Overview

Dataset statistics

Number of variables15
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory128.4 B

Variable types

DateTime3
Categorical4
Text5
Numeric3

Dataset

Description샘플 데이터
Author경기신용보증재단
URLhttps://www.bigdata-region.kr/#/dataset/e3afbd6b-b6c7-46bc-beb5-76720198a91c

Alerts

기준년월 has constant value ""Constant
시도명 has constant value ""Constant
이행원금 is highly overall correlated with 이행이자금액 and 2 other fieldsHigh correlation
이행이자금액 is highly overall correlated with 이행원금 and 1 other fieldsHigh correlation
이행금액 is highly overall correlated with 이행원금 and 2 other fieldsHigh correlation
성별코드 is highly overall correlated with 이행원금 and 1 other fieldsHigh correlation
성별코드 is highly imbalanced (78.9%)Imbalance
이행이자금액 has unique valuesUnique
이행금액 has unique valuesUnique
보증번호 has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:00:51.867526
Analysis finished2023-12-10 14:00:55.416262
Duration3.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2023-04-01 00:00:00
Maximum2023-04-01 00:00:00
2023-12-10T23:00:55.485484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:55.629437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

이행청구등록일자
Date

Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum1996-12-21 00:00:00
Maximum2023-08-30 00:00:00
2023-12-10T23:00:55.811487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:55.990729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)

성별코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
M
29 
F
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
M 29
96.7%
F 1
 
3.3%

Length

2023-12-10T23:00:56.188322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:00:56.367262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 29
96.7%
f 1
 
3.3%

연령대코드
Categorical

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
60
14 
70
11 
50
3 
80
2 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row60
2nd row50
3rd row50
4th row60
5th row70

Common Values

ValueCountFrequency (%)
60 14
46.7%
70 11
36.7%
50 3
 
10.0%
80 2
 
6.7%

Length

2023-12-10T23:00:56.546810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:00:56.750754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
60 14
46.7%
70 11
36.7%
50 3
 
10.0%
80 2
 
6.7%

사업자등록번호
Text

Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:00:57.005775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters300
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

Unique18 ?
Unique (%)60.0%

Sample

1st row12481*****
2nd row59007*****
3rd row13081*****
4th row13481*****
5th row12881*****
ValueCountFrequency (%)
12481 3
 
10.0%
12381 3
 
10.0%
13081 3
 
10.0%
13481 3
 
10.0%
12736 1
 
3.3%
12581 1
 
3.3%
13603 1
 
3.3%
12728 1
 
3.3%
12509 1
 
3.3%
10481 1
 
3.3%
Other values (12) 12
40.0%
2023-12-10T23:00:57.531245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 150
50.0%
1 48
 
16.0%
8 26
 
8.7%
2 20
 
6.7%
3 19
 
6.3%
4 8
 
2.7%
0 8
 
2.7%
5 7
 
2.3%
9 5
 
1.7%
6 5
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 150
50.0%
Decimal Number 150
50.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 48
32.0%
8 26
17.3%
2 20
13.3%
3 19
 
12.7%
4 8
 
5.3%
0 8
 
5.3%
5 7
 
4.7%
9 5
 
3.3%
6 5
 
3.3%
7 4
 
2.7%
Other Punctuation
ValueCountFrequency (%)
* 150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 150
50.0%
1 48
 
16.0%
8 26
 
8.7%
2 20
 
6.7%
3 19
 
6.3%
4 8
 
2.7%
0 8
 
2.7%
5 7
 
2.3%
9 5
 
1.7%
6 5
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 150
50.0%
1 48
 
16.0%
8 26
 
8.7%
2 20
 
6.7%
3 19
 
6.3%
4 8
 
2.7%
0 8
 
2.7%
5 7
 
2.3%
9 5
 
1.7%
6 5
 
1.7%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
30 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 30
100.0%

Length

2023-12-10T23:00:57.756816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:00:57.903916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Text

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:00:58.102393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length4.2333333
Min length3

Characters and Unicode

Total characters127
Distinct characters39
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)43.3%

Sample

1st row화성시
2nd row성남시 중원구
3rd row부천시
4th row시흥시
5th row파주시
ValueCountFrequency (%)
군포시 4
 
