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 memory115.4 B

Variable types

Categorical4
Numeric4
Text3
Unsupported1
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/84c0161b-74b8-45a7-bc36-6846f912fa1c

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 가맹점우편번호High correlation
시도명 is highly imbalanced (78.9%)Imbalance
시군구명 has 1 (3.3%) missing valuesMissing
읍면동명 has 1 (3.3%) missing valuesMissing
결제상품명 has 30 (100.0%) missing valuesMissing
가맹점번호 has unique valuesUnique
가맹점우편번호 has unique valuesUnique
결제상품명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
위도 has 1 (3.3%) zerosZeros
경도 has 1 (3.3%) zerosZeros

Reproduction

Analysis started2023-12-10 14:15:01.549045
Analysis finished2023-12-10 14:15:06.291320
Duration4.74 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
2022-09-01
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-09-01
2nd row2022-09-01
3rd row2022-09-01
4th row2022-09-01
5th row2022-09-01

Common Values

ValueCountFrequency (%)
2022-09-01 30
100.0%

Length

2023-12-10T23:15:06.454616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:15:06.696656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-09-01 30
100.0%

가맹점번호
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0664288 × 108
Minimum7.0000145 × 108
Maximum7.9932742 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:15:06.901141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0000145 × 108
5-th percentile7.0000444 × 108
Q17.0001457 × 108
median7.0002357 × 108
Q37.0003367 × 108
95-th percentile7.5464774 × 108
Maximum7.9932742 × 108
Range99325973
Interquartile range (IQR)19102.25

Descriptive statistics

Standard deviation25194345
Coefficient of variation (CV)0.035653575
Kurtosis12.206626
Mean7.0664288 × 108
Median Absolute Deviation (MAD)10233.5
Skewness3.6599974
Sum2.1199286 × 1010
Variance6.3475501 × 1014
MonotonicityNot monotonic
2023-12-10T23:15:07.146308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
700001452 1
 
3.3%
700022692 1
 
3.3%
700040234 1
 
3.3%
700039689 1
 
3.3%
700038412 1
 
3.3%
700037249 1
 
3.3%
700036827 1
 
3.3%
700033722 1
 
3.3%
700033512 1
 
3.3%
700032648 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
700001452 1
3.3%
700004224 1
3.3%
700004696 1
3.3%
700005007 1
3.3%
700007588 1
3.3%
700011678 1
3.3%
700012032 1
3.3%
700013255 1
3.3%
700018504 1
3.3%
700019179 1
3.3%
ValueCountFrequency (%)
799327425 1
3.3%
799326607 1
3.3%
700040234 1
3.3%
700039689 1
3.3%
700038412 1
3.3%
700037249 1
3.3%
700036827 1
3.3%
700033722 1
3.3%
700033512 1
3.3%
700032648 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

2023-12-10T23:15:07.420252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:15:07.642316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
999999999999999 30
100.0%

가맹점업종명
Text

Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:15:07.865554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.7333333
Min length2

Characters and Unicode

Total characters142
Distinct characters51
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

Unique10 ?
Unique (%)33.3%

Sample

1st row학원
2nd row일반휴게음식
3rd row레저업소
4th row기타의료기관
5th row보건위생
ValueCountFrequency (%)
일반휴게음식 6
16.2%
유통업 4
10.8%
음료식품 4
10.8%
학원 3
 
8.1%
영리 3
 
8.1%
건축자재 2
 
5.4%
용역 2
 
5.4%
서비스 2
 
5.4%
전기제품 1
 
2.7%
의류 1
 
2.7%
Other values (9) 9
24.3%
2023-12-10T23:15:08.372186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
음 10
 
7.0%
식 10
 
7.0%
7
 
4.9%
일 6
 
4.2%
휴 6
 
4.2%
게 6
 
4.2%
반 6
 
4.2%
유 5
 
3.5%
업 5
 
3.5%
통 5
 
3.5%
Other values (41) 76
53.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 134
94.4%
Space Separator 7
 
4.9%
Other Punctuation 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
음 10
 
7.5%
식 10
 
7.5%
일 6
 
4.5%
휴 6
 
4.5%
게 6
 
4.5%
반 6
 
4.5%
유 5
 
3.7%
업 5
 
3.7%
통 5
 
3.7%
품 5
 
3.7%
Other values (39) 70
52.2%
Space Separator
ValueCountFrequency (%)
7
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 134
94.4%
Common 8
 
