Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells112
Missing cells (%)20.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory155.4 B

Variable types

Categorical9
Text6
Numeric2
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/b1caf624-9292-47f6-9f69-ae2a1ea0aecb

Alerts

정책주간결제시작일자 has constant value ""Constant
정책주간결제종료일자 has constant value ""Constant
시도명 has constant value ""Constant
위도 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
가맹점우편번호 is highly overall correlated with 회원코드 and 8 other fieldsHigh correlation
성별코드 is highly overall correlated with 가맹점우편번호High correlation
연령대코드 is highly overall correlated with 가맹점우편번호High correlation
가맹점번호 is highly overall correlated with 가맹점우편번호 and 4 other fieldsHigh correlation
결제금액 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
경도 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
사용여부 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
회원코드 is highly overall correlated with 가맹점우편번호High correlation
결제상품ID is highly overall correlated with 가맹점우편번호High correlation
가맹점번호 is highly imbalanced (68.6%)Imbalance
가맹점우편번호 is highly imbalanced (73.5%)Imbalance
위도 is highly imbalanced (73.5%)Imbalance
경도 is highly imbalanced (73.5%)Imbalance
사용여부 is highly imbalanced (53.1%)Imbalance
결제금액 is highly imbalanced (68.6%)Imbalance
가맹점업종명 has 28 (93.3%) missing valuesMissing
시도명 has 28 (93.3%) missing valuesMissing
시군구명 has 28 (93.3%) missing valuesMissing
읍면동명 has 28 (93.3%) missing valuesMissing
카드번호 has unique valuesUnique
회원코드 has unique valuesUnique

Reproduction

Analysis started2024-03-13 11:49:09.108166
Analysis finished2024-03-13 11:49:11.551707
Duration2.44 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
2023-05-01
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-05-01
2nd row2023-05-01
3rd row2023-05-01
4th row2023-05-01
5th row2023-05-01

Common Values

ValueCountFrequency (%)
2023-05-01 30
100.0%

Length

2024-03-13T20:49:11.629282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:49:11.740597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-05-01 30
100.0%

정책주간결제종료일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-05-07
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-05-07
2nd row2023-05-07
3rd row2023-05-07
4th row2023-05-07
5th row2023-05-07

Common Values

ValueCountFrequency (%)
2023-05-07 30
100.0%

Length

2024-03-13T20:49:11.894395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:49:12.021461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-05-07 30
100.0%

카드번호
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:49:12.261070image/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+++wB4Ki3vFVR1w/0fQvW4fYF7CPKOiof8wImM7o+E0=
2nd rowzzgM6wmU50B6eA5vzz1qoYkTrBTUr4UuaGi7zngz7Qc=
3rd rowlYrssE73ETccWWR/wsnObPihESlq23/6i6iLAijD9WE=
4th row3VvtIkwpyu6gi2zqSD39nAEEWJ19V4SYQgDXvxaF+Y0=
5th rowPRE8nCkGSYFU2/mcwJ6Nx6s+VfO5V7WHJ+Vt+1tM9T0=
ValueCountFrequency (%)
wb4ki3vfvr1w/0fqvw4fyf7cpkoiof8wimm7o+e0 1
 
3.3%
zzgm6wmu50b6ea5vzz1qoyktrbtur4uuagi7zngz7qc 1
 
3.3%
crpyn0yxhppx5lthfuxdxducvh+2jub47dbsax210 1
 
3.3%
15qu3xlafnhehgi5zv6zf/ulfapxa0hrmc7ediskys 1
 
3.3%
uwdgypsbbbuusf4qovme+jouw+w90ekhrlvzzamuxau 1
 
3.3%
sbtwkbfxjcuhoosygb1bondwi4wf9nthgjuwpoo/bti 1
 
3.3%
pnz/hnh7o1pdltnhtj9pmnzyvsgzf7mnsbo/+vjsc/m 1
 
3.3%
0kyjyxk3elu/oyorlacd9g86m8deemashgggtls8e 1
 
3.3%
fzlip+vrktjtsevcon5gqkufremwsif+bapbwp49meq 1
 
3.3%
azjwcywznqt/4ahklb/fnickoebdkchpgsn60/sk8gq 1
 
3.3%
Other values (20) 20
66.7%
2024-03-13T20:49:12.725282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
+ 39
 
