Overview

Dataset statistics

Number of variables13
Number of observations30
Missing cells26
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory114.4 B

Variable types

DateTime1
Numeric4
Categorical4
Text3
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/9782d88c-0437-4776-a467-0eedc9b0393d

Alerts

정책일간결제일자 has constant value ""Constant
시도명 has constant value ""Constant
결제상품ID is highly overall correlated with 사용여부 and 1 other fieldsHigh correlation
사용여부 is highly overall correlated with 결제상품ID and 1 other fieldsHigh correlation
결제금액 is highly overall correlated with 결제상품ID and 1 other fieldsHigh correlation
가맹점우편번호 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 가맹점우편번호High correlation
경도 is highly overall correlated with 가맹점우편번호High correlation
결제상품ID is highly imbalanced (64.1%)Imbalance
결제금액 is highly imbalanced (64.1%)Imbalance
결제상품명 has 26 (86.7%) missing valuesMissing
가맹점번호 has unique valuesUnique
가맹점우편번호 has unique valuesUnique

Reproduction

Analysis started2024-03-13 11:47:32.821534
Analysis finished2024-03-13 11:47:35.865041
Duration3.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정책일간결제일자
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2021-10-01 00:00:00
Maximum2021-10-01 00:00:00
2024-03-13T20:47:35.931124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:47:36.076663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

가맹점번호
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3927478 × 108
Minimum7.0000006 × 108
Maximum7.9063219 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:47:36.211769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0000006 × 108
5-th percentile7.0000048 × 108
Q17.0000316 × 108
median7.0000566 × 108
Q37.9062866 × 108
95-th percentile7.9063164 × 108
Maximum7.9063219 × 108
Range90632130
Interquartile range (IQR)90625494

Descriptive statistics

Standard deviation45676122
Coefficient of variation (CV)0.06178504
Kurtosis-2.0620556
Mean7.3927478 × 108
Median Absolute Deviation (MAD)5181
Skewness0.28344281
Sum2.2178243 × 1010
Variance2.0863081 × 1015
MonotonicityNot monotonic
2024-03-13T20:47:36.374452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
700000464 1
 
3.3%
790628833 1
 
3.3%
790632188 1
 
3.3%
700006614 1
 
3.3%
790631854 1
 
3.3%
700005536 1
 
3.3%
700005788 1
 
3.3%
790631378 1
 
3.3%
700005413 1
 
3.3%
790631132 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
700000058 1
3.3%
700000464 1
3.3%
700000498 1
3.3%
700002425 1
3.3%
700002572 1
3.3%
700002709 1
3.3%
700002872 1
3.3%
700003049 1
3.3%
700003501 1
3.3%
700003884 1
3.3%
ValueCountFrequency (%)
790632188 1
3.3%
790631854 1
3.3%
790631378 1
3.3%
790631132 1
3.3%
790630914 1
3.3%
790630874 1
3.3%
790628833 1
3.3%
790628710 1
3.3%
790628493 1
3.3%
790627731 1
3.3%

결제상품ID
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
999999999999999
26 
140000032000
 
1
140000018000
 
1
140000099000
 
1
140000046000
 
1

Length

Max length15
Median length15
Mean length14.6
Min length12

Unique

Unique4 ?
Unique (%)13.3%

Sample

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

Common Values

ValueCountFrequency (%)
999999999999999 26
86.7%
140000032000 1
 
3.3%
140000018000 1
 
3.3%
140000099000 1
 
3.3%
140000046000 1
 
3.3%

Length

2024-03-13T20:47:36.562675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:47:36.721134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
999999999999999 26
86.7%
140000032000 1
 
3.3%
140000018000 1
 
3.3%
140000099000 1
 
3.3%
140000046000 1
 
3.3%

가맹점업종명
Categorical

Distinct11
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
일반휴게음식
12 
보건위생
5 
음료식품
3 
유통업 영리
2 
레저업소
2 
Other values (6)
6 

Length

Max length6
Median length5.5
Mean length4.8333333
Min length2

Unique

Unique6 ?
Unique (%)20.0%

Sample

1st row일반휴게음식
2nd row일반휴게음식
3rd row일반휴게음식
4th row유통업 영리
5th row약국

Common Values

ValueCountFrequency (%)
일반휴게음식 12
40.0%
보건위생 5
16.7%
음료식품 3
 
10.0%
유통업 영리 2
 
6.7%
레저업소 2
 
6.7%
약국 1
 
3.3%
문화.취미 1
 
3.3%
용역 서비스 1
 
3.3%
의류 1
 
3.3%
건축자재 1
 
3.3%

Length

2024-03-13T20:47:36.907245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반휴게음식 12
36.4%
보건위생 5
15.2%
음료식품 3
 
