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

Categorical5
Numeric4
Text2
Unsupported1
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/c5390d25-0767-496c-9b21-f92c2e00fa30

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 위도 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 가맹점우편번호 and 2 other fieldsHigh correlation
경도 is highly overall correlated with 가맹점우편번호 and 2 other fieldsHigh correlation
가맹점업종명 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
시도명 is highly overall correlated with 위도 and 2 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 13:44:18.747027
Analysis finished2023-12-10 13:44:22.836301
Duration4.09 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일반일간결제일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2020-08-01
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-08-01
2nd row2020-08-01
3rd row2020-08-01
4th row2020-08-01
5th row2020-08-01

Common Values

ValueCountFrequency (%)
2020-08-01 30
100.0%

Length

2023-12-10T22:44:22.951971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:44:23.097162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-08-01 30
100.0%

가맹점번호
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0663091 × 108
Minimum7.0000073 × 108
Maximum7.9933396 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:44:23.298630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0000073 × 108
5-th percentile7.0000236 × 108
Q17.0000716 × 108
median7.0001082 × 108
Q37.0001227 × 108
95-th percentile7.5464049 × 108
Maximum7.9933396 × 108
Range99333231
Interquartile range (IQR)5115.25

Descriptive statistics

Standard deviation25199478
Coefficient of variation (CV)0.035661444
Kurtosis12.206632
Mean7.0663091 × 108
Median Absolute Deviation (MAD)3503.5
Skewness3.6599985
Sum2.1198927 × 1010
Variance6.3501371 × 1014
MonotonicityNot monotonic
2023-12-10T22:44:23.540680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
700000731 1
 
3.3%
700010566 1
 
3.3%
700015175 1
 
3.3%
700015060 1
 
3.3%
700014845 1
 
3.3%
700014497 1
 
3.3%
700014414 1
 
3.3%
700012366 1
 
3.3%
700011986 1
 
3.3%
700011686 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
700000731 1
3.3%
700001681 1
3.3%
700003195 1
3.3%
700004075 1
3.3%
700004700 1
3.3%
700005104 1
3.3%
700006516 1
3.3%
700007072 1
3.3%
700007407 1
3.3%
700007707 1
3.3%
ValueCountFrequency (%)
799333962 1
3.3%
799333921 1
3.3%
700015175 1
3.3%
700015060 1
3.3%
700014845 1
3.3%
700014497 1
3.3%
700014414 1
3.3%
700012366 1
3.3%
700011986 1
3.3%
700011686 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

2023-12-10T22:44:23.821507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:44:23.975768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
999999999999999 30
100.0%

가맹점업종명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
일반휴게음식
8 
자동차정비 유지
3 
음료식품
3 
용역 서비스
3 
의류
2 
Other values (8)
11 

Length

Max length8
Median length6
Mean length4.7666667
Min length2

Unique

Unique5 ?
Unique (%)16.7%

Sample

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

Common Values

ValueCountFrequency (%)
일반휴게음식 8
26.7%
자동차정비 유지 3
 
10.0%
음료식품 3
 
10.0%
용역 서비스 3
 
10.0%
의류 2
 
6.7%
가구 2
 
6.7%
보건위생 2
 
6.7%
레저업소 2
 
6.7%
전기제품 1
 
3.3%
직물 1
 
3.3%
Other values (3) 3
 
10.0%

Length

2023-12-10T22:44:24.245273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반휴게음식 8
22.2%
자동차정비 3
 
8.3%
유지 3
 
8.3%
음료식품 3
 
8.3%
용역 3
 
8.3%
서비스 3
 
8.3%
의류 2
 
5.6%
가구 2
 
5.6%
보건위생 2
 
5.6%
레저업소 2
 
5.6%
Other values (5) 5
13.9%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14461.6
Minimum10129
Maximum18487
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:44:24.511560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10129
5-th percentile10246.85
Q111509.25
median14979.5
Q317013.25
95-th percentile18418.6
Maximum18487
Range8358
Interquartile range (IQR)5504