3.0%
E 33
 
2.5%
= 30
 
2.3%
z 30
 
2.3%
w 30
 
2.3%
f 29
 
2.2%
s 28
 
2.1%
/ 27
 
2.0%
o 27
 
2.0%
V 26
 
2.0%
Other values (55) 1021
77.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 520
39.4%
Lowercase Letter 517
39.2%
Decimal Number 187
 
14.2%
Math Symbol 69
 
5.2%
Other Punctuation 27
 
2.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 33
 
6.3%
V 26
 
5.0%
B 26
 
5.0%
F 25
 
4.8%
N 23
 
4.4%
A 23
 
4.4%
M 22
 
4.2%
D 21
 
4.0%
P 21
 
4.0%
J 21
 
4.0%
Other values (16) 279
53.7%
Lowercase Letter
ValueCountFrequency (%)
z 30
 
5.8%
w 30
 
5.8%
f 29
 
5.6%
s 28
 
5.4%
o 27
 
5.2%
x 24
 
4.6%
h 23
 
4.4%
g 23
 
4.4%
i 22
 
4.3%
c 21
 
4.1%
Other values (16) 260
50.3%
Decimal Number
ValueCountFrequency (%)
7 26
13.9%
1 26
13.9%
9 21
11.2%
0 19
10.2%
4 19
10.2%
8 18
9.6%
5 16
8.6%
2 16
8.6%
6 15
8.0%
3 11
5.9%
Math Symbol
ValueCountFrequency (%)
+ 39
56.5%
= 30
43.5%
Other Punctuation
ValueCountFrequency (%)
/ 27
100.0%

Most occurring scripts

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

Most frequent character per script

Latin
ValueCountFrequency (%)
E 33
 
3.2%
z 30
 
2.9%
w 30
 
2.9%
f 29
 
2.8%
s 28
 
2.7%
o 27
 
2.6%
V 26
 
2.5%
B 26
 
2.5%
F 25
 
2.4%
x 24
 
2.3%
Other values (42) 759
73.2%
Common
ValueCountFrequency (%)
+ 39
13.8%
= 30
10.6%
/ 27
9.5%
7 26
9.2%
1 26
9.2%
9 21
7.4%
0 19
6.7%
4 19
6.7%
8 18
6.4%
5 16
 
5.7%
Other values (3) 42
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+ 39
 
3.0%
E 33
 
2.5%
= 30
 
2.3%
z 30
 
2.3%
w 30
 
2.3%
f 29
 
2.2%
s 28
 
2.1%
/ 27
 
2.0%
o 27
 
2.0%
V 26
 
2.0%
Other values (55) 1021
77.3%

회원코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0220872 × 109
Minimum3.0112501 × 109
Maximum3.0682476 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:49:12.903371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0112501 × 109
5-th percentile3.0148624 × 109
Q13.016638 × 109
median3.0183258 × 109
Q33.0222077 × 109
95-th percentile3.0445419 × 109
Maximum3.0682476 × 109
Range56997461
Interquartile range (IQR)5569663.5

Descriptive statistics

Standard deviation11521949
Coefficient of variation (CV)0.00381258
Kurtosis8.9134112
Mean3.0220872 × 109
Median Absolute Deviation (MAD)1762485.5
Skewness2.8498265
Sum9.0662615 × 1010
Variance1.3275531 × 1014
MonotonicityNot monotonic
2024-03-13T20:49:13.115889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3017019518 1
 
3.3%
3018486259 1
 
3.3%
3023511195 1
 
3.3%
3015569124 1
 
3.3%
3020136818 1
 
3.3%
3011250115 1
 
3.3%
3014284141 1
 
3.3%
3016617623 1
 
3.3%
3016335120 1
 
3.3%
3042671215 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3011250115 1
3.3%
3014284141 1
3.3%
3015569124 1
3.3%
3016335120 1
3.3%
3016560080 1
3.3%
3016566546 1
3.3%
3016576573 1
3.3%
3016617623 1
3.3%
3016699180 1
3.3%
3016891890 1
3.3%
ValueCountFrequency (%)
3068247576 1
3.3%
3046072383 1
3.3%
3042671215 1
3.3%
3032121234 1
3.3%
3024450115 1
3.3%
3024162127 1
3.3%
3023511195 1
3.3%
3022397116 1
3.3%
3021639355 1
3.3%
3020136818 1
3.3%

가맹점번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
999999999999999
27 
GG_4D8HE7
 
