Overview

Dataset statistics

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

Variable types

DateTime1
Numeric4
Categorical4
Text3
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/8b433659-12ec-46a6-8378-63d51da86fae

Alerts

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

Reproduction

Analysis started2023-12-10 14:02:24.645864
Analysis finished2023-12-10 14:02:28.459391
Duration3.81 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
Minimum2021-05-01 00:00:00
Maximum2021-05-01 00:00:00
2023-12-10T23:02:28.516276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:28.680398image/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%
Mean7.0001735 × 108
Minimum7.0000279 × 108
Maximum7.0002909 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:02:28.867837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0000279 × 108
5-th percentile7.0000438 × 108
Q17.0001213 × 108
median7.0001782 × 108
Q37.0002306 × 108
95-th percentile7.0002825 × 108
Maximum7.0002909 × 108
Range26299
Interquartile range (IQR)10927.5

Descriptive statistics

Standard deviation7419.775
Coefficient of variation (CV)1.0599416 × 10-5
Kurtosis-0.70454615
Mean7.0001735 × 108
Median Absolute Deviation (MAD)5599.5
Skewness-0.18531634
Sum2.1000521 × 1010
Variance55053061
MonotonicityNot monotonic
2023-12-10T23:02:29.069427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
700004075 1
 
3.3%
700018385 1
 
3.3%
700029090 1
 
3.3%
700028620 1
 
3.3%
700027788 1
 
3.3%
700026165 1
 
3.3%
700027721 1
 
3.3%
700025209 1
 
3.3%
700024579 1
 
3.3%
700023232 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
700002791 1
3.3%
700004075 1
3.3%
700004743 1
3.3%
700008110 1
3.3%
700010494 1
3.3%
700011471 1
3.3%
700011782 1
3.3%
700012040 1
3.3%
700012404 1
3.3%
700013174 1
3.3%
ValueCountFrequency (%)
700029090 1
3.3%
700028620 1
3.3%
700027788 1
3.3%
700027721 1
3.3%
700026165 1
3.3%
700025209 1
3.3%
700024579 1
3.3%
700023232 1
3.3%
700022538 1
3.3%
700020819 1
3.3%

결제상품ID
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
999999999999999
28 
140000077000
 
1
140000102000
 
1

Length

Max length15
Median length15
Mean length14.8
Min length12

Unique

Unique2 ?
Unique (%)6.7%

Sample

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

Common Values

ValueCountFrequency (%)
999999999999999 28
93.3%
140000077000 1
 
3.3%
140000102000 1
 
3.3%

Length

2023-12-10T23:02:29.257076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:29.426920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
999999999999999 28
93.3%
140000077000 1
 
3.3%
140000102000 1
 
3.3%

가맹점업종명
Categorical

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
일반휴게음식
8 
학원
4 
보건위생
4 
의류
2 
건축자재
2 
Other values (9)
10 

Length

Max length6
Median length5
Mean length4.2
Min length2

Unique

Unique8 ?
Unique (%)26.7%

Sample

1st row의류
2nd row의류
3rd row레저업소
4th row일반휴게음식
5th row문화.취미

Common Values

ValueCountFrequency (%)
일반휴게음식 8
26.7%
학원 4
13.3%
보건위생 4
13.3%
의류 2
 
6.7%
건축자재 2
 
6.7%
유통업 영리 2
 
6.7%
레저업소 1
 
3.3%
문화.취미 1
 
3.3%
가구 1
 
3.3%
숙박업 1
 
3.3%
Other values (4) 4
13.3%

Length

2023-12-10T23:02:29.607640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반휴게음식 8
25.0%
학원 4
12.5%
보건위생 4
12.5%
의류 2
 
6.2%
건축자재 2
 
6.2%
유통업 2
 
6.2%
영리 2
 
6.2%
레저업소 1
 
3.1%
문화.취미 1
 
3.1%
가구 1
 
3.1%
Other values (5) 5
15.6%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15056
Minimum10298
Maximum18258
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:02:29.810077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10298
5-th percentile11372.05
Q113262.75
median15371.5
Q316939.25
95-th percentile17819.85
Maximum18258
Range7960
Interquartile range (IQR)3676.5

