Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells94
Missing cells (%)17.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory156.4 B

Variable types

Categorical4
Text5
Numeric8
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/d6789a0e-1950-4144-94c6-1a4f02484e8a

Alerts

정책주간결제시작일자 has constant value ""Constant
정책주간결제종료일자 has constant value ""Constant
사용여부 is highly overall correlated with 회원코드 and 6 other fieldsHigh correlation
시도명 is highly overall correlated with 가맹점번호 and 1 other fieldsHigh correlation
회원코드 is highly overall correlated with 사용여부High correlation
가맹점번호 is highly overall correlated with 가맹점우편번호 and 5 other fieldsHigh correlation
가맹점우편번호 is highly overall correlated with 가맹점번호 and 3 other fieldsHigh correlation
위도 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
경도 is highly overall correlated with 가맹점번호 and 3 other fieldsHigh correlation
결제금액 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
가맹점업종명 has 23 (76.7%) missing valuesMissing
가맹점우편번호 has 23 (76.7%) missing valuesMissing
시군구명 has 24 (80.0%) missing valuesMissing
읍면동명 has 24 (80.0%) missing valuesMissing
카드번호 has unique valuesUnique
회원코드 has unique valuesUnique
위도 has 24 (80.0%) zerosZeros
경도 has 24 (80.0%) zerosZeros
결제금액 has 21 (70.0%) zerosZeros

Reproduction

Analysis started2024-03-13 11:57:33.962868
Analysis finished2024-03-13 11:57:41.604300
Duration7.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정책주간결제시작일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2022-08-08
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-08-08
2nd row2022-08-08
3rd row2022-08-08
4th row2022-08-08
5th row2022-08-08

Common Values

ValueCountFrequency (%)
2022-08-08 30
100.0%

Length

2024-03-13T20:57:41.664871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:57:41.755887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-08-08 30
100.0%

정책주간결제종료일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2022-08-14
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-08-14
2nd row2022-08-14
3rd row2022-08-14
4th row2022-08-14
5th row2022-08-14

Common Values

ValueCountFrequency (%)
2022-08-14 30
100.0%

Length

2024-03-13T20:57:41.859878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:57:41.950874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-08-14 30
100.0%

카드번호
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:57:42.185268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length44
Mean length44
Min length44

Characters and Unicode

Total characters1320
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st rowzTN356bDM/KjZHcjDaut2xLAJSwgaCaOhKHEWtngk14=
2nd row++HCWLRQHriKSA4IG8FFn1JzHkP/buv/yeDKFAuc4uw=
3rd rowxV9MtxyrnHpyYhSlVK8tVtS6fVjpO5TIGmz6u/6S+kI=
4th rowzTGTwu6gMZuKC5Ol/BNaq3kARbAfMzS7hxa3JvrF8sM=
5th rowzTFCV6T/1GCHD+7smh49QNsJw7AGp68JpXPdh8V0doM=
ValueCountFrequency (%)
ztn356bdm/kjzhcjdaut2xlajswgacaohkhewtngk14 1
 
3.3%
hcwlrqhriksa4ig8ffn1jzhkp/buv/yedkfauc4uw 1
 
3.3%
zsapeplhuzi9z3gutuxftqodl5lwo+p1emsjo9nwwrk 1
 
3.3%
zsbpgdwdmurprdjjwevcgcj7azpgv2hpkjhk+k6tcs0 1
 
3.3%
zsebybi1rhw6ok+3pytcio9aptsje3hkkimxbjdkoky 1
 
3.3%
zsg2uvu85gouwkcjuxdkcxtfsw/na+pzexhu9irmfty 1
 
3.3%
zsjbmhtw8/cwsnvnzkmhxkptdm/r+ambjhdbjtpi5io 1
 
3.3%
zsqljuyv2blntheumsyli78zeztdysl4i/e8bxwhq9w 1
 
3.3%
zssl3mtxzv5qsfuc/yekvozas5fnoo/zsyjvk3rzvjy 1
 
3.3%
zsstgfsjdv99ckgbj4x39gy1j2sntf6qiarc0ecv4qo 1
 
3.3%
Other values (20) 20
66.7%
2024-03-13T20:57:42.646238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 50
 
3.8%
z 45
 
3.4%
C 33
 
2.5%
= 30
 
2.3%
T 29
 
2.2%
H 27
 
2.0%
G 26
 
2.0%
j 25
 
1.9%
u 25
 
1.9%
k 25
 
1.9%
Other values (55) 1005
76.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 556
42.1%
Lowercase Letter 512
38.8%
Decimal Number 177
 
