Overview

Dataset statistics

Number of variables16
Number of observations1000
Missing cells4
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory136.8 KiB
Average record size in memory140.1 B

Variable types

Text1
Categorical7
Numeric7
DateTime1

Dataset

Description한국주택금융공사 주택연금부 업무 관련 공개 공공데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15072832/fileData.do

Alerts

REG_ENO has constant value ""Constant
PDUTY_BRCD is highly overall correlated with PDUTY_ENO and 4 other fieldsHigh correlation
ARCV_STRT_DY is highly overall correlated with PDUTY_ENO and 7 other fieldsHigh correlation
PDUTY_TEAM_CD is highly overall correlated with PDUTY_ENO and 4 other fieldsHigh correlation
ADV_RCV_TARIF is highly overall correlated with CUST_NO and 5 other fieldsHigh correlation
PNSN_PAYFORM_CD is highly overall correlated with GUARNT_TERM_DY and 2 other fieldsHigh correlation
PDUTY_ENO is highly overall correlated with PDUTY_BRCD and 2 other fieldsHigh correlation
CUST_NO is highly overall correlated with ARCV_STRT_DY and 1 other fieldsHigh correlation
ARCV_TERM_DY is highly overall correlated with ADV_RCV_DCNTHigh correlation
ADV_RCV_DCNT is highly overall correlated with ARCV_TERM_DYHigh correlation
GUARNT_TERM_DY is highly overall correlated with PDUTY_BRCD and 3 other fieldsHigh correlation
BBNK_GIRO_CD is highly overall correlated with BBNK_ORG_CD and 1 other fieldsHigh correlation
BBNK_ORG_CD is highly overall correlated with BBNK_GIRO_CD and 1 other fieldsHigh correlation
ARCV_STRT_DY is highly imbalanced (98.9%)Imbalance
ADV_RCV_TARIF is highly imbalanced (95.2%)Imbalance
INC_RATE is highly imbalanced (60.1%)Imbalance
GUARNT_TERM_DY is highly skewed (γ1 = -31.26709107)Skewed
GUARNT_NO has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:09:03.943202
Analysis finished2023-12-12 22:09:11.510198
Duration7.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

GUARNT_NO
Text

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-12-13T07:09:11.665951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters14000
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st rowRTOA2020000211
2nd rowRTHA2020000681
3rd rowRTHA2020000641
4th rowRQAD2020000596
5th rowRTQA2020000274
ValueCountFrequency (%)
rtoa2020000211 1
 
0.1%
rtaa2009000096 1
 
0.1%
rtha2009000119 1
 
0.1%
rqad2008000216 1
 
0.1%
rtha2009000117 1
 
0.1%
rtha2009000116 1
 
0.1%
rtma2009000028 1
 
0.1%
rtha2009000112 1
 
0.1%
rtma2009000027 1
 
0.1%
rtho2009000007 1
 
0.1%
Other values (990) 990
99.0%
2023-12-13T07:09:12.082721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5844
41.7%
2 1393
 
10.0%
A 1088
 
7.8%
R 1003
 
7.2%
T 567
 
4.0%
8 532
 
3.8%
1 517
 
3.7%
9 509
 
3.6%
Q 436
 
3.1%
D 434
 
3.1%
Other values (14) 1677
 
12.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10004
71.5%
Uppercase Letter 3996
 
28.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1088
27.2%
R 1003
25.1%
T 567
14.2%
Q 436
10.9%
D 434
 
10.9%
H 266
 
6.7%
O 89
 
2.2%
B 39
 
1.0%
M 25
 
0.6%
J 16
 
0.4%
Other values (4) 33
 
0.8%
Decimal Number
ValueCountFrequency (%)
0 5844
58.4%
2 1393
 
13.9%
8 532
 
5.3%
1 517
 
5.2%
9 509
 
5.1%
7 390
 
3.9%
3 261
 
2.6%
5 193
 
1.9%
4 192
 
1.9%
6 173
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 10004
71.5%
Latin 3996
 
28.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1088
27.2%
R 1003
25.1%
T 567
14.2%
Q 436
10.9%
D 434
 
