Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells30
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory156.4 B

Variable types

Categorical5
Text4
Numeric8
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/a73a8f3a-0be6-4ad9-970a-118eb8cb3096

Alerts

정책주간결제시작일자 has constant value ""Constant
정책주간결제종료일자 has constant value ""Constant
사용여부 is highly overall correlated with 가맹점번호 and 5 other fieldsHigh correlation
성별코드 is highly overall correlated with 시도명High correlation
시도명 is highly overall correlated with 회원코드 and 10 other fieldsHigh correlation
가맹점업종명 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
회원코드 is highly overall correlated with 시도명High correlation
가맹점번호 is highly overall correlated with 위도 and 5 other fieldsHigh correlation
연령대코드 is highly overall correlated with 시도명High correlation
결제상품ID is highly overall correlated with 시도명High correlation
가맹점우편번호 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 가맹점번호 and 6 other fieldsHigh correlation
경도 is highly overall correlated with 가맹점번호 and 5 other fieldsHigh correlation
결제금액 is highly overall correlated with 가맹점번호 and 3 other fieldsHigh correlation
가맹점우편번호 has 10 (33.3%) missing valuesMissing
시군구명 has 10 (33.3%) missing valuesMissing
읍면동명 has 10 (33.3%) missing valuesMissing
카드번호 has unique valuesUnique
회원코드 has unique valuesUnique
위도 has 10 (33.3%) zerosZeros
경도 has 10 (33.3%) zerosZeros
결제금액 has 10 (33.3%) zerosZeros

Reproduction

Analysis started2024-03-13 11:56:33.091318
Analysis finished2024-03-13 11:56:41.082205
Duration7.99 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
2021-03-01
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-03-01
2nd row2021-03-01
3rd row2021-03-01
4th row2021-03-01
5th row2021-03-01

Common Values

ValueCountFrequency (%)
2021-03-01 30
100.0%

Length

2024-03-13T20:56:41.157784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:56:41.256569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-03-01 30
100.0%

정책주간결제종료일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2021-03-07
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-03-07
2nd row2021-03-07
3rd row2021-03-07
4th row2021-03-07
5th row2021-03-07

Common Values

ValueCountFrequency (%)
2021-03-07 30
100.0%

Length

2024-03-13T20:56:41.362908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:56:41.475334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-03-07 30
100.0%

카드번호
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:56:41.686033image/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 row++5IeEX92t/OTjD88vQv7dFuzHnzSG2Wi0sbvL++yRo=
2nd rowytQK7AxHYVxI149mPlV34WeO96rI9AIZDrrZrTF9z9s=
3rd rowW+38kRNEjZrqZRPk0a8QgRtyARGV0SGIKTvLsXnJeWc=
4th row1VaJI+zwjOGuVtH7oldNFjUAroPCaEjF9s8AElWzTnU=
5th rowA79XV83Q1+PwsB8yBczAJ4nXQNPs/5IRicV4kgSEZ4c=
ValueCountFrequency (%)
5ieex92t/otjd88vqv7dfuzhnzsg2wi0sbvl++yro 1
 
3.3%
ytqk7axhyvxi149mplv34weo96ri9aizdrrzrtf9z9s 1
 
3.3%
d+yxd49bgkt7ovjqmlve0ynkbat0gujwor1qagpe2s 1
 
3.3%
awzyvexpbg5qc7uhzf2mv1gg/+yswyf2praluubtm 1
 
3.3%
p/9fvgp2pfpwacjbbwutjjj1cat8wk1yzr2taolrm4i 1
 
3.3%
my3jfarfdvfb2si+fpmwwudb0lprkyor5iq1oniohh8 1
 
3.3%
ktqcwgkhbzn0c/scvt2r7iun3qo0tieghep6dp6yeae 1
 
3.3%
ijo1fyhikp7vxxzov996tzp1vpw96ua5vxcldikm4xc 1
 
3.3%
gbuixvrymbscw2j+pkx9pmqhobgoic3hg+ufzaegura 1
 
3.3%
evv+wwffeaatofutvoosbu7wuwtixwgxawdkyytf5xs 1
 
3.3%
Other values (20) 20
66.7%
2024-03-13T20:56:42.161677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 32
 
2.4%
+ 31
 
2.3%
I 30
 
2.3%
w 30
 
2.3%
= 30
 
2.3%
r 28
 
2.1%
G 26
 
2.0%
A 25
 
1.9%
t 25
 
1.9%
T 25
 
1.9%
Other values (55) 1038
78.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 546
41.4%
Lowercase Letter 491
37.2%
Decimal Number 203
 
