Overview

Dataset statistics

Number of variables18
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory151.5 KiB
Average record size in memory155.1 B

Variable types

Categorical8
Numeric7
Boolean2
DateTime1

Dataset

Description한국주택금융공사 신탁자산부 업무 관련 공개 공공데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15073172/fileData.do

Alerts

LIQD_PLAN_CD has constant value ""Constant
FEE_CD has constant value ""Constant
EXPECT_DY has constant value ""Constant
MNG_FEE_CD has constant value ""Constant
TRNSFER_DY has constant value ""Constant
PAY_AMT has constant value ""Constant
MLTM_TRN_YN has constant value ""Constant
BANK_TRN_YN has constant value ""Constant
REG_ENO has constant value ""Constant
REG_DT has constant value ""Constant
APPRV_DY is highly overall correlated with LOAN_EXEC_DYHigh correlation
LOAN_EXEC_DY is highly overall correlated with APPRV_DYHigh correlation
LOAN_AMT is highly overall correlated with LOAN_RAMTHigh correlation
INT_RAT is highly overall correlated with HOLD_CDHigh correlation
LOAN_RAMT is highly overall correlated with LOAN_AMTHigh correlation
HOLD_CD is highly overall correlated with INT_RATHigh correlation
LOAN_ACC_NO has unique valuesUnique

Reproduction

Analysis started2023-12-12 03:09:22.173759
Analysis finished2023-12-12 03:09:31.127410
Duration8.95 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-34
1000 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKHFCMB2020S-34
2nd rowKHFCMB2020S-34
3rd rowKHFCMB2020S-34
4th rowKHFCMB2020S-34
5th rowKHFCMB2020S-34

Common Values

ValueCountFrequency (%)
KHFCMB2020S-34 1000
100.0%

Length

2023-12-12T12:09:31.227319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:09:31.360956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
khfcmb2020s-34 1000
100.0%

FEE_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
B00417
1000 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB00417
2nd rowB00417
3rd rowB00417
4th rowB00417
5th rowB00417

Common Values

ValueCountFrequency (%)
B00417 1000
100.0%

Length

2023-12-12T12:09:31.519747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:09:31.659067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b00417 1000
100.0%

EXPECT_DY
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
20201120
1000 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20201120
2nd row20201120
3rd row20201120
4th row20201120
5th row20201120

Common Values

ValueCountFrequency (%)
20201120 1000
100.0%

Length

2023-12-12T12:09:31.814217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:09:31.974911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20201120 1000
100.0%

MNG_FEE_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
6A
1000 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6A
2nd row6A
3rd row6A
4th row6A
5th row6A

Common Values

ValueCountFrequency (%)
6A 1000
100.0%

Length

2023-12-12T12:09:32.090139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:09:32.221626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
6a 1000
100.0%

LOAN_ACC_NO
Real number (ℝ)

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1215102 × 1013
Minimum1.5090405 × 1011
Maximum9.9310901 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T12:09:32.416163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5090405 × 1011
5-th percentile2.0550904 × 1013
Q14.3340901 × 1013
median6.6240904 × 1013
Q38.1070901 × 1013
95-th percentile8.96724 × 1013
Maximum9.9310901 × 1013
Range9.9159997 × 1013
Interquartile range (IQR)3.773 × 1013

Descriptive statistics

Standard deviation2.3727808 × 1013
Coefficient of variation (CV)0.38761363
Kurtosis-0.55337402
Mean6.1215102 × 1013
Median Absolute Deviation (MAD)1.6105001 × 1013
Skewness-0.6774793
Sum6.1215102 × 1016
Variance5.6300887 × 1026
MonotonicityNot monotonic
2023-12-12T12:09:32.633989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22640904049185 1
 