10.9%
H 266
 
6.7%
O 89
 
2.2%
B 39
 
1.0%
M 25
 
0.6%
J 16
 
0.4%
Other values (4) 33
 
0.8%
Common
ValueCountFrequency (%)
0 5844
58.4%
2 1393
 
13.9%
8 532
 
5.3%
1 517
 
5.2%
9 509
 
5.1%
7 390
 
3.9%
3 261
 
2.6%
5 193
 
1.9%
4 192
 
1.9%
6 173
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5844
41.7%
2 1393
 
10.0%
A 1088
 
7.8%
R 1003
 
7.2%
T 567
 
4.0%
8 532
 
3.8%
1 517
 
3.7%
9 509
 
3.6%
Q 436
 
3.1%
D 434
 
3.1%
Other values (14) 1677
 
12.0%

PDUTY_BRCD
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
QAD
153 
TAB
150 
TAC
115 
THB
114 
TAD
101 
Other values (20)
367 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTOA
2nd rowTHA
3rd rowTHA
4th rowQAD
5th rowTQA

Common Values

ValueCountFrequency (%)
QAD 153
15.3%
TAB 150
15.0%
TAC 115
11.5%
THB 114
11.4%
TAD 101
10.1%
THA 93
9.3%
TAA 91
9.1%
THO 57
 
5.7%
TOA 19
 
1.9%
TBA 16
 
1.6%
Other values (15) 91
9.1%

Length

2023-12-13T07:09:12.240366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
qad 153
15.3%
tab 150
15.0%
tac 115
11.5%
thb 114
11.4%
tad 101
10.1%
tha 93
9.3%
taa 91
9.1%
tho 57
 
5.7%
toa 19
 
1.9%
tba 16
 
1.6%
Other values (15) 91
9.1%

PDUTY_TEAM_CD
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
547 
3
267 
2
185 
A
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 547
54.7%
3 267
26.7%
2 185
 
18.5%
A 1
 
0.1%

Length

2023-12-13T07:09:12.382463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:09:12.505145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 547
54.7%
3 267
26.7%
2 185
 
18.5%
a 1
 
0.1%

PDUTY_ENO
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)5.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1789.7838
Minimum1174
Maximum2003
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T07:09:12.652028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1174
5-th percentile1475
Q11656
median1788
Q31977
95-th percentile2001
Maximum2003
Range829
Interquartile range (IQR)321

Descriptive statistics

Standard deviation184.18261
Coefficient of variation (CV)0.10290774
Kurtosis-0.64652024
Mean1789.7838
Median Absolute Deviation (MAD)182
Skewness-0.50803476
Sum1787994
Variance33923.234
MonotonicityNot monotonic
2023-12-13T07:09:13.142287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1977 142
14.2%
1689 120
12.0%
2001 98
9.8%
1970 85
 
8.5%
1788 65
 
6.5%
1917 57
 
5.7%
1650 52
 
5.2%
1475 51
 
5.1%
1554 50
 
5.0%
1656 49
 
4.9%
Other values (41) 230
23.0%
ValueCountFrequency (%)
1174 4
 
0.4%
1371 16
 
1.6%
1385 2
 
0.2%
1406 11
 
1.1%
1446 2
 
0.2%
1475 51
5.1%
1530 4
 
0.4%
1554 50
5.0%
1557 13
 
1.3%
1597 2
 
0.2%
ValueCountFrequency (%)
2003 11
 
1.1%
2001 98
9.8%
2000 8
 
0.8%
1999 1
 
0.1%
1977 142
14.2%
1973 1
 
0.1%
1970 85
8.5%
1968 5
 
0.5%
1965 1
 
0.1%
1956 5
 
0.5%

CUST_NO
Real number (ℝ)

HIGH CORRELATION 

Distinct999
Distinct (%)100.0%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean66232452
Minimum2102090
Maximum1.4473836 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T07:09:13.296896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2102090
5-th percentile20017857
Q165818900
median69686419
Q373971534
95-th percentile77432959
Maximum1.4473836 × 108
Range1.4263627 × 108
Interquartile range (IQR)8152633.5