15.4%
Math Symbol 61
 
4.6%
Other Punctuation 19
 
1.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 32
 
5.9%
I 30
 
5.5%
G 26
 
4.8%
A 25
 
4.6%
T 25
 
4.6%
V 24
 
4.4%
F 23
 
4.2%
Q 23
 
4.2%
X 22
 
4.0%
Y 22
 
4.0%
Other values (16) 294
53.8%
Lowercase Letter
ValueCountFrequency (%)
w 30
 
6.1%
r 28
 
5.7%
t 25
 
5.1%
o 24
 
4.9%
z 23
 
4.7%
y 23
 
4.7%
v 22
 
4.5%
b 22
 
4.5%
j 21
 
4.3%
a 21
 
4.3%
Other values (16) 252
51.3%
Decimal Number
ValueCountFrequency (%)
0 25
12.3%
5 23
11.3%
1 22
10.8%
6 22
10.8%
2 22
10.8%
9 20
9.9%
8 19
9.4%
3 18
8.9%
4 17
8.4%
7 15
7.4%
Math Symbol
ValueCountFrequency (%)
+ 31
50.8%
= 30
49.2%
Other Punctuation
ValueCountFrequency (%)
/ 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1037
78.6%
Common 283
 
21.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 32
 
3.1%
I 30
 
2.9%
w 30
 
2.9%
r 28
 
2.7%
G 26
 
2.5%
A 25
 
2.4%
t 25
 
2.4%
T 25
 
2.4%
V 24
 
2.3%
o 24
 
2.3%
Other values (42) 768
74.1%
Common
ValueCountFrequency (%)
+ 31
11.0%
= 30
10.6%
0 25
8.8%
5 23
8.1%
1 22
7.8%
6 22
7.8%
2 22
7.8%
9 20
 
7.1%
8 19
 
6.7%
/ 19
 
6.7%
Other values (3) 50
17.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 32
 
2.4%
+ 31
 
2.3%
I 30
 
2.3%
w 30
 
2.3%
= 30
 
2.3%
r 28
 
2.1%
G 26
 
2.0%
A 25
 
1.9%
t 25
 
1.9%
T 25
 
1.9%
Other values (55) 1038
78.6%

회원코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0187829 × 109
Minimum3.0018924 × 109
Maximum3.0336453 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:56:42.302848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0018924 × 109
5-th percentile3.0056635 × 109
Q13.015731 × 109
median3.0195649 × 109
Q33.0213615 × 109
95-th percentile3.0320394 × 109
Maximum3.0336453 × 109
Range31752916
Interquartile range (IQR)5630476.5

Descriptive statistics

Standard deviation7559136.2
Coefficient of variation (CV)0.0025040344
Kurtosis0.4057683
Mean3.0187829 × 109
Median Absolute Deviation (MAD)2738865
Skewness-0.19819116
Sum9.0563487 × 1010
Variance5.714054 × 1013
MonotonicityNot monotonic
2024-03-13T20:56:42.441155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3020719426 1
 
3.3%
3013904110 1
 
3.3%
3007359176 1
 
3.3%
3022597422 1
 
3.3%
3019339162 1
 
3.3%
3019169203 1
 
3.3%
3019790563 1
 
3.3%
3013747102 1
 
3.3%
3017119692 1
 
3.3%
3019326211 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3001892380 1
3.3%
3004276148 1
3.3%
3007359176 1
3.3%
3008249146 1
3.3%
3009273212 1
3.3%
3013747102 1
3.3%
3013904110 1
3.3%
3015268137 1
3.3%
3017119692 1
3.3%
3017538343 1
3.3%
ValueCountFrequency (%)
3033645296 1
3.3%
3032495227 1
3.3%
3031482229 1
3.3%
3029289243 1
3.3%
3023611198 1
3.3%
3022993101 1
3.3%
3022597422 1
3.3%
3021418188 1
3.3%
3021191445 1
3.3%
3020958074 1
3.3%

가맹점번호
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3333382 × 1014
Minimum7.0314888 × 108
Maximum1 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:56:42.605040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0314888 × 108
5-th percentile7.052359 × 108
Q17.1310403 × 108
median7.7014017 × 108
Q31 × 1015
95-th percentile1 × 1015
Maximum1 × 1015
Range9.999993 × 1014
Interquartile range (IQR)9.9999929 × 1014

Descriptive statistics

Standard deviation4.7946295 × 1014
Coefficient of variation (CV)1.4383867
Kurtosis-1.5535714
Mean3.3333382 × 1014
Median Absolute Deviation (MAD)58696240
Skewness0.74488049
Sum1.0000015 × 1016
Variance2.2988472 × 1029
MonotonicityNot monotonic
2024-03-13T20:56:42.724807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
999999999999999 10
33.3%
707317827 1
 
3.3%
710498623 1
 
3.3%
714590024 1
 
3.3%
720134139 1
 
3.3%
716293893 1
 
3.3%
721228511 1
 
3.3%
724202391 1
 
3.3%
712389235 1
 
3.3%
786852871 1
 
3.3%
Other values (11) 11
36.7%
ValueCountFrequency (%)
703148880 1
3.3%
703532497 1
3.3%
707317827 1
3.3%
710393912 1
3.3%
710498623 1
3.3%
712389235 1
3.3%
712433298 1
3.3%
712608700 1
3.3%
714590024 1
3.3%
716293893 1
3.3%
ValueCountFrequency (%)
999999999999999 10
33.3%
793931915 1
 
