
Sum9352560278
Variance4.77508091 × 1011
MonotonicityNot monotonic
2024-02-13T20:53:02.077643image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 1136
 
1.3%
200000 769
 
0.9%
40000 648
 
0.8%
60000 627
 
0.7%
20000 496
 
0.6%
30000 492
 
0.6%
80000 331
 
0.4%
400000 322
 
0.4%
120000 316
 
0.4%
300000 294
 
0.3%
Other values (20516) 38250
44.6%
(Missing) 42110
49.1%
ValueCountFrequency (%)
0.2 2
 
< 0.1%
400 1
 
< 0.1%
820 1
 
< 0.1%
1000 15
< 0.1%
1080 1
 
< 0.1%
ValueCountFrequency (%)
54833332 1
< 0.1%
46000000 1
< 0.1%
34128892 1
< 0.1%
24937030 1
< 0.1%
20731800 1
< 0.1%
Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:02.248627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters686328
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 rowea6782cc
4th rowea6782cc
5th row01f63ac8
ValueCountFrequency (%)
ea6782cc 67530
78.7%
01f63ac8 7405
 
8.6%
00135d9c 4794
 
5.6%
a55475b1 4060
 
4.7%
07b11743 1144
 
1.3%
9b63302f 593
 
0.7%
1cf4e481 182
 
0.2%
8b9a3257 45
 
0.1%
90c587b1 27
 
< 0.1%
436d55c2 11
 
< 0.1%
2024-02-13T20:53:02.532481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 147479
21.5%
a 79040
11.5%
6 75539
11.0%
8 75189
11.0%
7 73950
10.8%
2 68179
9.9%
e 67712
9.9%
1 18938
 
2.8%
0 18757
 
2.7%
5 17068
 
2.5%
Other values (6) 44477
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 373243
54.4%
Lowercase Letter 313085
45.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 75539
20.2%
8 75189
20.1%
7 73950
19.8%
2 68179
18.3%
1 18938
 
5.1%
0 18757
 
5.0%
5 17068
 
4.6%
3 14585
 
3.9%
4 5579
 
1.5%
9 5459
 
1.5%
Lowercase Letter
ValueCountFrequency (%)
c 147479
47.1%
a 79040
25.2%
e 67712
21.6%
f 8180
 
2.6%
b 5869
 
1.9%
d 4805
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 373243
54.4%
Latin 313085
45.6%

Most frequent character per script

Common
ValueCountFrequency (%)
6 75539
20.2%
8 75189
20.1%
7 73950
19.8%
2 68179
18.3%
1 18938
 
5.1%
0 18757
 
5.0%
5 17068
 
4.6%
3 14585
 
3.9%
4 5579
 
1.5%
9 5459
 
1.5%
Latin
ValueCountFrequency (%)
c 147479
47.1%
a 79040
25.2%
e 67712
21.6%
f 8180
 
2.6%
b 5869
 
1.9%
d 4805
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 686328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 147479
21.5%
a 79040
11.5%
6 75539
11.0%
8 75189
11.0%
7 73950
10.8%
2 68179
9.9%
e 67712
9.9%
1 18938
 
2.8%
0 18757
 
2.7%
5 17068
 
2.5%
Other values (6) 44477
 
6.5%

contractdate_551D
Text

MISSING 

Distinct4075
Distinct (%)5.0%
Missing3892
Missing (%)4.5%
Memory size670.4 KiB
2024-02-13T20:53:02.953307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters818990
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

Unique452 ?
Unique (%)0.6%

Sample

1st row2011-06-15
2nd row2019-01-04
3rd row2016-10-25
4th row2015-01-30
5th row2014-09-12
ValueCountFrequency (%)
2019-06-28 148
 
0.2%
2019-07-04 142
 
0.2%
2019-07-31 138
 
0.2%
2019-01-04 137
 
0.2%
2019-11-29 137
 
0.2%
2019-09-06 137
 
0.2%
2019-09-09 136
 
0.2%
2019-07-01 134
 
0.2%
2019-04-26 134
 
0.2%
2018-12-07 133
 
0.2%
Other values (4065) 80523
98.3%
2024-02-13T20:53:03.545427image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 190732
23.3%
- 163798
20.0%
1 145553
17.8%
2 139949
17.1%
9 47714
 
5.8%
8 34130
 
4.2%
7 23509
 
2.9%
3 22102
 
2.7%
6 18199
 
2.2%
4 16819
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 655192
80.0%
Dash Punctuation 163798
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 190732
29.1%
1 145553
22.2%
2 139949
21.4%
9 47714
 
7.3%
8 34130
 
5.2%
7 23509
 
3.6%
3 22102
 
3.4%
6 18199
 
2.8%
4 16819
 
2.6%
5 16485
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 163798
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 818990
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 190732
23.3%
- 163798
20.0%
1 145553
17.8%
2 139949
17.1%
9 47714
 
5.8%
8 34130
 
4.2%
7 23509
 
2.9%
3 22102
 
2.7%
6 18199
 
2.2%
4 16819
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 818990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 190732
23.3%
- 163798
20.0%
1 145553
17.8%
2 139949
17.1%
9 47714
 
5.8%
8 34130
 
4.2%
7 23509
 
2.9%
3 22102
 
2.7%
6 18199
 
2.2%
4 16819
 
2.1%
Distinct4537
Distinct (%)5.6%
Missing4079
Missing (%)4.8%
Memory size670.4 KiB
2024-02-13T20:53:04.080825image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters817120
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

Unique1679 ?
Unique (%)2.1%

Sample

1st row2031-06-13
2nd row2021-08-04
3rd row2019-10-25
4th row2021-01-30
5th row2021-09-12
ValueCountFrequency (%)
2021-10-14 283
 
0.3%
2021-11-14 248
 
0.3%
2021-05-14 245
 
0.3%
2021-09-14 226
 
0.3%
2022-03-14 190
 
0.2%
2024-07-14 175
 
0.2%
2021-08-14 162
 
0.2%
2022-01-14 160
 
0.2%
2020-12-15 149
 
0.2%
2021-01-04 149
 
0.2%
Other values (4527) 79725
97.6%
2024-02-13T20:53:04.617993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 209824
25.7%
0 209324
25.6%
- 163424
20.0%
1 107932
13.2%
9 23014
 
2.8%
3 21709
 
2.7%
4 19772
 
2.4%
8 16291
 
2.0%
5 15736
 
1.9%
7 15380
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 653696
80.0%
Dash Punctuation 163424
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 209824
32.1%
0 209324
32.0%
1 107932
16.5%
9 23014
 
3.5%
3 21709
 
3.3%
4 19772
 
3.0%
8 16291
 
2.5%
5 15736
 
2.4%
7 15380
 
2.4%
6 14714
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 163424
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 817120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 209824
25.7%
0 209324
25.6%
- 163424
20.0%
1 107932
13.2%
9 23014
 
2.8%
3 21709
 
2.7%
4 19772
 
2.4%
8 16291
 
2.0%
5 15736
 
1.9%
7 15380
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 817120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 209824
25.7%
0 209324
25.6%
- 163424
20.0%
1 107932
13.2%
9 23014
 
2.8%
3 21709
 
2.7%
4 19772
 
2.4%
8 16291
 
2.0%
5 15736
 
1.9%
7 15380
 
1.9%
Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:04.807845image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters686328
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row7241344e
2nd row7241344e
3rd row7241344e
4th row7241344e
5th row7241344e
ValueCountFrequency (%)
7241344e 74448
86.8%
a55475b1 4121
 
4.8%
8f3a197f 1906
 
2.2%
0dc85f9d 1243
 
1.4%
a52d5641 939
 
1.1%
dd67cff0 879
 
1.0%
b919198c 834
 
1.0%
04bf6e27 579
 
0.7%
885ce291 432
 
0.5%
83931972 177
 
0.2%
Other values (5) 233
 
0.3%
2024-02-13T20:53:05.102441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 229199
33.4%
1 83703
 
12.2%
7 82209
 
12.0%
3 76922
 
11.2%
2 76708
 
11.2%
e 75661
 
11.0%
5 15947
 
2.3%
f 7525
 
1.1%
a 6966
 
1.0%
9 6438
 
0.9%
Other values (6) 25050
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 581730
84.8%
Lowercase Letter 104598
 
15.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 229199
39.4%
1 83703
 
14.4%
7 82209
 
14.1%
3 76922
 
13.2%
2 76708
 
13.2%
5 15947
 
2.7%
9 6438
 
1.1%
8 5036
 
0.9%
0 2854
 
0.5%
6 2714
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
e 75661
72.3%
f 7525
 
7.2%
a 6966
 
6.7%
b 5536
 
5.3%
d 5404
 
5.2%
c 3506
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 581730
84.8%
Latin 104598
 
15.2%

Most frequent character per script

Common
ValueCountFrequency (%)
4 229199
39.4%
1 83703
 
14.4%
7 82209
 
14.1%
3 76922
 
13.2%
2 76708
 
13.2%
5 15947
 
2.7%
9 6438
 
1.1%
8 5036
 
0.9%
0 2854
 
0.5%
6 2714
 
0.5%
Latin
ValueCountFrequency (%)
e 75661
72.3%
f 7525
 
7.2%
a 6966
 
6.7%
b 5536
 
5.3%
d 5404
 
5.2%
c 3506
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 686328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 229199
33.4%
1 83703
 
