Overview

Dataset statistics

Number of variables13
Number of observations30
Missing cells25
Missing cells (%)6.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory114.4 B

Variable types

Categorical3
Numeric6
Text3
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/5c4c77b9-f10c-4ae6-a1bc-e1a7cadda13c

Alerts

정책일간결제일자 has constant value ""Constant
시도명 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 결제상품ID and 1 other fieldsHigh correlation
사용여부 is highly overall correlated with 결제상품ID and 1 other fieldsHigh correlation
결제상품명 has 25 (83.3%) missing valuesMissing
가맹점번호 has unique valuesUnique
결제금액 has 25 (83.3%) zerosZeros

Reproduction

Analysis started2023-12-10 13:45:49.848274
Analysis finished2023-12-10 13:45:57.345647
Duration7.5 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
2021-04-01
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-04-01
2nd row2021-04-01
3rd row2021-04-01
4th row2021-04-01
5th row2021-04-01

Common Values

ValueCountFrequency (%)
2021-04-01 30
100.0%

Length

2023-12-10T22:45:57.465873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:45:57.713479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-04-01 30
100.0%

가맹점번호
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0001485 × 108
Minimum7.0000212 × 108
Maximum7.0002411 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:45:57.889580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0000212 × 108
5-th percentile7.000045 × 108
Q17.0001228 × 108
median7.0001551 × 108
Q37.000177 × 108
95-th percentile7.0002349 × 108
Maximum7.0002411 × 108
Range21982
Interquartile range (IQR)5422

Descriptive statistics

Standard deviation5594.5005
Coefficient of variation (CV)7.9919741 × 10-6
Kurtosis-0.021333075
Mean7.0001485 × 108
Median Absolute Deviation (MAD)3199.5
Skewness-0.40094605
Sum2.1000445 × 1010
Variance31298436
MonotonicityNot monotonic
2023-12-10T22:45:58.091218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
700004347 1
 
3.3%
700016212 1
 
3.3%
700024107 1
 
3.3%
700023971 1
 
3.3%
700022894 1
 
3.3%
700020487 1
 
3.3%
700022472 1
 
3.3%
700020063 1
 
3.3%
700019412 1
 
3.3%
700017752 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
700002125 1
3.3%
700004347 1
3.3%
700004688 1
3.3%
700008566 1
3.3%
700009345 1
3.3%
700010217 1
3.3%
700010591 1
3.3%
700012251 1
3.3%
700012374 1
3.3%
700012642 1
3.3%
ValueCountFrequency (%)
700024107 1
3.3%
700023971 1
3.3%
700022894 1
3.3%
700022472 1
3.3%
700020487 1
3.3%
700020063 1
3.3%
700019412 1
3.3%
700017752 1
3.3%
700017559 1
3.3%
700016681 1
3.3%

결제상품ID
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3335667 × 1014
Minimum1.4000004 × 1011
Maximum1 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:45:58.255094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000004 × 1011
5-th percentile1.4000005 × 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
2023-12-10T22:45:58.418940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
999999999999999 25
83.3%
140000102000 1
 
3.3%
140000065000 1
 
3.3%
140000059000 1
 
3.3%
140000040000 1
 
3.3%
140000046000 1
 
3.3%
ValueCountFrequency (%)
140000040000 1
 
3.3%
140000046000 1
 
3.3%
140000059000 1
 
3.3%
140000065000 1
 
3.3%
140000102000 1
 
3.3%
999999999999999 25
83.3%
ValueCountFrequency (%)
999999999999999 25
83.3%
140000102000 1
 
3.3%
140000065000 1
 
3.3%
140000059000 1
 
3.3%
140000046000 1
 
3.3%
140000040000 1
 
3.3%

가맹점업종명
Categorical

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
학원
5 
유통업 영리
5 
보건위생
5 
일반휴게음식
3 
자동차정비 유지
3 
Other values (7)
9 

Length

Max length8
Median length6
Mean length4.4666667
Min length2

Unique

Unique5 ?
Unique (%)16.7%

Sample

1st row학원
2nd row유통업 영리
3rd row보건위생
4th row보건위생
5th row학원

Common Values

ValueCountFrequency (%)
학원 5
16.7%
유통업 영리 5
16.7%
보건위생 5
16.7%
일반휴게음식 3
10.0%
자동차정비 유지 3
10.0%
건축자재 2
 