3.3%
790489019 1
 
3.3%
786852871 1
 
3.3%
781664133 1
 
3.3%
771262485 1
 
3.3%
769017852 1
 
3.3%
724202391 1
 
3.3%
721228511 1
 
3.3%
720134139 1
 
3.3%

성별코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
F
17 
M
13 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
F 17
56.7%
M 13
43.3%

Length

2024-03-13T20:56:42.851177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:56:42.950600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 17
56.7%
m 13
43.3%

연령대코드
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.666667
Minimum10
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:56:43.052583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile14.5
Q120
median40
Q350
95-th percentile50
Maximum60
Range50
Interquartile range (IQR)30

Descriptive statistics

Standard deviation13.817364
Coefficient of variation (CV)0.38740272
Kurtosis-1.0281758
Mean35.666667
Median Absolute Deviation (MAD)10
Skewness-0.31626879
Sum1070
Variance190.91954
MonotonicityNot monotonic
2024-03-13T20:56:43.168313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
40 9
30.0%
50 8
26.7%
20 7
23.3%
30 3
 
10.0%
10 2
 
6.7%
60 1
 
3.3%
ValueCountFrequency (%)
10 2
 
6.7%
20 7
23.3%
30 3
 
10.0%
40 9
30.0%
50 8
26.7%
60 1
 
3.3%
ValueCountFrequency (%)
60 1
 
3.3%
50 8
26.7%
40 9
30.0%
30 3
 
10.0%
20 7
23.3%
10 2
 
6.7%

결제상품ID
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4000006 × 1011
Minimum1.4000002 × 1011
Maximum1.4000013 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:56:43.290853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000002 × 1011
5-th percentile1.4000002 × 1011
Q11.4000003 × 1011
median1.4000004 × 1011
Q31.4000011 × 1011
95-th percentile1.4000012 × 1011
Maximum1.4000013 × 1011
Range108000
Interquartile range (IQR)76000

Descriptive statistics

Standard deviation39642.135
Coefficient of variation (CV)2.8315799 × 10-7
Kurtosis-1.12291
Mean1.4000006 × 1011
Median Absolute Deviation (MAD)16000
Skewness0.79888273
Sum4.2000018 × 1012
Variance1.5714989 × 109
MonotonicityNot monotonic
2024-03-13T20:56:43.420106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
140000030000 4
13.3%
140000046000 4
13.3%
140000038000 2
 
6.7%
140000116000 2
 
6.7%
140000018000 2
 
6.7%
140000124000 2
 
6.7%
140000034000 2
 
6.7%
140000126000 1
 
3.3%
140000028000 1
 
3.3%
140000024000 1
 
3.3%
Other values (9) 9
30.0%
ValueCountFrequency (%)
140000018000 2
6.7%
140000020000 1
 
3.3%
140000022000 1
 
3.3%
140000024000 1
 
3.3%
140000026000 1
 
3.3%
140000028000 1
 
3.3%
140000030000 4
13.3%
140000034000 2
6.7%
140000038000 2
6.7%
140000046000 4
13.3%
ValueCountFrequency (%)
140000126000 1
 
3.3%
140000124000 2
6.7%
140000120000 1
 
3.3%
140000116000 2
6.7%
140000114000 1
 
3.3%
140000112000 1
 
3.3%
140000088000 1
 
3.3%
140000058000 1
 
3.3%
140000052000 1
 
3.3%
140000046000 4
13.3%

결제상품명
Text

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:56:43.602981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length6.8333333
Min length4

Characters and Unicode

Total characters205
Distinct characters64
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

Unique12 ?
Unique (%)40.0%

Sample

1st row양평통보
2nd row안양사랑페이
3rd row고양페이카드
4th row안산사랑상품권 다온
5th row안양사랑페이
ValueCountFrequency (%)
부천페이 4
 
11.1%
용인와이페이 4
 
11.1%
양평통보 2
 
5.6%
고양페이카드 2
 
5.6%
안산사랑상품권 2
 
5.6%
다온 2
 
5.6%
안양사랑페이 2
 
5.6%
행복화성지역화폐 2
 
5.6%
you 1
 
2.8%
과천토리 1
 
2.8%
Other values (14) 14
38.9%
2024-03-13T20:56:43.969433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 18
 
8.8%
페 14
 
6.8%
랑 11
 
5.4%
사 11
 
5.4%
화 7
 
3.4%
천 7
 
3.4%
6
 
2.9%
양 6
 
2.9%
카 5
 
2.4%
드 5
 
2.4%
Other values (54) 115
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 177
86.3%
Lowercase Letter 10
 