12.2%
7 82209
 
12.0%
3 76922
 
11.2%
2 76708
 
11.2%
e 75661
 
11.0%
5 15947
 
2.3%
f 7525
 
1.1%
a 6966
 
1.0%
9 6438
 
0.9%
Other values (6) 25050
 
3.6%
Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:05.282541image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters686328
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

Unique8 ?
Unique (%)< 0.1%

Sample

1st row724be82a
2nd row724be82a
3rd row4257cbed
4th row1c9c5356
5th row724be82a
ValueCountFrequency (%)
4257cbed 41626
48.5%
1c9c5356 37508
43.7%
a55475b1 3892
 
4.5%
724be82a 699
 
0.8%
5015212e 550
 
0.6%
6f84dcc8 395
 
0.5%
f4e17141 356
 
0.4%
c6678a8f 198
 
0.2%
60e784d6 155
 
0.2%
37b13a3f 148
 
0.2%
Other values (15) 264
 
0.3%
2024-02-13T20:53:05.575190image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 129438
18.9%
c 117747
17.2%
4 47723
 
7.0%
7 47290
 
6.9%
b 46499
 
6.8%
2 44267
 
6.4%
1 43932
 
6.4%
e 43460
 
6.3%
d 42291
 
6.2%
6 38755
 
5.6%
Other values (6) 84926
12.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 430188
62.7%
Lowercase Letter 256140
37.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 129438
30.1%
4 47723
 
11.1%
7 47290
 
11.0%
2 44267
 
10.3%
1 43932
 
10.2%
6 38755
 
9.0%
3 38135
 
8.9%
9 37651
 
8.8%
8 2079
 
0.5%
0 918
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
c 117747
46.0%
b 46499
 
18.2%
e 43460
 
17.0%
d 42291
 
16.5%
a 4995
 
2.0%
f 1148
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 430188
62.7%
Latin 256140
37.3%

Most frequent character per script

Common
ValueCountFrequency (%)
5 129438
30.1%
4 47723
 
11.1%
7 47290
 
11.0%
2 44267
 
10.3%
1 43932
 
10.2%
6 38755
 
9.0%
3 38135
 
8.9%
9 37651
 
8.8%
8 2079
 
0.5%
0 918
 
0.2%
Latin
ValueCountFrequency (%)
c 117747
46.0%
b 46499
 
18.2%
e 43460
 
17.0%
d 42291
 
16.5%
a 4995
 
2.0%
f 1148
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 686328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 129438
18.9%
c 117747
17.2%
4 47723
 
7.0%
7 47290
 
6.9%
b 46499
 
6.8%
2 44267
 
6.4%
1 43932
 
6.4%
e 43460
 
6.3%
d 42291
 
6.2%
6 38755
 
5.6%
Other values (6) 84926
12.4%

credlmt_1052A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct9634
Distinct (%)34.9%
Missing58210
Missing (%)67.9%
Infinite0
Infinite (%)0.0%
Mean178935.6498
Minimum0
Maximum796800000
Zeros7143
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:05.731201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median36184
Q3121778
95-th percentile348000
Maximum796800000
Range796800000
Interquartile range (IQR)121778

Descriptive statistics

Standard deviation5274021.553
Coefficient of variation (CV)29.47440356
Kurtosis19120.24246
Mean178935.6498
Median Absolute Deviation (MAD)36184
Skewness130.7937608
Sum4935224157
Variance2.781530334 × 1013
MonotonicityNot monotonic
2024-02-13T20:53:05.899228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7143
 
8.3%
10000 1308
 
1.5%
20000 881
 
1.0%
30000 514
 
0.6%
200000 465
 
0.5%
100000 367
 
0.4%
40000 302
 
0.4%
60000 200
 
0.2%
120000 183
 
0.2%
50000 174
 
0.2%
Other values (9624) 16044
 
18.7%
(Missing) 58210
67.9%
ValueCountFrequency (%)
0 7143
8.3%
31.4 1
 
< 0.1%
50 2
 
< 0.1%
326 1
 
< 0.1%
821.4 1
 
< 0.1%
ValueCountFrequency (%)
796800000 1
< 0.1%
260000000 1
< 0.1%
140000000 1
< 0.1%
119290000 1
< 0.1%
77800000 1
< 0.1%

credlmt_228A
Real number (ℝ)

MISSING  ZEROS 

Distinct3535
Distinct (%)21.9%
Missing69661
Missing (%)81.2%
Infinite0
Infinite (%)0.0%
Mean52317.52293
Minimum0
Maximum4420000
Zeros4484
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:06.064228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median22600
Q360000
95-th percentile179873.3745
Maximum4420000
Range4420000
Interquartile range (IQR)60000

Descriptive statistics

Standard deviation128082.4582
Coefficient of variation (CV)2.448175124
Kurtosis296.6663376
Mean52317.52293
Median Absolute Deviation (MAD)22600
Skewness13.50831079
Sum843881644.8
Variance1.640511609 × 1010
MonotonicityNot monotonic
2024-02-13T20:53:06.221364image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4484
 
5.2%
10000 1001
 
1.2%
20000 824
 
1.0%
30000 411
 
0.5%
40000 261
 
0.3%
50000 200
 
0.2%
60000 166
 
0.2%
58000 163
 
0.2%
70000 138
 
0.2%
46000 136
 
0.2%
Other values (3525) 8346
 
9.7%
(Missing) 69661
81.2%
ValueCountFrequency (%)
0 4484
5.2%
0.2 18
 
< 0.1%
2.8 1
 
< 0.1%
6.046 1
 
< 0.1%
10.400001 1
 
< 0.1%
ValueCountFrequency (%)
4420000 1
< 0.1%
4230000 1
< 0.1%
3200000 1
< 0.1%
2957235.2 2
< 0.1%
2760000 1
< 0.1%

credlmt_3940954A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct9659
Distinct (%)25.3%
Missing47573
Missing (%)55.5%
Infinite0
Infinite (%)0.0%
Mean130360.3435
Minimum0
Maximum300000000
Zeros9947
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:06.374401image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20000
Q378000
95-th percentile287418.783
Maximum300000000
Range300000000
Interquartile range (IQR)78000

Descriptive statistics

Standard deviation2570305.488
Coefficient of variation (CV)19.71692785
Kurtosis8440.214432
Mean130360.3435
Median Absolute Deviation (MAD)20000
Skewness84.25533078
Sum4982111610
Variance6.606470301 × 1012
MonotonicityNot monotonic
2024-02-13T20:53:06.535362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9947
 
11.6%
10000 3810
 
4.4%
20000 2996
 
3.5%
30000 1642
 
1.9%
200000 735
 
0.9%
100000 569
 
0.7%
40000 395
 
0.5%
60000 247
 
0.3%
80000 206
 
0.2%
120000 193
 
0.2%
Other values (9649) 17478
 
20.4%
(Missing) 47573
55.5%
ValueCountFrequency (%)
0 9947
11.6%
0.2 3
 
< 0.1%
16.6 1
 
< 0.1%
20.800001 1
 
< 0.1%
31.4 1
 
< 0.1%
ValueCountFrequency (%)
300000000 1
< 0.1%
260000000 1
< 0.1%
180000000 1
< 0.1%
119290000 1
< 0.1%
100000000 1
< 0.1%
Distinct151
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:06.850704image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.23032719
Min length8

Characters and Unicode

Total characters706088
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

Unique37 ?
Unique (%)< 0.1%

Sample

1st rowP164_34_168
2nd rowP164_34_168
3rd rowc5a72b57
4th rowb619fa46
5th row74bd67a8
ValueCountFrequency (%)
b619fa46 31183
36.3%
p0_31_66 19199
22.4%
p33_61_72 5889
 
6.9%
c5a72b57 5554
 
6.5%
a55475b1 3892
 
4.5%
a7a613e0 3586
 
4.2%
50babcd4 2481
 
2.9%
p133_127_114 2353
 
2.7%
74bd67a8 1478
 
1.7%
9325d851 1346
 
1.6%
Other values (141) 8830
 
10.3%
2024-02-13T20:53:07.339945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 119047
16.9%
1 81108
11.5%
a 58727
8.3%
_ 58104
8.2%
b 53206
 
7.5%
4 46882
 
6.6%
3 45311
 
6.4%
9 37144
 
5.3%
f 33749
 
4.8%
5 32379
 
4.6%
Other values (8) 140431
19.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 449596
63.7%
Lowercase Letter 169336
 
24.0%
Connector Punctuation 58104
 
8.2%
Uppercase Letter 29052
 
4.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 119047
26.5%
1 81108
18.0%
4 46882
 
10.4%
3 45311
 
10.1%
9 37144
 
8.3%
5 32379
 
7.2%
7 32317
 
7.2%
0 28319
 
6.3%
2 19149
 
4.3%
8 7940
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
a 58727
34.7%
b 53206
31.4%
f 33749
19.9%
c 10834
 