9.1%
유통업 2
 
6.1%
영리 2
 
6.1%
레저업소 2
 
6.1%
약국 1
 
3.0%
문화.취미 1
 
3.0%
용역 1
 
3.0%
서비스 1
 
3.0%
Other values (3) 3
 
9.1%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13441.267
Minimum10019
Maximum18531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:47:37.051674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10019
5-th percentile10155.1
Q111031
median13354.5
Q315260.75
95-th percentile18037
Maximum18531
Range8512
Interquartile range (IQR)4229.75

Descriptive statistics

Standard deviation2762.7357
Coefficient of variation (CV)0.20554132
Kurtosis-1.1411687
Mean13441.267
Median Absolute Deviation (MAD)2197.5
Skewness0.40998521
Sum403238
Variance7632708.3
MonotonicityNot monotonic
2024-03-13T20:47:37.338421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
10598 1
 
3.3%
15245 1
 
3.3%
13118 1
 
3.3%
17128 1
 
3.3%
10364 1
 
3.3%
18442 1
 
3.3%
17052 1
 
3.3%
11698 1
 
3.3%
12502 1
 
3.3%
18531 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
10019 1
3.3%
10111 1
3.3%
10209 1
3.3%
10270 1
3.3%
10364 1
3.3%
10387 1
3.3%
10598 1
3.3%
10905 1
3.3%
11409 1
3.3%
11453 1
3.3%
ValueCountFrequency (%)
18531 1
3.3%
18442 1
3.3%
17542 1
3.3%
17128 1
3.3%
17079 1
3.3%
17052 1
3.3%
16214 1
3.3%
15266 1
3.3%
15245 1
3.3%
14725 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

2024-03-13T20:47:37.544258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:47:37.739037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Text

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:47:38.002160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.3
Min length3

Characters and Unicode

Total characters159
Distinct characters35
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

Unique7 ?
Unique (%)23.3%

Sample

1st row고양시 덕양구
2nd row양주시
3rd row부천시
4th row부천시
5th row성남시 분당구
ValueCountFrequency (%)
고양시 5
 
10.9%
양주시 3
 
6.5%
성남시 3
 
6.5%
용인시 3
 
6.5%
김포시 2
 
4.3%
처인구 2
 
4.3%
의정부시 2
 
4.3%
일산서구 2
 
4.3%
분당구 2
 
4.3%
화성시 2
 
4.3%
Other values (14) 20
43.5%
2024-03-13T20:47:38.445191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
18.2%
16
 
10.1%
구 16
 
10.1%
양 13
 
8.2%
안 8
 
5.0%
성 6
 
3.8%
인 5
 
3.1%
고 5
 
3.1%
산 5
 
3.1%
부 4
 
2.5%
Other values (25) 52
32.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 143
89.9%
Space Separator 16
 
10.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
20.3%
구 16
 
11.2%
양 13
 
9.1%
안 8
 
5.6%
성 6
 
4.2%
인 5
 
3.5%
고 5
 
3.5%
산 5
 
3.5%
부 4
 
2.8%
주 4
 
2.8%
Other values (24) 48
33.6%
Space Separator
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 143
89.9%
Common 16
 
10.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
20.3%
구 16
 
11.2%
양 13
 
9.1%
안 8
 
5.6%
성 6
 
4.2%
인 5
 
3.5%
고 5
 
3.5%
산 5
 
3.5%
부 4
 
2.8%
주 4
 
2.8%
Other values (24) 48
33.6%
Common
ValueCountFrequency (%)
16
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 143
89.9%
ASCII 16
 
10.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
20.3%
구 16
 
11.2%
양 13
 
9.1%
안 8
 
5.6%
성 6
 
4.2%
인 5
 
3.5%
고 5
 
3.5%
산 5
 
3.5%
부 4
 
2.8%
주 4
 
2.8%
Other values (24) 48
33.6%
ASCII
ValueCountFrequency (%)
16
100.0%

읍면동명
Text

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:47:38.692352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9666667
Min length2