Descriptive statistics

Standard deviation2264.5143
Coefficient of variation (CV)0.1504061
Kurtosis-0.9214401
Mean15056
Median Absolute Deviation (MAD)1662.5
Skewness-0.51712159
Sum451680
Variance5128024.8
MonotonicityNot monotonic
2023-12-10T23:02:29.990031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
14548 2
 
6.7%
15382 1
 
3.3%
17128 1
 
3.3%
15361 1
 
3.3%
11443 1
 
3.3%
16953 1
 
3.3%
16862 1
 
3.3%
10298 1
 
3.3%
16988 1
 
3.3%
17369 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
10298 1
3.3%
11314 1
3.3%
11443 1
3.3%
11698 1
3.3%
12174 1
3.3%
12562 1
3.3%
13166 1
3.3%
13248 1
3.3%
13307 1
3.3%
14103 1
3.3%
ValueCountFrequency (%)
18258 1
3.3%
17868 1
3.3%
17761 1
3.3%
17369 1
3.3%
17128 1
3.3%
17080 1
3.3%
16988 1
3.3%
16953 1
3.3%
16898 1
3.3%
16862 1
3.3%

시도명
Categorical

HIGH CORRELATION  IMBALANCE 

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

Length

Max length4
Median length3
Mean length3.0666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2023-12-10T23:02:30.195780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:30.340412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 28
93.3%
none 2
 
6.7%

시군구명
Text

MISSING 

Distinct20
Distinct (%)71.4%
Missing2
Missing (%)6.7%
Memory size372.0 B
2023-12-10T23:02:30.563900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length5.3928571
Min length3

Characters and Unicode

Total characters151
Distinct characters39
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)53.6%

Sample

1st row동두천시
2nd row평택시
3rd row화성시
4th row성남시 수정구
5th row의정부시
ValueCountFrequency (%)
용인시 5
 
11.4%
수원시 4
 
9.1%
기흥구 4
 
9.1%
팔달구 3
 
6.8%
성남시 3
 
6.8%
안산시 2
 
4.5%
평택시 2
 
4.5%
중원구 2
 
4.5%
부천시 2
 
4.5%
남양주시 1
 
2.3%
Other values (16) 16
36.4%
2023-12-10T23:02:31.091991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 27
17.9%
16
 
10.6%
구 16
 
10.6%
원 7
 
4.6%
인 6
 
4.0%
양 6
 
4.0%
용 5
 
3.3%
수 5
 
3.3%
기 4
 
2.6%
흥 4
 
2.6%
Other values (29) 55
36.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 135
89.4%
Space Separator 16
 
10.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 27
20.0%
구 16
 
11.9%
원 7
 
5.2%
인 6
 
4.4%
양 6
 
4.4%
용 5
 
3.7%
수 5
 
3.7%
기 4
 
3.0%
흥 4
 
3.0%
안 4
 
3.0%
Other values (28) 51
37.8%
Space Separator
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 135
89.4%
Common 16
 
10.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 27
20.0%
구 16
 
11.9%
원 7
 
5.2%
인 6
 
4.4%
양 6
 
4.4%
용 5
 
3.7%
수 5
 
3.7%
기 4
 
3.0%
흥 4
 
3.0%
안 4
 
3.0%
Other values (28) 51
37.8%
Common
ValueCountFrequency (%)
16
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 135
89.4%
ASCII 16
 
10.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 27
20.0%
구 16
 
11.9%
원 7
 
5.2%
인 6
 
4.4%
양 6
 
4.4%
용 5
 
3.7%
수 5
 
3.7%
기 4
 
3.0%
흥 4
 
3.0%
안 4
 
3.0%
Other values (28) 51
37.8%
ASCII
ValueCountFrequency (%)
16
100.0%

읍면동명
Text

MISSING 

Distinct25
Distinct (%)89.3%
Missing2
Missing (%)6.7%
Memory size372.0 B
2023-12-10T23:02:31.367103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3
Min length2

Characters and Unicode

Total characters84
Distinct characters43
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

Unique23 ?
Unique (%)82.1%

Sample

1st row생연동
2nd row용이동
3rd row남양읍
4th row태평동
5th row의정부동
ValueCountFrequency (%)
중동 3
 