Descriptive statistics

Standard deviation17077376
Coefficient of variation (CV)0.25784001
Kurtosis7.6936424
Mean66232452
Median Absolute Deviation (MAD)4143429
Skewness-1.1679746
Sum6.6166219 × 1010
Variance2.9163678 × 1014
MonotonicityNot monotonic
2023-12-13T07:09:13.433044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144574200 1
 
0.1%
70756671 1
 
0.1%
74903666 1
 
0.1%
74904461 1
 
0.1%
74595537 1
 
0.1%
74311805 1
 
0.1%
72810683 1
 
0.1%
70311870 1
 
0.1%
69937731 1
 
0.1%
69819477 1
 
0.1%
Other values (989) 989
98.9%
ValueCountFrequency (%)
2102090 1
0.1%
2179175 1
0.1%
2236759 1
0.1%
2359429 1
0.1%
2444402 1
0.1%
4413334 1
0.1%
5194209 1
0.1%
7951071 1
0.1%
8042468 1
0.1%
8066884 1
0.1%
ValueCountFrequency (%)
144738363 1
0.1%
144635479 1
0.1%
144574200 1
0.1%
144504623 1
0.1%
144473990 1
0.1%
144469742 1
0.1%
144444424 1
0.1%
144352956 1
0.1%
144320904 1
0.1%
115145545 1
0.1%

ARCV_STRT_DY
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
20201024
999 
20201023
 
1

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row20201024
2nd row20201024
3rd row20201024
4th row20201024
5th row20201024

Common Values

ValueCountFrequency (%)
20201024 999
99.9%
20201023 1
 
0.1%

Length

2023-12-13T07:09:13.563787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:09:13.648434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20201024 999
99.9%
20201023 1
 
0.1%

ARCV_TERM_DY
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20201092
Minimum20201023
Maximum20201124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T07:09:13.740393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20201023
5-th percentile20201026
Q120201083
median20201110
Q320201118
95-th percentile20201123
Maximum20201124
Range101
Interquartile range (IQR)35

Descriptive statistics

Standard deviation37.426258
Coefficient of variation (CV)1.8526849 × 10-6
Kurtosis-0.72490391
Mean20201092
Median Absolute Deviation (MAD)9
Skewness-1.0724685
Sum2.0201092 × 1010
Variance1400.7248
MonotonicityNot monotonic
2023-12-13T07:09:13.852054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
20201119 125
 
12.5%
20201029 109
 
10.9%
20201112 98
 
9.8%
20201105 88
 
8.8%
20201028 39
 
3.9%
20201122 38
 
3.8%
20201027 37
 
3.7%
20201124 36
 
3.6%
20201118 35
 
3.5%
20201109 34
 
3.4%
Other values (14) 361
36.1%
ValueCountFrequency (%)
20201023 1
 
0.1%
20201025 33
 
3.3%
20201026 31
 
3.1%
20201027 37
 
3.7%
20201028 39
 
3.9%
20201029 109
10.9%
20201101 26
 
2.6%
20201102 22
 
2.2%
20201103 25
 
2.5%
20201104 27
 
2.7%
ValueCountFrequency (%)
20201124 36
 
3.6%
20201123 29
 
2.9%
20201122 38
 
3.8%
20201119 125
12.5%
20201118 35
 
3.5%
20201117 31
 
3.1%
20201116 22
 
2.2%
20201115 28
 
2.8%
20201112 98
9.8%
20201111 31
 
3.1%

ADV_RCV_DCNT
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.839
Minimum0
Maximum32
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T07:09:13.966285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q18.25
median18
Q326
95-th percentile31
Maximum32
Range32
Interquartile range (IQR)17.75

Descriptive statistics

Standard deviation9.2360372
Coefficient of variation (CV)0.54849084
Kurtosis-1.3018262
Mean16.839
Median Absolute Deviation (MAD)9
Skewness-0.0277195
Sum16839
Variance85.304383
MonotonicityNot monotonic
2023-12-13T07:09:14.093180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
27 125
 