Characters and Unicode

Total characters89
Distinct characters48
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

Unique26 ?
Unique (%)86.7%

Sample

1st row동산동
2nd row남면
3rd row괴안동
4th row송내동
5th row수내동
ValueCountFrequency (%)
관양동 2
 
6.7%
와동 2
 
6.7%
동산동 1
 
3.3%
서현동 1
 
3.3%
이동읍 1
 
3.3%
장항동 1
 
3.3%
반송동 1
 
3.3%
김량장동 1
 
3.3%
의정부동 1
 
3.3%
서종면 1
 
3.3%
Other values (18) 18
60.0%
2024-03-13T20:47:39.129938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 27
30.3%
면 4
 
4.5%
양 3
 
3.4%
관 2
 
2.2%
고 2
 
2.2%
서 2
 
2.2%
가 2
 
2.2%
정 2
 
2.2%
장 2
 
2.2%
읍 2
 
2.2%
Other values (38) 41
46.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 27
30.3%
면 4
 
4.5%
양 3
 
3.4%
관 2
 
2.2%
고 2
 
2.2%
서 2
 
2.2%
가 2
 
2.2%
정 2
 
2.2%
장 2
 
2.2%
읍 2
 
2.2%
Other values (38) 41
46.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 27
30.3%
면 4
 
4.5%
양 3
 
3.4%
관 2
 
2.2%
고 2
 
2.2%
서 2
 
2.2%
가 2
 
2.2%
정 2
 
2.2%
장 2
 
2.2%
읍 2
 
2.2%
Other values (38) 41
46.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 27
30.3%
면 4
 
4.5%
양 3
 
3.4%
관 2
 
2.2%
고 2
 
2.2%
서 2
 
2.2%
가 2
 
2.2%
정 2
 
2.2%
장 2
 
2.2%
읍 2
 
2.2%
Other values (38) 41
46.1%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.501233
Minimum37.046
Maximum37.875
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:47:39.284001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.046
5-th percentile37.16275
Q137.335
median37.484
Q337.6895
95-th percentile37.82085
Maximum37.875
Range0.829
Interquartile range (IQR)0.3545

Descriptive statistics

Standard deviation0.22960741
Coefficient of variation (CV)0.006122663
Kurtosis-1.0982747
Mean37.501233
Median Absolute Deviation (MAD)0.194
Skewness-0.19822917
Sum1125.037
Variance0.052719564
MonotonicityNot monotonic
2024-03-13T20:47:39.434448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
37.484 2
 
6.7%
37.643 1
 
3.3%
37.385 1
 
3.3%
37.457 1
 
3.3%
37.194 1
 
3.3%
37.661 1
 
3.3%
37.193 1
 
3.3%
37.235 1
 
3.3%
37.74 1
 
3.3%
37.622 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
37.046 1
3.3%
37.138 1
3.3%
37.193 1
3.3%
37.194 1
3.3%
37.235 1
3.3%
37.255 1
3.3%
37.297 1
3.3%
37.333 1
3.3%
37.341 1
3.3%
37.373 1
3.3%
ValueCountFrequency (%)
37.875 1
3.3%
37.833 1
3.3%
37.806 1
3.3%
37.751 1
3.3%
37.74 1
3.3%
37.711 1
3.3%
37.708 1
3.3%
37.691 1
3.3%
37.685 1
3.3%
37.669 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.96493
Minimum126.597
Maximum127.353
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:47:39.601367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.597
5-th percentile126.72035
Q1126.81375
median126.9845
Q3127.10725
95-th percentile127.206
Maximum127.353
Range0.756
Interquartile range (IQR)0.2935

Descriptive statistics

Standard deviation0.1841727
Coefficient of variation (CV)0.0014505792
Kurtosis-0.76486798
Mean126.96493
Median Absolute Deviation (MAD)0.141
Skewness-0.020015354
Sum3808.948
Variance0.033919582
MonotonicityNot monotonic
2024-03-13T20:47:39.795336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
126.884 2
 
