Overview

Dataset statistics

Number of variables14
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory120.2 KiB
Average record size in memory123.1 B

Variable types

Text2
Categorical6
Numeric6

Dataset

Description한국주택금융공사 유동화자산부 업무 관련 공개 공공데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15072982/fileData.do

Alerts

PAY_DY has constant value ""Constant
ASUSP_PAY_AMT is highly overall correlated with CASH_ASUSP_PAY_AMTHigh correlation
CASH_ASUSP_PAY_AMT is highly overall correlated with ASUSP_PAY_AMTHigh correlation
TOPRN_TOT_AMT is highly overall correlated with TOINT_TOT_AMT and 2 other fieldsHigh correlation
TOINT_TOT_AMT is highly overall correlated with TOPRN_TOT_AMT and 2 other fieldsHigh correlation
TOHFEE_TOT_AMT is highly overall correlated with CHFEE_TOT_AMTHigh correlation
CPRN_TOT_AMT is highly overall correlated with TOPRN_TOT_AMT and 2 other fieldsHigh correlation
CINT_TOT_AMT is highly overall correlated with TOPRN_TOT_AMT and 2 other fieldsHigh correlation
CHFEE_TOT_AMT is highly overall correlated with TOHFEE_TOT_AMTHigh correlation
OSUSP_PAY_AMT is highly imbalanced (94.6%)Imbalance
ASUSP_PAY_AMT is highly imbalanced (95.2%)Imbalance
CASH_OSUSP_PAY_AMT is highly imbalanced (98.9%)Imbalance
CASH_ASUSP_PAY_AMT is highly imbalanced (98.6%)Imbalance
TOPRN_TOT_AMT has 651 (65.1%) zerosZeros
TOINT_TOT_AMT has 651 (65.1%) zerosZeros
TOHFEE_TOT_AMT has 970 (97.0%) zerosZeros
CPRN_TOT_AMT has 596 (59.6%) zerosZeros
CINT_TOT_AMT has 596 (59.6%) zerosZeros
CHFEE_TOT_AMT has 956 (95.6%) zerosZeros

Reproduction

Analysis started2023-12-12 22:44:16.928714
Analysis finished2023-12-12 22:44:22.217625
Duration5.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

LIQD_PLAN_CD
Text

Distinct381
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-12-13T07:44:22.380394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters14000
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique150 ?
Unique (%)15.0%

Sample

1st rowKHFCMB2020S-28
2nd rowKHFCMB2020S-24
3rd rowKHFCMB2020S-20
4th rowKHFCMB2020S-19
5th rowKHFCMB2020S-18
ValueCountFrequency (%)
khfcmb2017s-04 15
 
1.5%
khfcmb2020s-03 15
 
1.5%
khfcmb2020s-06 11
 
1.1%
khfcmb2020s-15 10
 
1.0%
khfcmb2020s-12 9
 
0.9%
khfcmb2019s-09 9
 
0.9%
khfcmb2018s-14 8
 
0.8%
khfcmb2019s-23 8
 
0.8%
khfcmb2020s-08 8
 
0.8%
khfcmb2019s-14 8
 
0.8%
Other values (371) 899
89.9%
2023-12-13T07:44:22.773244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1889
13.5%
2 1601
11.4%
1 1127
8.1%
B 1006
7.2%
K 1000
7.1%
H 1000
7.1%
F 1000
7.1%
C 1000
7.1%
M 1000
7.1%
- 1000
7.1%
Other values (10) 2377
17.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7023
50.2%
Decimal Number 5977
42.7%
Dash Punctuation 1000
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1889
31.6%
2 1601
26.8%
1 1127
18.9%
9 243
 
4.1%
3 216
 
3.6%
7 189
 
3.2%
6 187
 
3.1%
8 179
 
3.0%
5 174
 
2.9%
4 172
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
B 1006
14.3%
K 1000
14.2%
H 1000
14.2%
F 1000
14.2%
C 1000
14.2%
M 1000
14.2%
S 754
10.7%
L 240
 
