Overview

Dataset statistics

Number of variables14
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory118.3 KiB
Average record size in memory121.1 B

Variable types

Text1
Categorical8
Numeric5

Dataset

Description한국주택금융공사 신탁자산부 업무 관련 공개 공공데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15073320/fileData.do

Alerts

FEE_DEDCT_AMT has constant value ""Constant
REG_ENO is highly overall correlated with DEMND_DY and 6 other fieldsHigh correlation
LOAN_ORG_CD is highly overall correlated with DEMND_DY and 4 other fieldsHigh correlation
REG_DT is highly overall correlated with DEMND_DY and 5 other fieldsHigh correlation
DEMND_DY is highly overall correlated with LOAN_ORG_CD and 4 other fieldsHigh correlation
FEE_RAT is highly overall correlated with FEE_DEMND_AMT and 1 other fieldsHigh correlation
CALC_DCNT is highly overall correlated with MNG_FEE_CD and 4 other fieldsHigh correlation
LOAN_AVG_RAMT is highly overall correlated with FEE_DEMND_AMT and 1 other fieldsHigh correlation
FEE_DEMND_AMT is highly overall correlated with FEE_RAT and 3 other fieldsHigh correlation
MNG_FEE_CD is highly overall correlated with FEE_RAT and 2 other fieldsHigh correlation
CALC_STRT_DY is highly overall correlated with DEMND_DY and 5 other fieldsHigh correlation
CALC_END_DY is highly overall correlated with DEMND_DY and 5 other fieldsHigh correlation
HOLD_CD is highly overall correlated with LOAN_AVG_RAMT and 2 other fieldsHigh correlation
CALC_STRT_DY is highly imbalanced (65.3%)Imbalance
CALC_END_DY is highly imbalanced (65.3%)Imbalance
REG_ENO is highly imbalanced (85.2%)Imbalance
HOLD_CD is highly imbalanced (82.9%)Imbalance
FEE_DEMND_AMT is highly skewed (γ1 = 29.46286763)Skewed
FEE_DEMND_AMT has 17 (1.7%) zerosZeros

Reproduction

Analysis started2023-12-12 16:43:36.973175
Analysis finished2023-12-12 16:43:41.438348
Duration4.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

LIQD_PLAN_CD
Text

Distinct128
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-12-13T01:43:41.626416image/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

Unique29 ?
Unique (%)2.9%

Sample

1st rowKHFCMB2006S-02
2nd rowKHFCMB2013L-01
3rd rowKHFCMB2012L-16
4th rowKHFCMB2012S-05
5th rowKHFCMB2011S-19
ValueCountFrequency (%)
khfcmb2009s-08 28
 
2.8%
khfcmb2005s-05 28
 
2.8%
khfcmb2008s-04 28
 
2.8%
khfcmb2006s-05 28
 
2.8%
khfcmb2004s-06 28
 
2.8%
khfcmb2006s-02 27
 
2.7%
khfcmb2009s-04 27
 
2.7%
khfcmb2008s-01 26
 
2.6%
khfcmb2006s-01 26
 
2.6%
khfcmb2007s-01 26
 
2.6%
Other values (118) 728
72.8%
2023-12-13T01:43:42.047974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2643
18.9%
2 1208
8.6%
B 1001
 
7.1%
K 1000
 
7.1%
H 1000
 
7.1%
- 1000
 
7.1%
M 1000
 
7.1%
C 1000
 
7.1%
F 1000
 
7.1%
S 927
 
6.6%
Other values (10) 2221
15.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7005
50.0%
Decimal Number 5995
42.8%
Dash Punctuation 1000
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2643
44.1%
2 1208
20.2%
1 623
 
10.4%
5 279
 
4.7%
4 257
 
4.3%
6 230
 
3.8%
3 216
 
3.6%
9 191
 
3.2%
8 180
 
3.0%
7 168
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
B 1001
14.3%
K 1000
14.3%
H 1000
14.3%
M 1000
14.3%
C 1000
14.3%
F 1000
14.3%
S 927
13.2%
L 72
 