6.4%
d 7214
 
4.3%
e 5606
 
3.3%
Connector Punctuation
ValueCountFrequency (%)
_ 58104
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 29052
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 507700
71.9%
Latin 198388
 
28.1%

Most frequent character per script

Common
ValueCountFrequency (%)
6 119047
23.4%
1 81108
16.0%
_ 58104
11.4%
4 46882
 
9.2%
3 45311
 
8.9%
9 37144
 
7.3%
5 32379
 
6.4%
7 32317
 
6.4%
0 28319
 
5.6%
2 19149
 
3.8%
Latin
ValueCountFrequency (%)
a 58727
29.6%
b 53206
26.8%
f 33749
17.0%
P 29052
14.6%
c 10834
 
5.5%
d 7214
 
3.6%
e 5606
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 706088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 119047
16.9%
1 81108
11.5%
a 58727
8.3%
_ 58104
8.2%
b 53206
 
7.5%
4 46882
 
6.6%
3 45311
 
6.4%
9 37144
 
5.3%
f 33749
 
4.8%
5 32379
 
4.6%
Other values (8) 140431
19.9%

credquantity_1099L
Real number (ℝ)

MISSING 

Distinct14
Distinct (%)< 0.1%
Missing32773
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean1.544739522
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:07.486914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum16
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8641899014
Coefficient of variation (CV)0.5594405328
Kurtosis11.37325653
Mean1.544739522
Median Absolute Deviation (MAD)0
Skewness2.476195909
Sum81899
Variance0.7468241857
MonotonicityNot monotonic
2024-02-13T20:53:07.621463image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 32682
38.1%
2 14562
17.0%
3 4054
 
4.7%
4 1117
 
1.3%
5 359
 
0.4%
6 137
 
0.2%
7 56
 
0.1%
8 31
 
< 0.1%
9 9
 
< 0.1%
10 5
 
< 0.1%
Other values (4) 6
 
< 0.1%
(Missing) 32773
38.2%
ValueCountFrequency (%)
1 32682
38.1%
2 14562
17.0%
3 4054
 
4.7%
4 1117
 
1.3%
5 359
 
0.4%
ValueCountFrequency (%)
16 1
 
< 0.1%
13 1
 
< 0.1%
12 2
 
< 0.1%
11 2
 
< 0.1%
10 5
< 0.1%

credquantity_984L
Real number (ℝ)

MISSING 

Distinct72
Distinct (%)0.2%
Missing39563
Missing (%)46.1%
Infinite0
Infinite (%)0.0%
Mean4.565220213
Minimum1
Maximum146
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:07.766781image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q36
95-th percentile14
Maximum146
Range145
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.170388073
Coefficient of variation (CV)1.132560497
Kurtosis37.57028269
Mean4.565220213
Median Absolute Deviation (MAD)2
Skewness3.974553327
Sum211041
Variance26.73291282
MonotonicityNot monotonic
2024-02-13T20:53:07.927817image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 13723
 
16.0%
2 8000
 
9.3%
3 5162
 
6.0%
4 3790
 
4.4%
5 3000
 
3.5%
6 2314
 
2.7%
7 1912
 
2.2%
8 1508
 
1.8%
9 1199
 
1.4%
10 1015
 
1.2%
Other values (62) 4605
 
5.4%
(Missing) 39563
46.1%
ValueCountFrequency (%)
1 13723
16.0%
2 8000
9.3%
3 5162
 
6.0%
4 3790
 
4.4%
5 3000
 
3.5%
ValueCountFrequency (%)
146 1
< 0.1%
93 1
< 0.1%
91 1
< 0.1%
82 2
< 0.1%
79 2
< 0.1%

debtpastduevalue_732A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct5853
Distinct (%)7.2%
Missing4574
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean3791.878351
Minimum0
Maximum41138710
Zeros74968
Zeros (%)87.4%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:08.089627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4262.212
Maximum41138710
Range41138710
Interquartile range (IQR)0

Descriptive statistics

Standard deviation158238.0605
Coefficient of variation (CV)41.73078508
Kurtosis57161.56818
Mean3791.878351
Median Absolute Deviation (MAD)0
Skewness227.1087697
Sum307964984
Variance2.503928379 × 1010
MonotonicityNot monotonic
2024-02-13T20:53:08.235628image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 74968
87.4%
0.2 22
 
< 0.1%
7800 14
 
< 0.1%
2600 12
 
< 0.1%
11200 10
 
< 0.1%
5200 10
 
< 0.1%
20 10
 
< 0.1%
10400 9
 
< 0.1%
8000 9
 
< 0.1%
14 8
 
< 0.1%
Other values (5843) 6145
 
7.2%
(Missing) 4574
 
5.3%
ValueCountFrequency (%)
0 74968
87.4%
0.022 1
 
< 0.1%
0.032 1
 
< 0.1%
0.051999997 1
 
< 0.1%
0.056 1
 
< 0.1%
ValueCountFrequency (%)
41138710 1
< 0.1%
14656629 1
< 0.1%
5044352 1
< 0.1%
4675593.5 1
< 0.1%
3076680.8 1
< 0.1%

debtvalue_227A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct41241
Distinct (%)94.4%
Missing42110
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean165118.3306
Minimum0
Maximum41619050
Zeros1090
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:08.385306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2530.4001
Q112946
median35893.414
Q3110801.625
95-th percentile697153.75
Maximum41619050
Range41619050
Interquartile range (IQR)97855.625

Descriptive statistics

Standard deviation550497.1326
Coefficient of variation (CV)3.333955295
Kurtosis1028.157633
Mean165118.3306
Median Absolute Deviation (MAD)28933.556
Skewness20.95291114
Sum7212533800
Variance3.03047093 × 1011
MonotonicityNot monotonic
2024-02-13T20:53:08.538333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1090
 
1.3%
60000 37
 
< 0.1%
100000 32
 
< 0.1%
20000 31
 
< 0.1%
2600 28
 
< 0.1%
5200 27
 
< 0.1%
8000 26
 
< 0.1%
40000 24
 
< 0.1%
4000 24
 
< 0.1%
200000 22
 
< 0.1%
Other values (41231) 42340
49.4%
(Missing) 42110
49.1%
ValueCountFrequency (%)
0 1090
1.3%
0.2 19
 
< 0.1%
2.75 1
 
< 0.1%
3.266 1
 
< 0.1%
5.4 1
 
< 0.1%
ValueCountFrequency (%)
41619050 1
< 0.1%
23793790 1
< 0.1%
22537060 1
< 0.1%
19998358 1
< 0.1%
19842902 1
< 0.1%

dpd_550P
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct4871
Distinct (%)9.2%
Missing32773
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean25696.7525
Minimum0
Maximum207823776
Zeros47788
Zeros (%)55.7%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:08.691306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile37316.35
Maximum207823776
Range207823776
Interquartile range (IQR)0

Descriptive statistics

Standard deviation968643.4449
Coefficient of variation (CV)37.69516965
Kurtosis40360.64974
Mean25696.7525
Median Absolute Deviation (MAD)0
Skewness192.5710284
Sum1362390424
Variance9.382701233 × 1011
MonotonicityNot monotonic
2024-02-13T20:53:08.845305image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47788
55.7%
1 12
 
< 0.1%
90 11
 
< 0.1%
45 7
 
< 0.1%
14400 7
 
< 0.1%
18 7
 
< 0.1%
4 7
 
< 0.1%
35100 7
 
< 0.1%
12780 6
 
< 0.1%
2 6
 
< 0.1%
Other values (4861) 5160
 
6.0%
(Missing) 32773
38.2%
ValueCountFrequency (%)
0 47788
55.7%
1 12
 
< 0.1%
2 6
 
< 0.1%
3 1
 
< 0.1%
4 7
 
< 0.1%
ValueCountFrequency (%)
207823776 1
< 0.1%
65954828 1
< 0.1%
21040168 1
< 0.1%
13845062 1
< 0.1%
12560014 1
< 0.1%

dpd_733P
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct42
Distinct (%)0.1%
Missing39563
Missing (%)46.1%
Infinite0
Infinite (%)0.0%
Mean372.2601021
Minimum0
Maximum15443942
Zeros46181
Zeros (%)53.8%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:08.988557image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15443942
Range15443942
Interquartile range (IQR)0

Descriptive statistics

Standard deviation71871.36749
Coefficient of variation (CV)193.0676081
Kurtosis46121.09907
Mean372.2601021
Median Absolute Deviation (MAD)0
Skewness214.6372448
Sum17208840
Variance5165493464
MonotonicityNot monotonic
2024-02-13T20:53:09.142966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 46181
53.8%
1 5
 
< 0.1%
180000 2
 
< 0.1%
32412 2
 
< 0.1%
3577 1
 
< 0.1%
11 1
 
< 0.1%
13966 1
 
< 0.1%
55583 1
 
< 0.1%
20175 1
 
< 0.1%
18108 1
 
< 0.1%
Other values (32) 32
 
< 0.1%
(Missing) 39563
46.1%
ValueCountFrequency (%)
0 46181
53.8%
1 5
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
15443942 1
< 0.1%
277950 1
< 0.1%
234000 1
< 0.1%
197133 1
< 0.1%
180000 2
< 0.1%

dpdmax_851P
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct20157
Distinct (%)24.8%
Missing4567
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean35378.96084
Minimum0
Maximum185124192
Zeros55895
Zeros (%)65.2%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:09.300969image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37073.5
95-th percentile82074.85
Maximum185124192
Range185124192
Interquartile range (IQR)7073.5