Descriptive statistics

Standard deviation3067.0706
Coefficient of variation (CV)0.21208376
Kurtosis-1.5860735
Mean14461.6
Median Absolute Deviation (MAD)2747
Skewness-0.19918306
Sum433848
Variance9406921.9
MonotonicityNot monotonic
2023-12-10T22:44:24.750964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
16827 2
 
6.7%
10551 1
 
3.3%
16626 1
 
3.3%
17067 1
 
3.3%
18451 1
 
3.3%
12991 1
 
3.3%
17420 1
 
3.3%
15552 1
 
3.3%
10500 1
 
3.3%
16512 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
10129 1
3.3%
10205 1
3.3%
10298 1
3.3%
10304 1
3.3%
10403 1
3.3%
10500 1
3.3%
10551 1
3.3%
11453 1
3.3%
11678 1
3.3%
11926 1
3.3%
ValueCountFrequency (%)
18487 1
3.3%
18451 1
3.3%
18379 1
3.3%
18146 1
3.3%
18120 1
3.3%
17420 1
3.3%
17067 1
3.3%
17052 1
3.3%
16897 1
3.3%
16827 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 row경기도
4th row경기도
5th rowNONE

Common Values

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

Length

2023-12-10T22:44:25.018481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:44:25.304062image/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
2023-12-10T22:44:25.640802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.2857143
Min length3

Characters and Unicode

Total characters148
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

Unique14 ?
Unique (%)50.0%

Sample

1st row고양시 덕양구
2nd row수원시 권선구
3rd row고양시 일산서구
4th row여주시
5th row양주시
ValueCountFrequency (%)
고양시 6
 
14.0%
용인시 5
 
11.6%
화성시 3
 
7.0%
덕양구 3
 
7.0%
기흥구 2
 
4.7%
오산시 2
 
4.7%
수지구 2
 
4.7%
일산동구 2
 
4.7%
수원시 2
 
4.7%
김포시 1
 
2.3%
Other values (15) 15
34.9%
2023-12-10T22:44:26.474027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 28
18.9%
구 16
 
10.8%
15
 
10.1%
양 11
 
7.4%
고 6
 
4.1%
산 6
 
4.1%
인 6
 
4.1%
용 5
 
3.4%
수 4
 
2.7%
성 3
 
2.0%
Other values (30) 48
32.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 133
89.9%
Space Separator 15
 
10.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 28
21.1%
구 16
 
12.0%
양 11
 
8.3%
고 6
 
4.5%
산 6
 
4.5%
인 6
 
4.5%
용 5
 
3.8%
수 4
 
3.0%
성 3
 
2.3%
화 3
 
2.3%
Other values (29) 45
33.8%
Space Separator
ValueCountFrequency (%)
15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 133
89.9%
Common 15
 
10.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 28
21.1%
구 16
 
12.0%
양 11
 
8.3%
고 6
 
4.5%
산 6
 
4.5%
인 6
 
4.5%
용 5
 
3.8%
수 4
 
3.0%
성 3
 
2.3%
화 3
 
2.3%
Other values (29) 45
33.8%
Common
ValueCountFrequency (%)
15
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 133
89.9%
ASCII 15
 
10.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 28
21.1%
구 16
 
12.0%
양 11
 
8.3%
고 6
 
4.5%
산 6
 
4.5%
인 6
 
4.5%
용 5
 
3.8%
수 4
 
3.0%
성 3
 
2.3%
화 3
 
2.3%
Other values (29) 45
33.8%
ASCII
ValueCountFrequency (%)
15
100.0%

읍면동명
Text

MISSING 

Distinct27
Distinct (%)96.4%
Missing2
Missing (%)6.7%
Memory size372.0 B
2023-12-10T22:44:26.852202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9285714
Min length2

Characters and Unicode

Total characters82
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

Unique26 ?
Unique (%)92.9%

Sample

1st row도내동
2nd row탑동
3rd row가좌동
4th row북내면
5th row고암동
ValueCountFrequency (%)
동천동 2
 
7.1%
식사동 1
 
3.6%
도내동 1
 
3.6%
반월동 1
 
3.6%
석우동 1
 
3.6%
감북동 1
 
3.6%
장호원읍 1
 
3.6%
본오동 1
 
3.6%
화정동 1
 
3.6%
하동 1
 
3.6%
Other values (17) 17
60.7%
2023-12-10T22:44:27.493234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 27
32.9%
장 3
 