4.9%
Space Separator 6
 
2.9%
Uppercase Letter 5
 
2.4%
Open Punctuation 3
 
1.5%
Close Punctuation 3
 
1.5%
Dash Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 18
 
10.2%
페 14
 
7.9%
랑 11
 
6.2%
사 11
 
6.2%
화 7
 
4.0%
천 7
 
4.0%
양 6
 
3.4%
카 5
 
2.8%
드 5
 
2.8%
폐 5
 
2.8%
Other values (39) 88
49.7%
Lowercase Letter
ValueCountFrequency (%)
a 3
30.0%
y 2
20.0%
u 1
 
10.0%
o 1
 
10.0%
k 1
 
10.0%
n 1
 
10.0%
h 1
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
P 2
40.0%
N 1
20.0%
T 1
20.0%
Y 1
20.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 176
85.9%
Latin 15
 
7.3%
Common 13
 
6.3%
Han 1
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 18
 
10.2%
페 14
 
8.0%
랑 11
 
6.2%
사 11
 
6.2%
화 7
 
4.0%
천 7
 
4.0%
양 6
 
3.4%
카 5
 
2.8%
드 5
 
2.8%
폐 5
 
2.8%
Other values (38) 87
49.4%
Latin
ValueCountFrequency (%)
a 3
20.0%
y 2
13.3%
P 2
13.3%
N 1
 
6.7%
T 1
 
6.7%
u 1
 
6.7%
o 1
 
6.7%
Y 1
 
6.7%
k 1
 
6.7%
n 1
 
6.7%
Common
ValueCountFrequency (%)
6
46.2%
( 3
23.1%
) 3
23.1%
- 1
 
7.7%
Han
ValueCountFrequency (%)
愛 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 176
85.9%
ASCII 28
 
13.7%
CJK 1
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 18
 
10.2%
페 14
 
8.0%
랑 11
 
6.2%
사 11
 
6.2%
화 7
 
4.0%
천 7
 
4.0%
양 6
 
3.4%
카 5
 
2.8%
드 5
 
2.8%
폐 5
 
2.8%
Other values (38) 87
49.4%
ASCII
ValueCountFrequency (%)
6
21.4%
( 3
10.7%
) 3
10.7%
a 3
10.7%
y 2
 
7.1%
P 2
 
7.1%
N 1
 
3.6%
T 1
 
3.6%
- 1
 
3.6%
u 1
 
3.6%
Other values (5) 5
17.9%
CJK
ValueCountFrequency (%)
愛 1
100.0%

가맹점업종명
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
10 
음료식품
6 
일반휴게음식
5 
신변잡화
2 
유통업 영리
2 
Other values (4)
5 

Length

Max length7
Median length4
Mean length4.6
Min length4

Unique

Unique3 ?
Unique (%)10.0%

Sample

1st row일반휴게음식
2nd row음료식품
3rd row음료식품
4th row신변잡화
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 10
33.3%
음료식품 6
20.0%
일반휴게음식 5
16.7%
신변잡화 2
 
6.7%
유통업 영리 2
 
6.7%
보건위생 2
 
6.7%
수리서비스 1
 
3.3%
서적문구 1
 
3.3%
유통업 비영리 1
 
3.3%

Length

2024-03-13T20:56:44.136913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:56:44.296183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 10
30.3%
음료식품 6
18.2%
일반휴게음식 5
15.2%
유통업 3
 
9.1%
신변잡화 2
 
6.1%
영리 2
 
6.1%
보건위생 2
 
6.1%
수리서비스 1
 
3.0%
서적문구 1
 
3.0%
비영리 1
 
3.0%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)100.0%
Missing10
Missing (%)33.3%
Infinite0
Infinite (%)0.0%
Mean14663.95
Minimum10494
Maximum18501
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:56:44.427202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10494
5-th percentile10880.65
Q112520.25
median14647.5
Q316846.75
95-th percentile17960.45
Maximum18501
Range8007
Interquartile range (IQR)4326.5

Descriptive statistics

Standard deviation2368.5854
Coefficient of variation (CV)0.16152438
Kurtosis-1.008774
Mean14663.95
Median Absolute Deviation (MAD)2143.5
Skewness-0.16726408
Sum293279
Variance5610197
MonotonicityNot monotonic
2024-03-13T20:56:44.536828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
16802 1
 
3.3%
18501 1
 
3.3%
11918 1
 
3.3%
14715 1
 
3.3%
15445 1
 
3.3%
12522 1
 
3.3%
13837 1
 
3.3%
10901 1
 
3.3%
16034 1
 
3.3%
12515 1
 
3.3%
Other values (10) 10
33.3%
(Missing) 10
33.3%
ValueCountFrequency (%)
10494 1
3.3%
10901 1
3.3%
11918 1
3.3%
12088 1
3.3%
12515 1
3.3%
12522 1
3.3%
13837 1
3.3%
14001 1
3.3%
14315 1
3.3%
14580 1
3.3%
ValueCountFrequency (%)
18501 1
3.3%
17932 1
3.3%
17151 1
3.3%
17052 1
3.3%
16981 1
3.3%
16802 1
3.3%
16034 1
3.3%
15495 1
3.3%
15445 1
3.3%
14715 1
3.3%

