Overview

Dataset statistics

Number of variables13
Number of observations30
Missing cells27
Missing cells (%)6.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory114.4 B

Variable types

Categorical2
Numeric6
Text4
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/3258496f-09d2-4c9d-8716-d46f47232ebc

Alerts

정책일간결제일자 has constant value ""Constant
결제상품ID is highly overall correlated with 결제금액 and 1 other fieldsHigh correlation
가맹점우편번호 is highly overall correlated with 위도High correlation
위도 is highly overall correlated with 가맹점우편번호High correlation
결제금액 is highly overall correlated with 결제상품IDHigh correlation
사용여부 is highly overall correlated with 결제상품IDHigh correlation
시도명 is highly imbalanced (78.9%)Imbalance
시군구명 has 1 (3.3%) missing valuesMissing
읍면동명 has 1 (3.3%) missing valuesMissing
결제상품명 has 25 (83.3%) missing valuesMissing
가맹점번호 has unique valuesUnique
가맹점우편번호 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique
위도 has 1 (3.3%) zerosZeros
경도 has 1 (3.3%) zerosZeros
결제금액 has 25 (83.3%) zerosZeros

Reproduction

Analysis started2024-03-13 11:54:00.338547
Analysis finished2024-03-13 11:54:04.789021
Duration4.45 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
2023-07-01
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-07-01
2nd row2023-07-01
3rd row2023-07-01
4th row2023-07-01
5th row2023-07-01

Common Values

ValueCountFrequency (%)
2023-07-01 30
100.0%

Length

2024-03-13T20:54:04.852286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:54:04.972043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-07-01 30
100.0%

가맹점번호
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0664819 × 108
Minimum7.000005 × 108
Maximum7.9932969 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:54:05.101801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.000005 × 108
5-th percentile7.0000923 × 108
Q17.0001786 × 108
median7.000262 × 108
Q37.0004507 × 108
95-th percentile7.5465328 × 108
Maximum7.9932969 × 108
Range99329190
Interquartile range (IQR)27206.5

Descriptive statistics

Standard deviation25193431
Coefficient of variation (CV)0.035652014
Kurtosis12.206623
Mean7.0664819 × 108
Median Absolute Deviation (MAD)13070.5
Skewness3.6599967
Sum2.1199446 × 1010
Variance6.3470898 × 1014
MonotonicityNot monotonic
2024-03-13T20:54:05.268252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
700000498 1
 
3.3%
700025242 1
 
3.3%
700050554 1
 
3.3%
700048394 1
 
3.3%
700048165 1
 
3.3%
700048108 1
 
3.3%
700046969 1
 
3.3%
700046059 1
 
3.3%
700042090 1
 
3.3%
700041790 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
700000498 1
3.3%
700007199 1
3.3%
700011721 1
3.3%
700012828 1
3.3%
700013434 1
3.3%
700014794 1
3.3%
700016723 1
3.3%
700017794 1
3.3%
700018059 1
3.3%
700018610 1
3.3%
ValueCountFrequency (%)
799329688 1
3.3%
799328235 1
3.3%
700050554 1
3.3%
700048394 1
3.3%
700048165 1
3.3%
700048108 1
3.3%
700046969 1
3.3%
700046059 1
3.3%
700042090 1
3.3%
700041790 1
3.3%

결제상품ID
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3335667 × 1014
Minimum1.4000007 × 1011
Maximum1 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:54:05.423500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000007 × 1011
5-th percentile1.4000009 × 1011
Q11 × 1015
median1 × 1015
Q31 × 1015
95-th percentile1 × 1015
Maximum1 × 1015
Range9.9986 × 1014
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.7899595 × 1014
Coefficient of variation (CV)0.45478241
Kurtosis1.6571429
Mean8.3335667 × 1014
Median Absolute Deviation (MAD)0
Skewness-1.8844151
Sum2.50007 × 1016
Variance1.4363793 × 1029
MonotonicityNot monotonic
2024-03-13T20:54:05.824820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
999999999999999 25
83.3%
140000080000 1
 
3.3%
140000635000 1
 
3.3%
140000106000 1
 
3.3%
140000072000 1
 
3.3%
140000154000 1
 
3.3%
ValueCountFrequency (%)
140000072000 1
 
3.3%
140000080000 1
 
3.3%
140000106000 1
 
3.3%
140000154000 1
 
3.3%
140000635000 1
 
3.3%
999999999999999 25
83.3%
ValueCountFrequency (%)
999999999999999 25
83.3%
140000635000 1
 