10.3%
성남시 3
 
7.7%
부천시 3
 
7.7%
화성시 2
 
5.1%
김포시 2
 
5.1%
안성시 2
 
5.1%
중원구 2
 
5.1%
양주시 2
 
5.1%
안산시 2
 
5.1%
의왕시 1
 
2.6%
Other values (16) 16
41.0%
2023-12-10T23:00:58.485646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 31
24.4%
9
 
7.1%
구 9
 
7.1%
포 7
 
5.5%
성 7
 
5.5%
안 6
 
4.7%
천 5
 
3.9%
군 4
 
3.1%
원 4
 
3.1%
양 4
 
3.1%
Other values (29) 41
32.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 118
92.9%
Space Separator 9
 
7.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 31
26.3%
구 9
 
7.6%
포 7
 
5.9%
성 7
 
5.9%
안 6
 
5.1%
천 5
 
4.2%
군 4
 
3.4%
원 4
 
3.4%
양 4
 
3.4%
남 3
 
2.5%
Other values (28) 38
32.2%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 118
92.9%
Common 9
 
7.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 31
26.3%
구 9
 
7.6%
포 7
 
5.9%
성 7
 
5.9%
안 6
 
5.1%
천 5
 
4.2%
군 4
 
3.4%
원 4
 
3.4%
양 4
 
3.4%
남 3
 
2.5%
Other values (28) 38
32.2%
Common
ValueCountFrequency (%)
9
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 118
92.9%
ASCII 9
 
7.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 31
26.3%
구 9
 
7.6%
포 7
 
5.9%
성 7
 
5.9%
안 6
 
5.1%
천 5
 
4.2%
군 4
 
3.4%
원 4
 
3.4%
양 4
 
3.4%
남 3
 
2.5%
Other values (28) 38
32.2%
ASCII
ValueCountFrequency (%)
9
100.0%

행정동명
Text

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:00:58.761094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9333333
Min length2

Characters and Unicode

Total characters88
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)76.7%

Sample

1st row장안면
2nd row상대원동
3rd row내동
4th row정왕동
5th row야당동
ValueCountFrequency (%)
당동 3
 
10.0%
송내동 2
 
6.7%
상대원동 2
 
6.7%
은현면 1
 
3.3%
장안면 1
 
3.3%
당정동 1
 
3.3%
풍무동 1
 
3.3%
팔곡이동 1
 
3.3%
망포동 1
 
3.3%
성곡동 1
 
3.3%
Other values (16) 16
53.3%
2023-12-10T23:00:59.463163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 23
26.1%
당 6
 
6.8%
면 6
 
6.8%
내 3
 
3.4%
안 3
 
3.4%
왕 2
 
2.3%
곡 2
 
2.3%
백 2
 
2.3%
야 2
 
2.3%
정 2
 
2.3%
Other values (33) 37
42.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 88
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 23
26.1%
당 6
 
6.8%
면 6
 
6.8%
내 3
 
3.4%
안 3
 
3.4%
왕 2
 
2.3%
곡 2
 
2.3%
백 2
 
2.3%
야 2
 
2.3%
정 2
 
2.3%
Other values (33) 37
42.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 88
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 23
26.1%
당 6
 
6.8%
면 6
 
6.8%
내 3
 
3.4%
안 3
 
3.4%
왕 2
 
2.3%
곡 2
 
2.3%
백 2
 
2.3%
야 2
 
2.3%
정 2
 
2.3%
Other values (33) 37
42.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 88
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 23
26.1%
당 6
 
6.8%
면 6
 
6.8%
내 3
 
3.4%
안 3
 
3.4%
왕 2
 
2.3%
곡 2
 
2.3%
백 2
 
2.3%
야 2
 
2.3%
정 2
 
2.3%
Other values (33) 37
42.0%

채권은행명
Categorical

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
스탠다드차타드은행
18 
중소기업은행
4 
농협은행
2 
한국씨티은행
2 
국민은행
2 
Other values (2)
2 

Length

Max length9
Median length9
Mean length7.4666667
Min length4

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row스탠다드차타드은행
2nd row농협은행
3rd row스탠다드차타드은행
4th row스탠다드차타드은행
5th row스탠다드차타드은행