6.7%
숙박업 2
 
6.7%
가구 1
 
3.3%
전기제품 1
 
3.3%
레저업소 1
 
3.3%
Other values (2) 2
 
6.7%

Length

2023-12-10T22:45:58.649269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
학원 5
13.2%
유통업 5
13.2%
영리 5
13.2%
보건위생 5
13.2%
일반휴게음식 3
7.9%
자동차정비 3
7.9%
유지 3
7.9%
건축자재 2
 
5.3%
숙박업 2
 
5.3%
가구 1
 
2.6%
Other values (4) 4
10.5%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14406.367
Minimum10079
Maximum18344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:45:58.836788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10079
5-th percentile10223.9
Q112466.25
median14069.5
Q316898
95-th percentile17865.7
Maximum18344
Range8265
Interquartile range (IQR)4431.75

Descriptive statistics

Standard deviation2718.8084
Coefficient of variation (CV)0.1887227
Kurtosis-1.4152562
Mean14406.367
Median Absolute Deviation (MAD)2780.5
Skewness-0.18252733
Sum432191
Variance7391918.9
MonotonicityNot monotonic
2023-12-10T22:45:59.102962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
16898 2
 
6.7%
10079 2
 
6.7%
13152 1
 
3.3%
16970 1
 
3.3%
12406 1
 
3.3%
17758 1
 
3.3%
16507 1
 
3.3%
10401 1
 
3.3%
12647 1
 
3.3%
17814 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
10079 2
6.7%
10401 1
3.3%
10432 1
3.3%
10925 1
3.3%
11184 1
3.3%
12108 1
3.3%
12406 1
3.3%
12647 1
3.3%
12759 1
3.3%
13144 1
3.3%
ValueCountFrequency (%)
18344 1
3.3%
17908 1
3.3%
17814 1
3.3%
17758 1
3.3%
17024 1
3.3%
17006 1
3.3%
16970 1
3.3%
16898 2
6.7%
16802 1
3.3%
16507 1
3.3%

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

2023-12-10T22:45:59.327045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:45:59.487149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Text

Distinct21
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:45:59.738199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7.5
Mean length5.3666667
Min length3

Characters and Unicode

Total characters161
Distinct characters42
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

Unique15 ?
Unique (%)50.0%

Sample

1st row성남시 중원구
2nd row수원시 장안구
3rd row성남시 분당구
4th row성남시 수정구
5th row부천시
ValueCountFrequency (%)
용인시 6
 
12.8%
성남시 5
 
10.6%
기흥구 4
 
8.5%
평택시 3
 
6.4%
수원시 3
 
6.4%
분당구 2
 
4.3%
김포시 2
 
4.3%
일산동구 2
 
4.3%
고양시 2
 
4.3%
중원구 2
 
4.3%
Other values (16) 16
34.0%
2023-12-10T22:46:00.296212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
18.0%
17
 
10.6%
구 17
 
10.6%
인 7
 
4.3%
용 6
 
3.7%
성 6
 
3.7%
남 6
 
3.7%
원 6
 
3.7%
수 5
 
3.1%
기 4
 
2.5%
Other values (32) 58
36.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 144
89.4%
Space Separator 17
 
10.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
20.1%
구 17
 
11.8%
인 7
 
4.9%
용 6
 
4.2%
성 6
 
4.2%
남 6
 
4.2%
원 6
 
4.2%
수 5
 
3.5%
기 4
 
2.8%
흥 4
 
2.8%
Other values (31) 54
37.5%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 144
89.4%
Common 17
 
10.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
20.1%
구 17
 
11.8%
인 7
 
4.9%
용 6
 
4.2%
성 6
 
4.2%
남 6
 
4.2%
원 6
 
4.2%
수 5
 
3.5%
기 4
 
2.8%
흥 4
 
2.8%
Other values (31) 54
37.5%
Common
ValueCountFrequency (%)
17
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 144
89.4%
ASCII 17
 
10.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
20.1%
구 17
 
11.8%
인 7
 
4.9%
용 6
 
4.2%
성 6
 
4.2%
남 6
 
4.2%
원 6
 
4.2%
수 5
 
3.5%
기 4
 
2.8%
흥 4
 
2.8%
Other values (31) 54
37.5%
ASCII
ValueCountFrequency (%)
17
100.0%

읍면동명
Text

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:46:00.611952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.8666667
Min length2

Characters and Unicode

Total characters86
Distinct characters42
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