3.3%
140000154000 1
 
3.3%
140000106000 1
 
3.3%
140000080000 1
 
3.3%
140000072000 1
 
3.3%

가맹점업종명
Text

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:54:05.993107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length5.1666667
Min length2

Characters and Unicode

Total characters155
Distinct characters55
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

Unique13 ?
Unique (%)43.3%

Sample

1st row약국
2nd row용역서비스
3rd row미용/위생
4th row숙박업
5th row일반유통
ValueCountFrequency (%)
일반/휴게 7
18.4%
음식 7
18.4%
미용/위생 4
 
10.5%
일반유통 2
 
5.3%
전자제품 2
 
5.3%
용역서비스 2
 
5.3%
건축자재 1
 
2.6%
약국 1
 
2.6%
의류 1
 
2.6%
서비스 1
 
2.6%
Other values (10) 10
26.3%
2024-03-13T20:54:06.342381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 15
 
9.7%
일 9
 
5.8%
반 9
 
5.8%
8
 
5.2%
휴 7
 
4.5%
게 7
 
4.5%
음 7
 
4.5%
식 7
 
4.5%
용 6
 
3.9%
미 5
 
3.2%
Other values (45) 75
48.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 132
85.2%
Other Punctuation 15
 
9.7%
Space Separator 8
 
5.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
일 9
 
6.8%
반 9
 
6.8%
휴 7
 
5.3%
게 7
 
5.3%
음 7
 
5.3%
식 7
 
5.3%
용 6
 
4.5%
미 5
 
3.8%
통 5
 
3.8%
위 4
 
3.0%
Other values (43) 66
50.0%
Other Punctuation
ValueCountFrequency (%)
/ 15
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 132
85.2%
Common 23
 
14.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
일 9
 
6.8%
반 9
 
6.8%
휴 7
 
5.3%
게 7
 
5.3%
음 7
 
5.3%
식 7
 
5.3%
용 6
 
4.5%
미 5
 
3.8%
통 5
 
3.8%
위 4
 
3.0%
Other values (43) 66
50.0%
Common
ValueCountFrequency (%)
/ 15
65.2%
8
34.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 132
85.2%
ASCII 23
 
14.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 15
65.2%
8
34.8%
Hangul
ValueCountFrequency (%)
일 9
 
6.8%
반 9
 
6.8%
휴 7
 
5.3%
게 7
 
5.3%
음 7
 
5.3%
식 7
 
5.3%
용 6
 
4.5%
미 5
 
3.8%
통 5
 
3.8%
위 4
 
3.0%
Other values (43) 66
50.0%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14635.1
Minimum11324
Maximum17852
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:54:06.494918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11324
5-th percentile11729.35
Q113306.5
median14489
Q316294.75
95-th percentile17614.95
Maximum17852
Range6528
Interquartile range (IQR)2988.25

Descriptive statistics

Standard deviation1925.2679
Coefficient of variation (CV)0.1315514
Kurtosis-1.0206205
Mean14635.1
Median Absolute Deviation (MAD)1794.5
Skewness-0.078890781
Sum439053
Variance3706656.5
MonotonicityNot monotonic
2024-03-13T20:54:06.617321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
13599 1
 
3.3%
14983 1
 
3.3%
17368 1
 
3.3%
12175 1
 
3.3%
14949 1
 
3.3%
13209 1
 
3.3%
11775 1
 
3.3%
12039 1
 
3.3%
12564 1
 
3.3%
15818 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
11324 1
3.3%
11692 1
3.3%
11775 1
3.3%
12039 1
3.3%
12175 1
3.3%
12249 1
3.3%
12564 1
3.3%
13209 1
3.3%
13599 1
3.3%
13807 1
3.3%
ValueCountFrequency (%)
17852 1
3.3%
17817 1
3.3%
17368 1
3.3%
16832 1
3.3%
16802 1
3.3%
16507 1
3.3%
16348 1
3.3%
16306 1
3.3%
16261 1
3.3%
15818 1
3.3%

시도명
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
29 
NONE
 
1

Length

Max length4
Median length3
Mean length3.0333333
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 29
96.7%
NONE 1
 
3.3%

Length

2024-03-13T20:54:06.746240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:54:06.841828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 29
96.7%
none 1
 