1
753278803
 
1
720153351
 
1

Length

Max length15
Median length15
Mean length14.4
Min length9

Unique

Unique3 ?
Unique (%)10.0%

Sample

1st row999999999999999
2nd row999999999999999
3rd row999999999999999
4th rowGG_4D8HE7
5th row999999999999999

Common Values

ValueCountFrequency (%)
999999999999999 27
90.0%
GG_4D8HE7 1
 
3.3%
753278803 1
 
3.3%
720153351 1
 
3.3%

Length

2024-03-13T20:49:13.291377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:49:13.430140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
999999999999999 27
90.0%
gg_4d8he7 1
 
3.3%
753278803 1
 
3.3%
720153351 1
 
3.3%

성별코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
F
24 
M
6 

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 24
80.0%
M 6
 
20.0%

Length

2024-03-13T20:49:13.578586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:49:13.694134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 24
80.0%
m 6
 
20.0%

연령대코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
12 
50
6 
20
5 
40
4 
30
3 

Length

Max length2
Median length2
Mean length1.6
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50
2nd row0
3rd row20
4th row30
5th row20

Common Values

ValueCountFrequency (%)
0 12
40.0%
50 6
20.0%
20 5
16.7%
40 4
 
13.3%
30 3
 
10.0%

Length

2024-03-13T20:49:13.821338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:49:13.988368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12
40.0%
50 6
20.0%
20 5
16.7%
40 4
 
13.3%
30 3
 
10.0%

결제상품ID
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4000013 × 1011
Minimum1.4000002 × 1011
Maximum1.400011 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:49:14.169454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000002 × 1011
5-th percentile1.4000003 × 1011
Q11.4000004 × 1011
median1.400001 × 1011
Q31.4000012 × 1011
95-th percentile1.4000041 × 1011
Maximum1.400011 × 1011
Range1087000
Interquartile range (IQR)79500

Descriptive statistics

Standard deviation213692.39
Coefficient of variation (CV)1.5263728 × 10-6
Kurtosis16.202769
Mean1.4000013 × 1011
Median Absolute Deviation (MAD)42000
Skewness3.9202054
Sum4.200004 × 1012
Variance4.5664438 × 1010
MonotonicityNot monotonic
2024-03-13T20:49:14.314529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
140000116000 6
20.0%
140000030000 4
13.3%
140000124000 2
 
6.7%
140000046000 2
 
6.7%
140000126000 2
 
6.7%
140000044000 1
 
3.3%
140000140000 1
 
3.3%
140000104000 1
 
3.3%
140000034000 1
 
3.3%
140000106000 1
 
3.3%
Other values (9) 9
30.0%
ValueCountFrequency (%)
140000018000 1
 
3.3%
140000024000 1
 
3.3%
140000030000 4
13.3%
140000034000 1
 
3.3%
140000038000 1
 
3.3%
140000044000 1
 
3.3%
140000046000 2
6.7%
140000052000 1
 
3.3%
140000056000 1
 
3.3%
140000080000 1
 
3.3%
ValueCountFrequency (%)
140001105000 1
 
3.3%
140000634000 1
 
3.3%
140000140000 1
 
3.3%
140000126000 2
 
6.7%
140000124000 2
 
6.7%
140000120000 1
 
3.3%
140000116000 6
20.0%
140000106000 1
 
3.3%
140000104000 1
 
3.3%
140000080000 1
 
3.3%

결제상품명
Text

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:49:14.536628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length11.5
Mean length7.7333333
Min length4

Characters and Unicode

Total characters232
Distinct characters65
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)46.7%

Sample

1st row안산사랑상품권 다온
2nd row고양페이카드
3rd row포천사랑상품권(통합)
4th row부천페이
5th row광주사랑카드
ValueCountFrequency (%)
행복화성지역화폐 6
16.7%
부천페이 4
 
11.1%
수원페이 2
 
5.6%
용인와이페이 2
 
5.6%
안산사랑상품권 2
 
5.6%
다온 2
 
5.6%
고양페이카드 1
 
2.8%
오산화폐 1
 
2.8%
양평통보 1
 
2.8%
광주사랑카드 1
 
2.8%
Other values (14) 14
38.9%
2024-03-13T20:49:14.952711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 17
 
7.3%
화 17
 
7.3%
페 13
 
5.6%
폐 9
 
3.9%
성 8
 
3.4%
지 8
 
3.4%
역 8
 
3.4%
행 7
 
3.0%
) 7
 
3.0%
복 7
 
3.0%
Other values (55) 131
56.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 195
84.1%
Lowercase Letter 10
 