1.0%
A 5
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7005
50.0%
Common 6995
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2643
37.8%
2 1208
17.3%
- 1000
 
14.3%
1 623
 
8.9%
5 279
 
4.0%
4 257
 
3.7%
6 230
 
3.3%
3 216
 
3.1%
9 191
 
2.7%
8 180
 
2.6%
Latin
ValueCountFrequency (%)
B 1001
14.3%
K 1000
14.3%
H 1000
14.3%
M 1000
14.3%
C 1000
14.3%
F 1000
14.3%
S 927
13.2%
L 72
 
1.0%
A 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2643
18.9%
2 1208
8.6%
B 1001
 
7.1%
K 1000
 
7.1%
H 1000
 
7.1%
- 1000
 
7.1%
M 1000
 
7.1%
C 1000
 
7.1%
F 1000
 
7.1%
S 927
 
6.6%
Other values (10) 2221
15.9%

LOAN_ORG_CD
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
I001
173 
B004
153 
B010
151 
B027
128 
B039
114 
Other values (8)
281 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC001
2nd rowF001
3rd rowF001
4th rowF001
5th rowF001

Common Values

ValueCountFrequency (%)
I001 173
17.3%
B004 153
15.3%
B010 151
15.1%
B027 128
12.8%
B039 114
11.4%
I002 76
7.6%
F001 63
 
6.3%
F002 35
 
3.5%
I003 32
 
3.2%
B088 27
 
2.7%
Other values (3) 48
 
4.8%

Length

2023-12-13T01:43:42.214856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
i001 173
17.3%
b004 153
15.3%
b010 151
15.1%
b027 128
12.8%
b039 114
11.4%
i002 76
7.6%
f001 63
 
6.3%
f002 35
 
3.5%
i003 32
 
3.2%
b088 27
 
2.7%
Other values (3) 48
 
4.8%

MNG_FEE_CD
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
SB
204 
S1
187 
S2
184 
TB
131 
S3
69 
Other values (7)
225 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSB
2nd rowTB
3rd rowTB
4th rowTB
5th rowTB

Common Values

ValueCountFrequency (%)
SB 204
20.4%
S1 187
18.7%
S2 184
18.4%
TB 131
13.1%
S3 69
 
6.9%
CL 46
 
4.6%
BB 44
 
4.4%
C1 43
 
4.3%
4B 38
 
3.8%
BE 28
 
2.8%
Other values (2) 26
 
2.6%

Length

2023-12-13T01:43:42.332856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sb 204
20.4%
s1 187
18.7%
s2 184
18.4%
tb 131
13.1%
s3 69
 
6.9%
cl 46
 
4.6%
bb 44
 
4.4%
c1 43
 
4.3%
4b 38
 
3.8%
be 28
 
2.8%
Other values (2) 26
 
2.6%

DEMND_DY
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20140133
Minimum20140101
Maximum20141202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T01:43:42.463520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20140101
5-th percentile20140101
Q120140101
median20140106
Q320140106
95-th percentile20140203
Maximum20141202
Range1101
Interquartile range (IQR)5

Descriptive statistics

Standard deviation114.75165
Coefficient of variation (CV)5.6976608 × 10-6
Kurtosis72.911204
Mean20140133
Median Absolute Deviation (MAD)5
Skewness8.1517321
Sum2.0140133 × 1010
Variance13167.94
MonotonicityNot monotonic
2023-12-13T01:43:42.590066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
20140101 428
42.8%
20140106 331
33.1%
20140203 180
18.0%
20140102 36
 