3.4%
A 23
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7023
50.2%
Common 6977
49.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1889
27.1%
2 1601
22.9%
1 1127
16.2%
- 1000
14.3%
9 243
 
3.5%
3 216
 
3.1%
7 189
 
2.7%
6 187
 
2.7%
8 179
 
2.6%
5 174
 
2.5%
Latin
ValueCountFrequency (%)
B 1006
14.3%
K 1000
14.2%
H 1000
14.2%
F 1000
14.2%
C 1000
14.2%
M 1000
14.2%
S 754
10.7%
L 240
 
3.4%
A 23
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1889
13.5%
2 1601
11.4%
1 1127
8.1%
B 1006
7.2%
K 1000
7.1%
H 1000
7.1%
F 1000
7.1%
C 1000
7.1%
M 1000
7.1%
- 1000
7.1%
Other values (10) 2377
17.0%

PAY_DY
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
20201102
1000 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20201102
2nd row20201102
3rd row20201102
4th row20201102
5th row20201102

Common Values

ValueCountFrequency (%)
20201102 1000
100.0%

Length

2023-12-13T07:44:22.932403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:44:23.029918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20201102 1000
100.0%

TREAT_ORG_CD
Categorical

Distinct13
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
B004
331 
I001
283 
B088
95 
B081
92 
B039
72 
Other values (8)
127 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st rowB004
2nd rowB004
3rd rowB004
4th rowB004
5th rowB004

Common Values

ValueCountFrequency (%)
B004 331
33.1%
I001 283
28.3%
B088 95
 
9.5%
B081 92
 
9.2%
B039 72
 
7.2%
I003 28
 
2.8%
B037 25
 
2.5%
F001 21
 
2.1%
I002 20
 
2.0%
I004 19
 
1.9%
Other values (3) 14
 
1.4%

Length

2023-12-13T07:44:23.121143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b004 331
33.1%
i001 283
28.3%
b088 95
 
9.5%
b081 92
 
9.2%
b039 72
 
7.2%
i003 28
 
2.8%
b037 25
 
2.5%
f001 21
 
2.1%
i002 20
 
2.0%
i004 19
 
1.9%
Other values (3) 14
 
1.4%

HOLD_CD
Text

Distinct880
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-12-13T07:44:23.294620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters14000
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique785 ?
Unique (%)78.5%

Sample

1st rowB004-2020-0084
2nd rowB004-2020-0077
3rd rowB004-2020-0069
4th rowB004-2020-0066
5th rowB004-2020-0063
ValueCountFrequency (%)
b004-2010-0001 4
 
0.4%
b004-2015-0016 4
 
0.4%
i001-2011-0031 4
 
0.4%
i001-2012-0002 3
 
0.3%
i001-2010-0020 3
 
0.3%
i001-2010-0008 3
 
0.3%
i001-2011-0024 3
 
0.3%
b004-2014-0036 3
 
0.3%
b004-2014-0032 3
 
0.3%
i001-2011-0001 3
 
0.3%
Other values (870) 967
96.7%
2023-12-13T07:44:23.595667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5410
38.6%
- 2000
 
14.3%
1 1602
 
11.4%
2 1419
 
10.1%
4 617
 
4.4%
B 615
 
4.4%
8 481
 
3.4%
3 361
 
2.6%
5 354
 
2.5%
I 350
 
2.5%
Other values (5) 791
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11000
78.6%
Dash Punctuation 2000
 
14.3%
Uppercase Letter 1000
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5410
49.2%
1 1602
 
14.6%
2 1419
 
12.9%
4 617
 
5.6%
8 481
 
4.4%
3 361
 
3.3%
5 354
 
3.2%
9 317
 
2.9%
7 223
 
2.0%
6 216
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
B 615
61.5%
I 350
35.0%
F 34
 