Unique22 ?
Unique (%)73.3%

Sample

1st row은행동
2nd row율전동
3rd row서현동
4th row단대동
5th row중동
ValueCountFrequency (%)
장항동 2
 
6.7%
보정동 2
 
6.7%
중동 2
 
6.7%
장기동 2
 
6.7%
배양동 1
 
3.3%
은행동 1
 
3.3%
북면 1
 
3.3%
신장동 1
 
3.3%
이의동 1
 
3.3%
능서면 1
 
3.3%
Other values (16) 16
53.3%
2023-12-10T22:46:01.315065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 27
31.4%
장 5
 
5.8%
정 4
 
4.7%
중 3
 
3.5%
서 2
 
2.3%
항 2
 
2.3%
단 2
 
2.3%
포 2
 
2.3%
곡 2
 
2.3%
읍 2
 
2.3%
Other values (32) 35
40.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 86
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 27
31.4%
장 5
 
5.8%
정 4
 
4.7%
중 3
 
3.5%
서 2
 
2.3%
항 2
 
2.3%
단 2
 
2.3%
포 2
 
2.3%
곡 2
 
2.3%
읍 2
 
2.3%
Other values (32) 35
40.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 86
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 27
31.4%
장 5
 
5.8%
정 4
 
4.7%
중 3
 
3.5%
서 2
 
2.3%
항 2
 
2.3%
단 2
 
2.3%
포 2
 
2.3%
곡 2
 
2.3%
읍 2
 
2.3%
Other values (32) 35
40.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 86
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 27
31.4%
장 5
 
5.8%
정 4
 
4.7%
중 3
 
3.5%
서 2
 
2.3%
항 2
 
2.3%
단 2
 
2.3%
포 2
 
2.3%
곡 2
 
2.3%
읍 2
 
2.3%
Other values (32) 35
40.7%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.410333
Minimum36.984
Maximum37.904
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:46:01.628583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.984
5-th percentile37.0315
Q137.2795
median37.357
Q337.60125
95-th percentile37.80815
Maximum37.904
Range0.92
Interquartile range (IQR)0.32175

Descriptive statistics

Standard deviation0.22726019
Coefficient of variation (CV)0.0060747973
Kurtosis-0.060525644
Mean37.410333
Median Absolute Deviation (MAD)0.089
Skewness0.32161444
Sum1122.31
Variance0.051647195
MonotonicityNot monotonic
2023-12-10T22:46:01.818614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
37.275 2
 
6.7%
37.46 1
 
3.3%
37.302 1
 
3.3%
37.904 1
 
3.3%
37.081 1
 
3.3%
37.32 1
 
3.3%
37.291 1
 
3.3%
37.659 1
 
3.3%
36.984 1
 
3.3%
37.271 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
36.984 1
3.3%
36.991 1
3.3%
37.081 1
3.3%
37.219 1
3.3%
37.271 1
3.3%
37.275 2
6.7%
37.279 1
3.3%
37.281 1
3.3%
37.291 1
3.3%
37.302 1
3.3%
ValueCountFrequency (%)
37.904 1
3.3%
37.85 1
3.3%
37.757 1
3.3%
37.659 1
3.3%
37.648 1
3.3%
37.646 1
3.3%
37.643 1
3.3%
37.634 1
3.3%
37.503 1
3.3%
37.46 1
3.3%

경도
Real number (ℝ)

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.04293
Minimum126.671
Maximum127.564
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:46:02.011719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.671
5-th percentile126.7153
Q1126.90075
median127.1015
Q3127.15325
95-th percentile127.3639
Maximum127.564
Range0.893
Interquartile range (IQR)0.2525

Descriptive statistics

Standard deviation0.2094645
Coefficient of variation (CV)0.0016487694
Kurtosis0.39342972
Mean127.04293
Median Absolute Deviation (MAD)0.076
Skewness0.12924124
Sum3811.288
Variance0.043875375
MonotonicityNot monotonic
2023-12-10T22:46:02.254542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
127.111 2
 
6.7%
126.772 2
 
6.7%
127.051 2
 
6.7%
127.169 1
 
3.3%
127.115 1
 
3.3%
127.453 1
 
3.3%
126.767 1
 
3.3%
127.564 1
 
3.3%
126.878 1
 
3.3%
127.151 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
126.671 1
3.3%
126.673 1
3.3%
126.767 1
3.3%
126.772 2
6.7%
126.778 1
3.3%
126.798 1
3.3%
126.878 1
3.3%
126.969 1
3.3%
126.989 1
3.3%
127.017 1
3.3%
ValueCountFrequency (%)
127.564 1
3.3%
127.453 1
3.3%
127.255 1
3.3%
127.187 1
3.3%
127.169 1
3.3%
127.166 1
3.3%
127.158 1
3.3%
127.154 1
3.3%
127.151 1
3.3%
127.125 1
3.3%