3.6%
20140103 11
 
1.1%
20141202 10
 
1.0%
20140307 4
 
0.4%
ValueCountFrequency (%)
20140101 428
42.8%
20140102 36
 
3.6%
20140103 11
 
1.1%
20140106 331
33.1%
20140203 180
18.0%
20140307 4
 
0.4%
20141202 10
 
1.0%
ValueCountFrequency (%)
20141202 10
 
1.0%
20140307 4
 
0.4%
20140203 180
18.0%
20140106 331
33.1%
20140103 11
 
1.1%
20140102 36
 
3.6%
20140101 428
42.8%

FEE_RAT
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23154
Minimum0
Maximum1.2
Zeros9
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T01:43:42.733130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02
Q10.05
median0.1
Q30.4
95-th percentile0.5
Maximum1.2
Range1.2
Interquartile range (IQR)0.35

Descriptive statistics

Standard deviation0.19365984
Coefficient of variation (CV)0.83639905
Kurtosis-0.32209522
Mean0.23154
Median Absolute Deviation (MAD)0.08
Skewness0.63259964
Sum231.54
Variance0.037504133
MonotonicityNot monotonic
2023-12-13T01:43:42.901046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.1 233
23.3%
0.05 191
19.1%
0.5 187
18.7%
0.4 170
17.0%
0.25 51
 
5.1%
0.02 46
 
4.6%
0.03 24
 
2.4%
0.2 23
 
2.3%
0.04 19
 
1.9%
0.0 9
 
0.9%
Other values (25) 47
 
4.7%
ValueCountFrequency (%)
0.0 9
 
0.9%
0.02 46
 
4.6%
0.03 24
 
2.4%
0.04 19
 
1.9%
0.05 191
19.1%
0.06 5
 
0.5%
0.07 8
 
0.8%
0.1 233
23.3%
0.13 1
 
0.1%
0.19 1
 
0.1%
ValueCountFrequency (%)
1.2 2
0.2%
0.75 2
0.2%
0.74 1
0.1%
0.7 2
0.2%
0.65 1
0.1%
0.61 1
0.1%
0.58 1
0.1%
0.57 1
0.1%
0.53 1
0.1%
0.52 1
0.1%

CALC_STRT_DY
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
20131201
805 
20140101
180 
20141101
 
10
20140201
 
4
20131126
 
1

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row20131201
2nd row20131201
3rd row20131201
4th row20131201
5th row20131201

Common Values

ValueCountFrequency (%)
20131201 805
80.5%
20140101 180
 
18.0%
20141101 10
 
1.0%
20140201 4
 
0.4%
20131126 1
 
0.1%

Length

2023-12-13T01:43:43.067098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:43:43.202979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20131201 805
80.5%
20140101 180
 
18.0%
20141101 10
 
1.0%
20140201 4
 
0.4%
20131126 1
 
0.1%

CALC_END_DY
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
20131231
805 
20140131
180 
20141130
 
10
20140228
 
4
20131229
 
1

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row20131231
2nd row20131231
3rd row20131231
4th row20131231
5th row20131231

Common Values

ValueCountFrequency (%)
20131231 805
80.5%
20140131 180
 
18.0%
20141130 10
 
1.0%
20140228 4
 
0.4%
20131229 1
 
0.1%

Length

2023-12-13T01:43:43.367052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:43:43.499153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20131231 805
80.5%
20140131 180
 
18.0%
20141130 10
 
1.0%
20140228 4
 
0.4%
20131229 1
 
0.1%

CALC_DCNT
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.798
Minimum0
Maximum36
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T01:43:43.618630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31
Q131
median31
Q331
95-th percentile31
Maximum36
Range36
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0667529
Coefficient of variation (CV)0.067106724
Kurtosis161.89627
Mean30.798
Median Absolute Deviation (MAD)0
Skewness-12.160269
Sum30798
Variance4.2714675
MonotonicityNot monotonic
2023-12-13T01:43:43.759684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
31 975
97.5%
30 10
 
