Overview

Dataset statistics

Number of variables11
Number of observations2494
Missing cells2019
Missing cells (%)7.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory221.8 KiB
Average record size in memory91.1 B

Variable types

Text1
Categorical3
DateTime4
Numeric3

Dataset

Description경상남도 거창군 전자예금압류 현황에 대한 데이터로 대장번호, 체납자구분, 대장상태, 제3채무자, 압류일자, 압류금액, 추심일자, 추심금액, 해제일자, 해제금액을 제공합니다.
URLhttps://www.data.go.kr/data/15041009/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
압류금액(원) is highly overall correlated with 추심금액(원) and 1 other fieldsHigh correlation
추심금액(원) is highly overall correlated with 압류금액(원)High correlation
해제금액(원) is highly overall correlated with 압류금액(원) and 1 other fieldsHigh correlation
대장상태 is highly overall correlated with 해제금액(원)High correlation
체납자구분 is highly imbalanced (64.7%)Imbalance
대장상태 is highly imbalanced (95.6%)Imbalance
추심일자 has 1398 (56.1%) missing valuesMissing
추심금액(원) has 578 (23.2%) missing valuesMissing
대장번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:42:36.219301
Analysis finished2023-12-12 16:42:38.146695
Duration1.93 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대장번호
Text

UNIQUE 

Distinct2494
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size19.6 KiB
2023-12-13T01:42:38.331081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters27434
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2494 ?
Unique (%)100.0%

Sample

1st row2015-000001
2nd row2015-000002
3rd row2015-000003
4th row2015-000007
5th row2015-000008
ValueCountFrequency (%)
2015-000001 1
 
< 0.1%
2020-000078 1
 
< 0.1%
2020-000072 1
 
< 0.1%
2020-000088 1
 
< 0.1%
2020-000073 1
 
< 0.1%
2020-000074 1
 
< 0.1%
2020-000075 1
 
< 0.1%
2020-000076 1
 
< 0.1%
2020-000077 1
 
< 0.1%
2020-000079 1
 
< 0.1%
Other values (2484) 2484
99.6%
2023-12-13T01:42:38.753181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11451
41.7%
2 4460
 
16.3%
1 3119
 
11.4%
- 2494
 
9.1%
3 1262
 
4.6%
6 986
 
3.6%
5 906
 
3.3%
7 834
 
3.0%
8 706
 
2.6%
4 642
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24940
90.9%
Dash Punctuation 2494
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11451
45.9%
2 4460
 
17.9%
1 3119
 
12.5%
3 1262
 
5.1%
6 986
 
4.0%
5 906
 
3.6%
7 834
 
3.3%
8 706
 
2.8%
4 642
 
2.6%
9 574
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 2494
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27434
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11451
41.7%
2 4460
 
16.3%
1 3119
 
11.4%
- 2494
 
9.1%
3 1262
 
4.6%
6 986
 
3.6%
5 906
 
3.3%
7 834
 
3.0%
8 706
 
2.6%
4 642
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27434
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11451
41.7%
2 4460
 
16.3%
1 3119
 
11.4%
- 2494
 
9.1%
3 1262
 
4.6%
6 986
 
3.6%
5 906
 
3.3%
7 834
 
3.0%
8 706
 
2.6%
4 642
 
2.3%

체납자구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.6 KiB
개인
2328 
법인
 
166

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개인 2328
93.3%
법인 166
 
6.7%

Length

2023-12-13T01:42:38.938766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:42:39.057462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 2328
93.3%
법인 166
 
6.7%

대장상태
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.6 KiB
해제완료
2474 
추심완료
 
19
압류완료
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
해제완료 2474
99.2%
추심완료 19
 
0.8%
압류완료 1
 
< 0.1%

Length

2023-12-13T01:42:39.171101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:42:39.266677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해제완료 2474
99.2%
추심완료 19
 
0.8%
압류완료 1
 
< 0.1%

제3채무자
Categorical

Distinct19
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size19.6 KiB
농협은행
745 
신한은행
435 
국민은행
375 
경남은행
225 
KEB하나은행
130 
Other values (14)
584 

Length

Max length8
Median length4
Mean length4.2574178
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSC은행
2nd rowKEB하나은행
3rd row신한은행
4th row신한은행
5th row신한은행