12.5%
6 109
 
10.9%
20 98
 
9.8%
13 88
 
8.8%
5 39
 
3.9%
30 38
 
3.8%
4 37
 
3.7%
32 36
 
3.6%
26 35
 
3.5%
17 34
 
3.4%
Other values (14) 361
36.1%
ValueCountFrequency (%)
0 1
 
0.1%
2 33
 
3.3%
3 31
 
3.1%
4 37
 
3.7%
5 39
 
3.9%
6 109
10.9%
9 26
 
2.6%
10 22
 
2.2%
11 25
 
2.5%
12 27
 
2.7%
ValueCountFrequency (%)
32 36
 
3.6%
31 29
 
2.9%
30 38
 
3.8%
27 125
12.5%
26 35
 
3.5%
25 31
 
3.1%
24 22
 
2.2%
23 28
 
2.8%
20 98
9.8%
19 31
 
3.1%

ADV_RCV_TARIF
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0.5
989 
0.75
 
9
1.0
 
1
0.0
 
1

Length

Max length4
Median length3
Mean length3.009
Min length3

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row0.75
2nd row0.75
3rd row0.75
4th row1.0
5th row0.75

Common Values

ValueCountFrequency (%)
0.5 989
98.9%
0.75 9
 
0.9%
1.0 1
 
0.1%
0.0 1
 
0.1%

Length

2023-12-13T07:09:14.207232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:09:14.303433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.5 989
98.9%
0.75 9
 
0.9%
1.0 1
 
0.1%
0.0 1
 
0.1%

GUARNT_TERM_DY
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct909
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20348050
Minimum0
Maximum20611011
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T07:09:14.402874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20270908
Q120330859
median20370563
Q320410411
95-th percentile20451006
Maximum20611011
Range20611011
Interquartile range (IQR)79551.75

Descriptive statistics

Standard deviation646534.47
Coefficient of variation (CV)0.03177378
Kurtosis985.01489
Mean20348050
Median Absolute Deviation (MAD)39842
Skewness-31.267091
Sum2.034805 × 1010
Variance4.1800682 × 1011
MonotonicityNot monotonic
2023-12-13T07:09:14.513390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20330404 3
 
0.3%
20311009 3
 
0.3%
20410109 3
 
0.3%
20351026 3
 
0.3%
20410423 3
 
0.3%
20351028 3
 
0.3%
20371120 3
 
0.3%
20371014 2
 
0.2%
20310410 2
 
0.2%
20301006 2
 
0.2%
Other values (899) 973
97.3%
ValueCountFrequency (%)
0 1
0.1%
20210423 1
0.1%
20220428 1
0.1%
20230403 1
0.1%
20230620 1
0.1%
20230904 2
0.2%
20230905 1
0.1%
20231004 1
0.1%
20231007 1
0.1%
20231105 1
0.1%
ValueCountFrequency (%)
20611011 1
0.1%
20531007 1
0.1%
20531005 1
0.1%
20500326 1
0.1%
20490910 1
0.1%
20490727 1
0.1%
20490527 1
0.1%
20490525 1
0.1%
20490507 1
0.1%
20490427 1
0.1%

BBNK_GIRO_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct720
Distinct (%)72.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean188726.68
Minimum30012
Maximum882639
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T07:09:14.626308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30012
5-th percentile34766.9
Q163704
median103868
Q3207942
95-th percentile814480.5
Maximum882639
Range852627
Interquartile range (IQR)144238

Descriptive statistics

Standard deviation222975.39
Coefficient of variation (CV)1.1814725
Kurtosis3.5130678
Mean188726.68
Median Absolute Deviation (MAD)63361
Skewness2.1467417
Sum1.8853796 × 108
Variance4.9718025 × 1010
MonotonicityNot monotonic
2023-12-13T07:09:14.738522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30371 11
 