3.4%
G 1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 2000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13000
92.9%
Latin 1000
 
7.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5410
41.6%
- 2000
 
15.4%
1 1602
 
12.3%
2 1419
 
10.9%
4 617
 
4.7%
8 481
 
3.7%
3 361
 
2.8%
5 354
 
2.7%
9 317
 
2.4%
7 223
 
1.7%
Latin
ValueCountFrequency (%)
B 615
61.5%
I 350
35.0%
F 34
 
3.4%
G 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5410
38.6%
- 2000
 
14.3%
1 1602
 
11.4%
2 1419
 
10.1%
4 617
 
4.4%
B 615
 
4.4%
8 481
 
3.4%
3 361
 
2.6%
5 354
 
2.5%
I 350
 
2.5%
Other values (5) 791
 
5.7%

TOPRN_TOT_AMT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct350
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19829617
Minimum0
Maximum1.5162822 × 109
Zeros651
Zeros (%)65.1%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T07:44:23.727712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3556916.25
95-th percentile1.0536379 × 108
Maximum1.5162822 × 109
Range1.5162822 × 109
Interquartile range (IQR)556916.25

Descriptive statistics

Standard deviation99013655
Coefficient of variation (CV)4.9932208
Kurtosis92.836824
Mean19829617
Median Absolute Deviation (MAD)0
Skewness8.5343376
Sum1.9829617 × 1010
Variance9.8037039 × 1015
MonotonicityNot monotonic
2023-12-13T07:44:23.856866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 651
65.1%
183689430 1
 
0.1%
104138813 1
 
0.1%
307021 1
 
0.1%
409791 1
 
0.1%
2094579 1
 
0.1%
154993283 1
 
0.1%
5833406 1
 
0.1%
7366866 1
 
0.1%
31997380 1
 
0.1%
Other values (340) 340
34.0%
ValueCountFrequency (%)
0 651
65.1%
23682 1
 
0.1%
38145 1
 
0.1%
40309 1
 
0.1%
47000 1
 
0.1%
49578 1
 
0.1%
49870 1
 
0.1%
50097 1
 
0.1%
50573 1
 
0.1%
57182 1
 
0.1%
ValueCountFrequency (%)
1516282196 1
0.1%
1227663269 1
0.1%
869749471 1
0.1%
766957849 1
0.1%
698877402 1
0.1%
673006637 1
0.1%
658703046 1
0.1%
650325810 1
0.1%
622884896 1
0.1%
579500741 1
0.1%

TOINT_TOT_AMT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct350
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1911592.9
Minimum0
Maximum2.0760531 × 108
Zeros651
Zeros (%)65.1%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T07:44:23.999351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3288517.25
95-th percentile7179141
Maximum2.0760531 × 108
Range2.0760531 × 108
Interquartile range (IQR)288517.25

Descriptive statistics

Standard deviation10529099
Coefficient of variation (CV)5.508024
Kurtosis181.88568
Mean1911592.9
Median Absolute Deviation (MAD)0
Skewness11.806519
Sum1.9115929 × 109
Variance1.1086194 × 1014
MonotonicityNot monotonic
2023-12-13T07:44:24.430052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 651
65.1%
360331 1
 
0.1%
334826 1
 
0.1%
208241 1
 
0.1%
401580 1
 
0.1%
1533435 1
 
0.1%
76014 1
 
0.1%
451705 1
 
0.1%
739750 1
 
0.1%
236906 1
 
0.1%
Other values (340) 340
34.0%
ValueCountFrequency (%)
0 651
65.1%
206 1
 
0.1%
784 1
 
0.1%
1121 1
 
0.1%
1991 1
 
0.1%
3483 1
 
0.1%
6306 1
 
0.1%
14630 1
 
0.1%
15461 1
 
0.1%
19731 1
 
0.1%
ValueCountFrequency (%)
207605307 1
0.1%
129292910 1
0.1%
89706838 1
0.1%
73723332 1
0.1%
72403323 1
0.1%
70941588 1
0.1%
69897057 1
0.1%
64606882 1
0.1%
62284309 1
0.1%
56910673 1
0.1%