1.0%
16 6
 
0.6%
28 4
 
0.4%
0 3
 
0.3%
36 1
 
0.1%
29 1
 
0.1%
ValueCountFrequency (%)
0 3
 
0.3%
16 6
 
0.6%
28 4
 
0.4%
29 1
 
0.1%
30 10
 
1.0%
31 975
97.5%
36 1
 
0.1%
ValueCountFrequency (%)
36 1
 
0.1%
31 975
97.5%
30 10
 
1.0%
29 1
 
0.1%
28 4
 
0.4%
16 6
 
0.6%
0 3
 
0.3%

LOAN_AVG_RAMT
Real number (ℝ)

HIGH CORRELATION 

Distinct796
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1850337 × 1010
Minimum0
Maximum3.4635321 × 1011
Zeros8
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T01:43:43.923613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29045720
Q12.4158601 × 108
median7.7207359 × 108
Q32.1454472 × 109
95-th percentile8.0094824 × 1010
Maximum3.4635321 × 1011
Range3.4635321 × 1011
Interquartile range (IQR)1.9038612 × 109

Descriptive statistics

Standard deviation4.2143537 × 1010
Coefficient of variation (CV)3.5563154
Kurtosis28.396481
Mean1.1850337 × 1010
Median Absolute Deviation (MAD)6.5297354 × 108
Skewness5.117653
Sum1.1850337 × 1013
Variance1.7760777 × 1021
MonotonicityNot monotonic
2023-12-13T01:43:44.142874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
0.8%
183358133 4
 
0.4%
248025833 4
 
0.4%
12038973 3
 
0.3%
258710518 3
 
0.3%
190114148 3
 
0.3%
95891184 3
 
0.3%
48282658 3
 
0.3%
21777005 3
 
0.3%
246429524 3
 
0.3%
Other values (786) 963
96.3%
ValueCountFrequency (%)
0 8
0.8%
2654262 2
 
0.2%
6432187 1
 
0.1%
6757938 1
 
0.1%
8235223 1
 
0.1%
8358269 3
 
0.3%
8894933 1
 
0.1%
8910539 1
 
0.1%
9241712 1
 
0.1%
12038973 3
 
0.3%
ValueCountFrequency (%)
346353209737 2
0.2%
341638214121 1
0.1%
298468001921 1
0.1%
296391717777 2
0.2%
265240389828 1
0.1%
260730301298 2
0.2%
249470031956 1
0.1%
245528737276 2
0.2%
244111501486 2
0.2%
224148301256 1
0.1%

FEE_DEMND_AMT
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct981
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3461535.2
Minimum0
Maximum1.2395655 × 109
Zeros17
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T01:43:44.314706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2048.7
Q119018.25
median82783
Q3501117.75
95-th percentile12592607
Maximum1.2395655 × 109
Range1.2395655 × 109
Interquartile range (IQR)482099.5

Descriptive statistics

Standard deviation40091864
Coefficient of variation (CV)11.582105
Kurtosis907.10783
Mean3461535.2
Median Absolute Deviation (MAD)77442.5
Skewness29.462868
Sum3.4615352 × 109
Variance1.6073576 × 1015
MonotonicityNot monotonic
2023-12-13T01:43:44.498899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
1.7%
1459 2
 
0.2%
4833 2
 
0.2%
8401 2
 
0.2%
330342 1
 
0.1%
214594 1
 
0.1%
94870 1
 
0.1%
605216 1
 
0.1%
59024 1
 
0.1%
5455623 1
 
0.1%
Other values (971) 971
97.1%
ValueCountFrequency (%)
0 17
1.7%
68 1
 
0.1%
355 1
 
0.1%
384 1
 
0.1%
505 1
 
0.1%
511 1
 
0.1%
533 1
 
0.1%
563 1
 
0.1%
592 1
 
0.1%
639 1
 
0.1%
ValueCountFrequency (%)
1239565474 1
0.1%
101959848 1
0.1%
88105483 1
0.1%
85497815 1
0.1%
82365640 1
0.1%
80132216 1
0.1%
70069163 1
0.1%
58031696 1
0.1%
50698674 1
0.1%
47670631 1
0.1%