13.4%
Math Symbol 51
 
3.9%
Other Punctuation 24
 
1.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 50
 
9.0%
C 33
 
5.9%
T 29
 
5.2%
H 27
 
4.9%
G 26
 
4.7%
D 25
 
4.5%
J 24
 
4.3%
M 24
 
4.3%
K 22
 
4.0%
A 22
 
4.0%
Other values (16) 274
49.3%
Lowercase Letter
ValueCountFrequency (%)
z 45
 
8.8%
j 25
 
4.9%
u 25
 
4.9%
k 25
 
4.9%
c 24
 
4.7%
o 24
 
4.7%
p 23
 
4.5%
t 23
 
4.5%
s 23
 
4.5%
i 20
 
3.9%
Other values (16) 255
49.8%
Decimal Number
ValueCountFrequency (%)
8 23
13.0%
9 22
12.4%
1 21
11.9%
6 21
11.9%
4 21
11.9%
5 16
9.0%
3 15
8.5%
7 13
7.3%
0 13
7.3%
2 12
6.8%
Math Symbol
ValueCountFrequency (%)
= 30
58.8%
+ 21
41.2%
Other Punctuation
ValueCountFrequency (%)
/ 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1068
80.9%
Common 252
 
19.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 50
 
4.7%
z 45
 
4.2%
C 33
 
3.1%
T 29
 
2.7%
H 27
 
2.5%
G 26
 
2.4%
j 25
 
2.3%
u 25
 
2.3%
k 25
 
2.3%
D 25
 
2.3%
Other values (42) 758
71.0%
Common
ValueCountFrequency (%)
= 30
11.9%
/ 24
9.5%
8 23
9.1%
9 22
8.7%
1 21
8.3%
6 21
8.3%
+ 21
8.3%
4 21
8.3%
5 16
6.3%
3 15
 
6.0%
Other values (3) 38
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 50
 
3.8%
z 45
 
3.4%
C 33
 
2.5%
= 30
 
2.3%
T 29
 
2.2%
H 27
 
2.0%
G 26
 
2.0%
j 25
 
1.9%
u 25
 
1.9%
k 25
 
1.9%
Other values (55) 1005
76.1%

회원코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0229958 × 109
Minimum3.0020088 × 109
Maximum3.0709729 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:57:42.785049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0020088 × 109
5-th percentile3.0041276 × 109
Q13.01699 × 109
median3.0182944 × 109
Q33.0219406 × 109
95-th percentile3.061423 × 109
Maximum3.0709729 × 109
Range68964162
Interquartile range (IQR)4950672.2

Descriptive statistics

Standard deviation16435638
Coefficient of variation (CV)0.0054368708
Kurtosis2.7797535
Mean3.0229958 × 109
Median Absolute Deviation (MAD)1550684
Skewness1.7636639
Sum9.0689874 × 1010
Variance2.7013019 × 1014
MonotonicityNot monotonic
2024-03-13T20:57:42.930211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3019431578 1
 
3.3%
3022631227 1
 
3.3%
3016657566 1
 
3.3%
3002008777 1
 
3.3%
3018008983 1
 
3.3%
3014243174 1
 
3.3%
3019164293 1
 
3.3%
3070972939 1
 
3.3%
3002460425 1
 
3.3%
3018259983 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3002008777 1
3.3%
3002460425 1
3.3%
3006165241 1
3.3%
3008992351 1
3.3%
3014243174 1
3.3%
3016657566 1
3.3%
3016829865 1
3.3%
3016939760 1
3.3%
3017140593 1
3.3%
3017670200 1
3.3%
ValueCountFrequency (%)
3070972939 1
3.3%
3061541952 1
3.3%
3061277591 1
3.3%
3046861489 1
3.3%
3036894242 1
3.3%
3022965128 1
3.3%
3022631227 1
3.3%
3022302147 1
3.3%
3020856121 1
3.3%
3019431578 1
3.3%

가맹점번호
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.273511 × 1014
Minimum7.1536234 × 108
Maximum1 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:57:43.065425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.1536234 × 108
5-th percentile7.2192345 × 108
Q14.1020247 × 1014
median1 × 1015
Q31 × 1015
95-th percentile1 × 1015
Maximum1 × 1015
Range9.9999928 × 1014
Interquartile range (IQR)5.8979753 × 1014

Descriptive statistics

Standard deviation4.3412395 × 1014
Coefficient of variation (CV)0.59685611
Kurtosis-0.83475025
Mean7.273511 × 1014
Median Absolute Deviation (MAD)0
Skewness-1.0529272
Sum2.1820533 × 1016
Variance1.884636 × 1029
MonotonicityNot monotonic
2024-03-13T20:57:43.170294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
999999999999999 21
70.0%
722362113 1
 
