Overview

Dataset statistics

Number of variables20
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory170.1 KiB
Average record size in memory174.1 B

Variable types

Categorical10
Numeric8
Boolean2

Dataset

Description한국주택금융공사 신탁자산부 업무 관련 공개 공공데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15073030/fileData.do

Alerts

LIQD_PLAN_CD has constant value ""Constant
RPT_OFFER_SEQ has constant value ""Constant
ONLIN_YN has constant value ""Constant
DOC_OFFER_DY has constant value ""Constant
REG_ENO has constant value ""Constant
REG_DT is highly overall correlated with LOAN_ACC_NO and 6 other fieldsHigh correlation
WRT_BASIS_DY is highly overall correlated with LOAN_ACC_NO and 6 other fieldsHigh correlation
ARCV_RECVBLE_DVCD is highly overall correlated with DEMND_DY and 1 other fieldsHigh correlation
LEDGER_PRCSS_YN is highly overall correlated with DEMND_DY and 1 other fieldsHigh correlation
TREAT_ORG_CD is highly overall correlated with LOAN_ACC_NO and 5 other fieldsHigh correlation
HOLD_CD is highly overall correlated with SEQ and 4 other fieldsHigh correlation
LOAN_ACC_NO is highly overall correlated with WRT_BASIS_DY and 2 other fieldsHigh correlation
SEQ is highly overall correlated with TELGRM_SEQ and 4 other fieldsHigh correlation
TELGRM_SEQ is highly overall correlated with SEQ and 2 other fieldsHigh correlation
OCCR_SEQ is highly overall correlated with HOLD_CD and 3 other fieldsHigh correlation
DEMND_DY is highly overall correlated with CALC_DCNT and 4 other fieldsHigh correlation
ICALC_TRGT_AMT is highly overall correlated with INT_CALC_AMTHigh correlation
CALC_DCNT is highly overall correlated with DEMND_DY and 1 other fieldsHigh correlation
INT_CALC_AMT is highly overall correlated with DEMND_DY and 2 other fieldsHigh correlation
DMCUT_CALC_DVCD is highly overall correlated with DEMND_DYHigh correlation
ARCV_RECVBLE_DVCD is highly imbalanced (91.9%)Imbalance
DMCUT_CALC_DVCD is highly imbalanced (97.1%)Imbalance
LEDGER_PRCSS_YN is highly imbalanced (91.9%)Imbalance
CALC_DCNT has 19 (1.9%) zerosZeros

Reproduction

Analysis started2023-12-12 20:26:33.384958
Analysis finished2023-12-12 20:26:43.661591
Duration10.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

LIQD_PLAN_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
KHFCMB2020S-31
1000 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKHFCMB2020S-31
2nd rowKHFCMB2020S-31
3rd rowKHFCMB2020S-31
4th rowKHFCMB2020S-31
5th rowKHFCMB2020S-31

Common Values

ValueCountFrequency (%)
KHFCMB2020S-31 1000
100.0%

Length

2023-12-13T05:26:43.738539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:26:43.847441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
khfcmb2020s-31 1000
100.0%

HOLD_CD
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
B023-2020-0034
452 
B023-2020-0035
260 
B027-2020-0006
96 
B003-2020-0072
90 
B003-2020-0073
88 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB023-2020-0035
2nd rowB023-2020-0034
3rd rowB023-2020-0035
4th rowB023-2020-0035
5th rowB023-2020-0035

Common Values

ValueCountFrequency (%)
B023-2020-0034 452
45.2%
B023-2020-0035 260
26.0%
B027-2020-0006 96
 
9.6%
B003-2020-0072 90
 
9.0%
B003-2020-0073 88
 
8.8%
B039-2020-0045 14
 
1.4%

Length

2023-12-13T05:26:43.953777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:26:44.064828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b023-2020-0034 452
45.2%
b023-2020-0035 260
26.0%
b027-2020-0006 96
 
9.6%
b003-2020-0072 90
 
9.0%
b003-2020-0073 88
 
8.8%
b039-2020-0045 14
 
1.4%

LOAN_ACC_NO
Real number (ℝ)

