Overview

Dataset statistics

Number of variables13
Number of observations30
Missing cells34
Missing cells (%)8.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory115.4 B

Variable types

DateTime1
Numeric4
Categorical4
Text2
Unsupported1
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/c03458ea-013a-41b4-b4f9-4f8323de6365

Alerts

일반일간결제일자 has constant value ""Constant
결제상품ID has constant value ""Constant
사용여부 has constant value ""Constant
결제금액 has constant value ""Constant
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
시도명 is highly imbalanced (64.7%)Imbalance
시군구명 has 2 (6.7%) missing valuesMissing
읍면동명 has 2 (6.7%) missing valuesMissing
결제상품명 has 30 (100.0%) missing valuesMissing
가맹점번호 has unique valuesUnique
결제상품명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
위도 has 2 (6.7%) zerosZeros
경도 has 2 (6.7%) zerosZeros

Reproduction

Analysis started2023-12-10 13:47:57.846362
Analysis finished2023-12-10 13:48:02.694433
Duration4.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일반일간결제일자
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2023-02-01 00:00:00
Maximum2023-02-01 00:00:00
2023-12-10T22:48:02.835282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:48:03.004242image/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%
Mean7.0663882 × 108
Minimum7.000005 × 108
Maximum7.9933132 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:48:03.253440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.000005 × 108
5-th percentile7.0000331 × 108
Q17.0001069 × 108
median7.0001776 × 108
Q37.0002901 × 108
95-th percentile7.546469 × 108
Maximum7.9933132 × 108
Range99330817
Interquartile range (IQR)18314

Descriptive statistics

Standard deviation25196552
Coefficient of variation (CV)0.035656903
Kurtosis12.206628
Mean7.0663882 × 108
Median Absolute Deviation (MAD)9651
Skewness3.6599977
Sum2.1199165 × 1010
Variance6.3486623 × 1014
MonotonicityNot monotonic
2023-12-10T22:48:03.527422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
700000504 1
 
3.3%
700017584 1
 
3.3%
700033198 1
 
3.3%
700032719 1
 
3.3%
700031041 1
 
3.3%
700030010 1
 
3.3%
700029667 1
 
3.3%
700029537 1
 
3.3%
700027412 1
 
3.3%
700027161 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
700000504 1
3.3%
700002166 1
3.3%
700004700 1
3.3%
700005147 1
3.3%
700005277 1
3.3%
700005666 1
3.3%
700008110 1
3.3%
700010672 1
3.3%
700010751 1
3.3%
700013320 1
3.3%
ValueCountFrequency (%)
799331321 1
3.3%
799330835 1
3.3%
700033198 1
3.3%
700032719 1
3.3%
700031041 1
3.3%
700030010 1
3.3%
700029667 1
3.3%
700029537 1
3.3%
700027412 1
3.3%
700027161 1
3.3%

결제상품ID
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
999999999999999
30 

Length

Max length15
Median length15
Mean length15
Min length15

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
999999999999999 30
100.0%

Length

2023-12-10T22:48:03.744128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:48:03.908548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
999999999999999 30
100.0%

가맹점업종명
Categorical

Distinct11
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
일반휴게음식
11 
음료식품
4 
문화.취미
3 
학원
3 
건축자재
2 
Other values (6)
7 

Length

Max length6
Median length5
Mean length4.6
Min length2

Unique

Unique5 ?
Unique (%)16.7%

Sample

1st row레저업소
2nd row건축자재
3rd row건축자재
4th row음료식품
5th row일반휴게음식

Common Values

ValueCountFrequency (%)
일반휴게음식 11
36.7%
음료식품 4
 
13.3%
문화.취미 3
 
10.0%
학원 3
 
10.0%
건축자재 2
 
6.7%
전기제품 2
 
6.7%
레저업소 1
 
3.3%
보건위생 1
 
3.3%
레져용품 1
 
3.3%
수리서비스 1
 
3.3%

Length

2023-12-10T22:48:04.140473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반휴게음식 11
36.7%
음료식품 4
 
13.3%
문화.취미 3
 
10.0%
학원 3
 
10.0%
건축자재 2
 
6.7%
전기제품 2
 
6.7%
레저업소 1
 
3.3%
보건위생 1
 
3.3%
레져용품 1
 
3.3%
수리서비스 1
 
3.3%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13444.2
Minimum10110
Maximum18144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:48:04.387433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10110
5-th percentile10434.7
Q111461
median13620
Q315349
95-th percentile17145
Maximum18144
Range8034
Interquartile range (IQR)3888

