Overview

Dataset statistics

Number of variables13
Number of observations30
Missing cells29
Missing cells (%)7.4%
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/2c2e5a85-31ad-404d-b433-b9584fd73c59

Alerts

정책일간결제일자 has constant value ""Constant
시도명 has constant value ""Constant
결제상품명 has constant value ""Constant
가맹점우편번호 is highly overall correlated with 위도High correlation
위도 is highly overall correlated with 가맹점우편번호High correlation
결제상품ID is highly imbalanced (78.9%)Imbalance
사용여부 is highly imbalanced (78.9%)Imbalance
결제금액 is highly imbalanced (78.9%)Imbalance
결제상품명 has 29 (96.7%) missing valuesMissing
가맹점번호 has unique valuesUnique
가맹점우편번호 has unique valuesUnique
위도 has unique valuesUnique

Reproduction

Analysis started2024-03-13 11:59:09.760848
Analysis finished2024-03-13 11:59:12.364476
Duration2.6 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
Minimum2021-12-01 00:00:00
Maximum2021-12-01 00:00:00
2024-03-13T20:59:12.418084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:12.514311image/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%
Mean7.5170636 × 108
Minimum7.0000062 × 108
Maximum7.9125381 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:12.619310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0000062 × 108
5-th percentile7.0000088 × 108
Q17.0000231 × 108
median7.9123111 × 108
Q37.9124857 × 108
95-th percentile7.9125228 × 108
Maximum7.9125381 × 108
Range91253192
Interquartile range (IQR)91246257

Descriptive statistics

Standard deviation45987083
Coefficient of variation (CV)0.061176925
Kurtosis-2.0620555
Mean7.5170636 × 108
Median Absolute Deviation (MAD)21128.5
Skewness-0.28344273
Sum2.2551191 × 1010
Variance2.1148118 × 1015
MonotonicityNot monotonic
2024-03-13T20:59:12.750468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
791243290 1
 
3.3%
791249802 1
 
3.3%
700003495 1
 
3.3%
791253811 1
 
3.3%
700003308 1
 
3.3%
791252631 1
 
3.3%
700002953 1
 
3.3%
791251851 1
 
3.3%
700002523 1
 
3.3%
791250531 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
700000619 1
3.3%
700000635 1
3.3%
700001186 1
3.3%
700001218 1
3.3%
700001640 1
3.3%
700001826 1
3.3%
700002158 1
3.3%
700002272 1
3.3%
700002442 1
3.3%
700002523 1
3.3%
ValueCountFrequency (%)
791253811 1
3.3%
791252631 1
3.3%
791251851 1
3.3%
791250531 1
3.3%
791250466 1
3.3%
791249878 1
3.3%
791249802 1
3.3%
791249130 1
3.3%
791246897 1
3.3%
791246660 1
3.3%

결제상품ID
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
999999999999999
29 
140000034000
 
1

Length

Max length15
Median length15
Mean length14.9
Min length12

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
999999999999999 29
96.7%
140000034000 1
 
3.3%

Length

2024-03-13T20:59:12.885553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:59:12.995291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
999999999999999 29
96.7%
140000034000 1
 
3.3%

가맹점업종명
Categorical

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
일반휴게음식
11 
의류
3 
서적문구
2 
레져용품
2 
음료식품
2 
Other values (9)
10 

Length

Max length8
Median length6
Mean length4.7666667
Min length2

Unique

Unique8 ?
Unique (%)26.7%

Sample

1st row의류
2nd row일반휴게음식
3rd row일반휴게음식
4th row일반휴게음식
5th row일반휴게음식

Common Values

ValueCountFrequency (%)
일반휴게음식 11
36.7%
의류 3
 
10.0%
서적문구 2
 
6.7%
레져용품 2
 
6.7%
음료식품 2
 
6.7%
자동차정비 유지 2
 
6.7%
보건위생 1
 
3.3%
문화.취미 1
 
3.3%
학원 1
 
3.3%
숙박업 1
 
3.3%
Other values (4) 4
 
13.3%

Length

2024-03-13T20:59:13.132083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반휴게음식 11
34.4%
의류 3
 
9.4%
서적문구 2
 
6.2%
레져용품 2
 
6.2%
음료식품 2
 
6.2%
자동차정비 2
 
6.2%
유지 2
 
6.2%
보건위생 1
 
3.1%
문화.취미 1
 
3.1%
학원 1
 
3.1%
Other values (5) 5
15.6%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13633.233
Minimum10079
Maximum17944
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:13.277563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10079
5-th percentile10392.25
Q111707.75
median13566.5
Q314678
95-th percentile17159.05
Maximum17944
Range7865
Interquartile range (IQR)2970.25