Common Values

ValueCountFrequency (%)
스탠다드차타드은행 18
60.0%
중소기업은행 4
 
13.3%
농협은행 2
 
6.7%
한국씨티은행 2
 
6.7%
국민은행 2
 
6.7%
SC제일은행 1
 
3.3%
우리은행 1
 
3.3%

Length

2023-12-10T23:00:59.711129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:00:59.907979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
스탠다드차타드은행 18
60.0%
중소기업은행 4
 
13.3%
농협은행 2
 
6.7%
한국씨티은행 2
 
6.7%
국민은행 2
 
6.7%
sc제일은행 1
 
3.3%
우리은행 1
 
3.3%

이행실행일대위변제일자
Date

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum1996-12-30 00:00:00
Maximum2023-09-05 00:00:00
2023-12-10T23:01:00.117716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:00.294002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)

이행원금
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79996005
Minimum10000000
Maximum1.7877015 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:01:00.460503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10000000
5-th percentile26350000
Q149250000
median72500000
Q31 × 108
95-th percentile1.511 × 108
Maximum1.7877015 × 108
Range1.6877015 × 108
Interquartile range (IQR)50750000

Descriptive statistics

Standard deviation45468107
Coefficient of variation (CV)0.56837972
Kurtosis-0.72682225
Mean79996005
Median Absolute Deviation (MAD)27500000
Skewness0.49911496
Sum2.3998802 × 109
Variance2.0673488 × 1015
MonotonicityNot monotonic
2023-12-10T23:01:00.653651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
100000000 7
23.3%
50000000 5
16.7%
140000000 2
 
6.7%
150000000 2
 
6.7%
70000000 2
 
6.7%
152000000 1
 
3.3%
28000000 1
 
3.3%
49000000 1
 
3.3%
75000000 1
 
3.3%
25000000 1
 
3.3%
Other values (7) 7
23.3%
ValueCountFrequency (%)
10000000 1
 
3.3%
25000000 1
 
3.3%
28000000 1
 
3.3%
28110000 1
 
3.3%
30000000 1
 
3.3%
35000000 1
 
3.3%
39000000 1
 
3.3%
49000000 1
 
3.3%
50000000 5
16.7%
70000000 2
 
6.7%
ValueCountFrequency (%)
178770152 1
 
3.3%
152000000 1
 
3.3%
150000000 2
 
6.7%
140000000 2
 
6.7%
100000000 7
23.3%
80000000 1
 
3.3%
75000000 1
 
3.3%
70000000 2
 
6.7%
50000000 5
16.7%
49000000 1
 
3.3%

이행이자금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2587182.2
Minimum149572
Maximum10885698
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:01:00.888710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum149572
5-th percentile453219.45
Q11100171
median1760547.5
Q33434930.8
95-th percentile7119862.3
Maximum10885698
Range10736126
Interquartile range (IQR)2334759.8

Descriptive statistics

Standard deviation2355465.1
Coefficient of variation (CV)0.91043649
Kurtosis5.5062961
Mean2587182.2
Median Absolute Deviation (MAD)1137329
Skewness2.155664
Sum77615466
Variance5.5482158 × 1012
MonotonicityNot monotonic
2023-12-10T23:01:01.102288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1630136 1
 
3.3%
956506 1
 
3.3%
5063013 1
 
3.3%
963356 1
 
3.3%
8802739 1
 
3.3%
3383561 1
 
3.3%
10885698 1
 
3.3%
3595205 1
 
3.3%
811232 1
 
3.3%
3538356 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
149572 1
3.3%
357504 1
3.3%
570205 1
3.3%
591780 1
3.3%
811232 1
3.3%
956506 1
3.3%
963356 1
3.3%
1078767 1
3.3%
1164383 1
3.3%
1179452 1
3.3%
ValueCountFrequency (%)
10885698 1
3.3%
8802739 1
3.3%
5063013 1
3.3%
3839884 1
3.3%
3836985 1
3.3%
3595205 1
3.3%
3538356 1
3.3%
3452054 1
3.3%
3383561 1
3.3%
3336986 1
3.3%