4.3%
Close Punctuation 7
 
3.0%
Open Punctuation 7
 
3.0%
Space Separator 6
 
2.6%
Uppercase Letter 5
 
2.2%
Dash Punctuation 1
 
0.4%
Connector Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 17
 
8.7%
화 17
 
8.7%
페 13
 
6.7%
폐 9
 
4.6%
성 8
 
4.1%
지 8
 
4.1%
역 8
 
4.1%
행 7
 
3.6%
복 7
 
3.6%
천 7
 
3.6%
Other values (39) 94
48.2%
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%
Y 1
20.0%
T 1
20.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 194
83.6%
Common 22
 
9.5%
Latin 15
 
6.5%
Han 1
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 17
 
8.8%
화 17
 
8.8%
페 13
 
6.7%
폐 9
 
4.6%
성 8
 
4.1%
지 8
 
4.1%
역 8
 
4.1%
행 7
 
3.6%
복 7
 
3.6%
천 7
 
3.6%
Other values (38) 93
47.9%
Latin
ValueCountFrequency (%)
a 3
20.0%
y 2
13.3%
P 2
13.3%
N 1
 
6.7%
u 1
 
6.7%
o 1
 
6.7%
Y 1
 
6.7%
k 1
 
6.7%
n 1
 
6.7%
h 1
 
6.7%
Common
ValueCountFrequency (%)
) 7
31.8%
( 7
31.8%
6
27.3%
- 1
 
4.5%
_ 1
 
4.5%
Han
ValueCountFrequency (%)
愛 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 194
83.6%
ASCII 37
 
15.9%
CJK 1
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 17
 
8.8%
화 17
 
8.8%
페 13
 
6.7%
폐 9
 
4.6%
성 8
 
4.1%
지 8
 
4.1%
역 8
 
4.1%
행 7
 
3.6%
복 7
 
3.6%
천 7
 
3.6%
Other values (38) 93
47.9%
ASCII
ValueCountFrequency (%)
) 7
18.9%
( 7
18.9%
6
16.2%
a 3
8.1%
y 2
 
5.4%
P 2
 
5.4%
N 1
 
2.7%
- 1
 
2.7%
u 1
 
2.7%
o 1
 
2.7%
Other values (6) 6
16.2%
CJK
ValueCountFrequency (%)
愛 1
100.0%

가맹점업종명
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing28
Missing (%)93.3%
Memory size372.0 B
2024-03-13T20:49:15.118174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length6
Min length4

Characters and Unicode

Total characters12
Distinct characters10
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

Unique2 ?
Unique (%)100.0%

Sample

1st row일반유통
2nd row일반/휴게 음식
ValueCountFrequency (%)
일반유통 1
33.3%
일반/휴게 1
33.3%
음식 1
33.3%
2024-03-13T20:49:15.567831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
일 2
16.7%
반 2
16.7%
유 1
8.3%
통 1
8.3%
/ 1
8.3%
휴 1
8.3%
게 1
8.3%
1
8.3%
음 1
8.3%
식 1
8.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10
83.3%
Other Punctuation 1
 
8.3%
Space Separator 1
 
8.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
일 2
20.0%
반 2
20.0%
유 1
10.0%
통 1
10.0%
휴 1
10.0%
게 1
10.0%
음 1
10.0%
식 1
10.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10
83.3%
Common 2
 
16.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
일 2
20.0%
반 2
20.0%
유 1
10.0%
통 1
10.0%
휴 1
10.0%
게 1
10.0%
음 1
10.0%
식 1
10.0%
Common
ValueCountFrequency (%)
/ 1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10
83.3%
ASCII 2
 
16.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
일 2
20.0%
반 2
20.0%
유 1
10.0%
통 1
10.0%
휴 1
10.0%
게 1
10.0%
음 1
10.0%
식 1
10.0%
ASCII
ValueCountFrequency (%)
/ 1
50.0%
1
50.0%

가맹점우편번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
28 
10939
 
1
16269
 
1

Length

Max length5
Median length4
Mean length4.0666667
Min length4

Unique

Unique2 ?
Unique (%)6.7%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 28
93.3%
10939 1
 
3.3%
16269 1
 
3.3%

Length

2024-03-13T20:49:15.740860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:49:15.841269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 28
93.3%
10939 1
 
