Overview

Dataset statistics

Number of variables13
Number of observations30
Missing cells32
Missing cells (%)8.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory114.4 B

Variable types

Categorical4
Numeric4
Text4
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/237a2ca8-575d-4fa9-a544-97ca655a00d8

Alerts

정책일간결제일자 has constant value ""Constant
결제상품ID is highly overall correlated with 사용여부 and 1 other fieldsHigh correlation
결제금액 is highly overall correlated with 결제상품ID and 1 other fieldsHigh correlation
사용여부 is highly overall correlated with 결제상품ID and 1 other fieldsHigh correlation
가맹점우편번호 is highly overall correlated with 위도High correlation
위도 is highly overall correlated with 가맹점우편번호 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 시도명High correlation
시도명 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
결제상품ID is highly imbalanced (73.5%)Imbalance
시도명 is highly imbalanced (64.7%)Imbalance
사용여부 is highly imbalanced (64.7%)Imbalance
결제금액 is highly imbalanced (73.5%)Imbalance
시군구명 has 2 (6.7%) missing valuesMissing
읍면동명 has 2 (6.7%) missing valuesMissing
결제상품명 has 28 (93.3%) missing valuesMissing
가맹점번호 has unique valuesUnique
가맹점우편번호 has unique valuesUnique
위도 has 2 (6.7%) zerosZeros
경도 has 2 (6.7%) zerosZeros

Reproduction

Analysis started2023-12-10 14:21:04.658984
Analysis finished2023-12-10 14:21:07.601623
Duration2.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정책일간결제일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2021-08-01
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-08-01
2nd row2021-08-01
3rd row2021-08-01
4th row2021-08-01
5th row2021-08-01

Common Values

ValueCountFrequency (%)
2021-08-01 30
100.0%

Length

2023-12-10T23:21:07.667392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:21:07.748201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-08-01 30
100.0%

가맹점번호
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4207243 × 108
Minimum7.0000055 × 108
Maximum7.9709108 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:21:07.840965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0000055 × 108
5-th percentile7.0000114 × 108
Q17.0000334 × 108
median7.0000573 × 108
Q37.9708342 × 108
95-th percentile7.9708887 × 108
Maximum7.9709108 × 108
Range97090527
Interquartile range (IQR)97080078

Descriptive statistics

Standard deviation48929862
Coefficient of variation (CV)0.065936775
Kurtosis-2.0620556
Mean7.4207243 × 108
Median Absolute Deviation (MAD)4541.5
Skewness0.28344281
Sum2.2262173 × 1010
Variance2.3941314 × 1015
MonotonicityNot monotonic
2023-12-10T23:21:07.957358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
700000731 1
 
3.3%
797084359 1
 
3.3%
797091080 1
 
3.3%
700006241 1
 
3.3%
797089487 1
 
3.3%
700005666 1
 
3.3%
700005788 1
 
3.3%
797088123 1
 
3.3%
700005649 1
 
3.3%
797086943 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
700000553 1
3.3%
700000731 1
3.3%
700001640 1
3.3%
700001794 1
3.3%
700001826 1
3.3%
700002312 1
3.3%
700002709 1
3.3%
700002855 1
3.3%
700004807 1
3.3%
700004856 1
3.3%
ValueCountFrequency (%)
797091080 1
3.3%
797089487 1
3.3%
797088123 1
3.3%
797086943 1
3.3%
797086901 1
3.3%
797085783 1
3.3%
797084359 1
3.3%
797083425 1
3.3%
797083409 1
3.3%
797083222 1
3.3%

결제상품ID
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
999999999999999
28 
140000018000
 
1
140000062000
 
1

Length

Max length15
Median length15
Mean length14.8
Min length12

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row140000018000
2nd row999999999999999
3rd row999999999999999
4th row999999999999999
5th row999999999999999

Common Values

ValueCountFrequency (%)
999999999999999 28
93.3%
140000018000 1
 
3.3%
140000062000 1
 
3.3%

Length

2023-12-10T23:21:08.106680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:21:08.196655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
999999999999999 28
93.3%
140000018000 1
 
3.3%
140000062000 1
 
3.3%

가맹점업종명
Text

Distinct17
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:21:08.320816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.3
Min length2

Characters and Unicode

Total characters129
Distinct characters45
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

Unique11 ?
Unique (%)36.7%

Sample

1st row일반휴게음식
2nd row일반휴게음식
3rd row자동차판매
4th row의류
5th row수리서비스
ValueCountFrequency (%)
일반휴게음식 9
29.0%
학원 2
 