10.7%
인계동 2
 
7.1%
팔달로2가 1
 
3.6%
평촌동 1
 
3.6%
덕계동 1
 
3.6%
영덕동 1
 
3.6%
주교동 1
 
3.6%
창전동 1
 
3.6%
권선동 1
 
3.6%
화도읍 1
 
3.6%
Other values (15) 15
53.6%
2023-12-10T23:02:31.852871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 24
28.6%
중 4
 
4.8%
읍 4
 
4.8%
계 3
 
3.6%
평 3
 
3.6%
광 2
 
2.4%
부 2
 
2.4%
덕 2
 
2.4%
양 2
 
2.4%
이 2
 
2.4%
Other values (33) 36
42.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 83
98.8%
Decimal Number 1
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 24
28.9%
중 4
 
4.8%
읍 4
 
4.8%
계 3
 
3.6%
평 3
 
3.6%
광 2
 
2.4%
부 2
 
2.4%
덕 2
 
2.4%
양 2
 
2.4%
이 2
 
2.4%
Other values (32) 35
42.2%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 83
98.8%
Common 1
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 24
28.9%
중 4
 
4.8%
읍 4
 
4.8%
계 3
 
3.6%
평 3
 
3.6%
광 2
 
2.4%
부 2
 
2.4%
덕 2
 
2.4%
양 2
 
2.4%
이 2
 
2.4%
Other values (32) 35
42.2%
Common
ValueCountFrequency (%)
2 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 83
98.8%
ASCII 1
 
1.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 24
28.9%
중 4
 
4.8%
읍 4
 
4.8%
계 3
 
3.6%
평 3
 
3.6%
광 2
 
2.4%
부 2
 
2.4%
덕 2
 
2.4%
양 2
 
2.4%
이 2
 
2.4%
Other values (32) 35
42.2%
ASCII
ValueCountFrequency (%)
2 1
100.0%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.903
Minimum0
Maximum37.905
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:02:32.054838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.6473
Q137.26225
median37.3185
Q337.48125
95-th percentile37.78725
Maximum37.905
Range37.905
Interquartile range (IQR)0.219

Descriptive statistics

Standard deviation9.4899041
Coefficient of variation (CV)0.27189365
Kurtosis12.192362
Mean34.903
Median Absolute Deviation (MAD)0.125
Skewness-3.6570331
Sum1047.09
Variance90.05828
MonotonicityNot monotonic
2023-12-10T23:02:32.236580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.0 2
 
6.7%
37.276 2
 
6.7%
37.501 2
 
6.7%
37.278 1
 
3.3%
37.318 1
 
3.3%
37.821 1
 
3.3%
37.277 1
 
3.3%
37.662 1
 
3.3%
37.28 1
 
3.3%
37.262 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
0.0 2
6.7%
36.994 1
3.3%
37.078 1
3.3%
37.203 1
3.3%
37.212 1
3.3%
37.251 1
3.3%
37.262 1
3.3%
37.263 1
3.3%
37.276 2
6.7%
37.277 1
3.3%
ValueCountFrequency (%)
37.905 1
3.3%
37.821 1
3.3%
37.746 1
3.3%
37.662 1
3.3%
37.654 1
3.3%
37.501 2
6.7%
37.483 1
3.3%
37.476 1
3.3%
37.45 1
3.3%
37.444 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.5836
Minimum0
Maximum127.5
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:02:32.462914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile57.04695
Q1126.8555
median127.0495
Q3127.13525
95-th percentile127.37955
Maximum127.5
Range127.5
Interquartile range (IQR)0.27975

Descriptive statistics

Standard deviation32.235068
Coefficient of variation (CV)0.27183411
Kurtosis12.205739
Mean118.5836
Median Absolute Deviation (MAD)0.0995
Skewness-3.6598135
Sum3557.508
Variance1039.0996
MonotonicityNot monotonic
2023-12-10T23:02:32.663072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 2
 