Descriptive statistics

Standard deviation2284.7271
Coefficient of variation (CV)0.16758512
Kurtosis-0.84367179
Mean13633.233
Median Absolute Deviation (MAD)1374.5
Skewness0.16982689
Sum408997
Variance5219977.8
MonotonicityNot monotonic
2024-03-13T20:59:13.412388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
16869 1
 
3.3%
14564 1
 
3.3%
13536 1
 
3.3%
12964 1
 
3.3%
13136 1
 
3.3%
13461 1
 
3.3%
12271 1
 
3.3%
11520 1
 
3.3%
14710 1
 
3.3%
16977 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
10079 1
3.3%
10345 1
3.3%
10450 1
3.3%
10468 1
3.3%
10929 1
3.3%
11006 1
3.3%
11106 1
3.3%
11520 1
3.3%
12271 1
3.3%
12571 1
3.3%
ValueCountFrequency (%)
17944 1
3.3%
17308 1
3.3%
16977 1
3.3%
16930 1
3.3%
16869 1
3.3%
16502 1
3.3%
15020 1
3.3%
14710 1
3.3%
14582 1
3.3%
14564 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

2024-03-13T20:59:13.564131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:59:13.651161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Text

Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:59:13.807660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7.5
Mean length4.9666667
Min length3

Characters and Unicode

Total characters149
Distinct characters40
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

Unique18 ?
Unique (%)60.0%

Sample

1st row용인시 수지구
2nd row고양시 덕양구
3rd row포천시
4th row양평군
5th row파주시
ValueCountFrequency (%)
성남시 5
 
11.4%
부천시 4
 
9.1%
분당구 4
 
9.1%
용인시 3
 
6.8%
고양시 3
 
6.8%
수지구 2
 
4.5%
안양시 2
 
4.5%
만안구 2
 
4.5%
기흥구 1
 
2.3%
연천군 1
 
2.3%
Other values (17) 17
38.6%
2024-03-13T20:59:14.185906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
19.5%
14
 
9.4%
구 14
 
9.4%
양 9
 
6.0%
천 7
 
4.7%
남 7
 
4.7%
성 5
 
3.4%
안 4
 
2.7%
분 4
 
2.7%
당 4
 
2.7%
Other values (30) 52
34.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 135
90.6%
Space Separator 14
 
9.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
21.5%
구 14
 
10.4%
양 9
 
6.7%
천 7
 
5.2%
남 7
 
5.2%
성 5
 
3.7%
안 4
 
3.0%
분 4
 
3.0%
당 4
 
3.0%
부 4
 
3.0%
Other values (29) 48
35.6%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 135
90.6%
Common 14
 
9.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
21.5%
구 14
 
10.4%
양 9
 
6.7%
천 7
 
5.2%
남 7
 
5.2%
성 5
 
3.7%
안 4
 
3.0%
분 4
 
3.0%
당 4
 
3.0%
부 4
 
3.0%
Other values (29) 48
35.6%
Common
ValueCountFrequency (%)
14
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 135
90.6%
ASCII 14
 
9.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
21.5%
구 14
 
10.4%
양 9
 
6.7%
천 7
 
5.2%
남 7
 
5.2%
성 5
 
3.7%
안 4
 
3.0%
분 4
 
3.0%
당 4
 
3.0%
부 4
 
3.0%
Other values (29) 48
35.6%
ASCII
ValueCountFrequency (%)
14
100.0%

읍면동명
Text

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:59:14.368052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9666667
Min length2

Characters and Unicode

Total characters89
Distinct characters49
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

Unique28 ?
Unique (%)93.3%

Sample

1st row죽전동
2nd row성사동
3rd row영북면
4th row강상면
5th row금촌동
ValueCountFrequency (%)
박달동 2
 
6.7%
죽전동 1
 
3.3%
원미동 1
 
3.3%
천현동 1
 
3.3%
양지동 1
 
3.3%
운중동 1
 
3.3%
와부읍 1
 
3.3%
장흥면 1
 
3.3%
심곡본동 1
 
3.3%
구갈동 1
 
3.3%
Other values (19) 19
63.3%
2024-03-13T20:59:14.709478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 24
27.0%
면 4
 
4.5%
현 4
 
4.5%
중 3
 
3.4%
박 2
 
2.2%
원 2
 
2.2%
달 2
 
2.2%
사 2
 
2.2%
백 2
 
2.2%
장 2
 
2.2%
Other values (39) 42
47.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 24
27.0%
면 4
 
4.5%
현 4
 
4.5%
중 3
 
3.4%
박 2
 
2.2%
원 2
 
2.2%
달 2
 
2.2%
사 2
 
2.2%
백 2
 
2.2%
장 2
 
2.2%
Other values (39) 42
47.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 24
27.0%
면 4
 