6.5%
수리서비스 2
 
6.5%
의류 2
 
6.5%
보건위생 2
 
6.5%
전기제품 2
 
6.5%
의원 1
 
3.2%
약국 1
 
3.2%
음료식품 1
 
3.2%
자동차판매 1
 
3.2%
Other values (8) 8
25.8%
2023-12-10T23:21:08.593498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
음 10
 
7.8%
식 10
 
7.8%
일 9
 
7.0%
휴 9
 
7.0%
게 9
 
7.0%
반 9
 
7.0%
기 5
 
3.9%
의 4
 
3.1%
품 4
 
3.1%
서 3
 
2.3%
Other values (35) 57
44.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 128
99.2%
Space Separator 1
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
음 10
 
7.8%
식 10
 
7.8%
일 9
 
7.0%
휴 9
 
7.0%
게 9
 
7.0%
반 9
 
7.0%
기 5
 
3.9%
의 4
 
3.1%
품 4
 
3.1%
서 3
 
2.3%
Other values (34) 56
43.8%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 128
99.2%
Common 1
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
음 10
 
7.8%
식 10
 
7.8%
일 9
 
7.0%
휴 9
 
7.0%
게 9
 
7.0%
반 9
 
7.0%
기 5
 
3.9%
의 4
 
3.1%
품 4
 
3.1%
서 3
 
2.3%
Other values (34) 56
43.8%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 128
99.2%
ASCII 1
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
음 10
 
7.8%
식 10
 
7.8%
일 9
 
7.0%
휴 9
 
7.0%
게 9
 
7.0%
반 9
 
7.0%
기 5
 
3.9%
의 4
 
3.1%
품 4
 
3.1%
서 3
 
2.3%
Other values (34) 56
43.8%
ASCII
ValueCountFrequency (%)
1
100.0%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14816.567
Minimum10126
Maximum18550
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:21:08.719198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10126
5-th percentile10386
Q112121.5
median15911.5
Q317012.75
95-th percentile18425.15
Maximum18550
Range8424
Interquartile range (IQR)4891.25

Descriptive statistics

Standard deviation2780.7577
Coefficient of variation (CV)0.18767895
Kurtosis-1.3083504
Mean14816.567
Median Absolute Deviation (MAD)1780.5
Skewness-0.41227078
Sum444497
Variance7732613.2
MonotonicityNot monotonic
2023-12-10T23:21:08.819659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
10551 1
 
3.3%
10905 1
 
3.3%
18550 1
 
3.3%
17552 1
 
3.3%
10251 1
 
3.3%
16650 1
 
3.3%
17052 1
 
3.3%
18363 1
 
3.3%
15389 1
 
3.3%
14711 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
10126 1
3.3%
10251 1
3.3%
10551 1
3.3%
10855 1
3.3%
10905 1
3.3%
11445 1
3.3%
12097 1
3.3%
12113 1
3.3%
12147 1
3.3%
12739 1
3.3%
ValueCountFrequency (%)
18550 1
3.3%
18476 1
3.3%
18363 1
3.3%
17832 1
3.3%
17552 1
3.3%
17308 1
3.3%
17052 1
3.3%
17051 1
3.3%
16898 1
3.3%
16841 1
3.3%

시도명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
28 
NONE
 
2

Length

Max length4
Median length3
Mean length3.0666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 28
93.3%
NONE 2
 
6.7%

Length

2023-12-10T23:21:08.928438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:21:09.013705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 28
93.3%
none 2
 
6.7%

시군구명
Text

MISSING 

Distinct20
Distinct (%)71.4%
Missing2
Missing (%)6.7%
Memory size372.0 B
2023-12-10T23:21:09.146534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length4.5714286
Min length3

Characters and Unicode

Total characters128
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

Unique14 ?
Unique (%)50.0%

Sample

1st row고양시 덕양구
2nd row화성시
3rd row양주시
4th row평택시
5th row이천시
ValueCountFrequency (%)
용인시 4
 
10.5%
화성시 3
 
7.9%
수원시 3
 
7.9%
남양주시 3
 
7.9%
의왕시 2
 
5.3%
파주시 2
 
5.3%
처인구 2
 
5.3%
권선구 2
 
5.3%
고양시 2
 
5.3%
장안구 1
 
2.6%
Other values (14) 14
36.8%
2023-12-10T23:21:09.433941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 28
21.9%
10
 
7.8%
구 10
 
7.8%
양 7
 
5.5%
주 7
 
5.5%
인 6
 
4.7%
성 5
 
3.9%
남 4
 
3.1%
용 4
 
3.1%
수 4
 
3.1%
Other values (29) 43
33.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 118
92.2%
Space Separator 10
 