3.3%
16269 1
 
3.3%

시도명
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing28
Missing (%)93.3%
Memory size372.0 B
2024-03-13T20:49:15.982512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters3
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

Unique0 ?
Unique (%)0.0%

Sample

1st row경기도
2nd row경기도
ValueCountFrequency (%)
경기도 2
100.0%
2024-03-13T20:49:16.569106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 2
33.3%
기 2
33.3%
도 2
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 2
33.3%
기 2
33.3%
도 2
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 2
33.3%
기 2
33.3%
도 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 2
33.3%
기 2
33.3%
도 2
33.3%

시군구명
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing28
Missing (%)93.3%
Memory size372.0 B
2024-03-13T20:49:16.811534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5
Min length3

Characters and Unicode

Total characters10
Distinct characters9
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

Unique2 ?
Unique (%)100.0%

Sample

1st row파주시
2nd row수원시 장안구
ValueCountFrequency (%)
파주시 1
33.3%
수원시 1
33.3%
장안구 1
33.3%
2024-03-13T20:49:17.283860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 2
20.0%
파 1
10.0%
주 1
10.0%
수 1
10.0%
원 1
10.0%
1
10.0%
장 1
10.0%
안 1
10.0%
구 1
10.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9
90.0%
Space Separator 1
 
10.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 2
22.2%
파 1
11.1%
주 1
11.1%
수 1
11.1%
원 1
11.1%
장 1
11.1%
안 1
11.1%
구 1
11.1%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9
90.0%
Common 1
 
10.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 2
22.2%
파 1
11.1%
주 1
11.1%
수 1
11.1%
원 1
11.1%
장 1
11.1%
안 1
11.1%
구 1
11.1%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9
90.0%
ASCII 1
 
10.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 2
22.2%
파 1
11.1%
주 1
11.1%
수 1
11.1%
원 1
11.1%
장 1
11.1%
안 1
11.1%
구 1
11.1%
ASCII
ValueCountFrequency (%)
1
100.0%

읍면동명
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing28
Missing (%)93.3%
Memory size372.0 B
2024-03-13T20:49:17.463875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters6
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

Unique2 ?
Unique (%)100.0%

Sample

1st row조리읍
2nd row영화동
ValueCountFrequency (%)
조리읍 1
50.0%
영화동 1
50.0%
2024-03-13T20:49:17.873948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
조 1
16.7%
리 1
16.7%
읍 1
16.7%
영 1
16.7%
화 1
16.7%
동 1
16.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
조 1
16.7%
리 1
16.7%
읍 1
16.7%
영 1
16.7%
화 1
16.7%
동 1
16.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
조 1
16.7%
리 1
16.7%
읍 1
16.7%
영 1
16.7%
화 1
16.7%
동 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
조 1
16.7%
리 1
16.7%
읍 1
16.7%
영 1
16.7%
화 1
16.7%
동 1
16.7%

위도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
0.0
28 
37.736
 
1
37.289
 
1

Length

Max length6
Median length3
Mean length3.2
Min length3

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 28
93.3%
37.736 1
 
3.3%
37.289 1
 
3.3%

Length

2024-03-13T20:49:18.060149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:49:18.203048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 28
93.3%
37.736 1
 
3.3%
37.289 1
 
3.3%

경도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
0.0
28 
126.825
 
1
127.013
 
1

Length

Max length7
Median length3
Mean length3.2666667
Min length3

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 28
93.3%
126.825 1
 
3.3%
127.013 1
 
3.3%

Length

2024-03-13T20:49:18.347967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:49:18.498908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 28
93.3%
126.825 1
 
3.3%
127.013 1
 
3.3%

사용여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
27 
True
3 
ValueCountFrequency (%)
False 27
90.0%
True 3
 
10.0%
2024-03-13T20:49:18.669027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
27 
24700
 
1
45000
 
1
21500
 
1

Length

Max length5
Median length1
Mean length1.4
Min length1

Unique

Unique3 ?
Unique (%)10.0%

Sample

1st row0
2nd row0
3rd row0
4th row24700
5th row0

Common Values

ValueCountFrequency (%)
0 27
90.0%
24700 1
 
3.3%
45000 1
 
3.3%
21500 1
 
3.3%

Length

2024-03-13T20:49:18.808365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:49:18.945175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 27
90.0%
24700 1
 