TOHFEE_TOT_AMT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21518.378
Minimum0
Maximum2882949
Zeros970
Zeros (%)97.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T07:44:24.576591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2882949
Range2882949
Interquartile range (IQR)0

Descriptive statistics

Standard deviation176734.21
Coefficient of variation (CV)8.2131752
Kurtosis120.87478
Mean21518.378
Median Absolute Deviation (MAD)0
Skewness10.271894
Sum21518378
Variance3.123498 × 1010
MonotonicityNot monotonic
2023-12-13T07:44:24.688984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 970
97.0%
1603345 1
 
0.1%
713873 1
 
0.1%
109051 1
 
0.1%
275927 1
 
0.1%
1108288 1
 
0.1%
16824 1
 
0.1%
1875082 1
 
0.1%
52029 1
 
0.1%
64517 1
 
0.1%
Other values (21) 21
 
2.1%
ValueCountFrequency (%)
0 970
97.0%
415 1
 
0.1%
800 1
 
0.1%
4335 1
 
0.1%
16824 1
 
0.1%
19660 1
 
0.1%
50485 1
 
0.1%
52029 1
 
0.1%
64517 1
 
0.1%
81678 1
 
0.1%
ValueCountFrequency (%)
2882949 1
0.1%
2053798 1
0.1%
1875082 1
0.1%
1603345 1
0.1%
1602987 1
0.1%
1320158 1
0.1%
1305397 1
0.1%
1171815 1
0.1%
1108288 1
0.1%
1096426 1
0.1%

CPRN_TOT_AMT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct405
Distinct (%)40.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23185329
Minimum0
Maximum1.5162822 × 109
Zeros596
Zeros (%)59.6%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T07:44:24.842788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3893273.5
95-th percentile1.3404603 × 108
Maximum1.5162822 × 109
Range1.5162822 × 109
Interquartile range (IQR)893273.5

Descriptive statistics

Standard deviation1.0452527 × 108
Coefficient of variation (CV)4.5082503
Kurtosis75.42371
Mean23185329
Median Absolute Deviation (MAD)0
Skewness7.6274367
Sum2.3185329 × 1010
Variance1.0925531 × 1016
MonotonicityNot monotonic
2023-12-13T07:44:24.986710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 596
59.6%
379987 1
 
0.1%
25057028 1
 
0.1%
3047019 1
 
0.1%
2907687 1
 
0.1%
1564128 1
 
0.1%
31244483 1
 
0.1%
643559 1
 
0.1%
277777 1
 
0.1%
678727 1
 
0.1%
Other values (395) 395
39.5%
ValueCountFrequency (%)
0 596
59.6%
23682 1
 
0.1%
40309 1
 
0.1%
44172 1
 
0.1%
47000 1
 
0.1%
49578 1
 
0.1%
49870 1
 
0.1%
50097 1
 
0.1%
50573 1
 
0.1%
58170 1
 
0.1%
ValueCountFrequency (%)
1516282196 1
0.1%
1227663269 1
0.1%
869749471 1
0.1%
766957849 1
0.1%
698877402 1
0.1%
673006637 1
0.1%
658703046 1
0.1%
650325810 1
0.1%
622884896 1
0.1%
582615717 1
0.1%

CINT_TOT_AMT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct405
Distinct (%)40.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2266734.9
Minimum0
Maximum2.0760531 × 108
Zeros596
Zeros (%)59.6%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T07:44:25.169035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3514275.25
95-th percentile8328127.5
Maximum2.0760531 × 108
Range2.0760531 × 108
Interquartile range (IQR)514275.25