6.7%
127.206 2
 
6.7%
126.81 1
 
3.3%
126.825 1
 
3.3%
127.126 1
 
3.3%
126.764 1
 
3.3%
127.073 1
 
3.3%
127.202 1
 
3.3%
127.051 1
 
3.3%
127.353 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
126.597 1
3.3%
126.719 1
3.3%
126.722 1
3.3%
126.745 1
3.3%
126.759 1
3.3%
126.764 1
3.3%
126.769 1
3.3%
126.81 1
3.3%
126.825 1
3.3%
126.826 1
3.3%
ValueCountFrequency (%)
127.353 1
3.3%
127.206 2
6.7%
127.202 1
3.3%
127.126 1
3.3%
127.125 1
3.3%
127.119 1
3.3%
127.111 1
3.3%
127.096 1
3.3%
127.075 1
3.3%
127.073 1
3.3%

결제상품명
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing26
Missing (%)86.7%
Memory size372.0 B
2024-03-13T20:47:40.048554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length6
Mean length7.25
Min length6

Characters and Unicode

Total characters29
Distinct characters20
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

Unique4 ?
Unique (%)100.0%

Sample

1st row안성사랑카드
2nd row고양페이카드
3rd row의정부사랑카드(통합)
4th row용인와이페이
ValueCountFrequency (%)
안성사랑카드 1
25.0%
고양페이카드 1
25.0%
의정부사랑카드(통합 1
25.0%
용인와이페이 1
25.0%
2024-03-13T20:47:40.618009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
카 3
 
10.3%
드 3
 
10.3%
이 3
 
10.3%
사 2
 
6.9%
랑 2
 
6.9%
페 2
 
6.9%
안 1
 
3.4%
( 1
 
3.4%
인 1
 
3.4%
용 1
 
3.4%
Other values (10) 10
34.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 27
93.1%
Open Punctuation 1
 
3.4%
Close Punctuation 1
 
3.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
카 3
 
11.1%
드 3
 
11.1%
이 3
 
11.1%
사 2
 
7.4%
랑 2
 
7.4%
페 2
 
7.4%
안 1
 
3.7%
인 1
 
3.7%
용 1
 
3.7%
합 1
 
3.7%
Other values (8) 8
29.6%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 27
93.1%
Common 2
 
6.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
카 3
 
11.1%
드 3
 
11.1%
이 3
 
11.1%
사 2
 
7.4%
랑 2
 
7.4%
페 2
 
7.4%
안 1
 
3.7%
인 1
 
3.7%
용 1
 
3.7%
합 1
 
3.7%
Other values (8) 8
29.6%
Common
ValueCountFrequency (%)
( 1
50.0%
) 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 27
93.1%
ASCII 2
 
6.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
카 3
 
11.1%
드 3
 
11.1%
이 3
 
11.1%
사 2
 
7.4%
랑 2
 
7.4%
페 2
 
7.4%
안 1
 
3.7%
인 1
 
3.7%
용 1
 
3.7%
합 1
 
3.7%
Other values (8) 8
29.6%
ASCII
ValueCountFrequency (%)
( 1
50.0%
) 1
50.0%

사용여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
26 
True
4 
ValueCountFrequency (%)
False 26
86.7%
True 4
 
13.3%
2024-03-13T20:47:41.277567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
26 
60000
 
1
28000
 
1
9300
 
1
8300
 
1

Length

Max length5
Median length1
Mean length1.4666667
Min length1

Unique

Unique4 ?
Unique (%)13.3%

Sample

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

Common Values

ValueCountFrequency (%)
0 26
86.7%
60000 1
 
3.3%
28000 1
 
3.3%
9300 1
 
3.3%
8300 1
 
3.3%

Length

2024-03-13T20:47:41.463538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:47:41.644147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 26
86.7%
60000 1
 