3.3%
45000 1
 
3.3%
21500 1
 
3.3%

Interactions

2024-03-13T20:49:10.244723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:10.026537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:10.348205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:10.134795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:49:19.054731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시군구명읍면동명위도경도사용여부결제금액
카드번호1.0001.0001.0001.0001.0001.0001.0000.0000.0000.0000.0001.0001.0001.0001.000
회원코드1.0001.0000.0000.3490.3280.8530.9170.0000.0000.0000.0000.0000.0000.0000.000
가맹점번호1.0000.0001.0000.6560.3270.0000.0000.0000.0000.0000.0001.0001.0001.0001.000
성별코드1.0000.3490.6561.0000.2720.3460.7580.0000.0000.0000.0000.1750.1750.2690.656
연령대코드1.0000.3280.3270.2721.0000.0000.614NaNNaNNaNNaN0.1390.1390.3120.327
결제상품ID1.0000.8530.0000.3460.0001.0001.000NaNNaNNaNNaN0.0000.0000.0000.000
결제상품명1.0000.9170.0000.7580.6141.0001.0000.0000.0000.0000.0000.7790.7790.0000.000
가맹점업종명0.0000.0000.0000.000NaNNaN0.0001.0000.0000.0000.0000.0000.000NaN0.000
가맹점우편번호0.0000.0000.0000.000NaNNaN0.0000.0001.0000.0000.0000.0000.000NaN0.000
시군구명0.0000.0000.0000.000NaNNaN0.0000.0000.0001.0000.0000.0000.000NaN0.000
읍면동명0.0000.0000.0000.000NaNNaN0.0000.0000.0000.0001.0000.0000.000NaN0.000
위도1.0000.0001.0000.1750.1390.0000.7790.0000.0000.0000.0001.0001.0000.5211.000
경도1.0000.0001.0000.1750.1390.0000.7790.0000.0000.0000.0001.0001.0000.5211.000
사용여부1.0000.0001.0000.2690.3120.0000.000NaNNaNNaNNaN0.5210.5211.0001.000
결제금액1.0000.0001.0000.6560.3270.0000.0000.0000.0000.0000.0001.0001.0001.0001.000
2024-03-13T20:49:19.238128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도가맹점우편번호성별코드연령대코드가맹점번호결제금액경도사용여부
위도1.0001.0000.2790.0730.9810.9811.0000.771
가맹점우편번호1.0001.0001.0001.0001.0001.0001.0001.000
성별코드0.2791.0001.0000.3070.4400.4400.2790.170
연령대코드0.0731.0000.3071.0000.2580.2580.0730.354
가맹점번호0.9811.0000.4400.2581.0001.0000.9810.964
결제금액0.9811.0000.4400.2581.0001.0000.9810.964
경도1.0001.0000.2790.0730.9810.9811.0000.771
사용여부0.7711.0000.1700.3540.9640.9640.7711.000
2024-03-13T20:49:19.394023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회원코드결제상품ID가맹점번호성별코드연령대코드가맹점우편번호위도경도사용여부결제금액
회원코드1.000-0.1590.0000.3440.1301.0000.0000.0000.0000.000
결제상품ID-0.1591.0000.0000.2220.0001.0000.0000.0000.0000.000
가맹점번호0.0000.0001.0000.4400.2581.0000.9810.9810.9641.000
성별코드0.3440.2220.4401.0000.3071.0000.2790.2790.1700.440
연령대코드0.1300.0000.2580.3071.0001.0000.0730.0730.3540.258
가맹점우편번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위도0.0000.0000.9810.2790.0731.0001.0001.0000.7710.981
경도0.0000.0000.9810.2790.0731.0001.0001.0000.7710.981
사용여부0.0000.0000.9640.1700.3541.0000.7710.7711.0000.964
결제금액0.0000.0001.0000.4400.2581.0000.9810.9810.9641.000