Descriptive statistics

Standard deviation11085240
Coefficient of variation (CV)4.8903998
Kurtosis148.87457
Mean2266734.9
Median Absolute Deviation (MAD)0
Skewness10.524274
Sum2.2667349 × 109
Variance1.2288255 × 1014
MonotonicityNot monotonic
2023-12-13T07:44:25.320091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 596
59.6%
194593 1
 
0.1%
14268640 1
 
0.1%
1268080 1
 
0.1%
3014747 1
 
0.1%
774965 1
 
0.1%
1047544 1
 
0.1%
776032 1
 
0.1%
175900 1
 
0.1%
585759 1
 
0.1%
Other values (395) 395
39.5%
ValueCountFrequency (%)
0 596
59.6%
206 1
 
0.1%
320 1
 
0.1%
3483 1
 
0.1%
6306 1
 
0.1%
14630 1
 
0.1%
15461 1
 
0.1%
17331 1
 
0.1%
19731 1
 
0.1%
21639 1
 
0.1%
ValueCountFrequency (%)
207605307 1
0.1%
129292910 1
0.1%
89706838 1
0.1%
73723332 1
0.1%
72403323 1
0.1%
72095583 1
0.1%
70941588 1
0.1%
69897057 1
0.1%
64606882 1
0.1%
62284309 1
0.1%

CHFEE_TOT_AMT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39273.531
Minimum0
Maximum3913906
Zeros956
Zeros (%)95.6%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T07:44:25.461316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3913906
Range3913906
Interquartile range (IQR)0

Descriptive statistics

Standard deviation278655.93
Coefficient of variation (CV)7.0952604
Kurtosis102.04669
Mean39273.531
Median Absolute Deviation (MAD)0
Skewness9.4084333
Sum39273531
Variance7.7649128 × 1010
MonotonicityNot monotonic
2023-12-13T07:44:25.604780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 956
95.6%
52029 1
 
0.1%
3913906 1
 
0.1%
2882949 1
 
0.1%
3685243 1
 
0.1%
1357 1
 
0.1%
1602987 1
 
0.1%
794803 1
 
0.1%
243838 1
 
0.1%
31259 1
 
0.1%
Other values (35) 35
 
3.5%
ValueCountFrequency (%)
0 956
95.6%
415 1
 
0.1%
800 1
 
0.1%
1357 1
 
0.1%
4335 1
 
0.1%
8518 1
 
0.1%
15109 1
 
0.1%
16824 1
 
0.1%
19113 1
 
0.1%
31259 1
 
0.1%
ValueCountFrequency (%)
3913906 1
0.1%
3685243 1
0.1%
3143268 1
0.1%
2882949 1
0.1%
2053798 1
0.1%
1901167 1
0.1%
1875082 1
0.1%
1603345 1
0.1%
1602987 1
0.1%
1427216 1
0.1%

OSUSP_PAY_AMT
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
990 
<NA>
 
9
190780
 
1

Length

Max length6
Median length1
Mean length1.032
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 990
99.0%
<NA> 9
 
0.9%
190780 1
 
0.1%

Length

2023-12-13T07:44:25.727993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:44:25.802760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 990
99.0%
na 9
 
0.9%
190780 1
 
0.1%

ASUSP_PAY_AMT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
989 
<NA>
 
9
3300530
 
1
2497910
 
1

Length

Max length7
Median length1
Mean length1.039
Min length1

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 989
98.9%
<NA> 9
 
0.9%
3300530 1
 
0.1%
2497910 1
 
0.1%

Length

2023-12-13T07:44:25.887214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:44:25.974388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 989
98.9%
na 9
 
0.9%
3300530 1
 
0.1%
2497910 1
 
0.1%

CASH_OSUSP_PAY_AMT
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
999 
190780
 
1

Length

Max length6
Median length1
Mean length1.005
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 999
99.9%
190780 1
 