REG_ENO
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1222
959 
1452
 
27
1298
 
10
1498
 
4

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1222
2nd row1222
3rd row1222
4th row1222
5th row1222

Common Values

ValueCountFrequency (%)
1222 959
95.9%
1452 27
 
2.7%
1298 10
 
1.0%
1498 4
 
0.4%

Length

2023-12-13T01:43:44.738888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:43:44.868151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1222 959
95.9%
1452 27
 
2.7%
1298 10
 
1.0%
1498 4
 
0.4%

REG_DT
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2014/01/06 10:16:24
181 
2014/01/06 13:22:08
173 
2014/02/04 15:17:35
153 
2014/01/06 10:16:23
137 
2014/01/06 15:39:12
97 
Other values (11)
259 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2014/01/06 18:40:58
2nd row2014/01/06 15:39:34
3rd row2014/01/06 15:39:34
4th row2014/01/06 15:39:34
5th row2014/01/06 15:39:34

Common Values

ValueCountFrequency (%)
2014/01/06 10:16:24 181
18.1%
2014/01/06 13:22:08 173
17.3%
2014/02/04 15:17:35 153
15.3%
2014/01/06 10:16:23 137
13.7%
2014/01/06 15:39:12 97
9.7%
2014/01/06 15:39:34 63
 
6.3%
2014/01/06 18:41:14 35
 
3.5%
2014/01/06 10:49:28 34
 
3.4%
2014/01/06 09:25:24 32
 
3.2%
2014/02/05 12:42:12 27
 
2.7%
Other values (6) 68
 
6.8%

Length

2023-12-13T01:43:45.026208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2014/01/06 802
40.1%
10:16:24 181
 
9.0%
13:22:08 173
 
8.6%
2014/02/04 153
 
7.6%
15:17:35 153
 
7.6%
10:16:23 137
 
6.9%
15:39:12 97
 
4.9%
15:39:34 63
 
3.1%
18:41:14 35
 
1.8%
10:49:28 34
 
1.7%
Other values (12) 172
 
8.6%

FEE_DEDCT_AMT
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1000
100.0%

Length

2023-12-13T01:43:45.166085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:43:45.270390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1000
100.0%

HOLD_CD
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct47
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0000-0000-0000
908 
B088-2014-0002
 
3
B088-2014-0001
 
3
B088-2014-0003
 
3
B10-2012-0005
 
2
Other values (42)
 
81

Length

Max length14
Median length14
Mean length13.986
Min length13

Unique

Unique3 ?
Unique (%)0.3%

Sample

1st row0000-0000-0000
2nd row0000-0000-0000
3rd row0000-0000-0000
4th row0000-0000-0000
5th row0000-0000-0000

Common Values

ValueCountFrequency (%)
0000-0000-0000 908
90.8%
B088-2014-0002 3
 
0.3%
B088-2014-0001 3
 
0.3%
B088-2014-0003 3
 
0.3%
B10-2012-0005 2
 
0.2%
B039-2013-0015 2
 
0.2%
B039-2013-0013 2
 
0.2%
B039-2013-0010 2
 
0.2%
B039-2013-0007 2
 
0.2%
B039-2013-0004 2
 
0.2%
Other values (37) 71
 
7.1%

Length

2023-12-13T01:43:45.401272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0000-0000-0000 908
90.8%
b088-2014-0003 3
 