3.3%
725500984 1
 
3.3%
721564542 1
 
3.3%
410386700038101 1
 
3.3%
410141058307801 1
 
3.3%
722938162 1
 
3.3%
791720606 1
 
3.3%
715362335 1
 
3.3%
729965840 1
 
3.3%
ValueCountFrequency (%)
715362335 1
 
3.3%
721564542 1
 
3.3%
722362113 1
 
3.3%
722938162 1
 
3.3%
725500984 1
 
3.3%
729965840 1
 
3.3%
791720606 1
 
3.3%
410141058307801 1
 
3.3%
410386700038101 1
 
3.3%
999999999999999 21
70.0%
ValueCountFrequency (%)
999999999999999 21
70.0%
410386700038101 1
 
3.3%
410141058307801 1
 
3.3%
791720606 1
 
3.3%
729965840 1
 
3.3%
725500984 1
 
3.3%
722938162 1
 
3.3%
722362113 1
 
3.3%
721564542 1
 
3.3%
715362335 1
 
3.3%

성별코드
Categorical

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
M
16 
F
14 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
M 16
53.3%
F 14
46.7%

Length

2024-03-13T20:57:43.299767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:57:43.405536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 16
53.3%
f 14
46.7%

연령대코드
Real number (ℝ)

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38
Minimum20
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:57:43.517163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q130
median35
Q350
95-th percentile65.5
Maximum70
Range50
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.715697
Coefficient of variation (CV)0.38725517
Kurtosis-0.31952744
Mean38
Median Absolute Deviation (MAD)10
Skewness0.64832075
Sum1140
Variance216.55172
MonotonicityNot monotonic
2024-03-13T20:57:43.627124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
30 9
30.0%
20 6
20.0%
40 6
20.0%
50 5
16.7%
60 2
 
6.7%
70 2
 
6.7%
ValueCountFrequency (%)
20 6
20.0%
30 9
30.0%
40 6
20.0%
50 5
16.7%
60 2
 
6.7%
70 2
 
6.7%
ValueCountFrequency (%)
70 2
 
6.7%
60 2
 
6.7%
50 5
16.7%
40 6
20.0%
30 9
30.0%
20 6
20.0%

결제상품ID
Real number (ℝ)

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4000011 × 1011
Minimum1.4000002 × 1011
Maximum1.4000063 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:57:43.735222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000002 × 1011
5-th percentile1.4000002 × 1011
Q11.4000005 × 1011
median1.4000011 × 1011
Q31.4000012 × 1011
95-th percentile1.4000013 × 1011
Maximum1.4000063 × 1011
Range614000
Interquartile range (IQR)75000

Descriptive statistics

Standard deviation106656.2
Coefficient of variation (CV)7.6182942 × 10-7
Kurtosis22.259516
Mean1.4000011 × 1011
Median Absolute Deviation (MAD)18000
Skewness4.3877185
Sum4.2000032 × 1012
Variance1.1375545 × 1010
MonotonicityNot monotonic
2024-03-13T20:57:43.852455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
140000124000 4
 
13.3%
140000126000 4
 
13.3%
140000112000 3
 
10.0%
140000024000 2
 
6.7%
140000046000 2
 
6.7%
140000634000 1
 
3.3%
140000078000 1
 
3.3%
140000020000 1
 
3.3%
140000044000 1
 
3.3%
140000092000 1
 
3.3%
Other values (10) 10
33.3%
ValueCountFrequency (%)
140000020000 1
3.3%
140000024000 2
6.7%
140000030000 1
3.3%
140000040000 1
3.3%
140000044000 1
3.3%
140000046000 2
6.7%
140000058000 1
3.3%
140000062000 1
3.3%
140000078000 1
3.3%
140000084000 1
3.3%
ValueCountFrequency (%)
140000634000 1
 
3.3%
140000126000 4
13.3%
140000124000 4
13.3%
140000120000 1
 
3.3%
140000116000 1
 
3.3%
140000114000 1
 
3.3%
140000112000 3
10.0%
140000104000 1
 
3.3%
140000102000 1
 
3.3%
140000092000 1
 
3.3%

결제상품명
Text

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:57:44.023423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length12
Mean length8.1
Min length4

Characters and Unicode

Total characters243
Distinct characters61
Distinct categories7 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)50.0%

Sample

1st row수원페이(수원이)
2nd row군포愛머니
3rd row의왕사랑상품권(통합)
4th row수원페이(통합)
5th row군포愛머니
ValueCountFrequency (%)
수원페이 4
 
10.0%
다온 4
 
10.0%
안산사랑상품권 4
 
10.0%
군포愛머니 3
 
7.5%
광주사랑카드 2
 
5.0%
용인와이페이 2
 
5.0%
thank 2
 
5.0%
you 2
 
5.0%
행복화성지역화폐 1
 
2.5%
수원페이(수원이 1
 
2.5%
Other values (15) 15
37.5%
2024-03-13T20:57:44.388384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 13
 
5.3%
사 12
 
4.9%
랑 12
 
4.9%
10
 
4.1%
페 10
 
4.1%
( 9
 
3.7%
) 9
 
3.7%
합 7
 
2.9%
통 7
 
2.9%
원 7
 
2.9%
Other values (51) 147
60.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 186
76.5%
Lowercase Letter 18
 