Descriptive statistics

Standard deviation877296.7937
Coefficient of variation (CV)24.7971329
Kurtosis29327.89094
Mean35378.96084
Median Absolute Deviation (MAD)0
Skewness157.2726863
Sum2873620715
Variance7.696496642 × 1011
MonotonicityNot monotonic
2024-02-13T20:53:09.456970image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 55895
65.2%
1 45
 
0.1%
3 32
 
< 0.1%
45 31
 
< 0.1%
4 26
 
< 0.1%
18 25
 
< 0.1%
8 24
 
< 0.1%
27 24
 
< 0.1%
7 24
 
< 0.1%
2 23
 
< 0.1%
Other values (20147) 25075
29.2%
(Missing) 4567
 
5.3%
ValueCountFrequency (%)
0 55895
65.2%
1 45
 
0.1%
2 23
 
< 0.1%
3 32
 
< 0.1%
4 26
 
< 0.1%
ValueCountFrequency (%)
185124192 1
< 0.1%
120050072 1
< 0.1%
65954828 1
< 0.1%
52138384 1
< 0.1%
38912444 1
< 0.1%

dpdmaxdatemonth_804T
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing4567
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean6.610262976
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:09.589968image/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 deviation3.485598536
Coefficient of variation (CV)0.527301039
Kurtosis-1.224323176
Mean6.610262976
Median Absolute Deviation (MAD)3
Skewness-0.06871844396
Sum536912
Variance12.14939715
MonotonicityNot monotonic
2024-02-13T20:53:09.733012image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 7592
8.8%
1 7175
8.4%
7 7101
8.3%
12 7058
8.2%
11 6999
8.2%
2 6853
8.0%
9 6825
8.0%
8 6780
7.9%
5 6685
7.8%
4 6375
7.4%
Other values (2) 11781
13.7%
ValueCountFrequency (%)
1 7175
8.4%
2 6853
8.0%
3 5519
6.4%
4 6375
7.4%
5 6685
7.8%
ValueCountFrequency (%)
12 7058
8.2%
11 6999
8.2%
10 7592
8.8%
9 6825
8.0%
8 6780
7.9%

dpdmaxdateyear_742T
Real number (ℝ)

MISSING 

Distinct15
Distinct (%)< 0.1%
Missing4567
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean2018.226226
Minimum1900
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:09.855633image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile2014
Q12018
median2019
Q32019
95-th percentile2020
Maximum2020
Range120
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.783527979
Coefficient of variation (CV)0.0008837106343
Kurtosis240.9401748
Mean2018.226226
Median Absolute Deviation (MAD)1
Skewness-5.317593134
Sum163928407
Variance3.180972051
MonotonicityNot monotonic
2024-02-13T20:53:09.983665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2019 36628
42.7%
2018 16003
18.7%
2020 12455
 
14.5%
2017 5490
 
6.4%
2016 3265
 
3.8%
2015 2992
 
3.5%
2014 1822
 
2.1%
2013 1400
 
1.6%
2012 636
 
0.7%
2011 404
 
0.5%
Other values (5) 129
 
0.2%
(Missing) 4567
 
5.3%
ValueCountFrequency (%)
1900 1
 
< 0.1%
2007 6
 
< 0.1%
2008 11
 
< 0.1%
2009 53
0.1%
2010 58
0.1%
ValueCountFrequency (%)
2020 12455
 
14.5%
2019 36628
42.7%
2018 16003
18.7%
2017 5490
 
6.4%
2016 3265
 
3.8%

installmentamount_644A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct83
Distinct (%)0.2%
Missing39563
Missing (%)46.1%
Infinite0
Infinite (%)0.0%
Mean347.6580292
Minimum0
Maximum11418603
Zeros46141
Zeros (%)53.8%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:10.129077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum11418603
Range11418603
Interquartile range (IQR)0

Descriptive statistics

Standard deviation54110.26234
Coefficient of variation (CV)155.6422052
Kurtosis42939.42488
Mean347.6580292
Median Absolute Deviation (MAD)0
Skewness204.4856852
Sum16071535.38
Variance2927920490
MonotonicityNot monotonic
2024-02-13T20:53:10.285586image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46141
53.8%
0.2 5
 
< 0.1%
7202.6 2
 
< 0.1%
3.8 1
 
< 0.1%
348081.72 1
 
< 0.1%
4.6 1
 
< 0.1%
15.2 1
 
< 0.1%
683310.44 1
 
< 0.1%
1638.4 1
 
< 0.1%
4024 1
 
< 0.1%
Other values (73) 73
 
0.1%
(Missing) 39563
46.1%
ValueCountFrequency (%)
0 46141
53.8%
0.2 5
 
< 0.1%
0.6 1
 
< 0.1%
1 1
 
< 0.1%
1.2 1
 
< 0.1%
ValueCountFrequency (%)
11418603 1
< 0.1%
2036521.2 1
< 0.1%
683310.44 1
< 0.1%
382066.44 1
< 0.1%
348081.72 1
< 0.1%

installmentamount_833A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct41972
Distinct (%)79.2%
Missing32773
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean156888.8613
Minimum0
Maximum69658536
Zeros10054
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:10.435586image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16116.5
median36264.387
Q3128580.45
95-th percentile618368.794
Maximum69658536
Range69658536
Interquartile range (IQR)122463.95

Descriptive statistics

Standard deviation626662.6669
Coefficient of variation (CV)3.994309485
Kurtosis4336.629935
Mean156888.8613
Median Absolute Deviation (MAD)36264.387
Skewness48.18237574
Sum8317933650
Variance3.927060981 × 1011
MonotonicityNot monotonic
2024-02-13T20:53:10.591622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10054
 
11.7%
200000 38
 
< 0.1%
100000 34
 
< 0.1%
20000 32
 
< 0.1%
10000 23
 
< 0.1%
30000 21
 
< 0.1%
60000 21
 
< 0.1%
40000 17
 
< 0.1%
4000 15
 
< 0.1%
6000 12
 
< 0.1%
Other values (41962) 42751
49.8%
(Missing) 32773
38.2%
ValueCountFrequency (%)
0 10054
11.7%
0.2 6
 
< 0.1%
0.4 1
 
< 0.1%
0.42 1
 
< 0.1%
0.578 1
 
< 0.1%
ValueCountFrequency (%)
69658536 1
< 0.1%
54055732 1
< 0.1%
41619050 1
< 0.1%
19842902 1
< 0.1%
19506366 1
< 0.1%

instlamount_892A
Real number (ℝ)

MISSING  SKEWED 

Distinct31159
Distinct (%)71.6%
Missing42298
Missing (%)49.3%
Infinite0
Infinite (%)0.0%
Mean9814.832671
Minimum0
Maximum41138710
Zeros529
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:10.757622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1085.6
Q12372.6
median4228.2
Q37804.8003
95-th percentile22573.2098
Maximum41138710
Range41138710
Interquartile range (IQR)5432.2003

Descriptive statistics

Standard deviation211973.8004
Coefficient of variation (CV)21.59729132
Kurtosis33114.22922
Mean9814.832671
Median Absolute Deviation (MAD)2238.6003
Skewness175.6797601
Sum426876517.4
Variance4.493289206 × 1010
MonotonicityNot monotonic
2024-02-13T20:53:10.925587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 529
 
0.6%
4098.8604 103
 
0.1%
2049.4302 88
 
0.1%
6148.288 77
 
0.1%
7477.314 70
 
0.1%
4200.492 62
 
0.1%
3074.144 62
 
0.1%
2100.246 55
 
0.1%
3150.3699 52
 
0.1%
4000 49
 
0.1%
Other values (31149) 42346
49.4%
(Missing) 42298
49.3%
ValueCountFrequency (%)
0 529
0.6%
0.2 17
 
< 0.1%
2.176 2
 
< 0.1%
5.438 1
 
< 0.1%
9.2 1
 
< 0.1%
ValueCountFrequency (%)
41138710 1
< 0.1%
14656629 1
< 0.1%
2606511.8 1
< 0.1%
1918291.6 1
< 0.1%
1773930.4 1
< 0.1%

interesteffectiverate_369L
Real number (ℝ)

MISSING 

Distinct2642
Distinct (%)27.8%
Missing76285
Missing (%)88.9%
Infinite0
Infinite (%)0.0%
Mean504.6123995
Minimum-1.1
Maximum73000
Zeros332
Zeros (%)0.4%
Negative1
Negative (%)< 0.1%
Memory size670.4 KiB
2024-02-13T20:53:11.070001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1.1
5-th percentile0.11
Q15.35
median23.55
Q340.59
95-th percentile52.41
Maximum73000
Range73001.1
Interquartile range (IQR)35.24

Descriptive statistics

Standard deviation5337.928146
Coefficient of variation (CV)10.57827384
Kurtosis153.6385345
Mean504.6123995
Median Absolute Deviation (MAD)17.49
Skewness12.25670472
Sum4796845.47
Variance28493476.89
MonotonicityNot monotonic
2024-02-13T20:53:11.226522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.12 1103
 