이행금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82513561
Minimum10149572
Maximum1.8261004 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:01:01.303467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10149572
5-th percentile26873990
Q150149120
median74789040
Q31.0354229 × 108
95-th percentile1.5936549 × 108
Maximum1.8261004 × 108
Range1.7246046 × 108
Interquartile range (IQR)53393174

Descriptive statistics

Standard deviation47062373
Coefficient of variation (CV)0.57035925
Kurtosis-0.74989931
Mean82513561
Median Absolute Deviation (MAD)28700342
Skewness0.50002615
Sum2.4754068 × 109
Variance2.2148669 × 1015
MonotonicityNot monotonic
2023-12-10T23:01:01.554616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
101620189 1
 
3.3%
35956506 1
 
3.3%
155063013 1
 
3.3%
50963356 1
 
3.3%
108802739 1
 
3.3%
103383561 1
 
3.3%
162885698 1
 
3.3%
103595205 1
 
3.3%
28811232 1
 
3.3%
143538356 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
10149572 1
3.3%
25570205 1
3.3%
28467504 1
3.3%
28811232 1
3.3%
31235753 1
3.3%
35956506 1
3.3%
40795068 1
3.3%
49877708 1
3.3%
50963356 1
3.3%
51078767 1
3.3%
ValueCountFrequency (%)
182610036 1
3.3%
162885698 1
3.3%
155063013 1
3.3%
153328944 1
3.3%
143538356 1
3.3%
143452054 1
3.3%
108802739 1
3.3%
103595205 1
3.3%
103383561 1
3.3%
102979452 1
3.3%

기업번호
Text

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:01:01.900346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters720
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)93.3%

Sample

1st rowkJq4ZAG9ui2/zbYC3gZ78Q==
2nd rowOxMN8BXAMhRdjZiXYHWSaA==
3rd row9zadD6tyl12bw1zcPznQNw==
4th rowuNgk1H/6j5ud/zb5V8vJFw==
5th row14GhAY3imeQXraU5IGuWkg==
ValueCountFrequency (%)
ed1lflspxmyl9trao4faew 2
 
6.7%
kjq4zag9ui2/zbyc3gz78q 1
 
3.3%
ynu8lapatclcwet3hn/g/w 1
 
3.3%
oed/a2vkojqhkm+kyb9zw 1
 
3.3%
synlgdsrqvkbjdkqd877qw 1
 
3.3%
ije89qdykqkfwtny5jjtug 1
 
3.3%
5q0ydih9axlvgvbe5fbclw 1
 
3.3%
pcghyfa21zjnzfyndgnisw 1
 
3.3%
1y8tve3wolbpct16ep0s1g 1
 
3.3%
6pojzxyempwsv6h/gujcmq 1
 
3.3%
Other values (19) 19
63.3%
2023-12-10T23:01:02.430998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
= 60
 
8.3%
g 20
 
2.8%
w 20
 
2.8%
l 16
 
2.2%
Q 16
 
2.2%
j 15
 
2.1%
F 15
 
2.1%
N 15
 
2.1%
C 15
 
2.1%
A 14
 
1.9%
Other values (55) 514
71.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 287
39.9%
Uppercase Letter 264
36.7%
Decimal Number 91
 
12.6%
Math Symbol 66
 
9.2%
Other Punctuation 12
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
g 20
 
7.0%
w 20
 
7.0%
l 16
 
5.6%
j 15
 
5.2%
d 14
 
4.9%
c 13
 
4.5%
y 13
 
4.5%
q 13
 
4.5%
i 12
 
4.2%
n 12
 
4.2%
Other values (16) 139
48.4%
Uppercase Letter
ValueCountFrequency (%)
Q 16
 
6.1%
F 15
 
5.7%
N 15
 
5.7%
C 15
 
5.7%
A 14
 
5.3%
Y 14
 
5.3%
O 13
 
4.9%
E 13
 
4.9%
T 13
 
4.9%
R 12
 
4.5%
Other values (16) 124
47.0%
Decimal Number
ValueCountFrequency (%)
8 13
14.3%
5 12
13.2%
9 11
12.1%
1 11
12.1%
0 10
11.0%
2 8
8.8%
7 7
7.7%
4 7
7.7%
6 6
6.6%
3 6
6.6%
Math Symbol
ValueCountFrequency (%)
= 60
90.9%
+ 6
 