3.7%
가 2
 
2.4%
정 2
 
2.4%
북 2
 
2.4%
내 2
 
2.4%
고 2
 
2.4%
천 2
 
2.4%
교 2
 
2.4%
갈 2
 
2.4%
Other values (35) 36
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 82
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 27
32.9%
장 3
 
3.7%
가 2
 
2.4%
정 2
 
2.4%
북 2
 
2.4%
내 2
 
2.4%
고 2
 
2.4%
천 2
 
2.4%
교 2
 
2.4%
갈 2
 
2.4%
Other values (35) 36
43.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 82
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 27
32.9%
장 3
 
3.7%
가 2
 
2.4%
정 2
 
2.4%
북 2
 
2.4%
내 2
 
2.4%
고 2
 
2.4%
천 2
 
2.4%
교 2
 
2.4%
갈 2
 
2.4%
Other values (35) 36
43.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 82
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 27
32.9%
장 3
 
3.7%
가 2
 
2.4%
정 2
 
2.4%
북 2
 
2.4%
내 2
 
2.4%
고 2
 
2.4%
천 2
 
2.4%
교 2
 
2.4%
갈 2
 
2.4%
Other values (35) 36
43.9%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.927967
Minimum0
Maximum37.833
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:44:27.693480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.70175
Q137.23525
median37.3335
Q337.62175
95-th percentile37.7264
Maximum37.833
Range37.833
Interquartile range (IQR)0.3865

Descriptive statistics

Standard deviation9.4967169
Coefficient of variation (CV)0.27189435
Kurtosis12.192199
Mean34.927967
Median Absolute Deviation (MAD)0.1795
Skewness-3.6570045
Sum1047.839
Variance90.187631
MonotonicityNot monotonic
2023-12-10T22:44:27.901298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0 2
 
6.7%
37.335 2
 
6.7%
37.629 1
 
3.3%
37.235 1
 
3.3%
37.274 1
 
3.3%
37.214 1
 
3.3%
37.517 1
 
3.3%
37.115 1
 
3.3%
37.293 1
 
3.3%
37.632 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
0.0 2
6.7%
37.115 1
3.3%
37.13 1
3.3%
37.158 1
3.3%
37.163 1
3.3%
37.214 1
3.3%
37.235 1
3.3%
37.236 1
3.3%
37.256 1
3.3%
37.274 1
3.3%
ValueCountFrequency (%)
37.833 1
3.3%
37.748 1
3.3%
37.7 1
3.3%
37.668 1
3.3%
37.663 1
3.3%
37.654 1
3.3%
37.632 1
3.3%
37.629 1
3.3%
37.6 1
3.3%
37.596 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.5647
Minimum0
Maximum127.694
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:44:28.156798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile57.0267
Q1126.8325
median127.0625
Q3127.099
95-th percentile127.4393
Maximum127.694
Range127.694
Interquartile range (IQR)0.2665

Descriptive statistics

Standard deviation32.230198
Coefficient of variation (CV)0.27183637
Kurtosis12.205232
Mean118.5647
Median Absolute Deviation (MAD)0.091
Skewness-3.659708
Sum3556.941
Variance1038.7856
MonotonicityNot monotonic
2023-12-10T22:44:28.395069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0 2
 
6.7%
127.099 2
 
6.7%
126.87 1
 
3.3%
127.066 1
 
3.3%
127.105 1
 
3.3%
127.079 1
 
3.3%
127.147 1
 
3.3%
127.631 1
 
3.3%
126.858 1
 
3.3%
126.831 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
0.0 2
6.7%
126.726 1
3.3%
126.757 1
3.3%
126.767 1
3.3%
126.774 1
3.3%
126.81 1
3.3%
126.831 1
3.3%
126.837 1
3.3%
126.858 1
3.3%
126.87 1
3.3%
ValueCountFrequency (%)
127.694 1
3.3%
127.631 1
3.3%
127.205 1
3.3%
127.147 1
3.3%
127.136 1
3.3%
127.11 1
3.3%
127.105 1
3.3%
127.099 2
6.7%
127.084 1
3.3%
127.079 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%
2023-12-10T22:44:28.580489image/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