7.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 28
23.7%
구 10
 
8.5%
양 7
 
5.9%
주 7
 
5.9%
인 6
 
5.1%
성 5
 
4.2%
남 4
 
3.4%
용 4
 
3.4%
수 4
 
3.4%
화 3
 
2.5%
Other values (28) 40
33.9%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 118
92.2%
Common 10
 
7.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 28
23.7%
구 10
 
8.5%
양 7
 
5.9%
주 7
 
5.9%
인 6
 
5.1%
성 5
 
4.2%
남 4
 
3.4%
용 4
 
3.4%
수 4
 
3.4%
화 3
 
2.5%
Other values (28) 40
33.9%
Common
ValueCountFrequency (%)
10
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 118
92.2%
ASCII 10
 
7.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 28
23.7%
구 10
 
8.5%
양 7
 
5.9%
주 7
 
5.9%
인 6
 
5.1%
성 5
 
4.2%
남 4
 
3.4%
용 4
 
3.4%
수 4
 
3.4%
화 3
 
2.5%
Other values (28) 40
33.9%
ASCII
ValueCountFrequency (%)
10
100.0%

읍면동명
Text

MISSING 

Distinct26
Distinct (%)92.9%
Missing2
Missing (%)6.7%
Memory size372.0 B
2023-12-10T23:21:09.624654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1785714
Min length3

Characters and Unicode

Total characters89
Distinct characters45
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

Unique24 ?
Unique (%)85.7%

Sample

1st row도내동
2nd row청계동
3rd row덕계동
4th row세교동
5th row사음동
ValueCountFrequency (%)
별내동 2
 
7.1%
김량장동 2
 
7.1%
풍덕천동 1
 
3.6%
공도읍 1
 
3.6%
성석동 1
 
3.6%
고색동 1
 
3.6%
안녕동 1
 
3.6%
심곡본동 1
 
3.6%
금촌동 1
 
3.6%
내손동 1
 
3.6%
Other values (16) 16
57.1%
2023-12-10T23:21:09.971740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 26
29.2%
내 5
 
5.6%
정 3
 
3.4%
별 2
 
2.2%
금 2
 
2.2%
도 2
 
2.2%
호 2
 
2.2%
읍 2
 
2.2%
촌 2
 
2.2%
고 2
 
2.2%
Other values (35) 41
46.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 26
29.2%
내 5
 
5.6%
정 3
 
3.4%
별 2
 
2.2%
금 2
 
2.2%
도 2
 
2.2%
호 2
 
2.2%
읍 2
 
2.2%
촌 2
 
2.2%
고 2
 
2.2%
Other values (35) 41
46.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 26
29.2%
내 5
 
5.6%
정 3
 
3.4%
별 2
 
2.2%
금 2
 
2.2%
도 2
 
2.2%
호 2
 
2.2%
읍 2
 
2.2%
촌 2
 
2.2%
고 2
 
2.2%
Other values (35) 41
46.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 26
29.2%
내 5
 
5.6%
정 3
 
3.4%
별 2
 
2.2%
금 2
 
2.2%
도 2
 
2.2%
호 2
 
2.2%
읍 2
 
2.2%
촌 2
 
2.2%
고 2
 
2.2%
Other values (35) 41
46.1%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.915533
Minimum0
Maximum37.811
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:21:10.097485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.65
Q137.235
median37.3435
Q337.62325
95-th percentile37.74205
Maximum37.811
Range37.811
Interquartile range (IQR)0.38825

Descriptive statistics

Standard deviation9.4935573
Coefficient of variation (CV)0.27190068
Kurtosis12.190776
Mean34.915533
Median Absolute Deviation (MAD)0.141
Skewness-3.6567108
Sum1047.466
Variance90.12763
MonotonicityNot monotonic
2023-12-10T23:21:10.216337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.0 2
 