9.1%
Other Punctuation
ValueCountFrequency (%)
/ 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 551
76.5%
Common 169
 
23.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
g 20
 
3.6%
w 20
 
3.6%
l 16
 
2.9%
Q 16
 
2.9%
j 15
 
2.7%
F 15
 
2.7%
N 15
 
2.7%
C 15
 
2.7%
A 14
 
2.5%
Y 14
 
2.5%
Other values (42) 391
71.0%
Common
ValueCountFrequency (%)
= 60
35.5%
8 13
 
7.7%
5 12
 
7.1%
/ 12
 
7.1%
9 11
 
6.5%
1 11
 
6.5%
0 10
 
5.9%
2 8
 
4.7%
7 7
 
4.1%
4 7
 
4.1%
Other values (3) 18
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
= 60
 
8.3%
g 20
 
2.8%
w 20
 
2.8%
l 16
 
2.2%
Q 16
 
2.2%
j 15
 
2.1%
F 15
 
2.1%
N 15
 
2.1%
C 15
 
2.1%
A 14
 
1.9%
Other values (55) 514
71.4%

보증번호
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:01:02.807396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters720
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st rowqzuG7UVveGUK09iqyL8+5g==
2nd rowZB5upR2NpgB4iQMlz5e5qQ==
3rd row0nVXf4MabRg1pJlhWhvhEA==
4th rowboTgv6uSsrku29EQPAdr2A==
5th rowqjyCC5wgwiam5I0Zy3U0Tg==
ValueCountFrequency (%)
qzug7uvveguk09iqyl8+5g 1
 
3.3%
zb5upr2npgb4iqmlz5e5qq 1
 
3.3%
dpzwx0aiqjitq12gmw6rba 1
 
3.3%
yo93iu+byaelm983gab7aa 1
 
3.3%
ypgssvo7taqaa/wumlyleq 1
 
3.3%
lroclhnscfjqxg9vs/zq3g 1
 
3.3%
vqzoam7cnmdr42ublm4dkg 1
 
3.3%
3nwpxfyvgi7ggrb6qiuihq 1
 
3.3%
y4b/ln/mxzykn46efn7+pg 1
 
3.3%
3u1/vqhiir4q1pcfbuqspw 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:01:03.378949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
= 60
 
8.3%
g 24
 
3.3%
A 18
 
2.5%
Q 16
 
2.2%
m 16
 
2.2%
G 16
 
2.2%
w 15
 
2.1%
V 15
 
2.1%
Z 15
 
2.1%
q 15
 
2.1%
Other values (55) 510
70.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 292
40.6%
Uppercase Letter 250
34.7%
Decimal Number 98
 
13.6%
Math Symbol 70
 
9.7%
Other Punctuation 10
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
g 24
 
8.2%
m 16
 
5.5%
w 15
 
5.1%
q 15
 
5.1%
v 14
 
4.8%
i 14
 
4.8%
a 13
 
4.5%
p 13
 
4.5%
d 12
 
4.1%
u 12
 
4.1%
Other values (16) 144
49.3%
Uppercase Letter
ValueCountFrequency (%)
A 18
 
7.2%
Q 16
 
6.4%
G 16
 
6.4%
V 15
 
6.0%
Z 15
 
6.0%
X 12
 
4.8%
U 11
 
4.4%
C 11
 
4.4%
E 11
 
4.4%
R 11
 
4.4%
Other values (16) 114
45.6%
Decimal Number
ValueCountFrequency (%)
7 13
13.3%
3 13
13.3%
8 10
10.2%
6 10
10.2%
5 9
9.2%
1 9
9.2%
9 9
9.2%
4 9
9.2%
2 8
8.2%
0 8
8.2%
Math Symbol
ValueCountFrequency (%)
= 60
85.7%
+ 10
 
14.3%
Other Punctuation
ValueCountFrequency (%)
/ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 542
75.3%
Common 178
 