0.1%

Length

2023-12-13T07:44:26.064290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:44:26.140301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 999
99.9%
190780 1
 
0.1%

CASH_ASUSP_PAY_AMT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
998 
3300530
 
1
2497910
 
1

Length

Max length7
Median length1
Mean length1.012
Min length1

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 998
99.8%
3300530 1
 
0.1%
2497910 1
 
0.1%

Length

2023-12-13T07:44:26.223143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:44:26.307500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 998
99.8%
3300530 1
 
0.1%
2497910 1
 
0.1%

Interactions

2023-12-13T07:44:21.250956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:17.579663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:18.290080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:18.983049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:19.752849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:20.577361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:21.364421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:17.716606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:18.397963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:19.121012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:19.887476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:20.690073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:21.480369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:17.822494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:18.499339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:19.234266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:20.026567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:20.789907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:21.600623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:17.931769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:18.620603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:19.360667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:20.160475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:20.888937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:21.706565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:18.041096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:18.733469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:19.476599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:20.307598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:21.026098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:21.806074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:18.153951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:18.852128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:19.607935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:20.429428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:21.149196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:44:26.362459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
TREAT_ORG_CDTOPRN_TOT_AMTTOINT_TOT_AMTTOHFEE_TOT_AMTCPRN_TOT_AMTCINT_TOT_AMTCHFEE_TOT_AMTOSUSP_PAY_AMTASUSP_PAY_AMTCASH_OSUSP_PAY_AMTCASH_ASUSP_PAY_AMT
TREAT_ORG_CD1.0000.0000.0000.0000.0000.0000.0000.0000.3340.0000.335
TOPRN_TOT_AMT0.0001.0000.8690.6030.9990.8540.4860.0000.5990.0000.599
TOINT_TOT_AMT0.0000.8691.0000.3460.8500.9980.1930.0000.2970.0000.298
TOHFEE_TOT_AMT0.0000.6030.3461.0000.5300.3110.9720.0000.0000.0000.000
CPRN_TOT_AMT0.0000.9990.8500.5301.0000.8520.6690.0000.5730.0000.573
CINT_TOT_AMT0.0000.8540.9980.3110.8521.0000.5570.0000.2700.0000.270
CHFEE_TOT_AMT0.0000.4860.1930.9720.6690.5571.0000.0000.0000.0000.000
OSUSP_PAY_AMT0.0000.0000.0000.0000.0000.0000.0001.0000.0000.7060.000
ASUSP_PAY_AMT0.3340.5990.2970.0000.5730.2700.0000.0001.0000.0001.000
CASH_OSUSP_PAY_AMT0.0000.0000.0000.0000.0000.0000.0000.7060.0001.0000.000
CASH_ASUSP_PAY_AMT0.3350.5990.2980.0000.5730.2700.0000.0001.0000.0001.000
2023-12-13T07:44:26.467159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ASUSP_PAY_AMTTREAT_ORG_CDCASH_OSUSP_PAY_AMTCASH_ASUSP_PAY_AMTOSUSP_PAY_AMT
ASUSP_PAY_AMT1.0000.1990.0001.0000.000
TREAT_ORG_CD0.1991.0000.0000.2000.000
CASH_OSUSP_PAY_AMT0.0000.0001.0000.0000.499
CASH_ASUSP_PAY_AMT1.0000.2000.0001.0000.000
OSUSP_PAY_AMT0.0000.0000.4990.0001.000
2023-12-13T07:44:26.554261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
TOPRN_TOT_AMTTOINT_TOT_AMTTOHFEE_TOT_AMTCPRN_TOT_AMTCINT_TOT_AMTCHFEE_TOT_AMTTREAT_ORG_CDOSUSP_PAY_AMTASUSP_PAY_AMTCASH_OSUSP_PAY_AMTCASH_ASUSP_PAY_AMT
TOPRN_TOT_AMT1.0000.9880.3140.8540.8420.1920.0000.0000.4660.0000.467
TOINT_TOT_AMT0.9881.0000.2710.8440.8570.1640.0000.0000.2090.0000.209
TOHFEE_TOT_AMT0.3140.2711.0000.2700.2300.7630.0000.0000.0000.0000.000
CPRN_TOT_AMT0.8540.8440.2701.0000.9810.3620.0000.0000.4390.0000.439
CINT_TOT_AMT0.8420.8570.2300.9811.0000.3190.0000.0000.1880.0000.188
CHFEE_TOT_AMT0.1920.1640.7630.3620.3191.0000.0000.0000.0000.0000.000
TREAT_ORG_CD0.0000.0000.0000.0000.0000.0001.0000.0000.1990.0000.200
OSUSP_PAY_AMT0.0000.0000.0000.0000.0000.0000.0001.0000.0000.4990.000
ASUSP_PAY_AMT0.4660.2090.0000.4390.1880.0000.1990.0001.0000.0001.000
CASH_OSUSP_PAY_AMT0.0000.0000.0000.0000.0000.0000.0000.4990.0001.0000.000
CASH_ASUSP_PAY_AMT0.4670.2090.0000.4390.1880.0000.2000.0001.0000.0001.000