Common Values

ValueCountFrequency (%)
농협은행 745
29.9%
신한은행 435
17.4%
국민은행 375
15.0%
경남은행 225
 
9.0%
KEB하나은행 130
 
5.2%
우리은행 123
 
4.9%
기업은행 117
 
4.7%
카카오뱅크 106
 
4.3%
대구은행 40
 
1.6%
우정사업본부 37
 
1.5%
Other values (9) 161
 
6.5%

Length

2023-12-13T01:42:39.369084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
농협은행 745
29.9%
신한은행 435
17.4%
국민은행 375
15.0%
경남은행 225
 
9.0%
keb하나은행 130
 
5.2%
우리은행 123
 
4.9%
기업은행 117
 
4.7%
카카오뱅크 106
 
4.3%
대구은행 40
 
1.6%
우정사업본부 37
 
1.5%
Other values (9) 161
 
6.5%

압류일자
Date

Distinct154
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size19.6 KiB
Minimum2015-07-20 00:00:00
Maximum2023-06-21 00:00:00
2023-12-13T01:42:39.483738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:42:39.606080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

압류금액(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct1074
Distinct (%)43.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2830368.2
Minimum20910
Maximum88382120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2023-12-13T01:42:39.738562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20910
5-th percentile169980.5
Q1401250
median699770
Q31378920
95-th percentile10502400
Maximum88382120
Range88361210
Interquartile range (IQR)977670

Descriptive statistics

Standard deviation8742971.9
Coefficient of variation (CV)3.0889874
Kurtosis56.441709
Mean2830368.2
Median Absolute Deviation (MAD)364280
Skewness6.917055
Sum7.0589384 × 109
Variance7.6439558 × 1013
MonotonicityNot monotonic
2023-12-13T01:42:39.890632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10502400 13
 
0.5%
88382120 12
 
0.5%
48891370 12
 
0.5%
25147960 12
 
0.5%
4963640 12
 
0.5%
1708240 12
 
0.5%
4815660 12
 
0.5%
682230 10
 
0.4%
1377450 10
 
0.4%
56640 10
 
0.4%
Other values (1064) 2379
95.4%
ValueCountFrequency (%)
20910 1
 
< 0.1%
56640 10
0.4%
63600 1
 
< 0.1%
66640 8
0.3%
102690 4
 
0.2%
102930 1
 
< 0.1%
105840 3
 
0.1%
107000 5
0.2%
114310 1
 
< 0.1%
114800 4
 
0.2%
ValueCountFrequency (%)
88382120 12
0.5%
87774750 1
 
< 0.1%
80960680 1
 
< 0.1%
72284480 2
 
0.1%
50666390 1
 
< 0.1%
48891370 12
0.5%
42355920 1
 
< 0.1%
37759561 1
 
< 0.1%
37355920 1
 
< 0.1%
33048490 2
 
0.1%

추심일자
Date

MISSING 

Distinct91
Distinct (%)8.3%
Missing1398
Missing (%)56.1%
Memory size19.6 KiB
Minimum2015-11-24 00:00:00
Maximum2023-06-23 00:00:00
2023-12-13T01:42:40.040677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:42:40.178377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

추심금액(원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct500
Distinct (%)26.1%
Missing578
Missing (%)23.2%
Infinite0
Infinite (%)0.0%
Mean2445054.7
Minimum50000
Maximum88382120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2023-12-13T01:42:40.333549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50000
5-th percentile50000
Q150000
median469530
Q3901250
95-th percentile9881920
Maximum88382120
Range88332120
Interquartile range (IQR)851250

Descriptive statistics

Standard deviation9383147.5
Coefficient of variation (CV)3.8376023
Kurtosis55.56599
Mean2445054.7
Median Absolute Deviation (MAD)419530
Skewness7.0726259
Sum4.6847249 × 109
Variance8.8043458 × 1013
MonotonicityNot monotonic
2023-12-13T01:42:40.491263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 750
30.1%
10502400 13
 
0.5%
4963640 12
 
0.5%
88382120 12
 
0.5%
1708240 12
 
0.5%
4815660 12
 
0.5%
48891370 12
 
0.5%
25147960 12
 
0.5%
682230 10
 
0.4%
3055670 10
 
0.4%
Other values (490) 1061
42.5%
(Missing) 578
23.2%
ValueCountFrequency (%)
50000 750
30.1%
56640 10
 