3.3%

시군구명
Text

MISSING 

Distinct18
Distinct (%)62.1%
Missing1
Missing (%)3.3%
Memory size372.0 B
2024-03-13T20:54:07.008323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.5517241
Min length3

Characters and Unicode

Total characters132
Distinct characters38
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

Unique9 ?
Unique (%)31.0%

Sample

1st row성남시 분당구
2nd row평택시
3rd row동두천시
4th row안산시 단원구
5th row부천시
ValueCountFrequency (%)
부천시 4
 
10.3%
수원시 4
 
10.3%
과천시 2
 
5.1%
성남시 2
 
5.1%
안산시 2
 
5.1%
의정부시 2
 
5.1%
시흥시 2
 
5.1%
장안구 2
 
5.1%
평택시 2
 
5.1%
남양주시 2
 
5.1%
Other values (12) 15
38.5%
2024-03-13T20:54:07.388772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 30
22.7%
구 10
 
7.6%
10
 
7.6%
천 8
 
6.1%
원 7
 
5.3%
수 6
 
4.5%
부 6
 
4.5%
남 4
 
3.0%
안 4
 
3.0%
양 3
 
2.3%
Other values (28) 44
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 122
92.4%
Space Separator 10
 
7.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 30
24.6%
구 10
 
8.2%
천 8
 
6.6%
원 7
 
5.7%
수 6
 
4.9%
부 6
 
4.9%
남 4
 
3.3%
안 4
 
3.3%
양 3
 
2.5%
평 3
 
2.5%
Other values (27) 41
33.6%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 122
92.4%
Common 10
 
7.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 30
24.6%
구 10
 
8.2%
천 8
 
6.6%
원 7
 
5.7%
수 6
 
4.9%
부 6
 
4.9%
남 4
 
3.3%
안 4
 
3.3%
양 3
 
2.5%
평 3
 
2.5%
Other values (27) 41
33.6%
Common
ValueCountFrequency (%)
10
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 122
92.4%
ASCII 10
 
7.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 30
24.6%
구 10
 
8.2%
천 8
 
6.6%
원 7
 
5.7%
수 6
 
4.9%
부 6
 
4.9%
남 4
 
3.3%
안 4
 
3.3%
양 3
 
2.5%
평 3
 
2.5%
Other values (27) 41
33.6%
ASCII
ValueCountFrequency (%)
10
100.0%

읍면동명
Text

MISSING 

Distinct29
Distinct (%)100.0%
Missing1
Missing (%)3.3%
Memory size372.0 B
2024-03-13T20:54:07.650931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.137931
Min length2

Characters and Unicode

Total characters91
Distinct characters50
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

Unique29 ?
Unique (%)100.0%

Sample

1st row수내동
2nd row비전동
3rd row보산동
4th row원곡동
5th row원종동
ValueCountFrequency (%)
수내동 1
 
3.4%
물왕동 1
 
3.4%
화도읍 1
 
3.4%
신천동 1
 
3.4%
상대원동 1
 
3.4%
신곡동 1
 
3.4%
오남읍 1
 
3.4%
양평읍 1
 
3.4%
산본동 1
 
3.4%
파장동 1
 
3.4%
Other values (19) 19
65.5%
2024-03-13T20:54:07.984017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 25
27.5%
읍 4
 
4.4%
천 3
 
3.3%
중 3
 
3.3%
원 3
 
3.3%
의 2
 
2.2%
신 2
 
2.2%
오 2
 
2.2%
고 2
 
2.2%
정 2
 
2.2%
Other values (40) 43
47.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 90
98.9%
Decimal Number 1
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 25
27.8%
읍 4
 
4.4%
천 3
 
3.3%
중 3
 
3.3%
원 3
 
3.3%
의 2
 
2.2%
신 2
 
2.2%
오 2
 
2.2%
고 2
 
2.2%
정 2
 
2.2%
Other values (39) 42
46.7%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 90
98.9%
Common 1
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 25
27.8%
읍 4
 
4.4%
천 3
 
3.3%
중 3
 
3.3%
원 3
 
3.3%
의 2
 
2.2%
신 2
 
2.2%
오 2
 
2.2%
고 2
 
2.2%
정 2
 
2.2%
Other values (39) 42
46.7%
Common
ValueCountFrequency (%)
2 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 90
98.9%
ASCII 1
 