시도명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
20 
<NA>
10 

Length

Max length4
Median length3
Mean length3.3333333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 20
66.7%
<NA> 10
33.3%

Length

2024-03-13T20:56:44.704233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:56:44.795757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 20
66.7%
na 10
33.3%

시군구명
Text

MISSING 

Distinct17
Distinct (%)85.0%
Missing10
Missing (%)33.3%
Memory size372.0 B
2024-03-13T20:56:44.939569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length4.65
Min length3

Characters and Unicode

Total characters93
Distinct characters36
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 (%)70.0%

Sample

1st row양평군
2nd row안양시 만안구
3rd row고양시 덕양구
4th row안산시 상록구
5th row부천시
ValueCountFrequency (%)
용인시 4
 
14.3%
부천시 2
 
7.1%
처인구 2
 
7.1%
양평군 2
 
7.1%
안산시 2
 
7.1%
의왕시 1
 
3.6%
수지구 1
 
3.6%
구리시 1
 
3.6%
단원구 1
 
3.6%
과천시 1
 
3.6%
Other values (11) 11
39.3%
2024-03-13T20:56:45.278863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 18
19.4%
구 9
 
9.7%
8
 
8.6%
양 6
 
6.5%
인 6
 
6.5%
용 4
 
4.3%
안 4
 
4.3%
천 3
 
3.2%
평 3
 
3.2%
주 2
 
2.2%
Other values (26) 30
32.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 85
91.4%
Space Separator 8
 
8.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 18
21.2%
구 9
 
10.6%
양 6
 
7.1%
인 6
 
7.1%
용 4
 
4.7%
안 4
 
4.7%
천 3
 
3.5%
평 3
 
3.5%
주 2
 
2.4%
부 2
 
2.4%
Other values (25) 28
32.9%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 85
91.4%
Common 8
 
8.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 18
21.2%
구 9
 
10.6%
양 6
 
7.1%
인 6
 
7.1%
용 4
 
4.7%
안 4
 
4.7%
천 3
 
3.5%
평 3
 
3.5%
주 2
 
2.4%
부 2
 
2.4%
Other values (25) 28
32.9%
Common
ValueCountFrequency (%)
8
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 85
91.4%
ASCII 8
 
8.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 18
21.2%
구 9
 
10.6%
양 6
 
7.1%
인 6
 
7.1%
용 4
 
4.7%
안 4
 
4.7%
천 3
 
3.5%
평 3
 
3.5%
주 2
 
2.4%
부 2
 
2.4%
Other values (25) 28
32.9%
ASCII
ValueCountFrequency (%)
8
100.0%

읍면동명
Text

MISSING 

Distinct19
Distinct (%)95.0%
Missing10
Missing (%)33.3%
Memory size372.0 B
2024-03-13T20:56:45.479872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3
Min length2

Characters and Unicode

Total characters60
Distinct characters33
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

Unique18 ?
Unique (%)90.0%

Sample

1st row용문면
2nd row안양동
3rd row행신동
4th row이동
5th row중동
ValueCountFrequency (%)
용문면 2
 
10.0%
동천동 1
 
5.0%
상하동 1
 
5.0%
인창동 1
 
5.0%
심곡본동 1
 
5.0%
초지동 1
 
5.0%
별양동 1
 
5.0%
동패동 1
 
5.0%
내손동 1
 
5.0%
별내면 1
 
5.0%
Other values (9) 9
45.0%
2024-03-13T20:56:45.793930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 18
30.0%
면 3
 
5.0%
용 2
 
3.3%
하 2
 
3.3%
내 2
 
3.3%
별 2
 
3.3%
양 2
 
3.3%
중 2
 
3.3%
문 2
 
3.3%
안 2
 
3.3%
Other values (23) 23
38.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 60
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 18
30.0%
면 3
 
5.0%
용 2
 
3.3%
하 2
 
3.3%
내 2
 
3.3%
별 2
 
3.3%
양 2
 
3.3%
중 2
 
3.3%
문 2
 
3.3%
안 2
 
3.3%
Other values (23) 23
38.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 60
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 18
30.0%
면 3
 
5.0%
용 2
 
3.3%
하 2
 
3.3%
내 2
 
3.3%
별 2
 
3.3%
양 2
 
3.3%
중 2
 
3.3%
문 2
 
3.3%
안 2
 
3.3%
Other values (23) 23
38.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 60
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 18
30.0%
면 3
 