0.1%
76320901064512 1
 
0.1%
25630904028402 1
 
0.1%
79750901037849 1
 
0.1%
81070901038192 1
 
0.1%
86020901057745 1
 
0.1%
81100901053831 1
 
0.1%
63090904023045 1
 
0.1%
76290901075014 1
 
0.1%
67520900028519 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
150904048743 1
0.1%
860904011621 1
0.1%
990904013277 1
0.1%
1090904012201 1
0.1%
1120904023134 1
0.1%
1140900000788 1
0.1%
1980900006601 1
0.1%
2220904016402 1
0.1%
2350904034337 1
0.1%
2480904036079 1
0.1%
ValueCountFrequency (%)
99310901021705 1
0.1%
99280901078131 1
0.1%
99280901077978 1
0.1%
98720900004061 1
0.1%
98690900011619 1
0.1%
98560900007149 1
0.1%
97590900008428 1
0.1%
96980901088914 1
0.1%
96980901088899 1
0.1%
96980901088860 1
0.1%

APPRV_DY
Real number (ℝ)

HIGH CORRELATION 

Distinct127
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20199532
Minimum20191208
Maximum20200513
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T12:09:32.823662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20191208
5-th percentile20191230
Q120200220
median20200313
Q320200329
95-th percentile20200413
Maximum20200513
Range9305
Interquartile range (IQR)109.25

Descriptive statistics

Standard deviation2533.7984
Coefficient of variation (CV)0.00012543847
Kurtosis6.8778885
Mean20199532
Median Absolute Deviation (MAD)86
Skewness-2.9750234
Sum2.0199532 × 1010
Variance6420134.3
MonotonicityNot monotonic
2023-12-12T12:09:33.029746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20191231 28
 
2.8%
20200317 27
 
2.7%
20200331 26
 
2.6%
20200323 23
 
2.3%
20200330 23
 
2.3%
20200327 22
 
2.2%
20200401 22
 
2.2%
20200316 22
 
2.2%
20200303 22
 
2.2%
20200402 22
 
2.2%
Other values (117) 763
76.3%
ValueCountFrequency (%)
20191208 1
 
0.1%
20191210 1
 
0.1%
20191216 1
 
0.1%
20191218 4
0.4%
20191219 2
0.2%
20191220 3
0.3%
20191222 1
 
0.1%
20191223 2
0.2%
20191224 1
 
0.1%
20191225 1
 
0.1%
ValueCountFrequency (%)
20200513 1
 
0.1%
20200509 1
 
0.1%
20200508 2
 
0.2%
20200507 5
0.5%
20200506 1
 
0.1%
20200505 2
 
0.2%
20200504 3
0.3%
20200501 1
 
0.1%
20200430 1
 
0.1%
20200429 4
0.4%

TRNSFER_DY
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
20201027
1000 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20201027
2nd row20201027
3rd row20201027
4th row20201027
5th row20201027

Common Values

ValueCountFrequency (%)
20201027 1000
100.0%

Length

2023-12-12T12:09:33.224441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:09:33.346863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20201027 1000
100.0%

LOAN_EXEC_DY
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20200464
Minimum20200122
Maximum20200522
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T12:09:33.502927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200122
5-th percentile20200227
Q120200504
median20200508
Q320200515
95-th percentile20200520
Maximum20200522
Range400
Interquartile range (IQR)11

Descriptive statistics

Standard deviation94.097578
Coefficient of variation (CV)4.6581888 × 10-6
Kurtosis1.7092956
Mean20200464
Median Absolute Deviation (MAD)7
Skewness-1.738161
Sum2.0200464 × 1010
Variance8854.3543
MonotonicityNot monotonic
2023-12-12T12:09:33.671134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200515 116
 