4.5%
현 4
 
4.5%
중 3
 
3.4%
박 2
 
2.2%
원 2
 
2.2%
달 2
 
2.2%
사 2
 
2.2%
백 2
 
2.2%
장 2
 
2.2%
Other values (39) 42
47.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 24
27.0%
면 4
 
4.5%
현 4
 
4.5%
중 3
 
3.4%
박 2
 
2.2%
원 2
 
2.2%
달 2
 
2.2%
사 2
 
2.2%
백 2
 
2.2%
장 2
 
2.2%
Other values (39) 42
47.2%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.499633
Minimum36.979
Maximum38.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:14.841950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.979
5-th percentile37.2728
Q137.37825
median37.482
Q337.62725
95-th percentile37.93865
Maximum38.09
Range1.111
Interquartile range (IQR)0.249

Descriptive statistics

Standard deviation0.2263262
Coefficient of variation (CV)0.0060354245
Kurtosis2.0238862
Mean37.499633
Median Absolute Deviation (MAD)0.113
Skewness0.81462667
Sum1124.989
Variance0.051223551
MonotonicityNot monotonic
2024-03-13T20:59:14.984752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
37.329 1
 
3.3%
37.493 1
 
3.3%
37.385 1
 
3.3%
37.532 1
 
3.3%
37.459 1
 
3.3%
37.392 1
 
3.3%
37.58 1
 
3.3%
37.715 1
 
3.3%
37.483 1
 
3.3%
37.271 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
36.979 1
3.3%
37.271 1
3.3%
37.275 1
3.3%
37.297 1
3.3%
37.306 1
3.3%
37.329 1
3.3%
37.362 1
3.3%
37.376 1
3.3%
37.385 1
3.3%
37.392 1
3.3%
ValueCountFrequency (%)
38.09 1
3.3%
38.084 1
3.3%
37.761 1
3.3%
37.715 1
3.3%
37.693 1
3.3%
37.653 1
3.3%
37.645 1
3.3%
37.643 1
3.3%
37.58 1
3.3%
37.532 1
3.3%

경도
Real number (ℝ)

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.99347
Minimum126.673
Maximum127.476
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:15.103024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.673
5-th percentile126.74935
Q1126.7985
median126.9845
Q3127.1145
95-th percentile127.348
Maximum127.476
Range0.803
Interquartile range (IQR)0.316

Descriptive statistics

Standard deviation0.20726291
Coefficient of variation (CV)0.0016320754
Kurtosis-0.41514618
Mean126.99347
Median Absolute Deviation (MAD)0.1585
Skewness0.49743756
Sum3809.804
Variance0.042957913
MonotonicityNot monotonic
2024-03-13T20:59:15.251167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
127.11 2
 
6.7%
127.107 1
 
3.3%
126.793 1
 
3.3%
127.211 1
 
3.3%
127.163 1
 
3.3%
127.076 1
 
3.3%
127.217 1
 
3.3%
126.949 1
 
3.3%
126.783 1
 
3.3%
127.127 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
126.673 1
3.3%
126.73 1
3.3%
126.773 1
3.3%
126.774 1
3.3%
126.776 1
3.3%
126.783 1
3.3%
126.789 1
3.3%
126.793 1
3.3%
126.815 1
3.3%
126.837 1
3.3%
ValueCountFrequency (%)
127.476 1
3.3%
127.411 1
3.3%
127.271 1
3.3%
127.217 1
3.3%
127.211 1
3.3%
127.163 1
3.3%
127.127 1
3.3%
127.116 1
3.3%
127.11 2
6.7%
127.107 1
3.3%

결제상품명
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing29
Missing (%)96.7%
Memory size372.0 B
2024-03-13T20:59:15.405573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6
Distinct characters6
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

Unique1 ?
Unique (%)100.0%

Sample

1st row안양사랑페이
ValueCountFrequency (%)
안양사랑페이 1
100.0%
2024-03-13T20:59:15.656422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
안 1
16.7%
양 1
16.7%
사 1
16.7%
랑 1
16.7%
페 1
16.7%
이 1
16.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
안 1
16.7%
양 1
16.7%
사 1
16.7%
랑 1
16.7%
페 1
16.7%
이 1
16.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
안 1
16.7%
양 1
16.7%
사 1
16.7%
랑 1
16.7%
페 1
16.7%
이 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
안 1
16.7%
양 1
16.7%
사 1
16.7%
랑 1
16.7%
페 1
16.7%
이 1
16.7%

사용여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
29 
True
 
1
ValueCountFrequency (%)
False 29
96.7%
True 1
 
3.3%
2024-03-13T20:59:15.765045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
29 
30000
 
1

Length

Max length5
Median length1
Mean length1.1333333
Min length1

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
0 29
96.7%
30000 1
 
3.3%

Length

2024-03-13T20:59:15.880098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:59:15.993730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 29
96.7%
30000 1
 