1.3%
0 332
 
0.4%
0.11 237
 
0.3%
5.11 156
 
0.2%
56 103
 
0.1%
26.8 87
 
0.1%
48.02 75
 
0.1%
30 70
 
0.1%
48.18 57
 
0.1%
26.28 55
 
0.1%
Other values (2632) 7231
 
8.4%
(Missing) 76285
88.9%
ValueCountFrequency (%)
-1.1 1
 
< 0.1%
0 332
0.4%
0.06 1
 
< 0.1%
0.07 2
 
< 0.1%
0.08 8
 
< 0.1%
ValueCountFrequency (%)
73000 20
< 0.1%
69350 26
< 0.1%
62415 1
 
< 0.1%
55480 2
 
< 0.1%
48545 1
 
< 0.1%

interestrateyearly_538L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct418
Distinct (%)1.5%
Missing56966
Missing (%)66.4%
Infinite0
Infinite (%)0.0%
Mean52.4933464
Minimum0
Maximum46334.1
Zeros4116
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:11.384314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median39
Q342
95-th percentile45
Maximum46334.1
Range46334.1
Interquartile range (IQR)35

Descriptive statistics

Standard deviation636.0472099
Coefficient of variation (CV)12.11672056
Kurtosis3006.402748
Mean52.4933464
Median Absolute Deviation (MAD)6
Skewness50.23514402
Sum1513120.71
Variance404556.0532
MonotonicityNot monotonic
2024-02-13T20:53:11.535324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 4662
 
5.4%
0 4116
 
4.8%
39 2894
 
3.4%
42 2778
 
3.2%
0.12 2191
 
2.6%
40 1339
 
1.6%
43.3 997
 
1.2%
43 940
 
1.1%
40.05 478
 
0.6%
41.75 415
 
0.5%
Other values (408) 8015
 
9.3%
(Missing) 56966
66.4%
ValueCountFrequency (%)
0 4116
4.8%
0.01 2
 
< 0.1%
0.02 67
 
0.1%
0.12 2191
2.6%
0.15 9
 
< 0.1%
ValueCountFrequency (%)
46334.1 1
 
< 0.1%
37133.4 5
< 0.1%
11907.9 3
< 0.1%
9816.7 4
< 0.1%
9816.6 1
 
< 0.1%

lastupdate_260D
Text

MISSING 

Distinct611
Distinct (%)0.7%
Missing3892
Missing (%)4.5%
Memory size670.4 KiB
2024-02-13T20:53:11.921366image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters818990
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

Unique31 ?
Unique (%)< 0.1%

Sample

1st row2019-01-20
2nd row2019-01-20
3rd row2019-01-10
4th row2019-01-19
5th row2019-01-13
ValueCountFrequency (%)
2019-11-23 1585
 
1.9%
2020-01-08 1124
 
1.4%
2019-06-14 1084
 
1.3%
2019-10-11 1071
 
1.3%
2019-10-26 1042
 
1.3%
2019-07-17 1017
 
1.2%
2019-12-06 985
 
1.2%
2020-09-01 897
 
1.1%
2019-07-14 879
 
1.1%
2019-06-13 857
 
1.0%
Other values (601) 71358
87.1%
2024-02-13T20:53:12.530198image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 221989
27.1%
- 163798
20.0%
2 162455
19.8%
1 120883
14.8%
9 56228
 
6.9%
3 18684
 
2.3%
6 18493
 
2.3%
7 17680
 
2.2%
5 14016
 
1.7%
4 12582
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 655192
80.0%
Dash Punctuation 163798
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 221989
33.9%
2 162455
24.8%
1 120883
18.5%
9 56228
 
8.6%
3 18684
 
2.9%
6 18493
 
2.8%
7 17680
 
2.7%
5 14016
 
2.1%
4 12582
 
1.9%
8 12182
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 163798
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 818990
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 221989
27.1%
- 163798
20.0%
2 162455
19.8%
1 120883
14.8%
9 56228
 
6.9%
3 18684
 
2.3%
6 18493
 
2.3%
7 17680
 
2.2%
5 14016
 
1.7%
4 12582
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 818990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 221989
27.1%
- 163798
20.0%
2 162455
19.8%
1 120883
14.8%
9 56228
 
6.9%
3 18684
 
2.3%
6 18493
 
2.3%
7 17680
 
2.2%
5 14016
 
1.7%
4 12582
 
1.5%

maxdebtpduevalodued_3940955A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct1473
Distinct (%)1.8%
Missing4567
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean15.07405233
Minimum0
Maximum147470.61
Zeros56144
Zeros (%)65.4%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:12.693095image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.2
95-th percentile14.6
Maximum147470.61
Range147470.61
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation1036.106798
Coefficient of variation (CV)68.73445674
Kurtosis20195.1149
Mean15.07405233
Median Absolute Deviation (MAD)0
Skewness141.928446
Sum1224374.826
Variance1073517.296
MonotonicityNot monotonic
2024-02-13T20:53:12.850376image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 56144
65.4%
0.2 6127
 
7.1%
0.4 1898
 
2.2%
0.6 1454
 
1.7%
0.8 1244
 
1.5%
1 895
 
1.0%
1.6 595
 
0.7%
1.4 491
 
0.6%
1.2 479
 
0.6%
1.8000001 406
 
0.5%
Other values (1463) 11491
 
13.4%
(Missing) 4567
 
5.3%
ValueCountFrequency (%)
0 56144
65.4%
0.2 6127
 
7.1%
0.4 1898
 
2.2%
0.6 1454
 
1.7%
0.8 1244
 
1.5%
ValueCountFrequency (%)
147470.61 2
< 0.1%
147448.8 2
< 0.1%
1004.4 1
< 0.1%
1003.60004 1
< 0.1%
967.8 1
< 0.1%

num_group1
Real number (ℝ)

ZEROS 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.011749484
Minimum0
Maximum20
Zeros36500
Zeros (%)42.5%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:12.990378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum20
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.234287688
Coefficient of variation (CV)1.21995386
Kurtosis8.072424295
Mean1.011749484
Median Absolute Deviation (MAD)1
Skewness1.993541989
Sum86799
Variance1.523466098
MonotonicityNot monotonic
2024-02-13T20:53:13.129681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 36500
42.5%
1 27670
32.3%
2 12365
 
14.4%
3 5506
 
6.4%
4 2185
 
2.5%
5 894
 
1.0%
6 370
 
0.4%
7 163
 
0.2%
8 67
 
0.1%
9 32
 
< 0.1%
Other values (11) 39
 
< 0.1%
ValueCountFrequency (%)
0 36500
42.5%
1 27670
32.3%
2 12365
 
14.4%
3 5506
 
6.4%
4 2185
 
2.5%
ValueCountFrequency (%)
20 1
< 0.1%
19 1
< 0.1%
18 1
< 0.1%
17 1
< 0.1%
16 2
< 0.1%

numberofinstls_810L
Real number (ℝ)

MISSING 

Distinct243
Distinct (%)0.6%
Missing42298
Missing (%)49.3%
Infinite0
Infinite (%)0.0%
Mean30.49626377
Minimum0
Maximum358
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:13.718192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q112
median22
Q336
95-th percentile84
Maximum358
Range358
Interquartile range (IQR)24

Descriptive statistics

Standard deviation36.0600697
Coefficient of variation (CV)1.182442216
Kurtosis19.30316366
Mean30.49626377
Median Absolute Deviation (MAD)10
Skewness3.874254909
Sum1326374
Variance1300.328627
MonotonicityNot monotonic
2024-02-13T20:53:13.871389image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 9274
 
10.8%
24 5881
 
6.9%
36 3546
 
4.1%
18 2644
 
3.1%
48 2503
 
2.9%
6 2361
 
2.8%
1 2029
 
2.4%
60 1985
 
2.3%
16 1799
 
2.1%
30 958
 
1.1%
Other values (233) 10513
 
12.3%
(Missing) 42298
49.3%
ValueCountFrequency (%)
0 9
 
< 0.1%
1 2029
2.4%
2 4
 
< 0.1%
3 651
 
0.8%
4 284
 
0.3%
ValueCountFrequency (%)
358 1
< 0.1%
356 1
< 0.1%
336 1
< 0.1%
328 1
< 0.1%
316 1
< 0.1%

overdueamountmax_950A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct1834
Distinct (%)2.3%
Missing4567
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean18.25158857
Minimum0
Maximum147470.61
Zeros56003
Zeros (%)65.3%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:14.049812image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.4
95-th percentile17.6
Maximum147470.61
Range147470.61
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation1036.772496
Coefficient of variation (CV)56.80450729
Kurtosis20143.54743
Mean18.25158857
Median Absolute Deviation (MAD)0
Skewness141.6572117
Sum1482467.03
Variance1074897.208
MonotonicityNot monotonic
2024-02-13T20:53:14.206682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 56003
65.3%
0.2 3794
 