1.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 25
27.8%
읍 4
 
4.4%
천 3
 
3.3%
중 3
 
3.3%
원 3
 
3.3%
의 2
 
2.2%
신 2
 
2.2%
오 2
 
2.2%
고 2
 
2.2%
정 2
 
2.2%
Other values (39) 42
46.7%
ASCII
ValueCountFrequency (%)
2 1
100.0%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.180733
Minimum0
Maximum37.916
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:54:08.106526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37.0082
Q137.3085
median37.399
Q337.52
95-th percentile37.7453
Maximum37.916
Range37.916
Interquartile range (IQR)0.2115

Descriptive statistics

Standard deviation6.8362197
Coefficient of variation (CV)0.18894641
Kurtosis29.946204
Mean36.180733
Median Absolute Deviation (MAD)0.1035
Skewness-5.4700892
Sum1085.422
Variance46.7339
MonotonicityNot monotonic
2024-03-13T20:54:08.233236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
37.373 1
 
3.3%
37.381 1
 
3.3%
37.28 1
 
3.3%
37.656 1
 
3.3%
37.438 1
 
3.3%
37.436 1
 
3.3%
37.748 1
 
3.3%
37.69 1
 
3.3%
37.491 1
 
3.3%
37.365 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
0.0 1
3.3%
37.001 1
3.3%
37.017 1
3.3%
37.278 1
3.3%
37.28 1
3.3%
37.291 1
3.3%
37.298 1
3.3%
37.308 1
3.3%
37.31 1
3.3%
37.326 1
3.3%
ValueCountFrequency (%)
37.916 1
3.3%
37.748 1
3.3%
37.742 1
3.3%
37.69 1
3.3%
37.656 1
3.3%
37.528 1
3.3%
37.526 1
3.3%
37.525 1
3.3%
37.505 1
3.3%
37.491 1
3.3%

경도
Real number (ℝ)

UNIQUE  ZEROS 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.77977
Minimum0
Maximum127.527
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:54:08.364248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile126.76965
Q1126.83425
median126.9995
Q3127.0885
95-th percentile127.37835
Maximum127.527
Range127.527
Interquartile range (IQR)0.25425

Descriptive statistics

Standard deviation23.190165
Coefficient of variation (CV)0.18887611
Kurtosis29.995596
Mean122.77977
Median Absolute Deviation (MAD)0.1325
Skewness-5.4766415
Sum3683.393
Variance537.78373
MonotonicityNot monotonic
2024-03-13T20:54:08.486858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
127.119 1
 
3.3%
126.841 1
 
3.3%
127.44 1
 
3.3%
127.303 1
 
3.3%
126.785 1
 
3.3%
127.169 1
 
3.3%
127.069 1
 
3.3%
127.214 1
 
3.3%
127.527 1
 
3.3%
126.929 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
0.0 1
3.3%
126.762 1
3.3%
126.779 1
3.3%
126.785 1
3.3%
126.8 1
3.3%
126.801 1
3.3%
126.812 1
3.3%
126.832 1
3.3%
126.841 1
3.3%
126.854 1
3.3%
ValueCountFrequency (%)
127.527 1
3.3%
127.44 1
3.3%
127.303 1
3.3%
127.214 1
3.3%
127.169 1
3.3%
127.119 1
3.3%
127.107 1
3.3%
127.095 1
3.3%
127.069 1
3.3%
127.063 1
3.3%

결제상품명
Text

MISSING 

Distinct5
Distinct (%)100.0%
Missing25
Missing (%)83.3%
Memory size372.0 B
2024-03-13T20:54:08.676332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length9
Mean length10.6
Min length8

Characters and Unicode

Total characters53
Distinct characters32
Distinct categories7 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st row부천페이(통합)
2nd row수원페이(수원이)
3rd row군포愛머니(통합)
4th row양평통보(통합)
5th rowThank You Pay-N(교통)
ValueCountFrequency (%)
부천페이(통합 1
14.3%
수원페이(수원이 1
14.3%
군포愛머니(통합 1
14.3%
양평통보(통합 1
14.3%
thank 1
14.3%
you 1
14.3%
pay-n(교통 1
14.3%
2024-03-13T20:54:08.964841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 5
 
9.4%
통 5
 
9.4%
) 5
 
9.4%
이 3
 
5.7%
합 3
 
5.7%
페 2
 
3.8%
수 2
 
3.8%
원 2
 
3.8%
a 2
 
3.8%
2
 
3.8%
Other values (22) 22
41.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28
52.8%
Lowercase Letter 8
 