1.1%
66811 9
 
0.9%
65113 9
 
0.9%
66497 7
 
0.7%
69193 6
 
0.6%
66390 6
 
0.6%
66387 6
 
0.6%
66507 5
 
0.5%
207476 5
 
0.5%
63539 5
 
0.5%
Other values (710) 930
93.0%
ValueCountFrequency (%)
30012 1
 
0.1%
30038 1
 
0.1%
30041 2
 
0.2%
30148 1
 
0.1%
30164 1
 
0.1%
30326 1
 
0.1%
30371 11
1.1%
30847 1
 
0.1%
30889 1
 
0.1%
31095 1
 
0.1%
ValueCountFrequency (%)
882639 1
0.1%
882613 1
0.1%
882532 1
0.1%
881517 2
0.2%
842543 1
0.1%
842297 1
0.1%
842200 2
0.2%
842051 1
0.1%
842022 2
0.2%
841816 1
0.1%

BBNK_ORG_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)0.9%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean24.761762
Minimum3
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T07:09:14.824794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q14
median4
Q320
95-th percentile88
Maximum88
Range85
Interquartile range (IQR)16

Descriptive statistics

Standard deviation32.64323
Coefficient of variation (CV)1.3182919
Kurtosis-0.23963794
Mean24.761762
Median Absolute Deviation (MAD)1
Skewness1.2707268
Sum24737
Variance1065.5805
MonotonicityNot monotonic
2023-12-13T07:09:14.903738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4 483
48.3%
88 139
 
13.9%
20 135
 
13.5%
11 100
 
10.0%
81 79
 
7.9%
3 57
 
5.7%
32 3
 
0.3%
34 2
 
0.2%
39 1
 
0.1%
(Missing) 1
 
0.1%
ValueCountFrequency (%)
3 57
 
5.7%
4 483
48.3%
11 100
 
10.0%
20 135
 
13.5%
32 3
 
0.3%
34 2
 
0.2%
39 1
 
0.1%
81 79
 
7.9%
88 139
 
13.9%
ValueCountFrequency (%)
88 139
 
13.9%
81 79
 
7.9%
39 1
 
0.1%
34 2
 
0.2%
32 3
 
0.3%
20 135
 
13.5%
11 100
 
10.0%
4 483
48.3%
3 57
 
5.7%

PNSN_PAYFORM_CD
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
546 
2
452 
6
 
1
<NA>
 
1

Length

Max length4
Median length1
Mean length1.003
Min length1

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row1
2nd row2
3rd row2
4th row6
5th row2

Common Values

ValueCountFrequency (%)
1 546
54.6%
2 452
45.2%
6 1
 
0.1%
<NA> 1
 
0.1%

Length

2023-12-13T07:09:15.003771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:09:15.087569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 546
54.6%
2 452
45.2%
6 1
 
0.1%
na 1
 
0.1%

REG_ENO
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1200
1000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1200
2nd row1200
3rd row1200
4th row1200
5th row1200

Common Values

ValueCountFrequency (%)
1200 1000
100.0%

Length

2023-12-13T07:09:15.177643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:09:15.253405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1200 1000
100.0%

REG_TS
Date

Distinct59
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2020-10-24 03:00:09
Maximum2020-10-24 03:03:23
2023-12-13T07:09:15.352558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:15.532909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

INC_RATE
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
860 
-3
131 
3
 
9

Length

Max length2
Median length1
Mean length1.131
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 860
86.0%
-3 131
 
13.1%
3 9
 
0.9%

Length

2023-12-13T07:09:15.663988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:09:15.750725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 860
86.0%
3 140
 