4.4%
0.4 1381
 
1.6%
0.8 1063
 
1.2%
0.6 1032
 
1.2%
1 884
 
1.0%
1.6 632
 
0.7%
1.4 514
 
0.6%
1.2 493
 
0.6%
2.2 480
 
0.6%
Other values (1824) 14948
 
17.4%
(Missing) 4567
 
5.3%
ValueCountFrequency (%)
0 56003
65.3%
0.2 3794
 
4.4%
0.4 1381
 
1.6%
0.6 1032
 
1.2%
0.8 1063
 
1.2%
ValueCountFrequency (%)
147470.61 2
< 0.1%
147463.4 1
< 0.1%
147448.8 1
< 0.1%
1016.2 1
< 0.1%
1004.4 1
< 0.1%

overdueamountmaxdatemonth_494T
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing4567
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean6.630675662
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:14.337682image/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 deviation3.494756984
Coefficient of variation (CV)0.5270589548
Kurtosis-1.231697148
Mean6.630675662
Median Absolute Deviation (MAD)3
Skewness-0.07286293658
Sum538570
Variance12.21332638
MonotonicityNot monotonic
2024-02-13T20:53:14.449682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 7730
9.0%
12 7188
8.4%
11 7128
8.3%
1 7115
8.3%
7 7036
8.2%
2 6827
8.0%
9 6730
7.8%
5 6710
7.8%
8 6631
7.7%
6 6284
7.3%
Other values (2) 11845
13.8%
ValueCountFrequency (%)
1 7115
8.3%
2 6827
8.0%
3 5667
6.6%
4 6178
7.2%
5 6710
7.8%
ValueCountFrequency (%)
12 7188
8.4%
11 7128
8.3%
10 7730
9.0%
9 6730
7.8%
8 6631
7.7%

overdueamountmaxdateyear_432T
Real number (ℝ)

MISSING 

Distinct15
Distinct (%)< 0.1%
Missing4567
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean2018.290825
Minimum1900
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:14.563682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile2015
Q12018
median2019
Q32019
95-th percentile2020
Maximum2020
Range120
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.741405269
Coefficient of variation (CV)0.0008628118641
Kurtosis265.4091463
Mean2018.290825
Median Absolute Deviation (MAD)1
Skewness-5.599562516
Sum163933654
Variance3.032492312
MonotonicityNot monotonic
2024-02-13T20:53:14.688173image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2019 37174
43.3%
2018 15397
17.9%
2020 13388
 
15.6%
2017 5232
 
6.1%
2016 3057
 
3.6%
2015 2922
 
3.4%
2014 1807
 
2.1%
2013 1230
 
1.4%
2012 532
 
0.6%
2011 395
 
0.5%
Other values (5) 90
 
0.1%
(Missing) 4567
 
5.3%
ValueCountFrequency (%)
1900 1
 
< 0.1%
2007 5
 
< 0.1%
2008 4
 
< 0.1%
2009 28
< 0.1%
2010 52
0.1%
ValueCountFrequency (%)
2020 13388
 
15.6%
2019 37174
43.3%
2018 15397
17.9%
2017 5232
 
6.1%
2016 3057
 
3.6%
Distinct5
Distinct (%)0.2%
Missing83764
Missing (%)97.6%
Memory size670.4 KiB
2024-02-13T20:53:14.882986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length42
Median length29
Mean length29.07745437
Min length29

Characters and Unicode

Total characters58940
Distinct characters34
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
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 rowЕжемесячные платежи - 30 дней
2nd rowЕжемесячные платежи - 30 дней
3rd rowЕжемесячные платежи - 30 дней
4th rowЕжемесячные платежи - 30 дней
5th rowЕжемесячные платежи - 30 дней
ValueCountFrequency (%)
2011
19.8%
дней 2011
19.8%
платежи 2011
19.8%
ежемесячные 2006
19.8%
30 2006
19.8%
взносы 10
 
0.1%
с 10
 
0.1%
нерегулярной 10
 
0.1%
периодичностью 10
 
0.1%
срока 6
 
0.1%
Other values (9) 40
 
0.4%
2024-02-13T20:53:15.240112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
е 10101
17.1%
8104
 
13.7%
н 4077
 
6.9%
ж 4019
 
6.8%
и 2049
 
3.5%
с 2048
 
3.5%
д 2042
 
3.5%
т 2035
 
3.5%
а 2027
 
3.4%
л 2026
 
3.4%
Other values (24) 20412
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42773
72.6%
Space Separator 8104
 
13.7%
Decimal Number 4025
 
6.8%
Uppercase Letter 2027
 
3.4%
Dash Punctuation 2011
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
е 10101
23.6%
н 4077
 
9.5%
ж 4019
 
9.4%
и 2049
 
4.8%
с 2048
 
4.8%
д 2042
 
4.8%
т 2035
 
4.8%
а 2027
 
4.7%
л 2026
 
4.7%
я 2022
 
4.7%
Other values (14) 10327
24.1%
Decimal Number
ValueCountFrequency (%)
0 2011
50.0%
3 2006
49.8%
1 3
 
0.1%
8 3
 
0.1%
9 2
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
Е 2008
99.1%
В 16
 
0.8%
П 3
 
0.1%
Space Separator
ValueCountFrequency (%)
8104
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2011
100.0%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic 44800
76.0%
Common 14140
 
24.0%

Most frequent character per script

Cyrillic
ValueCountFrequency (%)
е 10101
22.5%
н 4077
 
9.1%
ж 4019
 
9.0%
и 2049
 
4.6%
с 2048
 
4.6%
д 2042
 
4.6%
т 2035
 
4.5%
а 2027
 
4.5%
л 2026
 
4.5%
я 2022
 
4.5%
Other values (17) 12354
27.6%
Common
ValueCountFrequency (%)
8104
57.3%
- 2011
 
14.2%
0 2011
 
14.2%
3 2006
 
14.2%
1 3
 
< 0.1%
8 3
 
< 0.1%
9 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic 44800
76.0%
ASCII 14140
 
24.0%

Most frequent character per block

Cyrillic
ValueCountFrequency (%)
е 10101
22.5%
н 4077
 
9.1%
ж 4019
 
9.0%
и 2049
 
4.6%
с 2048
 
4.6%
д 2042
 
4.6%
т 2035
 
4.5%
а 2027
 
4.5%
л 2026
 
4.5%
я 2022
 
4.5%
Other values (17) 12354
27.6%
ASCII
ValueCountFrequency (%)
8104
57.3%
- 2011
 
14.2%
0 2011
 
14.2%
3 2006
 
14.2%
1 3
 
< 0.1%
8 3
 
< 0.1%
9 2
 
< 0.1%
Distinct10
Distinct (%)< 0.1%
Missing3272
Missing (%)3.8%
Memory size670.4 KiB
2024-02-13T20:53:15.407185image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters660152
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 rowa0b598e4
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 41057
49.8%
a0b598e4 39348
47.7%
3ecc50a0 1087
 
1.3%
d479a207 775
 
0.9%
842dca9f 161
 
0.2%
9c7cbdb6 60
 
0.1%
e24bdef1 14
 
< 0.1%
0a59e5b4 7
 
< 0.1%
e4c51201 5
 
< 0.1%
f50a4e2c 5
 
< 0.1%
2024-02-13T20:53:15.691354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 163630
24.8%
a 82440
12.5%
4 81372
12.3%
b 80546
12.2%
7 42667
 
6.5%
0 42314
 
6.4%
1 41081
 
6.2%
e 40480
 
6.1%
9 40351
 
6.1%
8 39509
 
6.0%
Other values (6) 5762
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 453031
68.6%
Lowercase Letter 207121
31.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 163630
36.1%
4 81372
18.0%
7 42667
 
9.4%
0 42314
 
9.3%
1 41081
 
9.1%
9 40351
 
8.9%
8 39509
 
8.7%
3 1087
 
0.2%
2 960
 
0.2%
6 60
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 82440
39.8%
b 80546
38.9%
e 40480
19.5%
c 2465
 
1.2%
d 1010
 
0.5%
f 180
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 453031
68.6%
Latin 207121
31.4%

Most frequent character per script

Common
ValueCountFrequency (%)
5 163630
36.1%
4 81372
18.0%
7 42667
 
9.4%
0 42314
 
9.3%
1 41081
 
9.1%
9 40351
 
8.9%
8 39509
 
8.7%
3 1087
 
0.2%
2 960
 
0.2%
6 60
 
< 0.1%
Latin
ValueCountFrequency (%)
a 82440
39.8%
b 80546
38.9%
e 40480
19.5%
c 2465
 
1.2%
d 1010
 
0.5%
f 180
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 660152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 163630
24.8%
a 82440
12.5%
4 81372
12.3%
b 80546
12.2%
7 42667
 
6.5%
0 42314
 
6.4%
1 41081
 
6.2%
e 40480
 
6.1%
9 40351
 
6.1%
8 39509
 
6.0%
Other values (6) 5762
 
0.9%

pmtdaysoverdue_1135P
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct1633
Distinct (%)2.0%
Missing4574
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean71.47861901
Minimum0
Maximum663618
Zeros75023
Zeros (%)87.4%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:15.844478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile17
Maximum663618
Range663618
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4664.51105
Coefficient of variation (CV)65.25743103
Kurtosis20163.3094
Mean71.47861901
Median Absolute Deviation (MAD)0
Skewness141.7641911
Sum5805279
Variance21757663.34
MonotonicityNot monotonic
2024-02-13T20:53:15.997275image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 75023
87.4%
1 541
 