5.0%
용 2
 
3.3%
하 2
 
3.3%
내 2
 
3.3%
별 2
 
3.3%
양 2
 
3.3%
중 2
 
3.3%
문 2
 
3.3%
안 2
 
3.3%
Other values (23) 23
38.3%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.937433
Minimum0
Maximum37.715
Zeros10
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:56:45.916178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median37.289
Q337.4725
95-th percentile37.67075
Maximum37.715
Range37.715
Interquartile range (IQR)37.4725

Descriptive statistics

Standard deviation17.935489
Coefficient of variation (CV)0.71921954
Kurtosis-1.5535719
Mean24.937433
Median Absolute Deviation (MAD)0.258
Skewness-0.74465151
Sum748.123
Variance321.68177
MonotonicityNot monotonic
2024-03-13T20:56:46.029848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.0 10
33.3%
37.49 1
 
3.3%
37.338 1
 
3.3%
37.169 1
 
3.3%
37.607 1
 
3.3%
37.48 1
 
3.3%
37.309 1
 
3.3%
37.484 1
 
3.3%
37.428 1
 
3.3%
37.715 1
 
3.3%
Other values (11) 11
36.7%
ValueCountFrequency (%)
0.0 10
33.3%
36.984 1
 
3.3%
37.169 1
 
3.3%
37.237 1
 
3.3%
37.241 1
 
3.3%
37.272 1
 
3.3%
37.306 1
 
3.3%
37.309 1
 
3.3%
37.338 1
 
3.3%
37.383 1
 
3.3%
ValueCountFrequency (%)
37.715 1
3.3%
37.709 1
3.3%
37.624 1
3.3%
37.607 1
3.3%
37.5 1
3.3%
37.49 1
3.3%
37.484 1
3.3%
37.48 1
3.3%
37.45 1
3.3%
37.428 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.689333
Minimum0
Maximum127.593
Zeros10
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:56:46.193352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median126.843
Q3127.1055
95-th percentile127.4218
Maximum127.593
Range127.593
Interquartile range (IQR)127.1055

Descriptive statistics

Standard deviation60.908456
Coefficient of variation (CV)0.71919868
Kurtosis-1.5535713
Mean84.689333
Median Absolute Deviation (MAD)0.294
Skewness-0.74484573
Sum2540.68
Variance3709.8401
MonotonicityNot monotonic
2024-03-13T20:56:46.332000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.0 10
33.3%
127.591 1
 
3.3%
127.08 1
 
3.3%
127.118 1
 
3.3%
127.138 1
 
3.3%
126.779 1
 
3.3%
126.815 1
 
3.3%
127.593 1
 
3.3%
126.993 1
 
3.3%
126.741 1
 
3.3%
Other values (11) 11
36.7%
ValueCountFrequency (%)
0.0 10
33.3%
126.741 1
 
3.3%
126.775 1
 
3.3%
126.779 1
 
3.3%
126.815 1
 
3.3%
126.838 1
 
3.3%
126.848 1
 
3.3%
126.883 1
 
3.3%
126.921 1
 
3.3%
126.922 1
 
3.3%
ValueCountFrequency (%)
127.593 1
3.3%
127.591 1
3.3%
127.215 1
3.3%
127.206 1
3.3%
127.138 1
3.3%
127.136 1
3.3%
127.118 1
3.3%
127.114 1
3.3%
127.08 1
3.3%
126.993 1
3.3%

사용여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
True
20 
False
10 
ValueCountFrequency (%)
True 20
66.7%
False 10
33.3%
2024-03-13T20:56:46.479100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26124.333
Minimum0
Maximum319000
Zeros10
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:56:46.603812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9240
Q322000
95-th percentile99000
Maximum319000
Range319000
Interquartile range (IQR)22000

Descriptive statistics

Standard deviation61087.884
Coefficient of variation (CV)2.3383519
Kurtosis19.403967
Mean26124.333
Median Absolute Deviation (MAD)9240
Skewness4.2035373
Sum783730
Variance3.7317296 × 109
MonotonicityNot monotonic
2024-03-13T20:56:46.723846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 10
33.3%
30000 1
 
3.3%
319000 1
 
3.3%
11800 1
 
3.3%
13000 1
 
3.3%
7350 1
 
3.3%
9480 1
 
3.3%
9900 1
 
3.3%
23000 1
 
3.3%
66000 1
 
3.3%
Other values (11) 11
36.7%
ValueCountFrequency (%)
0 10
33.3%
1200 1
 
3.3%
2000 1
 
3.3%
6000 1
 
3.3%
7350 1
 
3.3%
9000 1
 
3.3%
9480 1
 
3.3%
9900 1
 
3.3%
10000 1
 
3.3%
11800 1
 
3.3%
ValueCountFrequency (%)
319000 1
3.3%
126000 1
3.3%
66000 1
3.3%
41000 1
3.3%
35000 1
3.3%
33000 1
3.3%
30000 1
3.3%
23000 1
3.3%
19000 1
3.3%
13000 1
3.3%