0.3%
b088-2014-0002 3
 
0.3%
b088-2014-0001 3
 
0.3%
b027-2013-0008 2
 
0.2%
b027-2013-0010 2
 
0.2%
b027-2013-0005 2
 
0.2%
b088-2013-0021 2
 
0.2%
b088-2013-0022 2
 
0.2%
b027-2012-0009 2
 
0.2%
Other values (37) 71
 
7.1%

Interactions

2023-12-13T01:43:40.178598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:37.880502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:38.433763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:38.997712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:39.582084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:40.284767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:37.987454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:38.537198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:39.099032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:39.693735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:40.394503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:38.087589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:38.633342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:39.224032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:39.828637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:40.510237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:38.211841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:38.753253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:39.336077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:39.942041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:40.618260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:38.328813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:38.886224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:39.461599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:40.054900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:43:45.527400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LOAN_ORG_CDMNG_FEE_CDDEMND_DYFEE_RATCALC_STRT_DYCALC_END_DYCALC_DCNTLOAN_AVG_RAMTFEE_DEMND_AMTREG_ENOREG_DTHOLD_CD
LOAN_ORG_CD1.0000.6230.9290.5390.8750.8630.7020.5260.1680.9150.9890.687
MNG_FEE_CD0.6231.0000.5390.9480.4270.3990.7630.6000.7190.7080.7450.680
DEMND_DY0.9290.5391.0000.3911.0001.0000.7160.6630.0001.0001.0000.000
FEE_RAT0.5390.9480.3911.0000.3510.3210.6580.4610.8810.6250.6480.733
CALC_STRT_DY0.8750.4271.0000.3511.0000.9910.9590.5320.0190.8590.9680.000
CALC_END_DY0.8630.3991.0000.3210.9911.0000.8640.4830.0190.8590.9560.000
CALC_DCNT0.7020.7630.7160.6580.9590.8641.0000.5610.4730.7230.8360.751
LOAN_AVG_RAMT0.5260.6000.6630.4610.5320.4830.5611.0000.2710.4780.6600.952
FEE_DEMND_AMT0.1680.7190.0000.8810.0190.0190.4730.2711.0000.2740.1860.648
REG_ENO0.9150.7081.0000.6250.8590.8590.7230.4780.2741.0001.0000.806
REG_DT0.9890.7451.0000.6480.9680.9560.8360.6600.1861.0001.0000.892
HOLD_CD0.6870.6800.0000.7330.0000.0000.7510.9520.6480.8060.8921.000
2023-12-13T01:43:45.730709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
REG_ENOCALC_END_DYHOLD_CDLOAN_ORG_CDMNG_FEE_CDREG_DTCALC_STRT_DY
REG_ENO1.0000.8410.5370.8040.3980.9940.841
CALC_END_DY0.8411.0000.0000.6950.2330.8600.865
HOLD_CD0.5370.0001.0000.2750.2770.4730.000
LOAN_ORG_CD0.8040.6950.2751.0000.2930.9360.716
MNG_FEE_CD0.3980.2330.2770.2931.0000.3740.251
REG_DT0.9940.8600.4730.9360.3741.0000.895
CALC_STRT_DY0.8410.8650.0000.7160.2510.8951.000
2023-12-13T01:43:45.897592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DEMND_DYFEE_RATCALC_DCNTLOAN_AVG_RAMTFEE_DEMND_AMTLOAN_ORG_CDMNG_FEE_CDCALC_STRT_DYCALC_END_DYREG_ENOREG_DTHOLD_CD
DEMND_DY1.000-0.117-0.2420.100-0.0050.6880.2770.9990.9990.9990.9930.000
FEE_RAT-0.1171.000-0.0140.1180.5890.2740.7700.2190.1990.3150.2790.374
CALC_DCNT-0.242-0.0141.0000.0350.0440.4760.5560.7130.5070.6630.6170.453
LOAN_AVG_RAMT0.1000.1180.0351.0000.8270.2490.3020.2480.2200.3050.3280.716
FEE_DEMND_AMT-0.0050.5890.0440.8271.0000.1550.5670.0240.0240.1820.1450.536
LOAN_ORG_CD0.6880.2740.4760.2490.1551.0000.2930.7160.6950.8040.9360.275
MNG_FEE_CD0.2770.7700.5560.3020.5670.2931.0000.2510.2330.3980.3740.277
CALC_STRT_DY0.9990.2190.7130.2480.0240.7160.2511.0000.8650.8410.8950.000
CALC_END_DY0.9990.1990.5070.2200.0240.6950.2330.8651.0000.8410.8600.000
REG_ENO0.9990.3150.6630.3050.1820.8040.3980.8410.8411.0000.9940.537
REG_DT0.9930.2790.6170.3280.1450.9360.3740.8950.8600.9941.0000.473
HOLD_CD0.0000.3740.4530.7160.5360.2750.2770.0000.0000.5370.4731.000