7.4%
Space Separator 10
 
4.1%
Open Punctuation 9
 
3.7%
Close Punctuation 9
 
3.7%
Uppercase Letter 9
 
3.7%
Dash Punctuation 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 13
 
7.0%
사 12
 
6.5%
랑 12
 
6.5%
페 10
 
5.4%
합 7
 
3.8%
통 7
 
3.8%
원 7
 
3.8%
수 7
 
3.8%
주 6
 
3.2%
화 6
 
3.2%
Other values (36) 99
53.2%
Lowercase Letter
ValueCountFrequency (%)
a 5
27.8%
y 3
16.7%
u 2
 
11.1%
o 2
 
11.1%
k 2
 
11.1%
n 2
 
11.1%
h 2
 
11.1%
Uppercase Letter
ValueCountFrequency (%)
P 3
33.3%
N 2
22.2%
T 2
22.2%
Y 2
22.2%
Space Separator
ValueCountFrequency (%)
10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 183
75.3%
Common 30
 
12.3%
Latin 27
 
11.1%
Han 3
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 13
 
7.1%
사 12
 
6.6%
랑 12
 
6.6%
페 10
 
5.5%
합 7
 
3.8%
통 7
 
3.8%
원 7
 
3.8%
수 7
 
3.8%
주 6
 
3.3%
화 6
 
3.3%
Other values (35) 96
52.5%
Latin
ValueCountFrequency (%)
a 5
18.5%
P 3
11.1%
y 3
11.1%
N 2
 
7.4%
u 2
 
7.4%
T 2
 
7.4%
o 2
 
7.4%
k 2
 
7.4%
n 2
 
7.4%
h 2
 
7.4%
Common
ValueCountFrequency (%)
10
33.3%
( 9
30.0%
) 9
30.0%
- 2
 
6.7%
Han
ValueCountFrequency (%)
愛 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 183
75.3%
ASCII 57
 
23.5%
CJK 3
 
1.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 13
 
7.1%
사 12
 
6.6%
랑 12
 
6.6%
페 10
 
5.5%
합 7
 
3.8%
통 7
 
3.8%
원 7
 
3.8%
수 7
 
3.8%
주 6
 
3.3%
화 6
 
3.3%
Other values (35) 96
52.5%
ASCII
ValueCountFrequency (%)
10
17.5%
( 9
15.8%
) 9
15.8%
a 5
8.8%
P 3
 
5.3%
y 3
 
5.3%
N 2
 
3.5%
u 2
 
3.5%
- 2
 
3.5%
T 2
 
3.5%
Other values (5) 10
17.5%
CJK
ValueCountFrequency (%)
愛 3
100.0%

가맹점업종명
Text

MISSING 

Distinct5
Distinct (%)71.4%
Missing23
Missing (%)76.7%
Memory size372.0 B
2024-03-13T20:57:44.531327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.5714286
Min length4

Characters and Unicode

Total characters32
Distinct characters18
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

Unique3 ?
Unique (%)42.9%

Sample

1st row레져용품
2nd row음료식품
3rd row서적문구
4th row일반휴게음식
5th row일반휴게음식
ValueCountFrequency (%)
음료식품 2
28.6%
일반휴게음식 2
28.6%
레져용품 1
14.3%
서적문구 1
14.3%
레저업소 1
14.3%
2024-03-13T20:57:44.869400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
음 4
12.5%
식 4
12.5%
품 3
 
9.4%
일 2
 
6.2%
반 2
 
6.2%
휴 2
 
6.2%
게 2
 
6.2%
레 2
 
6.2%
료 2
 
6.2%
문 1
 
3.1%
Other values (8) 8
25.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
음 4
12.5%
식 4
12.5%
품 3
 
9.4%
일 2
 
6.2%
반 2
 
6.2%
휴 2
 
6.2%
게 2
 
6.2%
레 2
 
6.2%
료 2
 
6.2%
문 1
 
3.1%
Other values (8) 8
25.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
음 4
12.5%
식 4
12.5%
품 3
 
9.4%
일 2
 
6.2%
반 2
 
6.2%
휴 2
 
6.2%
게 2
 
6.2%
레 2
 
6.2%
료 2
 
6.2%
문 1
 
3.1%
Other values (8) 8
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
음 4
12.5%
식 4
12.5%
품 3
 
9.4%
일 2
 
6.2%
반 2
 
6.2%
휴 2
 
6.2%
게 2
 
6.2%
레 2
 
6.2%
료 2
 
6.2%
문 1
 
3.1%
Other values (8) 8
25.0%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)100.0%
Missing23
Missing (%)76.7%
Infinite0
Infinite (%)0.0%
Mean15651.571
Minimum12102
Maximum18598
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:57:44.974893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12102
5-th percentile12743.4
Q115030.5
median15885
Q316457.5
95-th percentile18070.9
Maximum18598
Range6496
Interquartile range (IQR)1427