5.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
음 10
 
7.5%
식 10
 
7.5%
일 6
 
4.5%
휴 6
 
4.5%
게 6
 
4.5%
반 6
 
4.5%
유 5
 
3.7%
업 5
 
3.7%
통 5
 
3.7%
품 5
 
3.7%
Other values (39) 70
52.2%
Common
ValueCountFrequency (%)
7
87.5%
. 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 134
94.4%
ASCII 8
 
5.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
음 10
 
7.5%
식 10
 
7.5%
일 6
 
4.5%
휴 6
 
4.5%
게 6
 
4.5%
반 6
 
4.5%
유 5
 
3.7%
업 5
 
3.7%
통 5
 
3.7%
품 5
 
3.7%
Other values (39) 70
52.2%
ASCII
ValueCountFrequency (%)
7
87.5%
. 1
 
12.5%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14269.933
Minimum10111
Maximum18405
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:15:08.562232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10111
5-th percentile10875.05
Q112423.5
median14503.5
Q315724.75
95-th percentile17617.85
Maximum18405
Range8294
Interquartile range (IQR)3301.25

Descriptive statistics

Standard deviation2182.6077
Coefficient of variation (CV)0.1529515
Kurtosis-0.67098355
Mean14269.933
Median Absolute Deviation (MAD)1618.5
Skewness-0.071335631
Sum428098
Variance4763776.3
MonotonicityNot monotonic
2023-12-10T23:15:08.745951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
14995 1
 
3.3%
11813 1
 
3.3%
14683 1
 
3.3%
15839 1
 
3.3%
12226 1
 
3.3%
11622 1
 
3.3%
12073 1
 
3.3%
13998 1
 
3.3%
16689 1
 
3.3%
15382 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
10111 1
3.3%
10826 1
3.3%
10935 1
3.3%
11622 1
3.3%
11697 1
3.3%
11813 1
3.3%
12073 1
3.3%
12226 1
3.3%
13016 1
3.3%
13288 1
3.3%
ValueCountFrequency (%)
18405 1
3.3%
18134 1
3.3%
16987 1
3.3%
16832 1
3.3%
16689 1
3.3%
16306 1
3.3%
16253 1
3.3%
15839 1
3.3%
15382 1
3.3%
15282 1
3.3%

시도명
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
29 
NONE
 
1

Length

Max length4
Median length3
Mean length3.0333333
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 29
96.7%
NONE 1
 
3.3%

Length

2023-12-10T23:15:08.915627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:15:09.068360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 29
96.7%
none 1
 
3.3%

시군구명
Text

MISSING 

Distinct20
Distinct (%)69.0%
Missing1
Missing (%)3.3%
Memory size372.0 B
2023-12-10T23:15:09.281763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length4.4137931
Min length3

Characters and Unicode

Total characters128
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 (%)51.7%

Sample

1st row시흥시
2nd row김포시
3rd row오산시
4th row부천시
5th row광명시
ValueCountFrequency (%)
부천시 5
 
13.2%
수원시 3
 
7.9%
의정부시 3
 
7.9%
용인시 2
 
5.3%
성남시 2
 
5.3%
파주시 2
 
5.3%
남양주시 2
 
5.3%
시흥시 2
 
5.3%
김포시 1
 
2.6%
상록구 1
 
2.6%
Other values (15) 15
39.5%
2023-12-10T23:15:09.805598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 31
24.2%
구 9
 
7.0%
9
 
7.0%
부 8
 
6.2%
남 5
 
3.9%
천 5
 
3.9%
수 5
 
3.9%
안 4
 
3.1%
주 4
 
3.1%
정 4
 
3.1%
Other values (29) 44
34.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 119
93.0%
Space Separator 9
 
7.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 31
26.1%
구 9
 
7.6%
부 8
 
6.7%
남 5
 
4.2%
천 5
 
4.2%
수 5
 
4.2%
안 4
 
3.4%
주 4
 
3.4%
정 4
 
3.4%
성 3
 
2.5%
Other values (28) 41
34.5%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 119
93.0%
Common 9
 
7.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 31
26.1%
구 9
 
7.6%
부 8
 
6.7%
남 5
 
4.2%
천 5
 
4.2%
수 5
 
4.2%
안 4
 
3.4%
주 4
 
3.4%
정 4
 
3.4%
성 3
 
2.5%
Other values (28) 41
34.5%
Common
ValueCountFrequency (%)
9
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 119
93.0%
ASCII 9
 