0.6%
2 237
 
0.3%
3 191
 
0.2%
4 150
 
0.2%
7 128
 
0.1%
8 117
 
0.1%
9 109
 
0.1%
5 107
 
0.1%
6 93
 
0.1%
Other values (1623) 4521
 
5.3%
(Missing) 4574
 
5.3%
ValueCountFrequency (%)
0 75023
87.4%
1 541
 
0.6%
2 237
 
0.3%
3 191
 
0.2%
4 150
 
0.2%
ValueCountFrequency (%)
663618 2
< 0.1%
663586 1
< 0.1%
663520 1
< 0.1%
4572 1
< 0.1%
4519 1
< 0.1%
Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:16.190850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters686328
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 rowe914c86c
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 38483
44.9%
f6e26148 24420
28.5%
dbcbe8f8 13463
 
15.7%
e914c86c 8921
 
10.4%
d01fcb1e 277
 
0.3%
5f8f7038 178
 
0.2%
a7c1fc40 17
 
< 0.1%
daad4854 14
 
< 0.1%
b03dfa05 13
 
< 0.1%
10984579 5
 
< 0.1%
2024-02-13T20:53:16.475786image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 115659
16.9%
1 72400
10.5%
4 71874
10.5%
b 65699
9.6%
8 60642
8.8%
6 57761
8.4%
e 47081
6.9%
7 38683
 
5.6%
f 38546
 
5.6%
a 38541
 
5.6%
Other values (6) 79442
11.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 451064
65.7%
Lowercase Letter 235264
34.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 115659
25.6%
1 72400
16.1%
4 71874
15.9%
8 60642
13.4%
6 57761
12.8%
7 38683
 
8.6%
2 24420
 
5.4%
9 8931
 
2.0%
0 503
 
0.1%
3 191
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
b 65699
27.9%
e 47081
20.0%
f 38546
16.4%
a 38541
16.4%
c 31616
13.4%
d 13781
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Common 451064
65.7%
Latin 235264
34.3%

Most frequent character per script

Common
ValueCountFrequency (%)
5 115659
25.6%
1 72400
16.1%
4 71874
15.9%
8 60642
13.4%
6 57761
12.8%
7 38683
 
8.6%
2 24420
 
5.4%
9 8931
 
2.0%
0 503
 
0.1%
3 191
 
< 0.1%
Latin
ValueCountFrequency (%)
b 65699
27.9%
e 47081
20.0%
f 38546
16.4%
a 38541
16.4%
c 31616
13.4%
d 13781
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 686328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 115659
16.9%
1 72400
10.5%
4 71874
10.5%
b 65699
9.6%
8 60642
8.8%
6 57761
8.4%
e 47081
6.9%
7 38683
 
5.6%
f 38546
 
5.6%
a 38541
 
5.6%
Other values (6) 79442
11.6%

pmtnumpending_403L
Real number (ℝ)

MISSING  ZEROS 

Distinct262
Distinct (%)0.6%
Missing42111
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean20.26467491
Minimum0
Maximum300
Zeros1130
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:16.623672image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q324
95-th percentile60
Maximum300
Range300
Interquartile range (IQR)19

Descriptive statistics

Standard deviation29.01741658
Coefficient of variation (CV)1.431921149
Kurtosis24.17629055
Mean20.26467491
Median Absolute Deviation (MAD)8
Skewness4.179117427
Sum885161
Variance842.0104648
MonotonicityNot monotonic
2024-02-13T20:53:16.775997image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3759
 
4.4%
6 1862
 
2.2%
5 1842
 
2.1%
2 1838
 
2.1%
3 1832
 
2.1%
10 1745
 
2.0%
9 1719
 
2.0%
7 1699
 
2.0%
4 1656
 
1.9%
8 1610
 
1.9%
Other values (252) 24118
28.1%
(Missing) 42111
49.1%
ValueCountFrequency (%)
0 1130
 
1.3%
1 3759
4.4%
2 1838
2.1%
3 1832
2.1%
4 1656
1.9%
ValueCountFrequency (%)
300 8
< 0.1%
299 3
 
< 0.1%
298 8
< 0.1%
297 5
< 0.1%
296 5
< 0.1%
Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:16.944378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.003450245
Min length8

Characters and Unicode

Total characters686624
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row96a8fdfe
2nd row96a8fdfe
3rd row96a8fdfe
4th row60c73645
5th row96a8fdfe
ValueCountFrequency (%)
60c73645 60144
70.1%
96a8fdfe 19764
 
23.0%
a55475b1 3892
 
4.5%
164ee705 562
 
0.7%
e19fdece 407
 
0.5%
5065c2b8 324
 
0.4%
28bfa260 296
 
0.3%
9e302002 179
 
0.2%
p188_162_121 74
 
0.1%
7a7d6960 70
 
0.1%
Other values (6) 79
 
0.1%
2024-02-13T20:53:17.261357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 141504
20.6%
5 73039
10.6%
7 64752
9.4%
4 64607
9.4%
0 61992
9.0%
c 60932
8.9%
3 60394
8.8%
f 40250
 
5.9%
a 24028
 
3.5%
e 22466
 
3.3%
Other values (8) 72660
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 513951
74.9%
Lowercase Letter 172451
 
25.1%
Connector Punctuation 148
 
< 0.1%
Uppercase Letter 74
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 141504
27.5%
5 73039
14.2%
7 64752
12.6%
4 64607
12.6%
0 61992
12.1%
3 60394
11.8%
8 20566
 
4.0%
9 20489
 
4.0%
1 5181
 
1.0%
2 1427
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
c 60932
35.3%
f 40250
23.3%
a 24028
 
13.9%
e 22466
 
13.0%
d 20241
 
11.7%
b 4534
 
2.6%
Connector Punctuation
ValueCountFrequency (%)
_ 148
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 74
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 514099
74.9%
Latin 172525
 
25.1%

Most frequent character per script

Common
ValueCountFrequency (%)
6 141504
27.5%
5 73039
14.2%
7 64752
12.6%
4 64607
12.6%
0 61992
12.1%
3 60394
11.7%
8 20566
 
4.0%
9 20489
 
4.0%
1 5181
 
1.0%
2 1427
 
0.3%
Latin
ValueCountFrequency (%)
c 60932
35.3%
f 40250
23.3%
a 24028
 
13.9%
e 22466
 
13.0%
d 20241
 
11.7%
b 4534
 
2.6%
P 74
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 686624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 141504
20.6%
5 73039
10.6%
7 64752
9.4%
4 64607
9.4%
0 61992
9.0%
c 60932
8.9%
3 60394
8.8%
f 40250
 
5.9%
a 24028
 
3.5%
e 22466
 
3.3%
Other values (8) 72660
10.6%

residualamount_1093A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing69666
Missing (%)81.2%
Infinite0
Infinite (%)0.0%
Mean0.01996899225
Minimum0
Maximum322
Zeros16124
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:17.381356image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum322
Range322
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.535747524
Coefficient of variation (CV)126.984251
Kurtosis16125
Mean0.01996899225
Median Absolute Deviation (MAD)0
Skewness126.984251
Sum322
Variance6.430015504
MonotonicityNot monotonic
2024-02-13T20:53:17.490237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 16124
 
18.8%
322 1
 
< 0.1%
(Missing) 69666
81.2%
ValueCountFrequency (%)
0 16124
18.8%
322 1
 
< 0.1%
ValueCountFrequency (%)
322 1
 
< 0.1%
0 16124
18.8%

residualamount_127A
Real number (ℝ)

MISSING  ZEROS 

Distinct17356
Distinct (%)62.9%
Missing58210
Missing (%)67.9%
Infinite0
Infinite (%)0.0%
Mean57942.29912
Minimum0
Maximum2187568.2
Zeros9632
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:17.630212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median14461.327
Q370925.4
95-th percentile234692.8
Maximum2187568.2
Range2187568.2
Interquartile range (IQR)70925.4

Descriptive statistics

Standard deviation110621.4657
Coefficient of variation (CV)1.90916597
Kurtosis43.15329607
Mean57942.29912
Median Absolute Deviation (MAD)14461.327
Skewness4.998826472
Sum1598106552
Variance1.223710867 × 1010
MonotonicityNot monotonic
2024-02-13T20:53:17.783232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9632
 
11.2%
200000 34
 
< 0.1%
100000 22
 
< 0.1%
20000 21
 
< 0.1%
10000 20
 
< 0.1%
30000 12
 
< 0.1%
40000 12
 
< 0.1%
60000 10
 
< 0.1%
4000 9
 
< 0.1%
110000 8
 
< 0.1%
Other values (17346) 17801
 
20.7%
(Missing) 58210
67.9%
ValueCountFrequency (%)
0 9632
11.2%
0.42 1
 
< 0.1%
0.578 1
 
< 0.1%
0.934 1
 
< 0.1%
1.36 1
 
< 0.1%
ValueCountFrequency (%)
2187568.2 1
< 0.1%
2036242.4 1
< 0.1%
2034024.6 1
< 0.1%
1568739.6 1
< 0.1%
1491263.1 1
< 0.1%

residualamount_3940956A
Real number (ℝ)