Missing values

2023-12-13T07:44:21.958965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:44:22.148265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

LIQD_PLAN_CDPAY_DYTREAT_ORG_CDHOLD_CDTOPRN_TOT_AMTTOINT_TOT_AMTTOHFEE_TOT_AMTCPRN_TOT_AMTCINT_TOT_AMTCHFEE_TOT_AMTOSUSP_PAY_AMTASUSP_PAY_AMTCASH_OSUSP_PAY_AMTCASH_ASUSP_PAY_AMT
0KHFCMB2020S-2820201102B004B004-2020-00840000000000
1KHFCMB2020S-2420201102B004B004-2020-0077491666313860049166631386000000
2KHFCMB2020S-2020201102B004B004-2020-0069719834541101071983454110100000
3KHFCMB2020S-1920201102B004B004-2020-00663582358235184603582358235184600000
4KHFCMB2020S-1820201102B004B004-2020-00632913312198719802913312198719800000
5KHFCMB2020S-1720201102B004B004-2020-005911082989029350110829890293500000
6KHFCMB2020S-1320201102B004B004-2020-00550000000000
7KHFCMB2020S-1320201102B004B004-2015-003217500035050220175000350502200000
8KHFCMB2020S-1320201102B004B004-2015-00262539975108893202539975108893200000
9KHFCMB2020S-1320201102B004B004-2015-002158170408110581704081100000
LIQD_PLAN_CDPAY_DYTREAT_ORG_CDHOLD_CDTOPRN_TOT_AMTTOINT_TOT_AMTTOHFEE_TOT_AMTCPRN_TOT_AMTCINT_TOT_AMTCHFEE_TOT_AMTOSUSP_PAY_AMTASUSP_PAY_AMTCASH_OSUSP_PAY_AMTCASH_ASUSP_PAY_AMT
990KHFCMB2018S-2320201102B088B088-2018-0063000500000054065500000
991KHFCMB2018S-2320201102B088B088-2018-006200033616165193700000
992KHFCMB2018S-2320201102B081B081-2018-0087270852078324552271387327085207832455227138730000
993KHFCMB2018S-2320201102B039B039-2018-00400000000000
994KHFCMB2018S-2020201102B088B088-2018-005800025000026275600000
995KHFCMB2018S-2020201102B088B088-2018-00570000000000
996KHFCMB2018S-2020201102B088B088-2018-00550000000000
997KHFCMB2018S-2020201102B081B081-2018-00782731519224523002731519224523000000
998KHFCMB2018S-2020201102B039B039-2018-00360000000000
999KHFCMB2018S-1820201102B088B088-2018-00510004915945636169000000