HIGH CORRELATION 

Distinct993
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1437912 × 1015
Minimum1.0073123 × 1010
Maximum9.9700491 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T05:26:44.211665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.0073123 × 1010
5-th percentile3.0163076 × 1010
Q16.0448019 × 1010
median6.5873134 × 1010
Q36.388804 × 1013
95-th percentile6.7302414 × 1015
Maximum9.9700491 × 1015
Range9.970039 × 1015
Interquartile range (IQR)6.3827592 × 1013

Descriptive statistics

Standard deviation2.5510455 × 1015
Coefficient of variation (CV)2.2303419
Kurtosis3.1831693
Mean1.1437912 × 1015
Median Absolute Deviation (MAD)1.0200038 × 1010
Skewness2.0950794
Sum1.1437912 × 1018
Variance6.507833 × 1030
MonotonicityNot monotonic
2023-12-13T05:26:44.372884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35873045164 5
 
0.5%
4900717063200032 2
 
0.2%
48073069165 2
 
0.2%
64473138957 2
 
0.2%
25573004355 1
 
0.1%
25073102330 1
 
0.1%
25173064272 1
 
0.1%
25173064443 1
 
0.1%
25573004311 1
 
0.1%
25573004816 1
 
0.1%
Other values (983) 983
98.3%
ValueCountFrequency (%)
10073122584 1
0.1%
10273024188 1
0.1%
11173088749 1
0.1%
11773066039 1
0.1%
11773066136 1
0.1%
12873089126 1
0.1%
15273038864 1
0.1%
15273038905 1
0.1%
15273039376 1
0.1%
15773119568 1
0.1%
ValueCountFrequency (%)
9970049073200031 1
0.1%
9970049003200032 1
0.1%
9970048883200031 1
0.1%
9970048873200033 1
0.1%
9970048803200033 1
0.1%
9970048763200032 1
0.1%
9970048423200031 1
0.1%
9940071623200031 1
0.1%
9930067433200032 1
0.1%
9930066813200033 1
0.1%

SEQ
Real number (ℝ)

HIGH CORRELATION 

Distinct986
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1587888.4
Minimum1587070
Maximum1588467
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T05:26:44.522903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1587070
5-th percentile1587105.9
Q11587526.8
median1587967.5
Q31588217.2
95-th percentile1588417.1
Maximum1588467
Range1397
Interquartile range (IQR)690.5

Descriptive statistics

Standard deviation403.54783
Coefficient of variation (CV)0.00025414118
Kurtosis-0.77098625
Mean1587888.4
Median Absolute Deviation (MAD)288
Skewness-0.56025052
Sum1.5878884 × 109
Variance162850.85
MonotonicityNot monotonic
2023-12-13T05:26:44.677486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1587076 2
 
0.2%
1587071 2
 
0.2%
1587080 2
 
0.2%
1587079 2
 
0.2%
1587078 2
 
0.2%
1587077 2
 
0.2%
1587083 2
 
0.2%
1587075 2
 
0.2%
1587074 2
 
0.2%
1587073 2
 
0.2%
Other values (976) 980
98.0%
ValueCountFrequency (%)
1587070 2
0.2%
1587071 2
0.2%
1587072 2
0.2%
1587073 2
0.2%
1587074 2
0.2%
1587075 2
0.2%
1587076 2
0.2%
1587077 2
0.2%
1587078 2
0.2%
1587079 2
0.2%
ValueCountFrequency (%)
1588467 1
0.1%
1588466 1
0.1%
1588465 1
0.1%
1588464 1
0.1%
1588463 1
0.1%
1588462 1
0.1%
1588461 1
0.1%
1588460 1
0.1%
1588459 1
0.1%
1588458 1
0.1%

WRT_BASIS_DY
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
20200922
822 
20200921
178 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20200922
2nd row20200922
3rd row20200922
4th row20200922
5th row20200922

Common Values

ValueCountFrequency (%)
20200922 822
82.2%
20200921 178
 
17.8%

Length

2023-12-13T05:26:44.817720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:26:44.899495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20200922 822
82.2%
20200921 178
 
17.8%

TREAT_ORG_CD
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
B023
712 
B003
178 
B027
96 
B039
 
14

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB023
2nd rowB023
3rd rowB023
4th rowB023
5th rowB023