결제상품명
Text

MISSING 

Distinct5
Distinct (%)100.0%
Missing25
Missing (%)83.3%
Memory size372.0 B
2023-12-10T22:46:02.513197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length8
Min length6

Characters and Unicode

Total characters40
Distinct characters19
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

Unique5 ?
Unique (%)100.0%

Sample

1st row수원페이(통합)
2nd row용인와이페이(통합)
3rd row평택사랑카드(통합)
4th row여주사랑카드
5th row용인와이페이
ValueCountFrequency (%)
수원페이(통합 1
20.0%
용인와이페이(통합 1
20.0%
평택사랑카드(통합 1
20.0%
여주사랑카드 1
20.0%
용인와이페이 1
20.0%
2023-12-10T22:46:03.226533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 5
12.5%
페 3
 
7.5%
( 3
 
7.5%
통 3
 
7.5%
합 3
 
7.5%
) 3
 
7.5%
인 2
 
5.0%
랑 2
 
5.0%
용 2
 
5.0%
와 2
 
5.0%
Other values (9) 12
30.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 34
85.0%
Open Punctuation 3
 
7.5%
Close Punctuation 3
 
7.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 5
14.7%
페 3
 
8.8%
통 3
 
8.8%
합 3
 
8.8%
인 2
 
5.9%
랑 2
 
5.9%
용 2
 
5.9%
와 2
 
5.9%
드 2
 
5.9%
카 2
 
5.9%
Other values (7) 8
23.5%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 34
85.0%
Common 6
 
15.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 5
14.7%
페 3
 
8.8%
통 3
 
8.8%
합 3
 
8.8%
인 2
 
5.9%
랑 2
 
5.9%
용 2
 
5.9%
와 2
 
5.9%
드 2
 
5.9%
카 2
 
5.9%
Other values (7) 8
23.5%
Common
ValueCountFrequency (%)
( 3
50.0%
) 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 34
85.0%
ASCII 6
 
15.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 5
14.7%
페 3
 
8.8%
통 3
 
8.8%
합 3
 
8.8%
인 2
 
5.9%
랑 2
 
5.9%
용 2
 
5.9%
와 2
 
5.9%
드 2
 
5.9%
카 2
 
5.9%
Other values (7) 8
23.5%
ASCII
ValueCountFrequency (%)
( 3
50.0%
) 3
50.0%

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%
2023-12-10T22:46:03.424755image/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%
Mean4626.6667
Minimum0
Maximum50000
Zeros25
Zeros (%)83.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:46:03.657355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile39350
Maximum50000
Range50000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13178.821
Coefficient of variation (CV)2.8484484
Kurtosis7.755301
Mean4626.6667
Median Absolute Deviation (MAD)0
Skewness2.9596169
Sum138800
Variance1.7368133 × 108
MonotonicityNot monotonic
2023-12-10T22:46:04.017689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 25
83.3%
50000 1
 