11.6%
20200508 105
 
10.5%
20200520 92
 
9.2%
20200511 78
 
7.8%
20200506 63
 
6.3%
20200512 57
 
5.7%
20200507 54
 
5.4%
20200504 54
 
5.4%
20200513 45
 
4.5%
20200518 40
 
4.0%
Other values (59) 296
29.6%
ValueCountFrequency (%)
20200122 1
 
0.1%
20200130 1
 
0.1%
20200131 7
0.7%
20200203 1
 
0.1%
20200205 1
 
0.1%
20200207 5
0.5%
20200210 2
 
0.2%
20200211 2
 
0.2%
20200213 1
 
0.1%
20200214 2
 
0.2%
ValueCountFrequency (%)
20200522 1
 
0.1%
20200520 92
9.2%
20200519 29
 
2.9%
20200518 40
 
4.0%
20200515 116
11.6%
20200514 39
 
3.9%
20200513 45
 
4.5%
20200512 57
5.7%
20200511 78
7.8%
20200508 105
10.5%

LOAN_EXPIRE_DY
Real number (ℝ)

Distinct155
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20451164
Minimum20300220
Maximum20500520
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T12:09:33.846377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20300220
5-th percentile20300511
Q120400508
median20500466
Q320500512
95-th percentile20500520
Maximum20500520
Range200300
Interquartile range (IQR)100004

Descriptive statistics

Standard deviation72312.833
Coefficient of variation (CV)0.0035358785
Kurtosis-0.58410615
Mean20451164
Median Absolute Deviation (MAD)54
Skewness-1.0070464
Sum2.0451164 × 1010
Variance5.2291458 × 109
MonotonicityNot monotonic
2023-12-12T12:09:34.026852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20500515 82
 
8.2%
20500508 64
 
6.4%
20500511 57
 
5.7%
20500520 56
 
5.6%
20500507 40
 
4.0%
20500506 39
 
3.9%
20500512 37
 
3.7%
20500504 37
 
3.7%
20500514 27
 
2.7%
20500518 23
 
2.3%
Other values (145) 538
53.8%
ValueCountFrequency (%)
20300220 2
0.2%
20300227 2
0.2%
20300302 1
0.1%
20300306 1
0.1%
20300312 1
0.1%
20300313 1
0.1%
20300316 1
0.1%
20300317 1
0.1%
20300320 2
0.2%
20300327 1
0.1%
ValueCountFrequency (%)
20500520 56
5.6%
20500519 15
 
1.5%
20500518 23
 
2.3%
20500515 82
8.2%
20500514 27
 
2.7%
20500513 23
 
2.3%
20500512 37
3.7%
20500511 57
5.7%
20500508 64
6.4%
20500507 40
4.0%

LOAN_AMT
Real number (ℝ)

HIGH CORRELATION 

Distinct240
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.51428 × 108
Minimum4000000
Maximum3.13 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T12:09:34.203239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4000000
5-th percentile45000000
Q195000000
median1.47 × 108
Q32 × 108
95-th percentile2.861 × 108
Maximum3.13 × 108
Range3.09 × 108
Interquartile range (IQR)1.05 × 108

Descriptive statistics

Standard deviation71200634
Coefficient of variation (CV)0.47019464
Kurtosis-0.68915126
Mean1.51428 × 108
Median Absolute Deviation (MAD)53000000
Skewness0.26009934
Sum1.51428 × 1011
Variance5.0695303 × 1015
MonotonicityNot monotonic
2023-12-12T12:09:34.376134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200000000 43
 