Common Values

ValueCountFrequency (%)
B023 712
71.2%
B003 178
 
17.8%
B027 96
 
9.6%
B039 14
 
1.4%

Length

2023-12-13T05:26:44.998604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:26:45.114146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b023 712
71.2%
b003 178
 
17.8%
b027 96
 
9.6%
b039 14
 
1.4%

RPT_OFFER_SEQ
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1000
100.0%

Length

2023-12-13T05:26:45.213765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:26:45.294754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1000
100.0%

ONLIN_YN
Boolean

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
1000 
ValueCountFrequency (%)
True 1000
100.0%
2023-12-13T05:26:45.372846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

TELGRM_SEQ
Real number (ℝ)

HIGH CORRELATION 

Distinct712
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean330.234
Minimum1
Maximum712
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T05:26:45.469499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18.95
Q1140.75
median352
Q3477
95-th percentile662.05
Maximum712
Range711
Interquartile range (IQR)336.25

Descriptive statistics

Standard deviation202.88567
Coefficient of variation (CV)0.61436942
Kurtosis-1.0917175
Mean330.234
Median Absolute Deviation (MAD)161
Skewness-0.015352062
Sum330234
Variance41162.596
MonotonicityNot monotonic
2023-12-13T05:26:45.617089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 3
 
0.3%
7 3
 
0.3%
14 3
 
0.3%
13 3
 
0.3%
12 3
 
0.3%
10 3
 
0.3%
9 3
 
0.3%
8 3
 
0.3%
6 3
 
0.3%
5 3
 
0.3%
Other values (702) 970
97.0%
ValueCountFrequency (%)
1 3
0.3%
2 3
0.3%
3 3
0.3%
4 3
0.3%
5 3
0.3%
6 3
0.3%
7 3
0.3%
8 3
0.3%
9 3
0.3%
10 3
0.3%
ValueCountFrequency (%)
712 1
0.1%
711 1
0.1%
710 1
0.1%
709 1
0.1%
708 1
0.1%
707 1
0.1%
706 1
0.1%
705 1
0.1%
704 1
0.1%
703 1
0.1%

DOC_OFFER_DY
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
20200922
1000 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20200922
2nd row20200922
3rd row20200922
4th row20200922
5th row20200922

Common Values

ValueCountFrequency (%)
20200922 1000
100.0%

Length

2023-12-13T05:26:45.740188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:26:45.826877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20200922 1000
100.0%

ARCV_RECVBLE_DVCD
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2
990 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 990
99.0%
1 10
 
1.0%

Length

2023-12-13T05:26:45.911225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:26:45.996716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 990
99.0%
1 10
 
1.0%

DMCUT_CALC_DVCD
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
997 
2
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 997
99.7%
2 3
 
0.3%

Length

2023-12-13T05:26:46.086463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:26:46.186030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 997
99.7%
2 3
 
0.3%

OCCR_SEQ
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.798
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T05:26:46.279781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q35
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2323614
Coefficient of variation (CV)0.25684899
Kurtosis1.4010098
Mean4.798
Median Absolute Deviation (MAD)0
Skewness-0.63683837
Sum4798
Variance1.5187147
MonotonicityNot monotonic
2023-12-13T05:26:46.400665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 541
54.1%
4 175
 
17.5%
7 97
 
9.7%
6 76
 
7.6%
2 45
 
4.5%
3 44
 
4.4%
1 20
 
2.0%
8 2
 
0.2%
ValueCountFrequency (%)
1 20
 
2.0%
2 45
 
4.5%
3 44
 
4.4%
4 175
 
17.5%
5 541
54.1%
6 76
 
7.6%
7 97
 
9.7%
8 2
 
0.2%
ValueCountFrequency (%)
8 2
 
0.2%
7 97
 
9.7%
6 76
 
7.6%
5 541
54.1%
4 175
 
17.5%
3 44
 
4.4%
2 45
 
4.5%
1 20
 
2.0%

DEMND_DY
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20200879
Minimum20200819
Maximum20201031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T05:26:46.507529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200819
5-th percentile20200824
Q120200825
median20200907
Q320200915
95-th percentile20200922
Maximum20201031
Range212
Interquartile range (IQR)90