6.7%
127.058 1
 
3.3%
126.838 1
 
3.3%
127.048 1
 
3.3%
127.073 1
 
3.3%
126.837 1
 
3.3%
127.144 1
 
3.3%
127.443 1
 
3.3%
127.026 1
 
3.3%
126.771 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0.0 2
6.7%
126.771 1
3.3%
126.772 1
3.3%
126.824 1
3.3%
126.837 1
3.3%
126.838 1
3.3%
126.853 1
3.3%
126.863 1
3.3%
126.96 1
3.3%
127.016 1
3.3%
ValueCountFrequency (%)
127.5 1
3.3%
127.443 1
3.3%
127.302 1
3.3%
127.208 1
3.3%
127.166 1
3.3%
127.154 1
3.3%
127.144 1
3.3%
127.138 1
3.3%
127.127 1
3.3%
127.115 1
3.3%

결제상품명
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing28
Missing (%)93.3%
Memory size372.0 B
2023-12-10T23:02:32.925603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9.5
Mean length9.5
Min length8

Characters and Unicode

Total characters19
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row안양사랑상품권(통합)
2nd row수원페이(통합)
ValueCountFrequency (%)
안양사랑상품권(통합 1
50.0%
수원페이(통합 1
50.0%
2023-12-10T23:02:33.355363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 2
 
10.5%
통 2
 
10.5%
합 2
 
10.5%
) 2
 
10.5%
안 1
 
5.3%
양 1
 
5.3%
사 1
 
5.3%
랑 1
 
5.3%
상 1
 
5.3%
품 1
 
5.3%
Other values (5) 5
26.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15
78.9%
Open Punctuation 2
 
10.5%
Close Punctuation 2
 
10.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
통 2
13.3%
합 2
13.3%
안 1
 
6.7%
양 1
 
6.7%
사 1
 
6.7%
랑 1
 
6.7%
상 1
 
6.7%
품 1
 
6.7%
권 1
 
6.7%
수 1
 
6.7%
Other values (3) 3
20.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15
78.9%
Common 4
 
21.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
통 2
13.3%
합 2
13.3%
안 1
 
6.7%
양 1
 
6.7%
사 1
 
6.7%
랑 1
 
6.7%
상 1
 
6.7%
품 1
 
6.7%
권 1
 
6.7%
수 1
 
6.7%
Other values (3) 3
20.0%
Common
ValueCountFrequency (%)
( 2
50.0%
) 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15
78.9%
ASCII 4
 
21.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 2
50.0%
) 2
50.0%
Hangul
ValueCountFrequency (%)
통 2
13.3%
합 2
13.3%
안 1
 
6.7%
양 1
 
6.7%
사 1
 
6.7%
랑 1
 
6.7%
상 1
 
6.7%
품 1
 
6.7%
권 1
 
6.7%
수 1
 
6.7%
Other values (3) 3
20.0%

사용여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
28 
True
 
2
ValueCountFrequency (%)
False 28
93.3%
True 2
 
6.7%
2023-12-10T23:02:33.518949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
28 
261000
 
1
11000
 
1

Length

Max length6
Median length1
Mean length1.3
Min length1

Unique

Unique2 ?
Unique (%)6.7%

Sample

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

Common Values

ValueCountFrequency (%)
0 28
93.3%
261000 1
 
3.3%
11000 1
 
3.3%

Length

2023-12-10T23:02:33.680187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:33.823463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28
93.3%
261000 1
 