Descriptive statistics

Standard deviation2039.1916
Coefficient of variation (CV)0.1302867
Kurtosis1.0804465
Mean15651.571
Median Absolute Deviation (MAD)956
Skewness-0.55530434
Sum109561
Variance4158302.3
MonotonicityNot monotonic
2024-03-13T20:57:45.077156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
16074 1
 
3.3%
15821 1
 
3.3%
12102 1
 
3.3%
16841 1
 
3.3%
18598 1
 
3.3%
14240 1
 
3.3%
15885 1
 
3.3%
(Missing) 23
76.7%
ValueCountFrequency (%)
12102 1
3.3%
14240 1
3.3%
15821 1
3.3%
15885 1
3.3%
16074 1
3.3%
16841 1
3.3%
18598 1
3.3%
ValueCountFrequency (%)
18598 1
3.3%
16841 1
3.3%
16074 1
3.3%
15885 1
3.3%
15821 1
3.3%
14240 1
3.3%
12102 1
3.3%

시도명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
23 
경기도
6 
NONE
 
1

Length

Max length4
Median length4
Mean length3.8
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row<NA>
2nd row<NA>
3rd row경기도
4th row<NA>
5th row경기도

Common Values

ValueCountFrequency (%)
<NA> 23
76.7%
경기도 6
 
20.0%
NONE 1
 
3.3%

Length

2024-03-13T20:57:45.217037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:57:45.325412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 23
76.7%
경기도 6
 
20.0%
none 1
 
3.3%

시군구명
Text

MISSING 

Distinct6
Distinct (%)100.0%
Missing24
Missing (%)80.0%
Memory size372.0 B
2024-03-13T20:57:45.454166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.8333333
Min length3

Characters and Unicode

Total characters23
Distinct characters18
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

Unique6 ?
Unique (%)100.0%

Sample

1st row의왕시
2nd row군포시
3rd row남양주시
4th row용인시 수지구
5th row화성시
ValueCountFrequency (%)
의왕시 1
14.3%
군포시 1
14.3%
남양주시 1
14.3%
용인시 1
14.3%
수지구 1
14.3%
화성시 1
14.3%
광명시 1
14.3%
2024-03-13T20:57:46.088784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 6
26.1%
의 1
 
4.3%
1
 
4.3%
광 1
 
4.3%
성 1
 
4.3%
화 1
 
4.3%
구 1
 
4.3%
지 1
 
4.3%
수 1
 
4.3%
인 1
 
4.3%
Other values (8) 8
34.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22
95.7%
Space Separator 1
 
4.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 6
27.3%
의 1
 
4.5%
광 1
 
4.5%
성 1
 
4.5%
화 1
 
4.5%
구 1
 
4.5%
지 1
 
4.5%
수 1
 
4.5%
인 1
 
4.5%
왕 1
 
4.5%
Other values (7) 7
31.8%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22
95.7%
Common 1
 
4.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 6
27.3%
의 1
 
4.5%
광 1
 
4.5%
성 1
 
4.5%
화 1
 
4.5%
구 1
 
4.5%
지 1
 
4.5%
수 1
 
4.5%
인 1
 
4.5%
왕 1
 
4.5%
Other values (7) 7
31.8%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22
95.7%
ASCII 1
 
4.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 6
27.3%
의 1
 
4.5%
광 1
 
4.5%
성 1
 
4.5%
화 1
 
4.5%
구 1
 
4.5%
지 1
 
4.5%
수 1
 
4.5%
인 1
 
4.5%
왕 1
 
4.5%
Other values (7) 7
31.8%
ASCII
ValueCountFrequency (%)
1
100.0%

읍면동명
Text

MISSING 

Distinct6
Distinct (%)100.0%
Missing24
Missing (%)80.0%
Memory size372.0 B
2024-03-13T20:57:46.247633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1666667
Min length3

Characters and Unicode

Total characters19
Distinct characters13
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

Unique6 ?
Unique (%)100.0%

Sample

1st row고천동
2nd row산본동
3rd row별내동
4th row풍덕천동
5th row향남읍
ValueCountFrequency (%)
고천동 1
16.7%
산본동 1
16.7%
별내동 1
16.7%
풍덕천동 1
16.7%
향남읍 1
16.7%
철산동 1
16.7%
2024-03-13T20:57:46.569543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 5
26.3%
천 2
 
10.5%
산 2
 
10.5%
고 1
 
5.3%
본 1
 
5.3%
별 1
 
5.3%
내 1
 
5.3%
풍 1
 
5.3%
덕 1
 
5.3%
향 1
 
5.3%
Other values (3) 3
15.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 5
26.3%
천 2
 