Descriptive statistics

Standard deviation46.090542
Coefficient of variation (CV)2.2816108 × 10-6
Kurtosis-1.022156
Mean20200879
Median Absolute Deviation (MAD)15
Skewness-0.044422269
Sum2.0200879 × 1010
Variance2124.338
MonotonicityNot monotonic
2023-12-13T05:26:46.629326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
20200824 188
18.8%
20200922 111
 
11.1%
20200909 74
 
7.4%
20200914 72
 
7.2%
20200919 67
 
6.7%
20200904 54
 
5.4%
20200825 44
 
4.4%
20200829 31
 
3.1%
20200826 29
 
2.9%
20200920 27
 
2.7%
Other values (30) 303
30.3%
ValueCountFrequency (%)
20200819 2
 
0.2%
20200820 1
 
0.1%
20200821 11
 
1.1%
20200822 25
 
2.5%
20200823 11
 
1.1%
20200824 188
18.8%
20200825 44
 
4.4%
20200826 29
 
2.9%
20200827 11
 
1.1%
20200828 10
 
1.0%
ValueCountFrequency (%)
20201031 5
 
0.5%
20201024 2
 
0.2%
20201023 2
 
0.2%
20200929 1
 
0.1%
20200924 2
 
0.2%
20200923 1
 
0.1%
20200922 111
11.1%
20200920 27
 
2.7%
20200919 67
6.7%
20200918 5
 
0.5%

ICALC_TRGT_AMT
Real number (ℝ)

HIGH CORRELATION 

Distinct857
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.589274 × 108
Minimum11993
Maximum4.9819199 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T05:26:46.787091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11993
5-th percentile40413982
Q197023597
median1.4774337 × 108
Q32.1325901 × 108
95-th percentile3.0919447 × 108
Maximum4.9819199 × 108
Range4.9818 × 108
Interquartile range (IQR)1.1623542 × 108

Descriptive statistics

Standard deviation86245654
Coefficient of variation (CV)0.5426733
Kurtosis0.77528017
Mean1.589274 × 108
Median Absolute Deviation (MAD)57589246
Skewness0.81644598
Sum1.589274 × 1011
Variance7.4383129 × 1015
MonotonicityNot monotonic
2023-12-13T05:26:46.922766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148885099 9
 
0.9%
99256733 8
 
0.8%
297770200 6
 
0.6%
238216163 5
 
0.5%
218364816 5
 
0.5%
99456988 5
 
0.5%
208439141 5
 
0.5%
178662122 4
 
0.4%
109182410 4
 
0.4%
143922266 4
 
0.4%
Other values (847) 945
94.5%
ValueCountFrequency (%)
11993 2
0.2%
12897 1
0.1%
57612 1
0.1%
8570806 1
0.1%
9259732 1
0.1%
19857693 1
0.1%
19863602 1
0.1%
21140667 1
0.1%
21142000 1
0.1%
21836482 1
0.1%
ValueCountFrequency (%)
498191992 1
0.1%
496363650 1
0.1%
495732117 1
0.1%
495472291 1
0.1%
470681782 1
0.1%
453703974 1
0.1%
427067441 1
0.1%
425314296 1
0.1%
418202910 1
0.1%
416960814 1
0.1%

CALC_DCNT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.045
Minimum0
Maximum35
Zeros19
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T05:26:47.057781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median17
Q328
95-th percentile29
Maximum35
Range35
Interquartile range (IQR)20

Descriptive statistics

Standard deviation9.9529914
Coefficient of variation (CV)0.5839244
Kurtosis-1.4450868
Mean17.045
Median Absolute Deviation (MAD)10
Skewness-0.19589378
Sum17045
Variance99.062037
MonotonicityNot monotonic
2023-12-13T05:26:47.201368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
28 192
19.2%
12 75
 
7.5%
7 74
 
7.4%
2 72
 
7.2%
27 54
 
5.4%
17 54
 
5.4%
23 39
 
3.9%
22 31
 
3.1%
10 30
 
3.0%
30 30
 
3.0%
Other values (25) 349
34.9%
ValueCountFrequency (%)
0 19
 
1.9%
1 29
 
2.9%
2 72
7.2%
3 11
 
1.1%
4 11
 
1.1%
5 15
 
1.5%
6 15
 
1.5%
7 74
7.4%
8 21
 
2.1%
9 25
 
2.5%
ValueCountFrequency (%)
35 1
 
0.1%
33 2
 
0.2%
32 1
 
0.1%
31 12
 
1.2%
30 30
 
3.0%
29 15
 
1.5%
28 192
19.2%
27 54
 
5.4%
26 30
 
3.0%
25 21
 
2.1%

INT_CALC_AMT
Real number (ℝ)