24.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
g 24
 
4.4%
A 18
 
3.3%
Q 16
 
3.0%
m 16
 
3.0%
G 16
 
3.0%
w 15
 
2.8%
V 15
 
2.8%
Z 15
 
2.8%
q 15
 
2.8%
v 14
 
2.6%
Other values (42) 378
69.7%
Common
ValueCountFrequency (%)
= 60
33.7%
7 13
 
7.3%
3 13
 
7.3%
+ 10
 
5.6%
/ 10
 
5.6%
8 10
 
5.6%
6 10
 
5.6%
5 9
 
5.1%
1 9
 
5.1%
9 9
 
5.1%
Other values (3) 25
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
= 60
 
8.3%
g 24
 
3.3%
A 18
 
2.5%
Q 16
 
2.2%
m 16
 
2.2%
G 16
 
2.2%
w 15
 
2.1%
V 15
 
2.1%
Z 15
 
2.1%
q 15
 
2.1%
Other values (55) 510
70.8%

Interactions

2023-12-10T23:00:54.395944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:53.407724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:53.846000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:54.552685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:53.543769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:54.021511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:54.716188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:53.689703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:54.175669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:01:03.560311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이행청구등록일자성별코드연령대코드사업자등록번호시군구명행정동명채권은행명이행실행일대위변제일자이행원금이행이자금액이행금액기업번호보증번호
이행청구등록일자1.0001.0000.3940.8150.7010.8280.0000.9640.9440.6380.9440.9551.000
성별코드1.0001.0000.0000.0001.0001.0000.0001.0001.0000.0001.0001.0001.000
연령대코드0.3940.0001.0000.5140.0000.0000.3960.8550.0000.0000.0001.0001.000
사업자등록번호0.8150.0000.5141.0000.9250.9630.5780.8360.0000.0000.0001.0001.000
시군구명0.7011.0000.0000.9251.0001.0000.5290.9510.8310.8200.8311.0001.000
행정동명0.8281.0000.0000.9631.0001.0000.0000.9770.3600.9150.3601.0001.000
채권은행명0.0000.0000.3960.5780.5290.0001.0000.0000.0000.5210.0000.0001.000
이행실행일대위변제일자0.9641.0000.8550.8360.9510.9770.0001.0000.0000.9000.0001.0001.000
이행원금0.9441.0000.0000.0000.8310.3600.0000.0001.0000.2051.0000.0001.000
이행이자금액0.6380.0000.0000.0000.8200.9150.5210.9000.2051.0000.2050.9641.000
이행금액0.9441.0000.0000.0000.8310.3600.0000.0001.0000.2051.0000.0001.000
기업번호0.9551.0001.0001.0001.0001.0000.0001.0000.0000.9640.0001.0001.000
보증번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T23:01:03.772684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
채권은행명연령대코드성별코드
채권은행명1.0000.2520.000
연령대코드0.2521.0000.000
성별코드0.0000.0001.000
2023-12-10T23:01:03.890846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이행원금이행이자금액이행금액성별코드연령대코드채권은행명
이행원금1.0000.7950.9870.8660.0000.000
이행이자금액0.7951.0000.8540.0000.0000.123
이행금액0.9870.8541.0000.8660.0000.000
성별코드0.8660.0000.8661.0000.0000.000
연령대코드0.0000.0000.0000.0001.0000.252
채권은행명0.0000.1230.0000.0000.2521.000

Missing values

2023-12-10T23:00:54.982168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:00:55.296083image/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