3.3%

Interactions

2024-03-13T20:59:11.291177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:10.200224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:10.586950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:10.931363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:11.430533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:10.292320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:10.668342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:11.004574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:11.535961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:10.392712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:10.757690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:11.102602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:11.643293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:10.483739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:10.836540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:11.195397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:59:16.060695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호결제상품ID가맹점업종명가맹점우편번호시군구명읍면동명위도경도사용여부결제금액
가맹점번호1.0000.0000.2200.0000.0000.0000.4540.0000.0000.000
결제상품ID0.0001.0000.0000.0000.0000.0000.0000.0000.6550.655
가맹점업종명0.2200.0001.0000.5000.7470.9270.5300.4640.0000.000
가맹점우편번호0.0000.0000.5001.0001.0001.0000.8560.9030.0000.000
시군구명0.0000.0000.7471.0001.0001.0000.9911.0000.0000.000
읍면동명0.0000.0000.9271.0001.0001.0001.0001.0000.0000.000
위도0.4540.0000.5300.8560.9911.0001.0000.6330.0000.000
경도0.0000.0000.4640.9031.0001.0000.6331.0000.0000.000
사용여부0.0000.6550.0000.0000.0000.0000.0000.0001.0000.655
결제금액0.0000.6550.0000.0000.0000.0000.0000.0000.6551.000
2024-03-13T20:59:16.214070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
결제상품ID사용여부결제금액가맹점업종명
결제상품ID1.0000.4540.4540.000
사용여부0.4541.0000.4540.000
결제금액0.4540.4541.0000.000
가맹점업종명0.0000.0000.0001.000
2024-03-13T20:59:16.360262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점우편번호위도경도결제상품ID가맹점업종명사용여부결제금액
가맹점번호1.0000.0990.091-0.1300.0000.0000.0000.000
가맹점우편번호0.0991.000-0.8390.1460.0000.1740.0000.000
위도0.091-0.8391.000-0.2800.0000.1820.0000.000
경도-0.1300.146-0.2801.0000.0000.1260.0000.000
결제상품ID0.0000.0000.0000.0001.0000.0000.4540.454
가맹점업종명0.0000.1740.1820.1260.0001.0000.0000.000
사용여부0.0000.0000.0000.0000.4540.0001.0000.454
결제금액0.0000.0000.0000.0000.4540.0000.4541.000

Missing values

2024-03-13T20:59:12.117060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:59:12.287384image/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
02021-12-01791243290999999999999999의류16869경기도용인시 수지구죽전동37.329127.107<NA>N0
12021-12-01791218650999999999999999일반휴게음식10468경기도고양시 덕양구성사동37.653126.837<NA>N0
22021-12-01791244344999999999999999일반휴게음식11106경기도포천시영북면38.09127.271<NA>N0
32021-12-01700000619999999999999999일반휴게음식12571경기도양평군강상면37.489127.476<NA>N0
42021-12-01791245710999999999999999일반휴게음식10929경기도파주시금촌동37.761126.774<NA>N0
52021-12-01700000635999999999999999보건위생14004경기도안양시 만안구박달동37.402126.912<NA>N0
62021-12-01791245728999999999999999문화.취미17944경기도평택시안중읍36.979126.919<NA>N0
72021-12-01791218935999999999999999학원10345경기도고양시 일산서구탄현동37.693126.773<NA>N0
82021-12-01791246311999999999999999숙박업14202경기도광명시광명동37.481126.854<NA>N0
92021-12-01700001186999999999999999의류13493경기도성남시 분당구삼평동37.401127.11<NA>N0
정책일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
202021-12-01791250466999999999999999일반휴게음식14582경기도부천시중동37.494126.776<NA>N0
212021-12-01700002442999999999999999레져용품10079경기도김포시장기동37.645126.673<NA>N0
222021-12-01791250531999999999999999서적문구16977경기도용인시 기흥구구갈동37.271127.127<NA>N0
232021-12-01700002523999999999999999레저업소14710경기도부천시심곡본동37.483126.783<NA>N0
242021-12-01791251851999999999999999음료식품11520경기도양주시장흥면37.715126.949<NA>N0
252021-12-01700002953999999999999999음료식품12271경기도남양주시와부읍37.58127.217<NA>N0
262021-12-01791252631999999999999999자동차정비 유지13461경기도성남시 분당구운중동37.392127.076<NA>N0
272021-12-01700003308999999999999999자동차정비 유지13136경기도성남시 수정구양지동37.459127.163<NA>N0
282021-12-01791253811999999999999999일반휴게음식12964경기도하남시천현동37.532127.211<NA>N0
292021-12-01700003495999999999999999레져용품13536경기도성남시 분당구백현동37.385127.11<NA>N0