HIGH CORRELATION 

Distinct956
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195485.67
Minimum0
Maximum918555
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T05:26:47.340033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16372.75
Q168188
median150721
Q3288371.25
95-th percentile523171
Maximum918555
Range918555
Interquartile range (IQR)220183.25

Descriptive statistics

Standard deviation163740.4
Coefficient of variation (CV)0.83760823
Kurtosis1.483391
Mean195485.67
Median Absolute Deviation (MAD)100373.5
Skewness1.2235447
Sum1.9548567 × 108
Variance2.681092 × 1010
MonotonicityNot monotonic
2023-12-13T05:26:47.474447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72611 4
 
0.4%
193631 4
 
0.4%
145223 3
 
0.3%
290447 3
 
0.3%
88043 3
 
0.3%
103650 3
 
0.3%
130701 2
 
0.2%
425990 2
 
0.2%
369837 2
 
0.2%
21238 2
 
0.2%
Other values (946) 972
97.2%
ValueCountFrequency (%)
0 2
0.2%
7 1
0.1%
8 1
0.1%
12 1
0.1%
25 1
0.1%
4263 1
0.1%
4495 1
0.1%
4999 1
0.1%
5083 1
0.1%
5107 1
0.1%
ValueCountFrequency (%)
918555 1
0.1%
867231 1
0.1%
864453 1
0.1%
860336 1
0.1%
852103 1
0.1%
808541 1
0.1%
793885 1
0.1%
693177 1
0.1%
685180 1
0.1%
670823 1
0.1%

LEDGER_PRCSS_YN
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
990 
False
 
10
ValueCountFrequency (%)
True 990
99.0%
False 10
 
1.0%
2023-12-13T05:26:47.639504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

REG_ENO
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1841
1000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1841
2nd row1841
3rd row1841
4th row1841
5th row1841

Common Values

ValueCountFrequency (%)
1841 1000
100.0%

Length

2023-12-13T05:26:47.749665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:26:47.855669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1841 1000
100.0%

REG_DT
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2020/10/13 14:54:00
712 
2020/10/13 14:53:58
178 
2020/10/13 14:53:59
110 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020/10/13 14:54:00
2nd row2020/10/13 14:54:00
3rd row2020/10/13 14:54:00
4th row2020/10/13 14:54:00
5th row2020/10/13 14:54:00

Common Values

ValueCountFrequency (%)
2020/10/13 14:54:00 712
71.2%
2020/10/13 14:53:58 178
 
17.8%
2020/10/13 14:53:59 110
 
11.0%

Length

2023-12-13T05:26:47.957388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:26:48.076927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020/10/13 1000
50.0%
14:54:00 712
35.6%
14:53:58 178
 