6.7%
37.297 2
 
6.7%
37.235 2
 
6.7%
37.711 1
 
3.3%
37.213 1
 
3.3%
37.007 1
 
3.3%
37.714 1
 
3.3%
37.248 1
 
3.3%
37.196 1
 
3.3%
37.483 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
0.0 2
6.7%
37.0 1
3.3%
37.007 1
3.3%
37.196 1
3.3%
37.201 1
3.3%
37.213 1
3.3%
37.235 2
6.7%
37.248 1
3.3%
37.268 1
3.3%
37.297 2
6.7%
ValueCountFrequency (%)
37.811 1
3.3%
37.765 1
3.3%
37.714 1
3.3%
37.711 1
3.3%
37.666 1
3.3%
37.656 1
3.3%
37.646 1
3.3%
37.629 1
3.3%
37.606 1
3.3%
37.483 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.55353
Minimum0
Maximum127.411
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:21:10.337103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile57.0312
Q1126.81675
median127.0185
Q3127.11675
95-th percentile127.25315
Maximum127.411
Range127.411
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation32.22687
Coefficient of variation (CV)0.2718339
Kurtosis12.205786
Mean118.55353
Median Absolute Deviation (MAD)0.1215
Skewness-3.6598233
Sum3556.606
Variance1038.5711
MonotonicityNot monotonic
2023-12-10T23:21:10.461678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 2
 
6.7%
127.109 1
 
3.3%
126.736 1
 
3.3%
127.156 1
 
3.3%
126.799 1
 
3.3%
126.981 1
 
3.3%
127.202 1
 
3.3%
126.988 1
 
3.3%
126.777 1
 
3.3%
126.77 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0.0 2
6.7%
126.736 1
3.3%
126.745 1
3.3%
126.766 1
3.3%
126.77 1
3.3%
126.777 1
3.3%
126.799 1
3.3%
126.87 1
3.3%
126.942 1
3.3%
126.943 1
3.3%
ValueCountFrequency (%)
127.411 1
3.3%
127.259 1
3.3%
127.246 1
3.3%
127.207 1
3.3%
127.202 1
3.3%
127.156 1
3.3%
127.124 1
3.3%
127.117 1
3.3%
127.116 1
3.3%
127.113 1
3.3%

결제상품명
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing28
Missing (%)93.3%
Memory size372.0 B
2023-12-10T23:21:10.638656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8.5
Mean length8.5
Min length6

Characters and Unicode

Total characters17
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row고양페이카드
2nd row의왕사랑상품권(통합)
ValueCountFrequency (%)
고양페이카드 1
50.0%
의왕사랑상품권(통합 1
50.0%
2023-12-10T23:21:10.918286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
고 1
 
5.9%
랑 1
 
5.9%
합 1
 
5.9%
통 1
 
5.9%
( 1
 
5.9%
권 1
 
5.9%
품 1
 
5.9%
상 1
 
5.9%
사 1
 
5.9%
양 1
 
5.9%
Other values (7) 7
41.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15
88.2%
Open Punctuation 1
 
5.9%
Close Punctuation 1
 
5.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
고 1
 
6.7%
랑 1
 
6.7%
합 1
 
6.7%
통 1
 
6.7%
권 1
 
6.7%
품 1
 
6.7%
상 1
 
6.7%
사 1
 
6.7%
양 1
 
6.7%
왕 1
 
6.7%
Other values (5) 5
33.3%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15
88.2%
Common 2
 
11.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
고 1
 
6.7%
랑 1
 
6.7%
합 1
 
6.7%
통 1
 
6.7%
권 1
 
6.7%
품 1
 
6.7%
상 1
 
6.7%
사 1
 
6.7%
양 1
 
6.7%
왕 1
 
6.7%
Other values (5) 5
33.3%
Common
ValueCountFrequency (%)
( 1
50.0%
) 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15
88.2%
ASCII 2
 
11.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
고 1
 
6.7%
랑 1
 
6.7%
합 1
 
6.7%
통 1
 
6.7%
권 1
 
6.7%
품 1
 
6.7%
상 1
 
6.7%
사 1
 
6.7%
양 1
 
6.7%
왕 1
 
6.7%
Other values (5) 5
33.3%
ASCII
ValueCountFrequency (%)
( 1
50.0%
) 1
50.0%

사용여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
28 
True
 
2
ValueCountFrequency (%)
False 28
93.3%
True 2
 
6.7%
2023-12-10T23:21:11.042465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
28 
4000
 
1
56500
 
1

Length

Max length5
Median length1
Mean length1.2333333
Min length1

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row4000
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28
93.3%
4000 1
 
3.3%
56500 1
 
3.3%

Length

2023-12-10T23:21:11.146370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:21:11.267078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28
93.3%
4000 1
 