4.3%
300000000 38
 
3.8%
100000000 33
 
3.3%
150000000 30
 
3.0%
140000000 25
 
2.5%
130000000 21
 
2.1%
70000000 20
 
2.0%
110000000 19
 
1.9%
80000000 19
 
1.9%
170000000 17
 
1.7%
Other values (230) 735
73.5%
ValueCountFrequency (%)
4000000 1
 
0.1%
10000000 1
 
0.1%
15000000 1
 
0.1%
16000000 1
 
0.1%
17000000 1
 
0.1%
18000000 1
 
0.1%
19000000 1
 
0.1%
20000000 8
0.8%
25000000 2
 
0.2%
26000000 1
 
0.1%
ValueCountFrequency (%)
313000000 1
 
0.1%
308000000 1
 
0.1%
300000000 38
3.8%
299000000 1
 
0.1%
298000000 2
 
0.2%
297000000 1
 
0.1%
292000000 2
 
0.2%
290000000 1
 
0.1%
289000000 1
 
0.1%
288000000 2
 
0.2%

INT_RAT
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.32244
Minimum1.58
Maximum2.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T12:09:34.510256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.58
5-th percentile1.98
Q12.23
median2.38
Q32.43
95-th percentile2.53
Maximum2.63
Range1.05
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.17270632
Coefficient of variation (CV)0.07436417
Kurtosis2.2433029
Mean2.32244
Median Absolute Deviation (MAD)0.1
Skewness-1.3377023
Sum2322.44
Variance0.029827474
MonotonicityNot monotonic
2023-12-12T12:09:34.645068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2.43 323
32.3%
2.23 137
13.7%
2.38 104
 
10.4%
2.53 91
 
9.1%
2.28 74
 
7.4%
2.18 74
 
7.4%
2.03 31
 
3.1%
2.33 30
 
3.0%
2.48 22
 
2.2%
2.08 18
 
1.8%
Other values (21) 96
 
9.6%
ValueCountFrequency (%)
1.58 1
 
0.1%
1.63 5
 
0.5%
1.68 4
 
0.4%
1.78 10
 
1.0%
1.83 7
 
0.7%
1.88 5
 
0.5%
1.9 1
 
0.1%
1.93 4
 
0.4%
1.98 18
1.8%
2.03 31
3.1%
ValueCountFrequency (%)
2.63 7
 
0.7%
2.58 3
 
0.3%
2.55 2
 
0.2%
2.53 91
 
9.1%
2.48 22
 
2.2%
2.45 10
 
1.0%
2.43 323
32.3%
2.4 3
 
0.3%
2.38 104
 
10.4%
2.33 30
 
3.0%

LOAN_RAMT
Real number (ℝ)

HIGH CORRELATION 

Distinct888
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4835226 × 108
Minimum3933336
Maximum3.104839 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T12:09:34.785768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3933336
5-th percentile41680480
Q192033337
median1.423262 × 108
Q31.9819411 × 108
95-th percentile2.8296706 × 108
Maximum3.104839 × 108
Range3.0655057 × 108
Interquartile range (IQR)1.0616077 × 108

Descriptive statistics

Standard deviation71190176
Coefficient of variation (CV)0.47987253
Kurtosis-0.68386
Mean1.4835226 × 108
Median Absolute Deviation (MAD)54923894
Skewness0.28469368
Sum1.4835226 × 1011
Variance5.0680411 × 1015
MonotonicityNot monotonic
2023-12-12T12:09:34.943965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
297746793 8
 
0.8%
138948503 5
 
0.5%
197345830 4
 
0.4%
96666668 4
 
0.4%
79399146 4
 
0.4%
119098717 4
 
0.4%
98135461 4
 
0.4%
98888892 4
 
0.4%
148873398 4
 
0.4%
198497862 3
 
0.3%
Other values (878) 956
95.6%
ValueCountFrequency (%)
3933336 1
0.1%
9922285 1
0.1%
13340663 1
0.1%
14750000 1
0.1%
15466668 1
0.1%
16807472 1
0.1%
17462165 1
0.1%
18288861 1
0.1%
19256399 1
0.1%
19624125 1
0.1%
ValueCountFrequency (%)
310483903 1
0.1%
307949048 1
0.1%
299953769 2
0.2%
299952924 2
0.2%
299952292 1
0.1%
299952076 2
0.2%
299951857 1
0.1%
299919327 1
0.1%
299917970 1
0.1%
299916299 1
0.1%

PAY_AMT
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
15000
1000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15000
2nd row15000
3rd row15000
4th row15000
5th row15000

Common Values

ValueCountFrequency (%)
15000 1000
100.0%

Length

2023-12-12T12:09:35.085691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:09:35.179883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
15000 1000
100.0%

HOLD_CD
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
B004-2020-0099
839 
B004-2020-0098
161 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB004-2020-0099
2nd rowB004-2020-0099
3rd rowB004-2020-0099
4th rowB004-2020-0099
5th rowB004-2020-0099