10.5%
산 2
 
10.5%
고 1
 
5.3%
본 1
 
5.3%
별 1
 
5.3%
내 1
 
5.3%
풍 1
 
5.3%
덕 1
 
5.3%
향 1
 
5.3%
Other values (3) 3
15.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 5
26.3%
천 2
 
10.5%
산 2
 
10.5%
고 1
 
5.3%
본 1
 
5.3%
별 1
 
5.3%
내 1
 
5.3%
풍 1
 
5.3%
덕 1
 
5.3%
향 1
 
5.3%
Other values (3) 3
15.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 19
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 5
26.3%
천 2
 
10.5%
산 2
 
10.5%
고 1
 
5.3%
본 1
 
5.3%
별 1
 
5.3%
내 1
 
5.3%
풍 1
 
5.3%
덕 1
 
5.3%
향 1
 
5.3%
Other values (3) 3
15.8%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4763333
Minimum0
Maximum37.651
Zeros24
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:57:46.692780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile37.4246
Maximum37.651
Range37.651
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.208456
Coefficient of variation (CV)2.0342133
Kurtosis0.52791302
Mean7.4763333
Median Absolute Deviation (MAD)0
Skewness1.5802185
Sum224.29
Variance231.29715
MonotonicityNot monotonic
2024-03-13T20:57:46.810209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.0 24
80.0%
37.351 1
 
3.3%
37.363 1
 
3.3%
37.651 1
 
3.3%
37.321 1
 
3.3%
37.129 1
 
3.3%
37.475 1
 
3.3%
ValueCountFrequency (%)
0.0 24
80.0%
37.129 1
 
3.3%
37.321 1
 
3.3%
37.351 1
 
3.3%
37.363 1
 
3.3%
37.475 1
 
3.3%
37.651 1
 
3.3%
ValueCountFrequency (%)
37.651 1
 
3.3%
37.475 1
 
3.3%
37.363 1
 
3.3%
37.351 1
 
3.3%
37.321 1
 
3.3%
37.129 1
 
3.3%
0.0 24
80.0%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.3962
Minimum0
Maximum127.113
Zeros24
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:57:46.965225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile127.03685
Maximum127.113
Range127.113
Interquartile range (IQR)0

Descriptive statistics

Standard deviation51.660726
Coefficient of variation (CV)2.0341912
Kurtosis0.52746058
Mean25.3962
Median Absolute Deviation (MAD)0
Skewness1.5801327
Sum761.886
Variance2668.8306
MonotonicityNot monotonic
2024-03-13T20:57:47.106503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.0 24
80.0%
126.967 1
 
3.3%
126.925 1
 
3.3%
127.113 1
 
3.3%
127.094 1
 
3.3%
126.919 1
 
3.3%
126.868 1
 
3.3%
ValueCountFrequency (%)
0.0 24
80.0%
126.868 1
 
3.3%
126.919 1
 
3.3%
126.925 1
 
3.3%
126.967 1
 
3.3%
127.094 1
 
3.3%
127.113 1
 
3.3%
ValueCountFrequency (%)
127.113 1
 
3.3%
127.094 1
 
3.3%
126.967 1
 
3.3%
126.925 1
 
3.3%
126.919 1
 
3.3%
126.868 1
 
3.3%
0.0 24
80.0%

사용여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
21 
True
9 
ValueCountFrequency (%)
False 21
70.0%
True 9
30.0%
2024-03-13T20:57:47.214695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18672.667
Minimum0
Maximum220000
Zeros21
Zeros (%)70.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:57:47.346799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33375
95-th percentile139375
Maximum220000
Range220000
Interquartile range (IQR)3375

Descriptive statistics

Standard deviation55115.786
Coefficient of variation (CV)2.9516826
Kurtosis10.723135
Mean18672.667
Median Absolute Deviation (MAD)0
Skewness3.3867636
Sum560180
Variance3.0377499 × 109
MonotonicityNot monotonic
2024-03-13T20:57:47.462808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 21
70.0%
212500 1
 