MISSING  ZEROS 

Distinct21888
Distinct (%)58.3%
Missing48272
Missing (%)56.3%
Infinite0
Infinite (%)0.0%
Mean43011.06728
Minimum0
Maximum2022909.2
Zeros14293
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:17.932535image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7483.786
Q341320.617
95-th percentile200000
Maximum2022909.2
Range2022909.2
Interquartile range (IQR)41320.617

Descriptive statistics

Standard deviation93146.19531
Coefficient of variation (CV)2.165633201
Kurtosis49.10909781
Mean43011.06728
Median Absolute Deviation (MAD)7483.786
Skewness5.45922263
Sum1613732233
Variance8676213700
MonotonicityNot monotonic
2024-02-13T20:53:18.104535image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14293
 
16.7%
200000 47
 
0.1%
10000 38
 
< 0.1%
100000 31
 
< 0.1%
20000 25
 
< 0.1%
40000 24
 
< 0.1%
30000 22
 
< 0.1%
4000 16
 
< 0.1%
18617.066 14
 
< 0.1%
60000 14
 
< 0.1%
Other values (21878) 22995
26.8%
(Missing) 48272
56.3%
ValueCountFrequency (%)
0 14293
16.7%
0.42 1
 
< 0.1%
0.578 1
 
< 0.1%
0.934 1
 
< 0.1%
1.36 1
 
< 0.1%
ValueCountFrequency (%)
2022909.2 1
< 0.1%
1523874.4 1
< 0.1%
1448925.4 1
< 0.1%
1442545.6 1
< 0.1%
1407042 1
< 0.1%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:18.284511image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.001596904
Min length8

Characters and Unicode

Total characters686465
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 rowfa4f56f1
2nd rowab3c25cf
3rd rowa55475b1
4th rowdaf49a8a
5th rowab3c25cf
ValueCountFrequency (%)
ab3c25cf 50456
58.8%
a55475b1 32773
38.2%
fa4f56f1 1487
 
1.7%
15f04f45 503
 
0.6%
daf49a8a 435
 
0.5%
p28_48_88 137
 
0.2%
2024-02-13T20:53:18.599118image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 151268
22.0%
c 100912
14.7%
a 86021
12.5%
b 83229
12.1%
f 56358
 
8.2%
2 50593
 
7.4%
3 50456
 
7.4%
4 35838
 
5.2%
1 34763
 
5.1%
7 32773
 
4.8%
Other values (7) 4254
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 359099
52.3%
Lowercase Letter 326955
47.6%
Connector Punctuation 274
 
< 0.1%
Uppercase Letter 137
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 151268
42.1%
2 50593
 
14.1%
3 50456
 
14.1%
4 35838
 
10.0%
1 34763
 
9.7%
7 32773
 
9.1%
6 1487
 
0.4%
8 983
 
0.3%
0 503
 
0.1%
9 435
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
c 100912
30.9%
a 86021
26.3%
b 83229
25.5%
f 56358
17.2%
d 435
 
0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 274
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 137
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 359373
52.4%
Latin 327092
47.6%

Most frequent character per script

Common
ValueCountFrequency (%)
5 151268
42.1%
2 50593
 
14.1%
3 50456
 
14.0%
4 35838
 
10.0%
1 34763
 
9.7%
7 32773
 
9.1%
6 1487
 
0.4%
8 983
 
0.3%
0 503
 
0.1%
9 435
 
0.1%
Latin
ValueCountFrequency (%)
c 100912
30.9%
a 86021
26.3%
b 83229
25.4%
f 56358
17.2%
d 435
 
0.1%
P 137
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 686465
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 151268
22.0%
c 100912
14.7%
a 86021
12.5%
b 83229
12.1%
f 56358
 
8.2%
2 50593
 
7.4%
3 50456
 
7.4%
4 35838
 
5.2%
1 34763
 
5.1%
7 32773
 
4.8%
Other values (7) 4254
 
0.6%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:18.766795image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters686328
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowab3c25cf
2nd rowab3c25cf
3rd rowa55475b1
4th rowab3c25cf
5th rowab3c25cf
ValueCountFrequency (%)
ab3c25cf 44337
51.7%
a55475b1 39563
46.1%
fa4f56f1 1116
 
1.3%
daf49a8a 499
 
0.6%
15f04f45 272
 
0.3%
71ddaa88 4
 
< 0.1%
2024-02-13T20:53:19.058125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 164686
24.0%
c 88674
12.9%
a 86521
12.6%
b 83900
12.2%
f 48728
 
7.1%
3 44337
 
6.5%
2 44337
 
6.5%
4 41722
 
6.1%
1 40955
 
6.0%
7 39567
 
5.8%
Other values (5) 2901
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 377998
55.1%
Lowercase Letter 308330
44.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 164686
43.6%
3 44337
 
11.7%
2 44337
 
11.7%
4 41722
 
11.0%
1 40955
 
10.8%
7 39567
 
10.5%
6 1116
 
0.3%
8 507
 
0.1%
9 499
 
0.1%
0 272
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
c 88674
28.8%
a 86521
28.1%
b 83900
27.2%
f 48728
15.8%
d 507
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 377998
55.1%
Latin 308330
44.9%

Most frequent character per script

Common
ValueCountFrequency (%)
5 164686
43.6%
3 44337
 
11.7%
2 44337
 
11.7%
4 41722
 
11.0%
1 40955
 
10.8%
7 39567
 
10.5%
6 1116
 
0.3%
8 507
 
0.1%
9 499
 
0.1%
0 272
 
0.1%
Latin
ValueCountFrequency (%)
c 88674
28.8%
a 86521
28.1%
b 83900
27.2%
f 48728
15.8%
d 507
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 686328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 164686
24.0%
c 88674
12.9%
a 86521
12.6%
b 83900
12.2%
f 48728
 
7.1%
3 44337
 
6.5%
2 44337
 
6.5%
4 41722
 
6.1%
1 40955
 
6.0%
7 39567
 
5.8%
Other values (5) 2901
 
0.4%

totalamount_503A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct25797
Distinct (%)48.7%
Missing32773
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean257008.9193
Minimum0
Maximum796800000
Zeros7143
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:19.212079image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120000
median68379.6
Q3200000
95-th percentile836789.874
Maximum796800000
Range796800000
Interquartile range (IQR)180000

Descriptive statistics

Standard deviation3876513.325
Coefficient of variation (CV)15.08318596
Kurtosis34067.19054
Mean257008.9193
Median Absolute Deviation (MAD)60594.205
Skewness171.5859115
Sum1.362609888 × 1010
Variance1.502735556 × 1013
MonotonicityNot monotonic
2024-02-13T20:53:19.380645image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7143
 
8.3%
10000 1362
 
1.6%
20000 1007
 
1.2%
200000 749
 
0.9%
100000 727
 
0.8%
30000 655
 
0.8%
40000 475
 
0.6%
60000 382
 
0.4%
120000 298
 
0.3%
80000 272
 
0.3%
Other values (25787) 39948
46.6%
(Missing) 32773
38.2%
ValueCountFrequency (%)
0 7143
8.3%
31.4 1
 
< 0.1%
50 2
 
< 0.1%
326 1
 
< 0.1%
820 1
 
< 0.1%
ValueCountFrequency (%)
796800000 1
< 0.1%
260000000 1
< 0.1%
140000000 1
< 0.1%
119290000 1
< 0.1%
77800000 1
< 0.1%

totalamount_881A
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct29137
Distinct (%)63.0%
Missing39563
Missing (%)46.1%
Infinite0
Infinite (%)0.0%
Mean293763.1638
Minimum0
Maximum139080000
Zeros4481
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size670.4 KiB
2024-02-13T20:53:19.542659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q125721.7
median84441.0025
Q3276102.2
95-th percentile1117177.02
Maximum139080000
Range139080000
Interquartile range (IQR)250380.5

Descriptive statistics

Standard deviation1202831.312
Coefficient of variation (CV)4.094561402
Kurtosis5010.635403
Mean293763.1638
Median Absolute Deviation (MAD)74441.0025
Skewness55.21379928
Sum1.358008354 × 1010
Variance1.446803165 × 1012
MonotonicityNot monotonic
2024-02-13T20:53:19.699135image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4481
 
5.2%
10000 1058
 
1.2%
20000 948
 
1.1%
30000 531
 
0.6%
40000 388
 
0.5%
60000 311
 
0.4%
50000 265
 
0.3%
100000 227
 
0.3%
70000 179
 
0.2%
80000 170
 
0.2%
Other values (29127) 37670
43.9%
(Missing) 39563
46.1%
ValueCountFrequency (%)
0 4481
5.2%
0.2 18
 
< 0.1%
2.8 1
 
< 0.1%
6.046 1
 
< 0.1%
10.400001 1
 
< 0.1%
ValueCountFrequency (%)
139080000 1
< 0.1%
86780410 1
< 0.1%
72567220 1
< 0.1%
58100000 1
< 0.1%
56399550 1
< 0.1%