Common Values

ValueCountFrequency (%)
B004-2020-0099 839
83.9%
B004-2020-0098 161
 
16.1%

Length

2023-12-12T12:09:35.275069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:09:35.383373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b004-2020-0099 839
83.9%
b004-2020-0098 161
 
16.1%

MLTM_TRN_YN
Boolean

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
1000 
ValueCountFrequency (%)
False 1000
100.0%
2023-12-12T12:09:35.473065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

BANK_TRN_YN
Boolean

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
1000 
ValueCountFrequency (%)
False 1000
100.0%
2023-12-12T12:09:35.550949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

REG_ENO
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1769
1000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1769
2nd row1769
3rd row1769
4th row1769
5th row1769

Common Values

ValueCountFrequency (%)
1769 1000
100.0%

Length

2023-12-12T12:09:35.658394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:09:35.753868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1769 1000
100.0%

REG_DT
Date

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2020-10-28 11:22:37
Maximum2020-10-28 11:22:37
2023-12-12T12:09:35.842340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:35.939612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T12:09:29.048361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:22.723210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:23.760797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:24.674166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:25.656488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:26.768376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:27.942384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:29.215533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:22.890900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:23.914259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:24.797870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:25.821180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:26.979897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:28.088723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:29.381619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:23.058246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:24.029400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:24.919123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:25.981377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:27.134698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:28.228058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:29.570568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:23.218175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:24.173624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:25.051109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:26.145984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:27.280127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:28.396085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:29.749116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:23.372092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:24.320293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:25.198342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:26.294951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:27.477054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:28.559141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:30.254771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:23.528249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:24.444132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:25.343416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:26.448501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:27.641723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:28.726228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:30.408453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:23.634046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:24.551567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:25.487669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:26.584373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:27.785567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:28.879808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:09:36.015651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LOAN_ACC_NOAPPRV_DYLOAN_EXEC_DYLOAN_EXPIRE_DYLOAN_AMTINT_RATLOAN_RAMTHOLD_CD
LOAN_ACC_NO1.0000.3770.3430.1170.2630.3400.2630.646
APPRV_DY0.3771.0000.7010.2030.2970.1230.2920.147
LOAN_EXEC_DY0.3430.7011.0000.2670.2520.1410.2370.280
LOAN_EXPIRE_DY0.1170.2030.2671.0000.4820.8480.4980.068
LOAN_AMT0.2630.2970.2520.4821.0000.3160.9990.122
INT_RAT0.3400.1230.1410.8480.3161.0000.3150.835
LOAN_RAMT0.2630.2920.2370.4980.9990.3151.0000.101
HOLD_CD0.6460.1470.2800.0680.1220.8350.1011.000
2023-12-12T12:09:36.153365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LOAN_ACC_NOAPPRV_DYLOAN_EXEC_DYLOAN_EXPIRE_DYLOAN_AMTINT_RATLOAN_RAMTHOLD_CD
LOAN_ACC_NO1.0000.000-0.071-0.033-0.0220.137-0.0260.499
APPRV_DY0.0001.0000.6400.249-0.2640.002-0.2550.101
LOAN_EXEC_DY-0.0710.6401.0000.426-0.212-0.066-0.1950.302
LOAN_EXPIRE_DY-0.0330.2490.4261.0000.2390.3640.2560.081
LOAN_AMT-0.022-0.264-0.2120.2391.0000.2170.9910.093
INT_RAT0.1370.002-0.0660.3640.2171.0000.2210.665
LOAN_RAMT-0.026-0.255-0.1950.2560.9910.2211.0000.078
HOLD_CD0.4990.1010.3020.0810.0930.6650.0781.000