15.1%
Open Punctuation 5
 
9.4%
Close Punctuation 5
 
9.4%
Uppercase Letter 4
 
7.5%
Space Separator 2
 
3.8%
Dash Punctuation 1
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
통 5
17.9%
이 3
10.7%
합 3
10.7%
페 2
 
7.1%
수 2
 
7.1%
원 2
 
7.1%
부 1
 
3.6%
평 1
 
3.6%
보 1
 
3.6%
천 1
 
3.6%
Other values (7) 7
25.0%
Lowercase Letter
ValueCountFrequency (%)
a 2
25.0%
o 1
12.5%
k 1
12.5%
u 1
12.5%
y 1
12.5%
n 1
12.5%
h 1
12.5%
Uppercase Letter
ValueCountFrequency (%)
Y 1
25.0%
N 1
25.0%
P 1
25.0%
T 1
25.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 27
50.9%
Common 13
24.5%
Latin 12
22.6%
Han 1
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
통 5
18.5%
이 3
11.1%
합 3
11.1%
페 2
 
7.4%
수 2
 
7.4%
원 2
 
7.4%
부 1
 
3.7%
평 1
 
3.7%
보 1
 
3.7%
천 1
 
3.7%
Other values (6) 6
22.2%
Latin
ValueCountFrequency (%)
a 2
16.7%
o 1
8.3%
k 1
8.3%
Y 1
8.3%
N 1
8.3%
u 1
8.3%
P 1
8.3%
y 1
8.3%
n 1
8.3%
h 1
8.3%
Common
ValueCountFrequency (%)
( 5
38.5%
) 5
38.5%
2
 
15.4%
- 1
 
7.7%
Han
ValueCountFrequency (%)
愛 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 27
50.9%
ASCII 25
47.2%
CJK 1
 
1.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 5
20.0%
) 5
20.0%
a 2
 
8.0%
2
 
8.0%
o 1
 
4.0%
k 1
 
4.0%
Y 1
 
4.0%
N 1
 
4.0%
u 1
 
4.0%
P 1
 
4.0%
Other values (5) 5
20.0%
Hangul
ValueCountFrequency (%)
통 5
18.5%
이 3
11.1%
합 3
11.1%
페 2
 
7.4%
수 2
 
7.4%
원 2
 
7.4%
부 1
 
3.7%
평 1
 
3.7%
보 1
 
3.7%
천 1
 
3.7%
Other values (6) 6
22.2%
CJK
ValueCountFrequency (%)
愛 1
100.0%

사용여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
25 
True
5 
ValueCountFrequency (%)
False 25
83.3%
True 5
 
16.7%
2024-03-13T20:54:09.073390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29157.333
Minimum0
Maximum783720
Zeros25
Zeros (%)83.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:54:09.167907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile43575
Maximum783720
Range783720
Interquartile range (IQR)0

Descriptive statistics

Standard deviation142985.78
Coefficient of variation (CV)4.9039387
Kurtosis29.562982
Mean29157.333
Median Absolute Deviation (MAD)0
Skewness5.4217725
Sum874720
Variance2.0444932 × 1010
MonotonicityNot monotonic
2024-03-13T20:54:09.307550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 25
83.3%
1500 1
 