14.0%

Interactions

2023-12-13T07:09:10.101710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:05.237915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:05.994318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:06.742468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:07.522838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:08.399817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:09.243618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:10.226572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:05.336870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:06.098455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:06.834014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:07.647840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:08.510360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:09.354296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:10.354830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:05.442989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:06.209394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:06.946429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:07.768017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:08.629919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:09.471899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:10.455981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:05.540801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:06.317716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:07.061337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:07.896988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:08.764965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:09.578485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:10.578598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:05.635766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:06.412902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:07.186950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:08.032760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:08.905247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:09.726600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:10.699547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:05.788295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:06.519455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:07.298373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:08.166957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:09.008895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:09.887081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:10.798966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:05.895378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:06.639182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:07.386416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:08.285114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:09.128965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:09:09.992138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:09:15.831558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PDUTY_BRCDPDUTY_TEAM_CDPDUTY_ENOCUST_NOARCV_STRT_DYARCV_TERM_DYADV_RCV_DCNTADV_RCV_TARIFGUARNT_TERM_DYBBNK_GIRO_CDBBNK_ORG_CDPNSN_PAYFORM_CDREG_TSINC_RATE
PDUTY_BRCD1.0000.9970.9750.2351.0000.0000.0400.8061.0000.5410.4730.0000.6690.112
PDUTY_TEAM_CD0.9971.0000.7360.1061.0000.0000.0790.8941.0000.2500.1390.2010.8310.000
PDUTY_ENO0.9750.7361.0000.144NaN0.0000.0790.000NaN0.1180.1020.1260.0000.080
CUST_NO0.2350.1060.1441.000NaN0.1690.0980.790NaN0.2890.2810.3470.7580.298
ARCV_STRT_DY1.0001.000NaNNaN1.000NaN0.0961.0000.706NaNNaNNaN1.0000.000
ARCV_TERM_DY0.0000.0000.0000.169NaN1.0000.9900.087NaN0.0000.0000.0000.0000.000
ADV_RCV_DCNT0.0400.0790.0790.0980.0960.9901.0000.1440.0960.0220.0000.0000.2460.000
ADV_RCV_TARIF0.8060.8940.0000.7901.0000.0870.1441.0001.0000.5220.3180.9430.9540.000
GUARNT_TERM_DY1.0001.000NaNNaN0.706NaN0.0961.0001.000NaNNaNNaN1.0000.000
BBNK_GIRO_CD0.5410.2500.1180.289NaN0.0000.0220.522NaN1.0000.9180.0000.1060.000
BBNK_ORG_CD0.4730.1390.1020.281NaN0.0000.0000.318NaN0.9181.0000.0000.1660.000
PNSN_PAYFORM_CD0.0000.2010.1260.347NaN0.0000.0000.943NaN0.0000.0001.0000.3290.046
REG_TS0.6690.8310.0000.7581.0000.0000.2460.9541.0000.1060.1660.3291.0000.138
INC_RATE0.1120.0000.0800.2980.0000.0000.0000.0000.0000.0000.0000.0460.1381.000
2023-12-13T07:09:15.995560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PDUTY_BRCDINC_RATEARCV_STRT_DYPDUTY_TEAM_CDADV_RCV_TARIFPNSN_PAYFORM_CD
PDUTY_BRCD1.0000.0560.9880.9820.5680.000
INC_RATE0.0561.0000.0000.0000.0000.013
ARCV_STRT_DY0.9880.0001.0000.9990.9991.000
PDUTY_TEAM_CD0.9820.0000.9991.0000.5760.063
ADV_RCV_TARIF0.5680.0000.9990.5761.0000.707
PNSN_PAYFORM_CD0.0000.0131.0000.0630.7071.000
2023-12-13T07:09:16.118319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PDUTY_ENOCUST_NOARCV_TERM_DYADV_RCV_DCNTGUARNT_TERM_DYBBNK_GIRO_CDBBNK_ORG_CDPDUTY_BRCDPDUTY_TEAM_CDARCV_STRT_DYADV_RCV_TARIFPNSN_PAYFORM_CDINC_RATE
PDUTY_ENO1.0000.0020.0040.0040.0240.0410.0420.7160.6321.0000.1020.0730.042
CUST_NO0.0021.0000.0240.0240.1770.0650.0580.0830.0641.0000.7070.2340.196
ARCV_TERM_DY0.0040.0241.0001.000-0.030-0.037-0.0330.0000.0000.0000.0670.0000.000
ADV_RCV_DCNT0.0040.0241.0001.000-0.030-0.037-0.0330.0180.0570.0730.0860.0000.000
GUARNT_TERM_DY0.0240.177-0.030-0.0301.000-0.047-0.0440.9880.9990.4990.9991.0000.000
BBNK_GIRO_CD0.0410.065-0.037-0.037-0.0471.0000.8950.2460.1061.0000.2510.0000.000
BBNK_ORG_CD0.0420.058-0.033-0.033-0.0440.8951.0000.2480.1051.0000.2530.0000.000
PDUTY_BRCD0.7160.0830.0000.0180.9880.2460.2481.0000.9820.9880.5680.0000.056
PDUTY_TEAM_CD0.6320.0640.0000.0570.9990.1060.1050.9821.0000.9990.5760.0630.000
ARCV_STRT_DY1.0001.0000.0000.0730.4991.0001.0000.9880.9991.0000.9991.0000.000
ADV_RCV_TARIF0.1020.7070.0670.0860.9990.2510.2530.5680.5760.9991.0000.7070.000
PNSN_PAYFORM_CD0.0730.2340.0000.0001.0000.0000.0000.0000.0631.0000.7071.0000.013
INC_RATE0.0420.1960.0000.0000.0000.0000.0000.0560.0000.0000.0000.0131.000