Descriptive statistics

Standard deviation2326.5966
Coefficient of variation (CV)0.17305579
Kurtosis-1.0636985
Mean13444.2
Median Absolute Deviation (MAD)1862
Skewness0.23429888
Sum403326
Variance5413051.8
MonotonicityNot monotonic
2023-12-10T22:48:04.627757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
14948 2
 
6.7%
10902 1
 
3.3%
10525 1
 
3.3%
14001 1
 
3.3%
18144 1
 
3.3%
14598 1
 
3.3%
12701 1
 
3.3%
10460 1
 
3.3%
15447 1
 
3.3%
11692 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
10110 1
3.3%
10414 1
3.3%
10460 1
3.3%
10525 1
3.3%
10557 1
3.3%
10902 1
3.3%
11307 1
3.3%
11453 1
3.3%
11485 1
3.3%
11692 1
3.3%
ValueCountFrequency (%)
18144 1
3.3%
17550 1
3.3%
16650 1
3.3%
16282 1
3.3%
15517 1
3.3%
15447 1
3.3%
15445 1
3.3%
15441 1
3.3%
15073 1
3.3%
14948 2
6.7%

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

Common Values

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

Length

2023-12-10T22:48:04.896634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:48:05.161632image/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
2023-12-10T22:48:05.471319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.1785714
Min length3

Characters and Unicode

Total characters145
Distinct characters33
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 row파주시
2nd row고양시 덕양구
3rd row양주시
4th row남양주시
5th row안산시 단원구
ValueCountFrequency (%)
안산시 4
 
9.5%
고양시 4
 
9.5%
덕양구 3
 
7.1%
시흥시 3
 
7.1%
안양시 3
 
7.1%
단원구 3
 
7.1%
남양주시 2
 
4.8%
동안구 2
 
4.8%
수원시 2
 
4.8%
의정부시 1
 
2.4%
Other values (15) 15
35.7%
2023-12-10T22:48:05.973064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 31
21.4%
구 15
10.3%
14
 
9.7%
양 13
 
9.0%
안 12
 
8.3%
산 5
 
3.4%
주 5
 
3.4%
원 5
 
3.4%
고 4
 
2.8%
동 4
 
2.8%
Other values (23) 37
25.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 131
90.3%
Space Separator 14
 
9.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 31
23.7%
구 15
11.5%
양 13
 
9.9%
안 12
 
9.2%
산 5
 
3.8%
주 5
 
3.8%
원 5
 
3.8%
고 4
 
3.1%
동 4
 
3.1%
단 3
 
2.3%
Other values (22) 34
26.0%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 131
90.3%
Common 14
 
9.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 31
23.7%
구 15
11.5%
양 13
 
9.9%
안 12
 
9.2%
산 5
 
3.8%
주 5
 
3.8%
원 5
 
3.8%
고 4
 
3.1%
동 4
 
3.1%
단 3
 
2.3%
Other values (22) 34
26.0%
Common
ValueCountFrequency (%)
14
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 131
90.3%
ASCII 14
 
9.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 31
23.7%
구 15
11.5%
양 13
 
9.9%
안 12
 
9.2%
산 5
 
3.8%
주 5
 
3.8%
원 5
 
3.8%
고 4
 
3.1%
동 4
 
3.1%
단 3
 
2.3%
Other values (22) 34
26.0%
ASCII
ValueCountFrequency (%)
14
100.0%

읍면동명
Text

MISSING 

Distinct25
Distinct (%)89.3%
Missing2
Missing (%)6.7%
Memory size372.0 B
2023-12-10T22:48:06.280086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.0714286
Min length2

Characters and Unicode

Total characters86
Distinct characters45
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

Unique23 ?
Unique (%)82.1%

Sample

1st row동패동
2nd row행신동
3rd row고암동
4th row평내동
5th row초지동
ValueCountFrequency (%)
초지동 3
 