3.3%
6000 1
 
3.3%
783720 1
 
3.3%
60000 1
 
3.3%
23500 1
 
3.3%
ValueCountFrequency (%)
0 25
83.3%
1500 1
 
3.3%
6000 1
 
3.3%
23500 1
 
3.3%
60000 1
 
3.3%
783720 1
 
3.3%
ValueCountFrequency (%)
783720 1
 
3.3%
60000 1
 
3.3%
23500 1
 
3.3%
6000 1
 
3.3%
1500 1
 
3.3%
0 25
83.3%

Interactions

2024-03-13T20:54:03.765176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:00.796295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:01.435165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:01.990675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:02.554079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:03.213557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:03.853740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:00.916842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:01.550559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:02.069834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:02.640209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:03.304582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:03.947975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:01.023033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:01.661491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:02.163222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:02.720605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:03.390903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:04.053706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:01.119629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:01.743332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:02.254498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:02.811488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:03.485828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:04.147419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:01.207962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:01.824954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:02.365024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:02.932861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:03.584518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:04.244892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:01.307737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:01.903426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:02.451406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:03.086752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:03.666389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:54:09.422459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
가맹점번호1.0000.0000.0000.0000.0000.0001.0000.0000.000NaN0.0000.000
결제상품ID0.0001.0000.3250.0000.0000.4301.0000.0000.000NaN0.9720.112
가맹점업종명0.0000.3251.0000.3040.0000.5621.0000.0000.0001.0000.0001.000
가맹점우편번호0.0000.0000.3041.0000.0001.0001.0000.0000.0001.0000.0000.000
시도명0.0000.0000.0000.0001.000NaNNaN0.6550.655NaN0.0000.000
시군구명0.0000.4300.5621.000NaN1.0001.000NaNNaN1.0000.6501.000
읍면동명1.0001.0001.0001.000NaN1.0001.000NaNNaN1.0001.0001.000
위도0.0000.0000.0000.0000.655NaNNaN1.0000.655NaN0.0000.000
경도0.0000.0000.0000.0000.655NaNNaN0.6551.000NaN0.0000.000
결제상품명NaNNaN1.0001.000NaN1.0001.000NaNNaN1.000NaN1.000
사용여부0.0000.9720.0000.0000.0000.6501.0000.0000.000NaN1.0000.000
결제금액0.0000.1121.0000.0000.0001.0001.0000.0000.0001.0000.0001.000
2024-03-13T20:54:09.563756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용여부시도명
사용여부1.0000.000
시도명0.0001.000
2024-03-13T20:54:09.651048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호결제상품ID가맹점우편번호위도경도결제금액시도명사용여부
가맹점번호1.000-0.0530.123-0.0830.3810.0740.0000.000
결제상품ID-0.0531.0000.089-0.118-0.098-0.9920.0000.875
가맹점우편번호0.1230.0891.000-0.758-0.084-0.0810.0000.000
위도-0.083-0.118-0.7581.0000.0540.0970.4540.000
경도0.381-0.098-0.0840.0541.0000.1190.4540.000
결제금액0.074-0.992-0.0810.0970.1191.0000.0000.000
시도명0.0000.0000.0000.4540.4540.0001.0000.000
사용여부0.0000.8750.0000.0000.0000.0000.0001.000

Missing values

2024-03-13T20:54:04.397504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:54:04.590662image/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.
2024-03-13T20:54:04.733363image/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
02023-07-01700000498999999999999999약국13599경기도성남시 분당구수내동37.373127.119<NA>N0
12023-07-01799328235999999999999999용역서비스17852경기도평택시비전동37.001127.107<NA>N0
22023-07-01700007199999999999999999미용/위생11324경기도동두천시보산동37.916127.056<NA>N0
32023-07-01700011721999999999999999숙박업15382경기도안산시 단원구원곡동37.326126.801<NA>N0
42023-07-01700012828999999999999999일반유통14428경기도부천시원종동37.526126.8<NA>N0
52023-07-01700013434999999999999999가구16802경기도용인시 수지구동천동37.343127.063<NA>N0
62023-07-01700014794999999999999999전자제품12249NONE<NA><NA>0.00.0<NA>N0
72023-07-01799329688999999999999999일반/휴게 음식15470경기도안산시 단원구고잔동37.31126.832<NA>N0
82023-07-01700016723140000080000일반유통14537경기도부천시중동37.505126.762부천페이(통합)Y1500
92023-07-01700017794999999999999999기타유통14441경기도부천시오정동37.528126.779<NA>N0
정책일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
202023-07-01700037507999999999999999일반/휴게 음식17817경기도평택시안중읍37.017126.945<NA>N0
212023-07-01700041679999999999999999문화/취미16348경기도수원시 장안구파장동37.308126.993<NA>N0
222023-07-01700041790140000106000대형유통15818경기도군포시산본동37.365126.929군포愛머니(통합)Y783720
232023-07-01700042090140000072000일반/휴게 음식12564경기도양평군양평읍37.491127.527양평통보(통합)Y60000
242023-07-01700046059140000154000레저/스포츠 서비스12039경기도남양주시오남읍37.69127.214Thank You Pay-N(교통)Y23500
252023-07-01700046969999999999999999직물/침구류11775경기도의정부시신곡동37.748127.069<NA>N0
262023-07-01700048108999999999999999용역서비스13209경기도성남시 중원구상대원동37.436127.169<NA>N0
272023-07-01700048165999999999999999미용/위생14949경기도시흥시신천동37.438126.785<NA>N0
282023-07-01700048394999999999999999일반/휴게 음식12175경기도남양주시화도읍37.656127.303<NA>N0
292023-07-01700050554999999999999999미용/위생17368경기도이천시창전동37.28127.44<NA>N0