7.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 31
26.1%
구 9
 
7.6%
부 8
 
6.7%
남 5
 
4.2%
천 5
 
4.2%
수 5
 
4.2%
안 4
 
3.4%
주 4
 
3.4%
정 4
 
3.4%
성 3
 
2.5%
Other values (28) 41
34.5%
ASCII
ValueCountFrequency (%)
9
100.0%

읍면동명
Text

MISSING 

Distinct27
Distinct (%)93.1%
Missing1
Missing (%)3.3%
Memory size372.0 B
2023-12-10T23:15:10.120461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3
Min length2

Characters and Unicode

Total characters87
Distinct characters50
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

Unique25 ?
Unique (%)86.2%

Sample

1st row능곡동
2nd row사우동
3rd row오산동
4th row원종동
5th row광명동
ValueCountFrequency (%)
의정부동 2
 
6.9%
중동 2
 
6.9%
능곡동 1
 
3.4%
사우동 1
 
3.4%
당동 1
 
3.4%
평내동 1
 
3.4%
진접읍 1
 
3.4%
안양동 1
 
3.4%
망포동 1
 
3.4%
신천동 1
 
3.4%
Other values (17) 17
58.6%
2023-12-10T23:15:10.723075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 26
29.9%
의 3
 
3.4%
읍 3
 
3.4%
춘 2
 
2.3%
정 2
 
2.3%
천 2
 
2.3%
평 2
 
2.3%
안 2
 
2.3%
부 2
 
2.3%
원 2
 
2.3%
Other values (40) 41
47.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 87
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 26
29.9%
의 3
 
3.4%
읍 3
 
3.4%
춘 2
 
2.3%
정 2
 
2.3%
천 2
 
2.3%
평 2
 
2.3%
안 2
 
2.3%
부 2
 
2.3%
원 2
 
2.3%
Other values (40) 41
47.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 87
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 26
29.9%
의 3
 
3.4%
읍 3
 
3.4%
춘 2
 
2.3%
정 2
 
2.3%
천 2
 
2.3%
평 2
 
2.3%
안 2
 
2.3%
부 2
 
2.3%
원 2
 
2.3%
Other values (40) 41
47.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 87
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 26
29.9%
의 3
 
3.4%
읍 3
 
3.4%
춘 2
 
2.3%
정 2
 
2.3%
천 2
 
2.3%
평 2
 
2.3%
안 2
 
2.3%
부 2
 
2.3%
원 2
 
2.3%
Other values (40) 41
47.1%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.228333
Minimum0
Maximum37.849
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:15:11.168725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37.1787
Q137.329
median37.462
Q337.59775
95-th percentile37.74075
Maximum37.849
Range37.849
Interquartile range (IQR)0.26875

Descriptive statistics

Standard deviation6.844806
Coefficient of variation (CV)0.18893516
Kurtosis29.954101
Mean36.228333
Median Absolute Deviation (MAD)0.147
Skewness-5.4711366
Sum1086.85
Variance46.851369
MonotonicityNot monotonic
2023-12-10T23:15:11.484063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
37.738 2
 
6.7%
37.368 1
 
3.3%
37.743 1
 
3.3%
37.481 1
 
3.3%
37.358 1
 
3.3%
37.643 1
 
3.3%
37.699 1
 
3.3%
37.394 1
 
3.3%
37.242 1
 
3.3%
0.0 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0.0 1
3.3%
37.149 1
3.3%
37.215 1
3.3%
37.242 1
3.3%
37.282 1
3.3%
37.283 1
3.3%
37.298 1
3.3%
37.327 1
3.3%
37.335 1
3.3%
37.358 1
3.3%
ValueCountFrequency (%)
37.849 1
3.3%
37.743 1
3.3%
37.738 2
6.7%
37.737 1
3.3%
37.699 1
3.3%
37.643 1
3.3%
37.621 1
3.3%
37.528 1
3.3%
37.52 1
3.3%
37.518 1
3.3%

경도
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.73463
Minimum0
Maximum127.236
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:15:11.712143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile126.74695
Q1126.80725
median126.976
Q3127.08975
95-th percentile127.18405
Maximum127.236
Range127.236
Interquartile range (IQR)0.2825

Descriptive statistics

Standard deviation23.181364
Coefficient of variation (CV)0.18887386
Kurtosis29.997177
Mean122.73463
Median Absolute Deviation (MAD)0.1585
Skewness-5.4768514
Sum3682.039
Variance537.37564
MonotonicityNot monotonic
2023-12-10T23:15:11.958432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
127.095 2
 