8.9%
14:53:59 110
 
5.5%

Interactions

2023-12-13T05:26:41.830251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:34.631655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:36.062962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:37.070036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:38.076816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:39.082194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:39.958392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:40.839371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:41.968301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:34.774552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:36.184592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:37.205569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:38.177424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:39.186999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:40.078407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:40.948332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:42.101541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:34.912073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:36.300007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:37.352212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:38.301615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:39.318643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:40.179107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:41.068997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:42.214131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:35.370480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:36.427057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:37.475484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:38.423453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:39.439238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:40.292655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:41.187554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:42.360308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:35.509403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:36.550028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:37.600386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:38.562435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:39.560652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:40.408228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:41.300212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:42.467643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:35.643668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:36.659734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:37.727398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:38.683266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:39.651139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:40.533982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:41.426332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:42.587769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:35.777944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:36.784655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:37.848072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:38.810310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:39.749613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:40.643531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:41.559319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:42.714289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:35.924417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:36.946365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:37.966242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:38.938057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:39.855806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:40.745342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:41.700649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:26:48.191956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
HOLD_CDLOAN_ACC_NOSEQWRT_BASIS_DYTREAT_ORG_CDTELGRM_SEQARCV_RECVBLE_DVCDDMCUT_CALC_DVCDOCCR_SEQDEMND_DYICALC_TRGT_AMTCALC_DCNTINT_CALC_AMTLEDGER_PRCSS_YNREG_DT
HOLD_CD1.0000.8310.8551.0001.0000.6160.0640.0000.8330.2630.3110.3660.1690.0641.000
LOAN_ACC_NO0.8311.0000.8351.0000.7390.6500.1130.0000.6460.0950.2810.2400.0000.1130.941
SEQ0.8550.8351.0001.0000.8950.9040.0670.0000.7700.3350.2820.2790.1460.0671.000
WRT_BASIS_DY1.0001.0001.0001.0001.0000.7410.0000.0000.9980.1300.3100.3540.1140.0001.000
TREAT_ORG_CD1.0000.7390.8951.0001.0000.6880.0000.0000.9800.2520.3030.4180.1210.0001.000
TELGRM_SEQ0.6160.6500.9040.7410.6881.0000.0890.0000.5600.3030.2140.2240.0000.0890.732
ARCV_RECVBLE_DVCD0.0640.1130.0670.0000.0000.0891.0000.6530.5860.5820.0580.2380.0000.9970.000
DMCUT_CALC_DVCD0.0000.0000.0000.0000.0000.0000.6531.0000.5560.4720.0000.1370.1240.6530.000
OCCR_SEQ0.8330.6460.7700.9980.9800.5600.5860.5561.0000.4850.2720.3210.1810.5860.959
DEMND_DY0.2630.0950.3350.1300.2520.3030.5820.4720.4851.0000.0000.9400.6260.5820.324
ICALC_TRGT_AMT0.3110.2810.2820.3100.3030.2140.0580.0000.2720.0001.0000.1170.8050.0580.354
CALC_DCNT0.3660.2400.2790.3540.4180.2240.2380.1370.3210.9400.1171.0000.6740.2380.377
INT_CALC_AMT0.1690.0000.1460.1140.1210.0000.0000.1240.1810.6260.8050.6741.0000.0000.157
LEDGER_PRCSS_YN0.0640.1130.0670.0000.0000.0890.9970.6530.5860.5820.0580.2380.0001.0000.000
REG_DT1.0000.9411.0001.0001.0000.7320.0000.0000.9590.3240.3540.3770.1570.0001.000
2023-12-13T05:26:48.383223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
REG_DTWRT_BASIS_DYARCV_RECVBLE_DVCDDMCUT_CALC_DVCDLEDGER_PRCSS_YNTREAT_ORG_CDHOLD_CD
REG_DT1.0000.9990.0000.0000.0000.9990.998
WRT_BASIS_DY0.9991.0000.0000.0000.0000.9990.998
ARCV_RECVBLE_DVCD0.0000.0001.0000.4530.9490.0000.046
DMCUT_CALC_DVCD0.0000.0000.4531.0000.4530.0000.000
LEDGER_PRCSS_YN0.0000.0000.9490.4531.0000.0000.046
TREAT_ORG_CD0.9990.9990.0000.0000.0001.0000.999
HOLD_CD0.9980.9980.0460.0000.0460.9991.000
2023-12-13T05:26:48.895491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LOAN_ACC_NOSEQTELGRM_SEQOCCR_SEQDEMND_DYICALC_TRGT_AMTCALC_DCNTINT_CALC_AMTHOLD_CDWRT_BASIS_DYTREAT_ORG_CDARCV_RECVBLE_DVCDDMCUT_CALC_DVCDLEDGER_PRCSS_YNREG_DT
LOAN_ACC_NO1.000-0.2300.4250.2600.284-0.060-0.149-0.1380.4460.9980.5740.0810.0000.0810.704
SEQ-0.2301.0000.740-0.028-0.409-0.1030.1510.0310.6300.9960.8130.0670.0000.0670.997
TELGRM_SEQ0.4250.7401.0000.352-0.269-0.0540.017-0.0490.3820.5790.4880.0680.0000.0680.596
OCCR_SEQ0.260-0.0280.3521.000-0.1490.222-0.154-0.0170.6580.9530.8070.4420.4180.4420.972
DEMND_DY0.284-0.409-0.269-0.1491.000-0.054-0.804-0.6370.1820.1600.2080.7020.5720.7020.259
ICALC_TRGT_AMT-0.060-0.103-0.0540.222-0.0541.000-0.0500.5430.1670.2380.1840.0450.0000.0450.226
CALC_DCNT-0.1490.1510.017-0.154-0.804-0.0501.0000.7190.2130.3360.2730.1890.1050.1890.265
INT_CALC_AMT-0.1380.031-0.049-0.017-0.6370.5430.7191.0000.0890.0870.0720.0000.0940.0000.094
HOLD_CD0.4460.6300.3820.6580.1820.1670.2130.0891.0000.9980.9990.0460.0000.0460.998
WRT_BASIS_DY0.9980.9960.5790.9530.1600.2380.3360.0870.9981.0000.9990.0000.0000.0000.999
TREAT_ORG_CD0.5740.8130.4880.8070.2080.1840.2730.0720.9990.9991.0000.0000.0000.0000.999
ARCV_RECVBLE_DVCD0.0810.0670.0680.4420.7020.0450.1890.0000.0460.0000.0001.0000.4530.9490.000
DMCUT_CALC_DVCD0.0000.0000.0000.4180.5720.0000.1050.0940.0000.0000.0000.4531.0000.4530.000
LEDGER_PRCSS_YN0.0810.0670.0680.4420.7020.0450.1890.0000.0460.0000.0000.9490.4531.0000.000
REG_DT0.7040.9970.5960.9720.2590.2260.2650.0940.9980.9990.9990.0000.0000.0001.000