Missing values

2024-03-13T20:49:10.518136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:49:10.890327image/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.
2024-03-13T20:49:11.439322image/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
02023-05-012023-05-07+++wB4Ki3vFVR1w/0fQvW4fYF7CPKOiof8wImM7o+E0=3017019518999999999999999F50140000124000안산사랑상품권 다온<NA><NA><NA><NA><NA>0.00.0N0
12023-05-012023-05-07zzgM6wmU50B6eA5vzz1qoYkTrBTUr4UuaGi7zngz7Qc=3017272147999999999999999F0140000018000고양페이카드<NA><NA><NA><NA><NA>0.00.0N0
22023-05-012023-05-07lYrssE73ETccWWR/wsnObPihESlq23/6i6iLAijD9WE=3018887772999999999999999F20140000056000포천사랑상품권(통합)<NA><NA><NA><NA><NA>0.00.0N0
32023-05-012023-05-073VvtIkwpyu6gi2zqSD39nAEEWJ19V4SYQgDXvxaF+Y0=3017441443GG_4D8HE7M30140000030000부천페이<NA><NA><NA><NA><NA>0.00.0Y24700
42023-05-012023-05-07PRE8nCkGSYFU2/mcwJ6Nx6s+VfO5V7WHJ+Vt+1tM9T0=3024162127999999999999999M20140000024000광주사랑카드<NA><NA><NA><NA><NA>0.00.0N0
52023-05-012023-05-07++/PZX9DubyC8nr0En0fFUlZYN8LX/EBFo1UEFZ6hdk=3021639355999999999999999F0140000038000양평통보<NA><NA><NA><NA><NA>0.00.0N0
62023-05-012023-05-07++ALLp1WDzNWlBHGt2gbZdBacNMl6/fO8xRtCLz/VDs=3016576573999999999999999F40140000126000수원페이<NA><NA><NA><NA><NA>0.00.0N0
72023-05-012023-05-07zziix17qXhD+rBNX8IMy7xgOMNTJYZ+HsFfYcgepkTA=3068247576999999999999999F20140000634000수원페이(수원이)<NA><NA><NA><NA><NA>0.00.0N0
82023-05-012023-05-07EVIxyj2R8zOCtpPQhp2wNFoNMlu19U5Kky4lyWRXDr8=3024450115999999999999999F0140000080000부천페이(통합)<NA><NA><NA><NA><NA>0.00.0N0
92023-05-012023-05-07PRdguWmzbYfU1wY5RtEFNao1f3Vb+sHBYLjzOwEiioM=3032121234999999999999999M40140000030000부천페이<NA><NA><NA><NA><NA>0.00.0N0
정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
202023-05-012023-05-07V+5+vyg7s5wxWJpsi0lu7wW94WSA/JZcESnN1EOSoGA=3016699180999999999999999F0140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
212023-05-012023-05-07aZJwCYwzNQt/4AHKlB/fnIcKOeBDkchPGsN60/Sk8GQ=3018433535999999999999999F0140000124000안산사랑상품권 다온<NA><NA><NA><NA><NA>0.00.0N0
222023-05-012023-05-07fzlip+VRkTJtseVCON5gqkUFreMWsIF+bApBwP49meQ=3042671215999999999999999F0140000106000군포愛머니(통합)<NA><NA><NA><NA><NA>0.00.0N0
232023-05-012023-05-07++0kyjYXK3eLu/oyOrLACD9G86m8DEEmAShGGgtlS8E=3016335120999999999999999F0140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
242023-05-012023-05-07pNz/HNh7o1pdLtNHTj9pMNZyVsGzf7MnSbO/+vJsC/M=3016617623999999999999999F50140000034000안양사랑페이<NA><NA><NA><NA><NA>0.00.0N0
252023-05-012023-05-07sBTWkbfXjcUHoOSygB1BoNdwI4wf9nThGjuWpoO/bTI=3014284141720153351F50140000126000수원페이일반/휴게 음식16269경기도수원시 장안구영화동37.289127.013Y21500
262023-05-012023-05-07uwDGyPsBBbUusf4qovMe+Jouw+W90EKHRlvZZaMUxaU=3011250115999999999999999F40140000104000Thank You Pay-N(통합)<NA><NA><NA><NA><NA>0.00.0N0
272023-05-012023-05-07+15qu3XLAfNHehGi5zV6zF/uLFAPxa0hrmc7edIskys=3020136818999999999999999F0140000046000용인와이페이<NA><NA><NA><NA><NA>0.00.0N0
282023-05-012023-05-07++CRPyN0yxHPpX5LthFUxDxDUCvh+2jub47dbSAx210=3015569124999999999999999M20140000140000행복화성지역화폐_화이트<NA><NA><NA><NA><NA>0.00.0N0
292023-05-012023-05-07+190xwof4i+5AVdg8ZuLfa/ywGHVTqzRFBhhpwmcMco=3023511195999999999999999M40140000044000오산화폐 오색전<NA><NA><NA><NA><NA>0.00.0N0