3.3%
6300 1
 
3.3%
5500 1
 
3.3%
30000 1
 
3.3%
47000 1
 
3.3%
ValueCountFrequency (%)
0 25
83.3%
5500 1
 
3.3%
6300 1
 
3.3%
30000 1
 
3.3%
47000 1
 
3.3%
50000 1
 
3.3%
ValueCountFrequency (%)
50000 1
 
3.3%
47000 1
 
3.3%
30000 1
 
3.3%
6300 1
 
3.3%
5500 1
 
3.3%
0 25
83.3%

Interactions

2023-12-10T22:45:56.026238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:50.651999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:51.646787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:53.065138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:53.844204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:54.742541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:56.167440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:50.815084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:51.789537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:53.201817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:53.968438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:54.902478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:56.313522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:50.952314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:51.948533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:53.341329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:54.118582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:55.238093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:56.437269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:51.122940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:52.245017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:53.472633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:54.254626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:55.536273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:56.562962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:51.319500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:52.373305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:53.596022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:54.433236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:55.719618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:56.702263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:51.448136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:52.883424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:53.720642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:54.576899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:55.908162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:46:04.180212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호결제상품ID가맹점업종명가맹점우편번호시군구명읍면동명위도경도결제상품명사용여부결제금액
가맹점번호1.0000.0000.4420.4640.0000.0000.0000.6141.0000.2560.000
결제상품ID0.0001.0000.0000.0000.0000.4600.0000.371NaN0.9721.000
가맹점업종명0.4420.0001.0000.6380.8350.5800.0000.0001.0000.0000.000
가맹점우편번호0.4640.0000.6381.0001.0000.9530.7960.5241.0000.0000.000
시군구명0.0000.0000.8351.0001.0000.9920.9330.9521.0000.0000.723
읍면동명0.0000.4600.5800.9530.9921.0000.9880.9901.0000.6570.878
위도0.0000.0000.0000.7960.9330.9881.0000.6711.0000.0580.000
경도0.6140.3710.0000.5240.9520.9900.6711.0001.0000.4580.784
결제상품명1.000NaN1.0001.0001.0001.0001.0001.0001.000NaN1.000
사용여부0.2560.9720.0000.0000.0000.6570.0580.458NaN1.0001.000
결제금액0.0001.0000.0000.0000.7230.8780.0000.7841.0001.0001.000
2023-12-10T22:46:04.396882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용여부가맹점업종명
사용여부1.0000.000
가맹점업종명0.0001.000
2023-12-10T22:46:05.107657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호결제상품ID가맹점우편번호위도경도결제금액가맹점업종명사용여부
가맹점번호1.000-0.3020.311-0.3430.0930.2760.0000.135
결제상품ID-0.3021.000-0.2530.411-0.184-0.9870.0000.875
가맹점우편번호0.311-0.2531.000-0.8750.1030.2510.2210.000
위도-0.3430.411-0.8751.000-0.063-0.3990.0000.000
경도0.093-0.1840.103-0.0631.0000.1620.0000.388
결제금액0.276-0.9870.251-0.3990.1621.0000.0000.964
가맹점업종명0.0000.0000.2210.0000.0000.0001.0000.000
사용여부0.1350.8750.0000.0000.3880.9640.0001.000

Missing values

2023-12-10T22:45:56.906274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:45:57.228459image/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

정책일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
02021-04-01700004347999999999999999학원13152경기도성남시 중원구은행동37.46127.169<NA>N0
12021-04-01700002125999999999999999유통업 영리16357경기도수원시 장안구율전동37.302126.969<NA>N0
22021-04-01700004688999999999999999보건위생13591경기도성남시 분당구서현동37.385127.125<NA>N0
32021-04-01700009345999999999999999보건위생13144경기도성남시 수정구단대동37.449127.158<NA>N0
42021-04-01700008566999999999999999학원14548경기도부천시중동37.503126.772<NA>N0
52021-04-01700010217999999999999999학원10079경기도김포시장기동37.646126.673<NA>N0
62021-04-01700010591999999999999999건축자재12759경기도광주시역동37.4127.255<NA>N0
72021-04-01700012251999999999999999보건위생13555경기도성남시 분당구정자동37.371127.107<NA>N0
82021-04-01700012374999999999999999일반휴게음식10925경기도파주시금촌동37.757126.772<NA>N0
92021-04-01700012642999999999999999일반휴게음식10079경기도김포시장기동37.643126.671<NA>N0
정책일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
202021-04-01700016681999999999999999전기제품15390경기도안산시 단원구원곡동37.327126.798<NA>N0
212021-04-01700017559999999999999999레저업소17006경기도용인시 기흥구중동37.271127.151<NA>N0
222021-04-01700017752140000059000유통업 영리17814경기도평택시포승읍36.984126.878평택사랑카드(통합)Y5500
232021-04-01700019412140000040000자동차정비 유지12647경기도여주시능서면37.275127.564여주사랑카드Y30000
242021-04-01700020063999999999999999음료식품10401경기도고양시 일산동구장항동37.659126.767<NA>N0
252021-04-01700022472999999999999999신변잡화16507경기도수원시 영통구이의동37.291127.051<NA>N0
262021-04-01700020487140000046000보건위생16898경기도용인시 기흥구보정동37.32127.111용인와이페이Y47000
272021-04-01700022894999999999999999숙박업17758경기도평택시신장동37.081127.051<NA>N0
282021-04-01700023971999999999999999숙박업12406경기도가평군북면37.904127.453<NA>N0
292021-04-01700024107999999999999999보건위생16970경기도용인시 기흥구구갈동37.275127.111<NA>N0