Missing values

2023-12-13T07:09:10.976834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:09:11.249610image/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.
2023-12-13T07:09:11.421040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

GUARNT_NOPDUTY_BRCDPDUTY_TEAM_CDPDUTY_ENOCUST_NOARCV_STRT_DYARCV_TERM_DYADV_RCV_DCNTADV_RCV_TARIFGUARNT_TERM_DYBBNK_GIRO_CDBBNK_ORG_CDPNSN_PAYFORM_CDREG_ENOREG_TSINC_RATE
0RTOA2020000211TOA220031445742002020102420201122300.752045092734032034112002020/10/24 03:03:230
1RTHA2020000681THA111741447383632020102420201122300.752061101120636720212002020/10/24 03:03:230
2RTHA2020000641THA118791443529562020102420201119270.752037091681443281212002020/10/24 03:03:230
3RQAD2020000596QAD119771444444242020102420201119271.02048101111049511612002020/10/24 03:03:230
4RTQA2020000274TQA218741444739902020102420201119270.752053100739534639212002020/10/24 03:03:230
5RTAD2020000612TAD116501444697422020102420201119270.752047100720587620112002020/10/24 03:03:230
6RTAC2020000698TAC317881445046232020102420201119270.752042101810086111112002020/10/24 03:03:230
7RTAD2020000634TAD116501446354792020102420201119270.752045101221301788212002020/10/24 03:03:230
8RTPA2020000506TPA116551151455452020102420201119270.75205310051959604212002020/10/24 03:03:230
9RTAB2020000713TAB316891443209042020102420201119270.752044100520253620212002020/10/24 03:03:230
GUARNT_NOPDUTY_BRCDPDUTY_TEAM_CDPDUTY_ENOCUST_NOARCV_STRT_DYARCV_TERM_DYADV_RCV_DCNTADV_RCV_TARIFGUARNT_TERM_DYBBNK_GIRO_CDBBNK_ORG_CDPNSN_PAYFORM_CDREG_ENOREG_TSINC_RATE
990RTHO2009000059THO1191775215560202010242020102960.52031071511566511212002020/10/24 03:01:050
991RQAD2010000128TAD11656785794522020102420201118260.52032040126306788212002020/10/24 03:01:050
992RQAD2010000127QAD11977742590642020102420201112200.520480401687964112002020/10/24 03:01:050
993RTHA2010000065TAA11799775770602020102420201119270.52028033181219181112002020/10/24 03:01:050
994RTHA2010000051TAA11799783994122020102420201104120.52032031911586911212002020/10/24 03:01:050
995RQAD2010000059TAD1165024444022020102420201122300.520480204407014112002020/10/24 03:01:050
996RTHO2010000002THO11917777589312020102420201119270.520370107690094112002020/10/24 03:01:050
997RTHO2010000001THO1191724790422202010242020102740.52044010526822488112002020/10/24 03:01:050
998RQAD2009000521TAB31689774882982020102420201110180.520331202652074112002020/10/24 03:01:050
999RQAD2009000528TAC31554771126872020102420201119270.52040120984220020212002020/10/24 03:01:060