Missing values

2023-12-13T05:26:43.207998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:26:43.534596image/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_CDHOLD_CDLOAN_ACC_NOSEQWRT_BASIS_DYTREAT_ORG_CDRPT_OFFER_SEQONLIN_YNTELGRM_SEQDOC_OFFER_DYARCV_RECVBLE_DVCDDMCUT_CALC_DVCDOCCR_SEQDEMND_DYICALC_TRGT_AMTCALC_DCNTINT_CALC_AMTLEDGER_PRCSS_YNREG_ENOREG_DT
0KHFCMB2020S-31B023-2020-003580073070812158846720200922B0231Y712202009222152020082518170322427376661Y18412020/10/13 14:54:00
1KHFCMB2020S-31B023-2020-003479573117704158846620200922B0231Y711202009222152020082411910808128232358Y18412020/10/13 14:54:00
2KHFCMB2020S-31B023-2020-003579573117533158846520200922B0231Y71020200922215202008249335783428217834Y18412020/10/13 14:54:00
3KHFCMB2020S-31B023-2020-003579573117522158846420200922B0231Y70920200922215202008247299788228170328Y18412020/10/13 14:54:00
4KHFCMB2020S-31B023-2020-003579573117470158846320200922B0231Y7082020092221520200914158634280792840Y18412020/10/13 14:54:00
5KHFCMB2020S-31B023-2020-003579573117458158846220200922B0231Y707202009222152020091998400000215378Y18412020/10/13 14:54:00
6KHFCMB2020S-31B023-2020-003478473018383158846120200922B0231Y70620200922215202009091052121391287964Y18412020/10/13 14:54:00
7KHFCMB2020S-31B023-2020-003578473018060158846020200922B0231Y705202009222152020082918075666223346450Y18412020/10/13 14:54:00
8KHFCMB2020S-31B023-2020-003478473018015158845920200922B0231Y704202009222152020082410672595428187791Y18412020/10/13 14:54:00
9KHFCMB2020S-31B023-2020-003578473017882158845820200922B0231Y703202009222152020082410199232628238762Y18412020/10/13 14:54:00
LIQD_PLAN_CDHOLD_CDLOAN_ACC_NOSEQWRT_BASIS_DYTREAT_ORG_CDRPT_OFFER_SEQONLIN_YNTELGRM_SEQDOC_OFFER_DYARCV_RECVBLE_DVCDDMCUT_CALC_DVCDOCCR_SEQDEMND_DYICALC_TRGT_AMTCALC_DCNTINT_CALC_AMTLEDGER_PRCSS_YNREG_ENOREG_DT
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