Missing values

2023-12-12T12:09:30.641423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:09:30.982910image/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_CDFEE_CDEXPECT_DYMNG_FEE_CDLOAN_ACC_NOAPPRV_DYTRNSFER_DYLOAN_EXEC_DYLOAN_EXPIRE_DYLOAN_AMTINT_RATLOAN_RAMTPAY_AMTHOLD_CDMLTM_TRN_YNBANK_TRN_YNREG_ENOREG_DT
0KHFCMB2020S-34B00417202011206A22640904049185202001292020102720200320205003202500000002.4324583333615000B004-2020-0099NN17692020/10/28 11:22:37
1KHFCMB2020S-34B00417202011206A67850901154669202003182020102720200515205005151650000002.4316316666815000B004-2020-0099NN17692020/10/28 11:22:37
2KHFCMB2020S-34B00417202011206A63650904033088202002252020102720200508205005081400000002.4313894850315000B004-2020-0099NN17692020/10/28 11:22:37
3KHFCMB2020S-34B00417202011206A21650904019004202002082020102720200325205003251450000002.4314258333815000B004-2020-0099NN17692020/10/28 11:22:37
4KHFCMB2020S-34B00417202011206A5010090402707120200312202010272020050420300504380000002.183641667015000B004-2020-0099NN17692020/10/28 11:22:37
5KHFCMB2020S-34B00417202011206A53820904018891202003292020102720200511204005111620000002.3815930000015000B004-2020-0099NN17692020/10/28 11:22:37
6KHFCMB2020S-34B00417202011206A55860904031247202002272020102720200504203505041260000002.0812300849715000B004-2020-0099NN17692020/10/28 11:22:37
7KHFCMB2020S-34B00417202011206A1090904012201202003192020102720200508205005083000000002.4329995207615000B004-2020-0099NN17692020/10/28 11:22:37
8KHFCMB2020S-34B00417202011206A8619090403084320200327202010272020051420500514700000002.336946517615000B004-2020-0099NN17692020/10/28 11:22:37
9KHFCMB2020S-34B00417202011206A81010904021456202003122020102720200515205005151630000002.4316177575815000B004-2020-0099NN17692020/10/28 11:22:37
LIQD_PLAN_CDFEE_CDEXPECT_DYMNG_FEE_CDLOAN_ACC_NOAPPRV_DYTRNSFER_DYLOAN_EXEC_DYLOAN_EXPIRE_DYLOAN_AMTINT_RATLOAN_RAMTPAY_AMTHOLD_CDMLTM_TRN_YNBANK_TRN_YNREG_ENOREG_DT
990KHFCMB2020S-34B00417202011206A4275090400581420200307202010272020051120500511800000002.437939914615000B004-2020-0099NN17692020/10/28 11:22:37
991KHFCMB2020S-34B00417202011206A64220904021723202001302020102720200228205002282650000002.3326140689515000B004-2020-0099NN17692020/10/28 11:22:37
992KHFCMB2020S-34B00417202011206A4053090402584520200330202010272020051920500519780000002.057713333615000B004-2020-0099NN17692020/10/28 11:22:37
993KHFCMB2020S-34B00417202011206A6695090400556820200330202010272020051920500519560000002.235537778015000B004-2020-0099NN17692020/10/28 11:22:37
994KHFCMB2020S-34B00417202011206A48040904019751202003092020102720200508205005082120000002.4321040773315000B004-2020-0099NN17692020/10/28 11:22:37
995KHFCMB2020S-34B00417202011206A7803090002444220200330202010272020052020300520790000002.287665155915000B004-2020-0098NN17692020/10/28 11:22:37
996KHFCMB2020S-34B00417202011206A81030900029201202003092020102720200511205005112340000002.4323005203715000B004-2020-0099NN17692020/10/28 11:22:37
997KHFCMB2020S-34B00417202011206A53260904020944202002262020102720200504205005041200000002.4311909871715000B004-2020-0099NN17692020/10/28 11:22:37
998KHFCMB2020S-34B00417202011206A77390901095405202003062020102720200508203505081870000002.2818351331015000B004-2020-0099NN17692020/10/28 11:22:37
999KHFCMB2020S-34B00417202011206A581090401917220200402202010272020051820300518500000001.984849068415000B004-2020-0099NN17692020/10/28 11:22:37