3.3%
28000 1
 
3.3%
9300 1
 
3.3%
8300 1
 
3.3%

Interactions

2024-03-13T20:47:34.938025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:47:33.434746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:47:33.934912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:47:34.378327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:47:35.081817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:47:33.534028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:47:34.047755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:47:34.475075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:47:35.220092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:47:33.662601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:47:34.138114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:47:34.608822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:47:35.319284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:47:33.780654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:47:34.228480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:47:34.777738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:47:41.759912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호결제상품ID가맹점업종명가맹점우편번호시군구명읍면동명위도경도결제상품명사용여부결제금액
가맹점번호1.0000.0500.0000.0000.0000.0000.0000.0001.0000.0000.050
결제상품ID0.0501.0000.0000.0000.5491.0000.7870.0001.0001.0001.000
가맹점업종명0.0000.0001.0000.5440.6510.8870.6100.2551.0000.0000.000
가맹점우편번호0.0000.0000.5441.0001.0001.0000.9620.8251.0000.0000.000
시군구명0.0000.5490.6511.0001.0001.0000.9720.9491.0000.0490.549
읍면동명0.0001.0000.8871.0001.0001.0001.0001.0001.0001.0001.000
위도0.0000.7870.6100.9620.9721.0001.0000.5281.0000.4260.787
경도0.0000.0000.2550.8250.9491.0000.5281.0001.0000.3140.000
결제상품명1.0001.0001.0001.0001.0001.0001.0001.0001.000NaN1.000
사용여부0.0001.0000.0000.0000.0491.0000.4260.314NaN1.0001.000
결제금액0.0501.0000.0000.0000.5491.0000.7870.0001.0001.0001.000
2024-03-13T20:47:41.949519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
결제상품ID사용여부결제금액가맹점업종명
결제상품ID1.0000.9451.0000.000
사용여부0.9451.0000.9450.000
결제금액1.0000.9451.0000.000
가맹점업종명0.0000.0000.0001.000
2024-03-13T20:47:42.102320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점우편번호위도경도결제상품ID가맹점업종명사용여부결제금액
가맹점번호1.0000.131-0.0630.0450.0250.0000.0000.025
가맹점우편번호0.1311.000-0.8370.5130.0000.2570.0000.000
위도-0.063-0.8371.000-0.3370.3840.2800.2590.384
경도0.0450.513-0.3371.0000.0000.0000.2560.000
결제상품ID0.0250.0000.3840.0001.0000.0000.9451.000
가맹점업종명0.0000.2570.2800.0000.0001.0000.0000.000
사용여부0.0000.0000.2590.2560.9450.0001.0000.945
결제금액0.0250.0000.3840.0001.0000.0000.9451.000

Missing values

2024-03-13T20:47:35.560914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:47:35.792985image/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
02021-10-01700000464999999999999999일반휴게음식10598경기도고양시 덕양구동산동37.643126.884<NA>N0
12021-10-01700000058999999999999999일반휴게음식11409경기도양주시남면37.875126.995<NA>N0
22021-10-01790626605999999999999999일반휴게음식14725경기도부천시괴안동37.484126.81<NA>N0
32021-10-01700002425999999999999999유통업 영리14723경기도부천시송내동37.484126.769<NA>N0
42021-10-01700000498999999999999999약국13599경기도성남시 분당구수내동37.373127.119<NA>N0
52021-10-01790627261999999999999999보건위생13937경기도안양시 동안구관양동37.405126.958<NA>N0
62021-10-01700002572999999999999999문화.취미10270경기도고양시 덕양구고양동37.708126.902<NA>N0
72021-10-01790627553999999999999999음료식품11485경기도양주시삼숭동37.806127.096<NA>N0
82021-10-01700002709999999999999999일반휴게음식16214경기도수원시 장안구연무동37.297127.028<NA>N0
92021-10-01790627731999999999999999용역 서비스17079경기도용인시 기흥구보라동37.255127.111<NA>N0
정책일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
202021-10-01790630914999999999999999일반휴게음식10019경기도김포시통진읍37.691126.597<NA>N0
212021-10-01700005121140000018000일반휴게음식10387경기도고양시 일산서구주엽동37.669126.759고양페이카드Y28000
222021-10-01790631132999999999999999건축자재18531경기도화성시팔탄면37.138126.884<NA>N0
232021-10-01700005413999999999999999학원12502경기도양평군서종면37.622127.353<NA>N0
242021-10-01790631378140000099000유통업 영리11698경기도의정부시의정부동37.74127.051의정부사랑카드(통합)Y9300
252021-10-01700005788999999999999999레저업소17052경기도용인시 처인구김량장동37.235127.202<NA>N0
262021-10-01700005536999999999999999보건위생18442경기도화성시반송동37.193127.073<NA>N0
272021-10-01790631854999999999999999일반휴게음식10364경기도고양시 일산동구장항동37.661126.764<NA>N0
282021-10-01700006614140000046000일반휴게음식17128경기도용인시 처인구이동읍37.194127.206용인와이페이Y8300
292021-10-01790632188999999999999999일반휴게음식13118경기도성남시 수정구복정동37.457127.126<NA>N0