10.7%
신천동 2
 
7.1%
평촌동 1
 
3.6%
동패동 1
 
3.6%
상봉암동 1
 
3.6%
상동 1
 
3.6%
남한산성면 1
 
3.6%
주교동 1
 
3.6%
의정부동 1
 
3.6%
삼송동 1
 
3.6%
Other values (15) 15
53.6%
2023-12-10T22:48:06.782394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 26
30.2%
초 3
 
3.5%
지 3
 
3.5%
평 3
 
3.5%
신 3
 
3.5%
천 2
 
2.3%
양 2
 
2.3%
고 2
 
2.3%
암 2
 
2.3%
상 2
 
2.3%
Other values (35) 38
44.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 86
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 26
30.2%
초 3
 
3.5%
지 3
 
3.5%
평 3
 
3.5%
신 3
 
3.5%
천 2
 
2.3%
양 2
 
2.3%
고 2
 
2.3%
암 2
 
2.3%
상 2
 
2.3%
Other values (35) 38
44.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 86
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 26
30.2%
초 3
 
3.5%
지 3
 
3.5%
평 3
 
3.5%
신 3
 
3.5%
천 2
 
2.3%
양 2
 
2.3%
고 2
 
2.3%
암 2
 
2.3%
상 2
 
2.3%
Other values (35) 38
44.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 86
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 26
30.2%
초 3
 
3.5%
지 3
 
3.5%
평 3
 
3.5%
신 3
 
3.5%
천 2
 
2.3%
양 2
 
2.3%
고 2
 
2.3%
암 2
 
2.3%
상 2
 
2.3%
Other values (35) 38
44.2%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.9975
Minimum0
Maximum37.953
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:48:07.044358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.6527
Q137.31025
median37.4425
Q337.65075
95-th percentile37.79205
Maximum37.953
Range37.953
Interquartile range (IQR)0.3405

Descriptive statistics

Standard deviation9.5154347
Coefficient of variation (CV)0.27188898
Kurtosis12.193402
Mean34.9975
Median Absolute Deviation (MAD)0.1605
Skewness-3.6572574
Sum1049.925
Variance90.543497
MonotonicityNot monotonic
2023-12-10T22:48:07.243868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 2
 
6.7%
37.711 1
 
3.3%
37.384 1
 
3.3%
37.398 1
 
3.3%
37.491 1
 
3.3%
37.474 1
 
3.3%
37.657 1
 
3.3%
37.441 1
 
3.3%
37.307 1
 
3.3%
37.742 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0.0 2
6.7%
37.006 1
3.3%
37.248 1
3.3%
37.299 1
3.3%
37.303 1
3.3%
37.307 1
3.3%
37.309 1
3.3%
37.314 1
3.3%
37.337 1
3.3%
37.384 1
3.3%
ValueCountFrequency (%)
37.953 1
3.3%
37.833 1
3.3%
37.742 1
3.3%
37.711 1
3.3%
37.657 1
3.3%
37.654 1
3.3%
37.653 1
3.3%
37.652 1
3.3%
37.647 1
3.3%
37.618 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.4796
Minimum0
Maximum127.302
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:48:07.449939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile57.02445
Q1126.78
median126.8765
Q3127.059
95-th percentile127.23715
Maximum127.302
Range127.302
Interquartile range (IQR)0.279

Descriptive statistics

Standard deviation32.206758
Coefficient of variation (CV)0.27183379
Kurtosis12.205813
Mean118.4796
Median Absolute Deviation (MAD)0.132
Skewness-3.6598288
Sum3554.388
Variance1037.2753
MonotonicityNot monotonic
2023-12-10T22:48:07.668943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0 2
 
6.7%
126.78 2
 
6.7%
126.738 1
 
3.3%
127.062 1
 
3.3%
126.921 1
 
3.3%
126.75 1
 
3.3%
127.23 1
 
3.3%
126.832 1
 
3.3%
126.819 1
 
3.3%
127.05 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
0.0 2
6.7%
126.721 1
3.3%
126.738 1
3.3%
126.739 1
3.3%
126.75 1
3.3%
126.777 1
3.3%
126.78 2
6.7%
126.815 1
3.3%
126.819 1
3.3%
126.82 1
3.3%
ValueCountFrequency (%)
127.302 1
3.3%
127.243 1
3.3%
127.23 1
3.3%
127.169 1
3.3%
127.139 1
3.3%
127.127 1
3.3%
127.075 1
3.3%
127.062 1
3.3%
127.05 1
3.3%
127.017 1
3.3%