3.3%
56500 1
 
3.3%

Interactions

2023-12-10T23:21:06.843521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:05.184671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:05.608475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:06.323456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:06.967860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:05.284354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:05.719804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:06.447809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:07.075893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:05.383512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:05.817539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:06.580351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:07.168587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:05.492693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:06.193467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:06.716796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:21:11.355045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
가맹점번호1.0000.0000.2880.0000.0000.0000.0000.0000.000NaN0.0000.000
결제상품ID0.0001.0000.0000.0000.0000.6471.0000.0000.0000.0001.0001.000
가맹점업종명0.2880.0001.0000.0001.0000.3610.0001.0001.000NaN0.0000.000
가맹점우편번호0.0000.0000.0001.0000.3940.9901.0000.3940.3940.0000.0000.000
시도명0.0000.0001.0000.3941.000NaNNaN0.9060.906NaN0.0000.000
시군구명0.0000.6470.3610.990NaN1.0001.000NaNNaN0.0000.5080.647
읍면동명0.0001.0000.0001.000NaN1.0001.000NaNNaN0.0001.0001.000
위도0.0000.0001.0000.3940.906NaNNaN1.0000.906NaN0.0000.000
경도0.0000.0001.0000.3940.906NaNNaN0.9061.000NaN0.0000.000
결제상품명NaN0.000NaN0.000NaN0.0000.000NaNNaN1.000NaN0.000
사용여부0.0001.0000.0000.0000.0000.5081.0000.0000.000NaN1.0001.000
결제금액0.0001.0000.0000.0000.0000.6471.0000.0000.0000.0001.0001.000
2023-12-10T23:21:11.482374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
결제상품ID결제금액사용여부시도명
결제상품ID1.0001.0000.9820.000
결제금액1.0001.0000.9820.000
사용여부0.9820.9821.0000.000
시도명0.0000.0000.0001.000
2023-12-10T23:21:11.584676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점우편번호위도경도결제상품ID시도명사용여부결제금액
가맹점번호1.0000.0270.139-0.1460.0000.0000.0000.000
가맹점우편번호0.0271.000-0.8030.2910.0000.2590.0000.000
위도0.139-0.8031.000-0.0040.0000.7210.0000.000
경도-0.1460.291-0.0041.0000.0000.7210.0000.000
결제상품ID0.0000.0000.0000.0001.0000.0000.9821.000
시도명0.0000.2590.7210.7210.0001.0000.0000.000
사용여부0.0000.0000.0000.0000.9820.0001.0000.982
결제금액0.0000.0000.0000.0001.0000.0000.9821.000

Missing values

2023-12-10T23:21:07.290062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:21:07.448481image/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.
2023-12-10T23:21:07.551362image/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

정책일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
02021-08-01700000731140000018000일반휴게음식10551경기도고양시 덕양구도내동37.629126.87고양페이카드Y4000
12021-08-01700000553999999999999999일반휴게음식18476경기도화성시청계동37.201127.109<NA>N0
22021-08-01797082161999999999999999자동차판매11445경기도양주시덕계동37.811127.045<NA>N0
32021-08-01700001794999999999999999의류17832경기도평택시세교동37.0127.078<NA>N0
42021-08-01700001640999999999999999수리서비스17308경기도이천시사음동37.297127.411<NA>N0
52021-08-01797082293999999999999999보건위생15812경기도군포시금정동37.365126.943<NA>N0
62021-08-01700001826999999999999999의류13597경기도성남시 분당구수내동37.376127.116<NA>N0
72021-08-01797083069999999999999999약국17051경기도용인시 처인구김량장동37.235127.207<NA>N0
82021-08-01700002312999999999999999음료식품10126경기도김포시고촌읍37.606126.766<NA>N0
92021-08-01797083222999999999999999수리서비스16385경기도수원시 권선구호매실동37.268126.942<NA>N0
정책일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
202021-08-01797086901999999999999999전기제품12113경기도남양주시별내동37.646127.124<NA>N0
212021-08-01700005244999999999999999전기제품10855경기도파주시금촌동37.765126.77<NA>N0
222021-08-01797086943999999999999999일반휴게음식14711경기도부천시심곡본동37.483126.777<NA>N0
232021-08-01700005649999999999999999레져용품15389NONE<NA><NA>0.00.0<NA>N0
242021-08-01797088123999999999999999기타18363경기도화성시안녕동37.196126.988<NA>N0
252021-08-01700005788999999999999999레저업소17052경기도용인시 처인구김량장동37.235127.202<NA>N0
262021-08-01700005666999999999999999일반휴게음식16650경기도수원시 권선구고색동37.248126.981<NA>N0
272021-08-01797089487999999999999999가구10251경기도고양시 일산동구성석동37.714126.799<NA>N0
282021-08-01700006241999999999999999일반휴게음식17552경기도안성시공도읍37.007127.156<NA>N0
292021-08-01797091080999999999999999일반휴게음식18550경기도화성시송산면37.213126.736<NA>N0