3.3%
5300 1
 
3.3%
220000 1
 
3.3%
11900 1
 
3.3%
47480 1
 
3.3%
1500 1
 
3.3%
7500 1
 
3.3%
50000 1
 
3.3%
4000 1
 
3.3%
ValueCountFrequency (%)
0 21
70.0%
1500 1
 
3.3%
4000 1
 
3.3%
5300 1
 
3.3%
7500 1
 
3.3%
11900 1
 
3.3%
47480 1
 
3.3%
50000 1
 
3.3%
212500 1
 
3.3%
220000 1
 
3.3%
ValueCountFrequency (%)
220000 1
 
3.3%
212500 1
 
3.3%
50000 1
 
3.3%
47480 1
 
3.3%
11900 1
 
3.3%
7500 1
 
3.3%
5300 1
 
3.3%
4000 1
 
3.3%
1500 1
 
3.3%
0 21
70.0%

Interactions

2024-03-13T20:57:40.297655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:34.631676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:35.418148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:36.206254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:36.991343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:37.677217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:38.508173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:39.541268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:40.412968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:34.737020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:35.503644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:36.302310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:37.087385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:37.770384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:38.604821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:39.626482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:40.502674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:34.835898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:35.595540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:36.395300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:37.194940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:37.861637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:38.688732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:39.709684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:40.595721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:34.942464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:35.690362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:36.499695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:37.269800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:38.007418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:38.781363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:39.787165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:40.685518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:35.068694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:35.851350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:36.615951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:37.352445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:38.135192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:39.176690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:39.876872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:40.771551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:35.161480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:35.955200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:36.709171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:37.428181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:38.239741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:39.284391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:39.962658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:40.866627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:35.253737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:36.040398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:36.811955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:37.510977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:38.335500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:39.370129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:40.066389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:40.968533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:35.336452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:36.128807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:36.917306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:37.601561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:38.432073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:39.457108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:40.180663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:57:47.552839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
카드번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
회원코드1.0001.0000.6560.3180.0000.1810.9120.8591.000NaN1.0001.0000.6290.6290.7280.522
가맹점번호1.0000.6561.0000.0000.0000.0000.750NaNNaNNaNNaNNaN0.6170.6171.0000.880
성별코드1.0000.3180.0001.0000.0000.1410.4320.4650.0000.0001.0001.0000.0000.0000.0000.000
연령대코드1.0000.0000.0000.0001.0000.2880.8520.5440.7590.0001.0001.0000.0000.0000.0000.000
결제상품ID1.0000.1810.0000.1410.2881.0001.0000.7041.0000.0001.0001.0000.0000.0000.0000.000
결제상품명1.0000.9120.7500.4320.8521.0001.0001.0001.0000.0001.0001.0000.7180.7180.6581.000
가맹점업종명1.0000.859NaN0.4650.5440.7041.0001.0001.0000.0001.0001.0000.0000.000NaN1.000
가맹점우편번호1.0001.000NaN0.0000.7591.0001.0001.0001.0000.0001.0001.0000.0000.000NaN1.000
시도명1.000NaNNaN0.0000.0000.0000.0000.0000.0001.000NaNNaN0.2930.293NaN0.000
시군구명1.0001.000NaN1.0001.0001.0001.0001.0001.000NaN1.0001.000NaNNaNNaN1.000
읍면동명1.0001.000NaN1.0001.0001.0001.0001.0001.000NaN1.0001.000NaNNaNNaN1.000
위도1.0000.6290.6170.0000.0000.0000.7180.0000.0000.293NaNNaN1.0000.9860.8600.340
경도1.0000.6290.6170.0000.0000.0000.7180.0000.0000.293NaNNaN0.9861.0000.8600.340
사용여부1.0000.7281.0000.0000.0000.0000.658NaNNaNNaNNaNNaN0.8600.8601.0000.351
결제금액1.0000.5220.8800.0000.0000.0001.0001.0001.0000.0001.0001.0000.3400.3400.3511.000
2024-03-13T20:57:47.730283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용여부성별코드시도명
사용여부1.0000.0001.000
성별코드0.0001.0000.000
시도명1.0000.0001.000
2024-03-13T20:57:47.829911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회원코드가맹점번호연령대코드결제상품ID가맹점우편번호위도경도결제금액성별코드시도명사용여부
회원코드1.0000.1760.4910.0710.2860.0640.079-0.1450.2250.0000.511
가맹점번호0.1761.0000.0740.2750.679-0.849-0.839-0.9760.0001.0000.982
연령대코드0.4910.0741.000-0.1340.3300.0440.078-0.0890.0000.0000.000
결제상품ID0.0710.275-0.1341.000-0.342-0.239-0.223-0.2590.2610.0000.000
가맹점우편번호0.2860.6790.330-0.3421.000-0.786-0.107-0.5000.0000.0001.000
위도0.064-0.8490.044-0.239-0.7861.0000.9870.7810.0000.0910.658
경도0.079-0.8390.078-0.223-0.1070.9871.0000.7680.0000.0910.658
결제금액-0.145-0.976-0.089-0.259-0.5000.7810.7681.0000.0000.0000.548
성별코드0.2250.0000.0000.2610.0000.0000.0000.0001.0000.0000.000
시도명0.0001.0000.0000.0000.0000.0910.0910.0000.0001.0001.000
사용여부0.5110.9820.0000.0001.0000.6580.6580.5480.0001.0001.000

Missing values

2024-03-13T20:57:41.105704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:57:41.381541image/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.
2024-03-13T20:57:41.526824image/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