Interactions

2024-03-13T20:56:39.548644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:33.735268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:34.626965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:35.441281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:36.521203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:37.279102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:38.137379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:38.890295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:39.635421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:33.842930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:34.731364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:35.572907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:36.606100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:37.376693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:38.234763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:38.971397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:39.756300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:33.971453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:34.824869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:35.970513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:36.701675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:37.485959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:38.345391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:39.047581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:39.878263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:34.097715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:34.918810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:36.046850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:36.809243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:37.633295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:38.436170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:39.128583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:39.967919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:34.226335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:35.011341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:36.149515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:36.903503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:37.751539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:38.538290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:39.209670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:40.054630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:34.322050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:35.099529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:36.249605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:37.024371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:37.856006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:38.634053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:39.294831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:40.137321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:34.411330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:35.205380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:36.344150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:37.114949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:37.955828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:38.712784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:39.388098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:40.217137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:34.498793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:35.334740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:36.425911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:37.196326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:38.053065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:38.797458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:56:39.464562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:56:46.857469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시군구명읍면동명위도경도사용여부결제금액
카드번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
회원코드1.0001.0000.0000.5390.2780.7030.8440.4730.7840.9811.0000.0000.0000.0000.385
가맹점번호1.0000.0001.0000.1210.2140.0000.354NaNNaNNaNNaN0.9920.9920.9920.000
성별코드1.0000.5390.1211.0000.0000.0000.0000.0000.1530.7301.0000.0000.0000.0000.000
연령대코드1.0000.2780.2140.0001.0000.2880.5560.5930.5080.0000.0000.1880.1880.1880.000
결제상품ID1.0000.7030.0000.0000.2881.0001.0000.4310.8281.0001.0000.0000.0000.0000.242
결제상품명1.0000.8440.3540.0000.5561.0001.0000.9070.9481.0001.0000.2820.2820.2820.000
가맹점업종명1.0000.473NaN0.0000.5930.4310.9071.0000.3750.7510.692NaNNaNNaN0.000
가맹점우편번호1.0000.784NaN0.1530.5080.8280.9480.3751.0001.0001.000NaNNaNNaN0.892
시군구명1.0000.981NaN0.7300.0001.0001.0000.7511.0001.0001.000NaNNaNNaN0.490
읍면동명1.0001.000NaN1.0000.0001.0001.0000.6921.0001.0001.000NaNNaNNaN1.000
위도1.0000.0000.9920.0000.1880.0000.282NaNNaNNaNNaN1.0000.9930.9930.000
경도1.0000.0000.9920.0000.1880.0000.282NaNNaNNaNNaN0.9931.0000.9930.000
사용여부1.0000.0000.9920.0000.1880.0000.282NaNNaNNaNNaN0.9930.9931.0000.000
결제금액1.0000.3850.0000.0000.0000.2420.0000.0000.8920.4901.0000.0000.0000.0001.000
2024-03-13T20:56:47.007283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용여부성별코드시도명가맹점업종명
사용여부1.0000.0001.0001.000
성별코드0.0001.0001.0000.000
시도명1.0001.0001.0001.000
가맹점업종명1.0000.0001.0001.000
2024-03-13T20:56:47.106584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회원코드가맹점번호연령대코드결제상품ID가맹점우편번호위도경도결제금액성별코드가맹점업종명시도명사용여부
회원코드1.0000.148-0.0260.2260.065-0.0120.148-0.0800.4170.2281.0000.000
가맹점번호0.1481.0000.1870.056-0.236-0.648-0.742-0.7290.0001.0001.0000.922
연령대코드-0.0260.1871.000-0.228-0.159-0.0990.014-0.1280.0000.3221.0000.099
결제상품ID0.2260.056-0.2281.0000.376-0.173-0.026-0.0610.0000.0001.0000.000
가맹점우편번호0.065-0.236-0.1590.3761.000-0.9370.186-0.0810.0000.0001.0001.000
위도-0.012-0.648-0.099-0.173-0.9371.0000.6140.7230.0001.0001.0000.922
경도0.148-0.7420.014-0.0260.1860.6141.0000.7010.0001.0001.0000.922
결제금액-0.080-0.729-0.128-0.061-0.0810.7230.7011.0000.0000.0001.0000.000
성별코드0.4170.0000.0000.0000.0000.0000.0000.0001.0000.0001.0000.000
가맹점업종명0.2281.0000.3220.0000.0001.0001.0000.0000.0001.0001.0001.000
시도명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사용여부0.0000.9220.0990.0001.0000.9220.9220.0000.0001.0001.0001.000