Missing values

2023-12-13T01:43:41.099353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:43:41.345984image/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_CDLOAN_ORG_CDMNG_FEE_CDDEMND_DYFEE_RATCALC_STRT_DYCALC_END_DYCALC_DCNTLOAN_AVG_RAMTFEE_DEMND_AMTREG_ENOREG_DTFEE_DEDCT_AMTHOLD_CD
0KHFCMB2006S-02C001SB201401060.520131201201312313177790369733034212222014/01/06 18:40:5800000-0000-0000
1KHFCMB2013L-01F001TB201401060.42013120120131231319404574030319497912222014/01/06 15:39:3400000-0000-0000
2KHFCMB2012L-16F001TB201401060.4201312012013123131381436291191295838412222014/01/06 15:39:3400000-0000-0000
3KHFCMB2012S-05F001TB201401060.420131201201312313116086720470546507812222014/01/06 15:39:3400000-0000-0000
4KHFCMB2011S-19F001TB201401060.420131201201312313110621478195360839312222014/01/06 15:39:3400000-0000-0000
5KHFCMB2011S-08F001TB201401060.42013120120131231318765775588297796312222014/01/06 15:39:3400000-0000-0000
6KHFCMB2011S-08F0014B201401060.0320131201201312313126542626812222014/01/06 15:39:3400000-0000-0000
7KHFCMB2011S-08F001BB201401060.25201312012013123131265426256312222014/01/06 15:39:3400000-0000-0000
8KHFCMB2010S-16F001TB201401060.42013120120131231314095423712139132212222014/01/06 15:39:3400000-0000-0000
9KHFCMB2010S-01F001TB201401060.420131201201312313151887769917627612222014/01/06 15:39:3400000-0000-0000
LIQD_PLAN_CDLOAN_ORG_CDMNG_FEE_CDDEMND_DYFEE_RATCALC_STRT_DYCALC_END_DYCALC_DCNTLOAN_AVG_RAMTFEE_DEMND_AMTREG_ENOREG_DTFEE_DEDCT_AMTHOLD_CD
990KHFCMB2013S-26B088C1201402030.13201401012014013131281342675931063314522014/02/05 12:42:120B088-2013-0022
991KHFCMB2013S-26B088CL201402030.1201401012014013131281342675923894914522014/02/05 12:42:120B088-2013-0022
992KHFCMB2013S-26B088C1201402030.0201401012014013131260730301298014522014/02/05 12:42:120B088-2013-0021
993KHFCMB2013S-26B088CL201402030.12014010120140131312607303012982214421714522014/02/05 12:42:120B088-2013-0021
994KHFCMB2013S-19B088C1201402030.612014010120140131314524517833234407214522014/02/05 12:42:120B088-2013-0017
995KHFCMB2013S-19B088CL201402030.1201401012014013131452451783338427414522014/02/05 12:42:120B088-2013-0017
996KHFCMB2013S-19B088C1201402030.4520140101201401313111811358913451420414522014/02/05 12:42:120B088-2013-0016
997KHFCMB2013S-19B088CL201402030.120140101201401313111811358913100315714522014/02/05 12:42:120B088-2013-0016
998KHFCMB2013S-15B088C1201402030.72014010120140131317037016747418365114522014/02/05 12:42:120B088-2013-0011
999KHFCMB2013S-15B088CL201402030.1201401012014013131703701674759766414522014/02/05 12:42:120B088-2013-0011