2023-12-10T22:44:28.775659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:44:29.019752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

Interactions

2023-12-10T22:44:21.110259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:19.270611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:19.916186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:20.510525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:21.264271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:19.472445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:20.049313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:20.681001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:21.413749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:19.603745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:20.222422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:20.825724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:21.552688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:19.740795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:20.382310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:20.971679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:44:29.162241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도
가맹점번호1.0000.0000.5880.0001.0001.0000.0000.000
가맹점업종명0.0001.0000.4780.7750.8971.0000.7750.775
가맹점우편번호0.5880.4781.0000.6081.0001.0000.6080.608
시도명0.0000.7750.6081.000NaNNaN0.9060.906
시군구명1.0000.8971.000NaN1.0001.000NaNNaN
읍면동명1.0001.0001.000NaN1.0001.000NaNNaN
위도0.0000.7750.6080.906NaNNaN1.0000.906
경도0.0000.7750.6080.906NaNNaN0.9061.000
2023-12-10T22:44:29.405252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점업종명시도명
가맹점업종명1.0000.574
시도명0.5741.000
2023-12-10T22:44:29.544643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점우편번호위도경도가맹점업종명시도명
가맹점번호1.0000.334-0.2640.1430.0000.000
가맹점우편번호0.3341.000-0.8190.5070.2280.399
위도-0.264-0.8191.000-0.2070.5740.721
경도0.1430.507-0.2071.0000.5740.721
가맹점업종명0.0000.2280.5740.5741.0000.574
시도명0.0000.3990.7210.7210.5741.000

Missing values

2023-12-10T22:44:22.115924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:44:22.544347image/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.
2023-12-10T22:44:22.732995image/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
02020-08-01700000731999999999999999일반휴게음식10551경기도고양시 덕양구도내동37.629126.87<NA>N0
12020-08-01799333921999999999999999일반휴게음식16626경기도수원시 권선구탑동37.256126.972<NA>N0
22020-08-01700001681999999999999999자동차정비 유지10205경기도고양시 일산서구가좌동37.7126.726<NA>N0
32020-08-01700003195999999999999999일반휴게음식12614경기도여주시북내면37.332127.694<NA>N0
42020-08-01700004075999999999999999의류15382NONE<NA><NA>0.00.0<NA>N0
52020-08-01700004700999999999999999음료식품11453경기도양주시고암동37.833127.075<NA>N0
62020-08-01700005104999999999999999일반휴게음식11678경기도의정부시가능동37.748127.042<NA>N0
72020-08-01799333962999999999999999가구14545경기도부천시상동37.504126.757<NA>N0
82020-08-01700006516999999999999999일반휴게음식18120경기도오산시궐동37.158127.059<NA>N0
92020-08-01700007072999999999999999전기제품11926경기도구리시인창동37.6127.136<NA>N0
일반일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
202020-08-01700011297999999999999999레저업소16897경기도용인시 기흥구보정동37.322127.11<NA>N0
212020-08-01700011497999999999999999직물14577NONE<NA><NA>0.00.0<NA>N0
222020-08-01700011686999999999999999의류17052경기도용인시 처인구김량장동37.236127.205<NA>N0
232020-08-01700011986999999999999999학원16512경기도수원시 영통구하동37.292127.068<NA>N0
242020-08-01700012366999999999999999일반휴게음식10500경기도고양시 덕양구화정동37.632126.831<NA>N0
252020-08-01700014414999999999999999건강식품15552경기도안산시 상록구본오동37.293126.858<NA>N0
262020-08-01700014497999999999999999레저업소17420경기도이천시장호원읍37.115127.631<NA>N0
272020-08-01700014845999999999999999자동차정비 유지12991경기도하남시감북동37.517127.147<NA>N0
282020-08-01700015060999999999999999일반휴게음식18451경기도화성시석우동37.214127.079<NA>N0
292020-08-01700015175999999999999999자동차판매17067경기도용인시 기흥구신갈동37.274127.105<NA>N0