Missing values

2024-03-13T20:56:40.345670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:56:40.570917image/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.
2024-03-13T20:56:40.719527image/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
02021-03-012021-03-07++5IeEX92t/OTjD88vQv7dFuzHnzSG2Wi0sbvL++yRo=3020719426707317827F40140000038000양평통보일반휴게음식12515경기도양평군용문면37.49127.591Y30000
12021-03-012021-03-07ytQK7AxHYVxI149mPlV34WeO96rI9AIZDrrZrTF9z9s=3004276148781664133F40140000034000안양사랑페이음료식품14001경기도안양시 만안구안양동37.397126.921Y19000
22021-03-012021-03-07W+38kRNEjZrqZRPk0a8QgRtyARGV0SGIKTvLsXnJeWc=3032495227793931915F50140000018000고양페이카드음료식품10494경기도고양시 덕양구행신동37.624126.838Y33000
32021-03-012021-03-071VaJI+zwjOGuVtH7oldNFjUAroPCaEjF9s8AElWzTnU=3023611198771262485M50140000124000안산사랑상품권 다온신변잡화15495경기도안산시 상록구이동37.306126.848Y6000
42021-03-012021-03-07A79XV83Q1+PwsB8yBczAJ4nXQNPs/5IRicV4kgSEZ4c=3017538343999999999999999M40140000034000안양사랑페이<NA><NA><NA><NA><NA>0.00.0N0
52021-03-012021-03-07IjKi3OL2Py50tj2WCOxk5X0ezkj6LzbfIchlQgr8G2o=3015268137710393912M20140000030000부천페이일반휴게음식14580경기도부천시중동37.5126.775Y126000
62021-03-012021-03-07++Iu/iBxVF5Nd2KMLnHQPNR1J5BIZEfp5IyZF5KvRTk=3033645296769017852M40140000046000용인와이페이음료식품17052경기도용인시 처인구김량장동37.237127.206Y35000
72021-03-012021-03-07ytR5wtlcHMmz06PWbAnr+wXF+wUydxUzusrAxZo4g9o=3001892380703148880M50140000020000광명사랑화폐유통업 영리14315경기도광명시소하동37.45126.883Y9000
82021-03-012021-03-076ewBQFSQlU6v47XF6uSrRz0s8f4/WBcuQcvblg2fTLE=3020019768703532497F10140000058000평택사랑카드(통합)보건위생17932경기도평택시안중읍36.984126.922Y10000
92021-03-012021-03-07F0IGPMTL52nfG7JIu5lXgNN6DAuFVXeAmH9mxO0y4gE=3031482229720066559F50140000046000용인와이페이일반휴게음식17151경기도용인시 처인구고림동37.241127.215Y12000
정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
202021-03-012021-03-07CLK1yISY0JCrBQfQGLpTS+gBXPuGYkx98CrEL05MRjY=3021418188721228511F20140000124000안산사랑상품권 다온신변잡화15445경기도안산시 단원구초지동37.309126.815Y9480
212021-03-012021-03-07EVV+wwfFEaaTofUTvoosbu7wUWtIXwgxawDKYyTf5Xs=3009273212999999999999999F50140000030000부천페이<NA><NA><NA><NA><NA>0.00.0N0
222021-03-012021-03-07GbuixVRymBScw2J+PKX9PmQhObGoiC3hg+UfzaeGUrA=3019326211716293893M30140000030000부천페이유통업 영리14715경기도부천시심곡본동37.48126.779Y7350
232021-03-012021-03-07Ijo1FYHiKP7VXXzoV996tZp1vpw96Ua5vxClDIkM4Xc=3017119692999999999999999M40140000052000포천사랑상품권<NA><NA><NA><NA><NA>0.00.0N0
242021-03-012021-03-07KtQCWgKhbzN0C/SCvt2R7IUN3QO0tIEgheP6dp6yeaE=3013747102999999999999999M20140000112000군포愛머니<NA><NA><NA><NA><NA>0.00.0N0
252021-03-012021-03-07My3JfaRfdVFb2si+fpMwwUdb0LPrKyOr5Iq1Oniohh8=3019790563999999999999999F50140000030000부천페이<NA><NA><NA><NA><NA>0.00.0N0
262021-03-012021-03-07P/9fVgP2PFpwAcJbbwUtjjJ1caT8WK1yZr2tAOLrM4I=3019169203999999999999999M40140000024000광주사랑카드<NA><NA><NA><NA><NA>0.00.0N0
272021-03-012021-03-07++awZYVexPBG5qC7UHzF2mv1Gg/+ySwYF2PralUUBtM=3019339162720134139F20140000028000구리사랑카드일반휴게음식11918경기도구리시인창동37.607127.138Y13000
282021-03-012021-03-07+D+YxD49BGKT7ovJqMlve0YnKbAt0gUjWOr1QaGPe2s=3022597422999999999999999M50140000126000수원페이<NA><NA><NA><NA><NA>0.00.0N0
292021-03-012021-03-07/KPHQEdkqZbYrwNjzTaflG6ltX+ZLDA4Pn5B03dYRxA=3007359176714590024F40140000116000행복화성지역화폐일반휴게음식18501경기도화성시산척동37.169127.118Y11800