정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
02022-08-082022-08-14zTN356bDM/KjZHcjDaut2xLAJSwgaCaOhKHEWtngk14=3019431578999999999999999M20140000634000수원페이(수원이)<NA><NA><NA><NA><NA>0.00.0N0
12022-08-082022-08-14++HCWLRQHriKSA4IG8FFn1JzHkP/buv/yeDKFAuc4uw=3017140593999999999999999M30140000112000군포愛머니<NA><NA><NA><NA><NA>0.00.0N0
22022-08-082022-08-14xV9MtxyrnHpyYhSlVK8tVtS6fVjpO5TIGmz6u/6S+kI=3036894242722362113M30140000062000의왕사랑상품권(통합)레져용품16074경기도의왕시고천동37.351126.967Y212500
32022-08-082022-08-14zTGTwu6gMZuKC5Ol/BNaq3kARbAfMzS7hxa3JvrF8sM=3018064380999999999999999M40140000102000수원페이(통합)<NA><NA><NA><NA><NA>0.00.0N0
42022-08-082022-08-14zTFCV6T/1GCHD+7smh49QNsJw7AGp68JpXPdh8V0doM=3020856121725500984M50140000112000군포愛머니음료식품15821경기도군포시산본동37.363126.925Y5300
52022-08-082022-08-14zTE3aBSRz+UIcg/4XliCc/DxZa4wKDu3cGKtCrm7tF0=3018328816721564542F40140000114000Thank You Pay-N서적문구12102경기도남양주시별내동37.651127.113Y220000
62022-08-082022-08-14zT7WQx+Mp8QC79YuJCmi+O4OlhS5Mqq5028TkZNsmyw=3061277591999999999999999M60140000040000여주사랑카드<NA><NA><NA><NA><NA>0.00.0N0
72022-08-082022-08-14++GshZ/F/1CmTRQ08aZLDuox+oMFMEEodTcpTe1pA1c=3017858246999999999999999M20140000030000부천페이<NA><NA><NA><NA><NA>0.00.0N0
82022-08-082022-08-14zT4sqjfcok2LLQ6+BHZ1S9DfiiR8QzlaGuv2ZYjHECQ=3008992351410386700038101M30140000024000광주사랑카드<NA><NA><NA><NA><NA>0.00.0Y11900
92022-08-082022-08-14zRv97p/cMjaDKHYaotYBtbteT9HRkiLNnxxViIWn/Yo=3018761275999999999999999F50140000046000용인와이페이<NA><NA><NA><NA><NA>0.00.0N0
정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
202022-08-082022-08-14zSUMxBcP7k/9duPJAN40fn8HvfC2TP3agn1CtgzVdqY=3019128789999999999999999M40140000126000수원페이<NA><NA><NA><NA><NA>0.00.0N0
212022-08-082022-08-14zSStGFsjDV99CkGBJ4x39GY1J2Sntf6qIarc0eCV4qo=3016829865999999999999999F40140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
222022-08-082022-08-14zSSl3MtXzV5QSfUC/yEKVozaS5fNoo/zSyjVK3rzvJY=3018259983999999999999999F30140000126000수원페이<NA><NA><NA><NA><NA>0.00.0N0
232022-08-082022-08-14zSQlJUYV2BlnTHeUmsyLI78ZeZTdysL4i/e8bXWHQ9w=3002460425999999999999999M20140000058000평택사랑카드(통합)<NA><NA><NA><NA><NA>0.00.0N0
242022-08-082022-08-14zSJBmhtW8/CwSNvNzKmHXKPTDm/r+AMbjHDBjtPI5io=3070972939791720606F50140000092000행복화성지역화폐(통합)일반휴게음식18598경기도화성시향남읍37.129126.919Y7500
252022-08-082022-08-14zSG2UVu85GOuwKcJuXdKCXtFSW/NA+pZeXHU9irmftY=3019164293999999999999999F70140000044000오산화폐 오색전<NA><NA><NA><NA><NA>0.00.0N0
262022-08-082022-08-14zSEBYBi1RHw6ok+3pYTCio9APTsje3hkKImxBJdKOkY=3014243174715362335M20140000020000광명사랑화폐레저업소14240경기도광명시철산동37.475126.868Y50000
272022-08-082022-08-14zSBpGdWDMuRPrDjjwevcgCj7azpGV2HpKJHk+k6tcS0=3018008983999999999999999F30140000126000수원페이<NA><NA><NA><NA><NA>0.00.0N0
282022-08-082022-08-14zSAPEPlHuZI9Z3GuTUXFTqOdl5LWo+p1EMSJo9NwWRk=3002008777729965840F30140000112000군포愛머니음료식품15885NONE<NA><NA>0.00.0Y4000
292022-08-082022-08-14zS9WDDrul+/SOiyjPD25flsvZ1CzeBEbuXNoROl4Ec8=3016657566999999999999999F50140000126000수원페이<NA><NA><NA><NA><NA>0.00.0N0