3.3%
11000 1
 
3.3%

Interactions

2023-12-10T23:02:27.435813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:25.666516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:26.296326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:26.861997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:27.532666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:25.781045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:26.430343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:26.989399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:27.658236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:25.915762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:26.570706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:27.144509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:27.779628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:26.171346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:26.709756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:27.292203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:02:33.935905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
가맹점번호1.0000.0000.6880.0000.3670.0000.5530.3670.367NaN0.5070.000
결제상품ID0.0001.0000.0000.5980.0000.0001.0000.0000.0000.0001.0001.000
가맹점업종명0.6880.0001.0000.0000.2990.0000.6770.2990.2990.0000.0000.000
가맹점우편번호0.0000.5980.0001.0000.0001.0000.9580.0000.0000.0000.2700.598
시도명0.3670.0000.2990.0001.000NaNNaN0.9060.906NaN0.0000.000
시군구명0.0000.0000.0001.000NaN1.0000.993NaNNaN0.0000.0000.000
읍면동명0.5531.0000.6770.958NaN0.9931.000NaNNaN0.0001.0001.000
위도0.3670.0000.2990.0000.906NaNNaN1.0000.906NaN0.0000.000
경도0.3670.0000.2990.0000.906NaNNaN0.9061.000NaN0.0000.000
결제상품명NaN0.0000.0000.000NaN0.0000.000NaNNaN1.000NaN0.000
사용여부0.5071.0000.0000.2700.0000.0001.0000.0000.000NaN1.0001.000
결제금액0.0001.0000.0000.5980.0000.0001.0000.0000.0000.0001.0001.000
2023-12-10T23:02:34.167371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
결제상품ID시도명사용여부가맹점업종명결제금액
결제상품ID1.0000.0000.9820.0001.000
시도명0.0001.0000.0000.1290.000
사용여부0.9820.0001.0000.0000.982
가맹점업종명0.0000.1290.0001.0000.000
결제금액1.0000.0000.9820.0001.000
2023-12-10T23:02:34.632318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점우편번호위도경도결제상품ID가맹점업종명시도명사용여부결제금액
가맹점번호1.0000.0480.043-0.0890.0000.3290.1010.4420.000
가맹점우편번호0.0481.000-0.8630.0380.2130.0000.0000.1440.213
위도0.043-0.8631.0000.0940.0000.1290.7210.0000.000
경도-0.0890.0380.0941.0000.0000.1290.7210.0000.000
결제상품ID0.0000.2130.0000.0001.0000.0000.0000.9821.000
가맹점업종명0.3290.0000.1290.1290.0001.0000.1290.0000.000
시도명0.1010.0000.7210.7210.0000.1291.0000.0000.000
사용여부0.4420.1440.0000.0000.9820.0000.0001.0000.982
결제금액0.0000.2130.0000.0001.0000.0000.0000.9821.000

Missing values

2023-12-10T23:02:27.954336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:02:28.228137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-10T23:02:28.380750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

정책일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
02021-05-01700004075999999999999999의류15382NONE<NA><NA>0.00.0<NA>N0
12021-05-01700002791999999999999999의류11314경기도동두천시생연동37.905127.058<NA>N0
22021-05-01700004743999999999999999레저업소17868경기도평택시용이동36.994127.138<NA>N0
32021-05-01700010494999999999999999일반휴게음식18258경기도화성시남양읍37.212126.824<NA>N0
42021-05-01700008110999999999999999문화.취미13307경기도성남시 수정구태평동37.444127.127<NA>N0
52021-05-01700011471999999999999999가구11698경기도의정부시의정부동37.746127.051<NA>N0
62021-05-01700011782999999999999999학원15303경기도안산시 상록구부곡동37.333126.863<NA>N0
72021-05-01700012040999999999999999숙박업16471경기도수원시 팔달구인계동37.276127.029<NA>N0
82021-05-01700012404999999999999999보건위생13166경기도성남시 중원구금광동37.45127.166<NA>N0
92021-05-01700013174999999999999999학원17080경기도용인시 기흥구보라동37.251127.105<NA>N0
정책일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
202021-05-01700020819999999999999999일반휴게음식12174경기도남양주시화도읍37.654127.302<NA>N0
212021-05-01700022538999999999999999유통업 영리14548경기도부천시중동37.501126.771<NA>N0
222021-05-01700023232999999999999999전기제품16571경기도수원시 권선구권선동37.262127.026<NA>N0
232021-05-01700024579999999999999999보건위생17369경기도이천시창전동37.28127.443<NA>N0
242021-05-01700025209999999999999999일반휴게음식16988경기도용인시 기흥구중동37.276127.144<NA>N0
252021-05-01700027721999999999999999일반휴게음식10298경기도고양시 덕양구주교동37.662126.837<NA>N0
262021-05-01700026165999999999999999건축자재16862NONE<NA><NA>0.00.0<NA>N0
272021-05-01700027788999999999999999광학제품16953경기도용인시 기흥구영덕동37.277127.073<NA>N0
282021-05-01700028620999999999999999일반휴게음식11443경기도양주시덕계동37.821127.048<NA>N0
292021-05-01700029090999999999999999일반휴게음식15361경기도안산시 단원구고잔동37.318126.838<NA>N0