기준년월이행청구등록일자성별코드연령대코드사업자등록번호시도명시군구명행정동명채권은행명이행실행일대위변제일자이행원금이행이자금액이행금액기업번호보증번호
02023-041996-12-21M6012481*****경기도화성시장안면스탠다드차타드은행1996-12-301000000001630136101620189kJq4ZAG9ui2/zbYC3gZ78Q==qzuG7UVveGUK09iqyL8+5g==
12023-042023-08-30M5059007*****경기도성남시 중원구상대원동농협은행2023-09-052811000035750428467504OxMN8BXAMhRdjZiXYHWSaA==ZB5upR2NpgB4iQMlz5e5qQ==
22023-041997-03-07M5013081*****경기도부천시내동스탠다드차타드은행1997-04-2410000000017260271017174819zadD6tyl12bw1zcPznQNw==0nVXf4MabRg1pJlhWhvhEA==
32023-041997-07-14F6013481*****경기도시흥시정왕동스탠다드차타드은행1997-08-191787701523839884182610036uNgk1H/6j5ud/zb5V8vJFw==boTgv6uSsrku29EQPAdr2A==
42023-041997-08-22M7012881*****경기도파주시야당동스탠다드차타드은행1997-09-09150000000333698615332894414GhAY3imeQXraU5IGuWkg==qjyCC5wgwiam5I0Zy3U0Tg==
52023-041998-03-12M6013525*****경기도용인시 처인구백암면한국씨티은행1998-04-081000000002482876102482876ZxhcCiJV8/5lnsZQtOloOg==kodvIswVInV7GCFZEV3C7g==
62023-041998-03-23M7012328*****경기도군포시당동스탠다드차타드은행1998-04-1050000000107876751078767c/LE5e8Cl9OljTTHingTsQ==1C/qVUUPLKz7ZK6YXUkV6g==
72023-042023-08-30M5063894*****경기도고양시 일산동구백석동중소기업은행2023-09-051000000014957210149572Sde0OINO6PMROw9wq5jnOg==JWCLwwYunjRuvVZ9Z/Zd1g==
82023-041998-04-06M6012985*****경기도성남시 중원구상대원동스탠다드차타드은행1998-04-2230000000123575331235753ckKKf8W5nLDFRTeTEg0+zQ==vBi6lJvYSG6s3WwRXciRyw==
92023-041998-04-09M7012681*****경기도이천시신둔면스탠다드차타드은행1998-05-168000000059178079829189xJ2FcLYxfdYpBL27wpjgnA==VRiuIT+7QXs+maZn5ESy3g==
기준년월이행청구등록일자성별코드연령대코드사업자등록번호시도명시군구명행정동명채권은행명이행실행일대위변제일자이행원금이행이자금액이행금액기업번호보증번호
202023-041998-06-18M6012381*****경기도군포시당동우리은행1998-06-3075000000286643877866438Ed1lFlspXmyl9TRao4FaEw==bGfVSUnQaGamlwzCtjhOmA==
212023-041998-07-03M6012509*****경기도안성시당왕동국민은행1998-08-1449000000218083551180835fXqRRxX8iQVwZHBYG0CxEQ==3U1/VqHiIR4q1PcfBUqsPw==
222023-041998-07-09M6013081*****경기도부천시송내동스탠다드차타드은행1998-08-0714000000035383561435383566POjZXyEmPwsV6h/gujcMQ==Y4B/ln/mxZYkN46efN7+pg==
232023-041998-07-16M7012728*****경기도포천시영중면스탠다드차타드은행1998-08-1328000000811232288112321y8tVe3WOLbPCT16Ep0S1g==3nWpXfyvGi7GgrB6QIuIhQ==
242023-041998-07-24M7013603*****경기도김포시사우동스탠다드차타드은행1998-09-081000000003595205103595205PCghYFA21zjNzfYnDgNISw==vQzoAM7Cnmdr42Ublm4dkg==
252023-041998-07-25M6013481*****경기도안산시 단원구성곡동중소기업은행1998-12-15152000000108856981628856985Q0YdiH9AXlVgvbe5FbCLw==lRocLhNscfJqXG9vs/zQ3g==
262023-041998-07-27M6012481*****경기도수원시 영통구망포동중소기업은행1998-08-211000000003383561103383561IjE89QdYkqkFwtNy5jjTug==yPGSSvO7tAQaA/WuMLYleQ==
272023-041998-07-27M6013481*****경기도안산시 상록구팔곡이동한국씨티은행1998-09-091000000008802739108802739SynLGdSRqvKbjdKqD877Qw==yo93Iu+byAeLM983GAb7AA==
282023-041998-08-18M6013681*****경기도김포시풍무동스탠다드차타드은행1998-09-285000000096335650963356+oeD/A2vKoJqHKM+kYB9zw==dpzwX0aiqjiTq12gmW6RBA==
292023-041998-08-20M7012481*****경기도화성시반월동스탠다드차타드은행1998-09-211500000005063013155063013nvNb3j5X/WA4ghYxmtkqSg==e0gCQJ1k8R7Zb9dKG8rGVg==