6.7%
127.142 2
 
6.7%
126.811 1
 
3.3%
126.806 1
 
3.3%
126.946 1
 
3.3%
127.236 1
 
3.3%
127.048 1
 
3.3%
127.178 1
 
3.3%
126.918 1
 
3.3%
127.052 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
0.0 1
3.3%
126.724 1
3.3%
126.775 1
3.3%
126.78 1
3.3%
126.787 1
3.3%
126.79 1
3.3%
126.805 1
3.3%
126.806 1
3.3%
126.811 1
3.3%
126.819 1
3.3%
ValueCountFrequency (%)
127.236 1
3.3%
127.189 1
3.3%
127.178 1
3.3%
127.142 2
6.7%
127.136 1
3.3%
127.095 2
6.7%
127.074 1
3.3%
127.052 1
3.3%
127.05 1
3.3%
127.048 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%
2023-12-10T23:15:12.180114image/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

2023-12-10T23:15:12.394324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:15:12.602706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

Interactions

2023-12-10T23:15:04.897639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:02.264311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:03.150792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:03.986270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:05.040231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:02.409337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:03.409219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:04.252862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:05.174540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:02.536263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:03.593204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:04.508168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:05.331613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:03.007728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:03.825287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:04.722088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:15:12.760516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도
가맹점번호1.0000.0000.0000.0000.0001.0000.0000.000
가맹점업종명0.0001.0000.0001.0000.5680.0001.0001.000
가맹점우편번호0.0000.0001.0000.0000.9850.9680.0000.000
시도명0.0001.0000.0001.000NaNNaN0.6550.655
시군구명0.0000.5680.985NaN1.0000.985NaNNaN
읍면동명1.0000.0000.968NaN0.9851.000NaNNaN
위도0.0001.0000.0000.655NaNNaN1.0000.655
경도0.0001.0000.0000.655NaNNaN0.6551.000
2023-12-10T23:15:13.182005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점우편번호위도경도시도명
가맹점번호1.000-0.1360.104-0.0950.000
가맹점우편번호-0.1361.000-0.9100.0010.000
위도0.104-0.9101.0000.0000.454
경도-0.0950.0010.0001.0000.454
시도명0.0000.0000.4540.4541.000

Missing values

2023-12-10T23:15:05.556888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:15:05.969812image/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.
2023-12-10T23:15:06.205948image/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
02022-09-01700001452999999999999999학원14995경기도시흥시능곡동37.368126.811<NA>N0
12022-09-01799326607999999999999999일반휴게음식10111경기도김포시사우동37.621126.724<NA>N0
22022-09-01700004224999999999999999레저업소18134경기도오산시오산동37.149127.074<NA>N0
32022-09-01700004696999999999999999기타의료기관14473경기도부천시원종동37.518126.805<NA>N0
42022-09-01700005007999999999999999보건위생14285경기도광명시광명동37.479126.852<NA>N0
52022-09-01700007588999999999999999일반휴게음식14407경기도부천시고강동37.528126.819<NA>N0
62022-09-01700011678999999999999999건축자재13016경기도하남시춘궁동37.52127.189<NA>N0
72022-09-01799327425999999999999999용역 서비스14557경기도부천시춘의동37.501126.787<NA>N0
82022-09-01700012032999999999999999음료식품10826경기도파주시법원읍37.849126.874<NA>N0
92022-09-01700013255999999999999999일반휴게음식18405경기도화성시병점동37.215127.044<NA>N0
일반일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
202022-09-01700029537999999999999999일반휴게음식14948경기도시흥시신천동37.441126.78<NA>N0
212022-09-01700032053999999999999999병원14534경기도부천시중동37.505126.775<NA>N0
222022-09-01700032648999999999999999의류15382NONE<NA><NA>0.00.0<NA>N0
232022-09-01700033512999999999999999유통업 영리16689경기도수원시 영통구망포동37.242127.052<NA>N0
242022-09-01700033722999999999999999음료식품13998경기도안양시 만안구안양동37.394126.918<NA>N0
252022-09-01700036827999999999999999음료식품12073경기도남양주시진접읍37.699127.178<NA>N0
262022-09-01700037249999999999999999유통업 비영리11622경기도의정부시의정부동37.738127.048<NA>N0
272022-09-01700038412999999999999999학원12226경기도남양주시평내동37.643127.236<NA>N0
282022-09-01700039689999999999999999유통업 영리15839경기도군포시당동37.358126.946<NA>N0
292022-09-01700040234999999999999999음료식품14683경기도부천시괴안동37.481126.806<NA>N0