결제상품명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing30
Missing (%)100.0%
Memory size402.0 B

사용여부
Boolean

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
30 
ValueCountFrequency (%)
False 30
100.0%
2023-12-10T22:48:07.901163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
30 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 30
100.0%

Length

2023-12-10T22:48:08.052022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:48:08.245159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

Interactions

2023-12-10T22:48:00.980836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:58.515262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:59.292786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:48:00.288264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:48:01.146811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:58.683848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:59.462473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:48:00.430369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:48:01.315011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:58.898452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:59.684400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:48:00.623578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:48:01.457901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:59.051773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:48:00.022481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:48:00.768267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:48:08.360805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도
가맹점번호1.0000.3940.0000.0000.0001.0000.0000.000
가맹점업종명0.3941.0000.0000.4610.6680.8950.4610.461
가맹점우편번호0.0000.0001.0000.2131.0001.0000.2130.213
시도명0.0000.4610.2131.000NaNNaN0.9060.906
시군구명0.0000.6681.000NaN1.0001.000NaNNaN
읍면동명1.0000.8951.000NaN1.0001.000NaNNaN
위도0.0000.4610.2130.906NaNNaN1.0000.906
경도0.0000.4610.2130.906NaNNaN0.9061.000
2023-12-10T22:48:08.550785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명가맹점업종명
시도명1.0000.352
가맹점업종명0.3521.000
2023-12-10T22:48:08.738061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점우편번호위도경도가맹점업종명시도명
가맹점번호1.0000.127-0.032-0.1480.1770.000
가맹점우편번호0.1271.000-0.8080.0060.0000.144
위도-0.032-0.8081.0000.2510.3520.721
경도-0.1480.0060.2511.0000.3520.721
가맹점업종명0.1770.0000.3520.3521.0000.352
시도명0.0000.1440.7210.7210.3521.000

Missing values

2023-12-10T22:48:02.133181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:48:02.409327image/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.
2023-12-10T22:48:02.583762image/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

일반일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
02023-02-01700000504999999999999999레저업소10902경기도파주시동패동37.711126.738<NA>N0
12023-02-01799330835999999999999999건축자재10525경기도고양시 덕양구행신동37.615126.834<NA>N0
22023-02-01700002166999999999999999건축자재11485NONE<NA><NA>0.00.0<NA>N0
32023-02-01700004700999999999999999음료식품11453경기도양주시고암동37.833127.075<NA>N0
42023-02-01700005147999999999999999일반휴게음식12219경기도남양주시평내동37.647127.243<NA>N0
52023-02-01700005277999999999999999음료식품15445경기도안산시 단원구초지동37.309126.815<NA>N0
62023-02-01700005666999999999999999일반휴게음식16650경기도수원시 권선구고색동37.248126.981<NA>N0
72023-02-01799331321999999999999999문화.취미15073경기도시흥시정왕동37.337126.739<NA>N0
82023-02-01700008110999999999999999문화.취미13307경기도성남시 수정구태평동37.444127.127<NA>N0
92023-02-01700010672999999999999999일반휴게음식10110경기도김포시사우동37.618126.721<NA>N0
일반일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
202023-02-01700026548999999999999999전기제품14948경기도시흥시신천동37.437126.78<NA>N0
212023-02-01700026732999999999999999음료식품10557경기도고양시 덕양구삼송동37.653126.887<NA>N0
222023-02-01700027161999999999999999의류11692경기도의정부시의정부동37.742127.05<NA>N0
232023-02-01700027412999999999999999문화.취미15447경기도안산시 단원구초지동37.307126.819<NA>N0
242023-02-01700029537999999999999999일반휴게음식14948경기도시흥시신천동37.441126.78<NA>N0
252023-02-01700029667999999999999999일반휴게음식10460경기도고양시 덕양구주교동37.657126.832<NA>N0
262023-02-01700030010999999999999999일반휴게음식12701경기도광주시남한산성면37.474127.23<NA>N0
272023-02-01700031041999999999999999학원14598경기도부천시상동37.491126.75<NA>N0
282023-02-01700032719999999999999999전기제품18144NONE<NA><NA>0.00.0<NA>N0
292023-02-01700033198999999999999999학원14001경기도안양시 만안구안양동37.398126.921<NA>N0