0.4%
63600 1
 
< 0.1%
66640 8
 
0.3%
79820 1
 
< 0.1%
102690 4
 
0.2%
102930 1
 
< 0.1%
107000 5
 
0.2%
114310 1
 
< 0.1%
114800 4
 
0.2%
ValueCountFrequency (%)
88382120 12
0.5%
87774750 1
 
< 0.1%
80960680 1
 
< 0.1%
72284480 2
 
0.1%
50666390 1
 
< 0.1%
48891370 12
0.5%
37759561 1
 
< 0.1%
37355920 1
 
< 0.1%
32026360 6
0.2%
26933580 2
 
0.1%

해제일자
Date

Distinct369
Distinct (%)14.9%
Missing20
Missing (%)0.8%
Memory size19.6 KiB
Minimum2015-07-21 00:00:00
Maximum2023-06-14 00:00:00
2023-12-13T01:42:40.627317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:42:41.063987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

해제금액(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct1070
Distinct (%)43.3%
Missing23
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean2809886.3
Minimum20910
Maximum88382120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2023-12-13T01:42:41.177853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20910
5-th percentile169820
Q1401250
median694470
Q31377450
95-th percentile10502400
Maximum88382120
Range88361210
Interquartile range (IQR)976200

Descriptive statistics

Standard deviation8611328.4
Coefficient of variation (CV)3.0646537
Kurtosis56.862739
Mean2809886.3
Median Absolute Deviation (MAD)360220
Skewness6.9165794
Sum6.9432291 × 109
Variance7.4154978 × 1013
MonotonicityNot monotonic
2023-12-13T01:42:41.297270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10502400 13
 
0.5%
4963640 12
 
0.5%
1708240 12
 
0.5%
4815660 12
 
0.5%
48891370 12
 
0.5%
25147960 12
 
0.5%
88382120 11
 
0.4%
682230 10
 
0.4%
3055670 10
 
0.4%
6021140 10
 
0.4%
Other values (1060) 2357
94.5%
(Missing) 23
 
0.9%
ValueCountFrequency (%)
20910 1
 
< 0.1%
56640 10
0.4%
63600 1
 
< 0.1%
66640 8
0.3%
102690 4
 
0.2%
102930 1
 
< 0.1%
105840 3
 
0.1%
107000 5
0.2%
114310 1
 
< 0.1%
114800 4
 
0.2%
ValueCountFrequency (%)
88382120 11
0.4%
87774750 1
 
< 0.1%
80960680 1
 
< 0.1%
72284480 2
 
0.1%
50666390 1
 
< 0.1%
48891370 12
0.5%
42355920 1
 
< 0.1%
37759561 1
 
< 0.1%
37355920 1
 
< 0.1%
33048490 2
 
0.1%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.6 KiB
Minimum2023-07-19 00:00:00
Maximum2023-07-19 00:00:00
2023-12-13T01:42:41.392405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:42:41.474092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T01:42:37.444100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:42:36.811440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:42:37.156740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:42:37.525971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:42:36.924727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:42:37.257097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:42:37.620352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:42:37.038702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:42:37.356600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:42:41.543531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납자구분대장상태제3채무자압류금액(원)추심일자추심금액(원)해제금액(원)
체납자구분1.0000.0960.2610.5210.8060.5620.530
대장상태0.0961.0000.0770.0000.9940.000NaN
제3채무자0.2610.0771.0000.0980.4410.0000.101
압류금액(원)0.5210.0000.0981.0000.7061.0001.000
추심일자0.8060.9940.4410.7061.0000.7400.706
추심금액(원)0.5620.0000.0001.0000.7401.0001.000
해제금액(원)0.530NaN0.1011.0000.7061.0001.000
2023-12-13T01:42:41.647492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납자구분제3채무자대장상태
체납자구분1.0000.2310.159
제3채무자0.2311.0000.040
대장상태0.1590.0401.000
2023-12-13T01:42:41.727477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
압류금액(원)추심금액(원)해제금액(원)체납자구분대장상태제3채무자
압류금액(원)1.0000.5021.0000.3920.0000.041
추심금액(원)0.5021.0000.4990.3940.0000.032
해제금액(원)1.0000.4991.0000.3991.0000.042
체납자구분0.3920.3940.3991.0000.1590.231
대장상태0.0000.0001.0000.1591.0000.040
제3채무자0.0410.0320.0420.2310.0401.000

Missing values

2023-12-13T01:42:37.751743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:42:37.938159image/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.
2023-12-13T01:42:38.079280image/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

대장번호체납자구분대장상태제3채무자압류일자압류금액(원)추심일자추심금액(원)해제일자해제금액(원)데이터기준일자
02015-000001개인해제완료SC은행2015-07-20561710<NA>5617102015-07-245617102023-07-19
12015-000002개인해제완료KEB하나은행2015-07-20561710<NA>5617102015-07-245617102023-07-19
22015-000003개인해제완료신한은행2015-07-20561710<NA>5617102015-07-245617102023-07-19
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