Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells116
Missing cells (%)21.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory156.4 B

Variable types

Categorical8
Text6
Numeric3
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/e975eb1a-1ee3-43aa-afc9-98605df552af

Alerts

정책주간결제시작일자 has constant value ""Constant
정책주간결제종료일자 has constant value ""Constant
가맹점업종명 has constant value ""Constant
시도명 has constant value ""Constant
시군구명 has constant value ""Constant
읍면동명 has constant value ""Constant
위도 is highly overall correlated with 가맹점번호 and 1 other fieldsHigh correlation
가맹점번호 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
결제금액 is highly overall correlated with 가맹점번호 and 3 other fieldsHigh correlation
경도 is highly overall correlated with 가맹점번호 and 1 other fieldsHigh correlation
사용여부 is highly overall correlated with 가맹점번호 and 1 other fieldsHigh correlation
회원코드 is highly overall correlated with 성별코드High correlation
성별코드 is highly overall correlated with 회원코드High correlation
가맹점번호 is highly imbalanced (73.5%)Imbalance
가맹점우편번호 is highly imbalanced (78.9%)Imbalance
위도 is highly imbalanced (78.9%)Imbalance
경도 is highly imbalanced (78.9%)Imbalance
사용여부 is highly imbalanced (64.7%)Imbalance
결제금액 is highly imbalanced (73.5%)Imbalance
가맹점업종명 has 29 (96.7%) missing valuesMissing
시도명 has 29 (96.7%) missing valuesMissing
시군구명 has 29 (96.7%) missing valuesMissing
읍면동명 has 29 (96.7%) missing valuesMissing
카드번호 has unique valuesUnique
회원코드 has unique valuesUnique
연령대코드 has 11 (36.7%) zerosZeros

Reproduction

Analysis started2024-03-13 11:54:50.444392
Analysis finished2024-03-13 11:54:52.769683
Duration2.33 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
2023-08-07
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-07
2nd row2023-08-07
3rd row2023-08-07
4th row2023-08-07
5th row2023-08-07

Common Values

ValueCountFrequency (%)
2023-08-07 30
100.0%

Length

2024-03-13T20:54:52.840540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:54:53.221141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-07 30
100.0%

정책주간결제종료일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-08-13
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-13
2nd row2023-08-13
3rd row2023-08-13
4th row2023-08-13
5th row2023-08-13

Common Values

ValueCountFrequency (%)
2023-08-13 30
100.0%

Length

2024-03-13T20:54:53.332052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:54:53.437104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-13 30
100.0%

카드번호
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:54:53.639272image/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 rowG5waM21XUUZQVW9KRCRHz58mpFwPEKbDJ0/XqPcaRfQ=
2nd rowHrgEkSP3kxQnjjNkwE22PJr9rzzu5KGGiKjl5jj2s2w=
3rd row2ZpiIyaxIgTwkdsWXyOQdFaja6pbTDjQw8mrAXVvGik=
4th rowG5zy+uQHJio+fyXkvBIuXo5AI2zyzqpNfdEZgBiLGFU=
5th rowLsDV5WVnl5UYO9MH9Hh3z4BlmsiWVAsOsb5ICwd8PLI=
ValueCountFrequency (%)
g5wam21xuuzqvw9krcrhz58mpfwpekbdj0/xqpcarfq 1
 
3.3%
hrgeksp3kxqnjjnkwe22pjr9rzzu5kggikjl5jj2s2w 1
 
3.3%
wptzi2lwukg5d+pauiwqyxvruxbercz1/dtvha6p5y4 1
 
3.3%
5jcqtratmfhztfdlnyndxjc7dw2jiciac5fvelsbvh8 1
 
3.3%
vhvlqkfidyopayfrdm624gygiv1f6b+ikoesql4bqim 1
 
3.3%
3lebeswlyrzr/m9a0f/heywuibic2qusv9f2l98td4y 1
 
3.3%
2z+6jsuu/zt++kz1pz8pi4e57uwejpb7taxvcn0urpe 1
 
3.3%
thjq4n9zjaufrotlm/lyi5j055lxndvt6n8r/5ldboy 1
 
3.3%
29da8ffl6i4csbrfsy+2guw3kvqmpovxz2x/iz8mopg 1
 
3.3%
ozrhmwaf62voiztsjfctpsgdkjiwewy7tmnn1bavbli 1
 
3.3%
Other values (20) 20
66.7%
2024-03-13T20:54:54.085072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 32
 
2.4%
= 30
 
2.3%
w 30
 
2.3%
z 29
 
2.2%
9 28
 
2.1%
M 27
 
2.0%
V 27
 
2.0%
F 26
 
2.0%
J 26
 
2.0%
2 26
 
2.0%
Other values (55) 1039
78.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 527
39.9%
Lowercase Letter 507
38.4%
Decimal Number 216
16.4%
Math Symbol 52
 
3.9%
Other Punctuation 18
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 30
 
5.9%
z 29
 
5.7%
i 25
 
4.9%
p 25
 
4.9%
d 24
 
4.7%
s 22
 
4.3%
f 22
 
4.3%
l 21
 
4.1%
c 21
 
4.1%
n 20
 
3.9%
Other values (16) 268
52.9%
Uppercase Letter
ValueCountFrequency (%)
M 27
 
5.1%
V 27
 
5.1%
F 26
 
4.9%
J 26
 
4.9%
I 25
 
4.7%
W 23
 
4.4%
N 22
 
4.2%
S 21
 
4.0%
P 21
 
4.0%
Q 21
 
4.0%
Other values (16) 288
54.6%
Decimal Number
ValueCountFrequency (%)
5 32
14.8%
9 28
13.0%
2 26
12.0%
8 22
10.2%
0 21
9.7%
4 20
9.3%
1 18
8.3%
6 18
8.3%
7 16
7.4%
3 15
6.9%
Math Symbol
ValueCountFrequency (%)
= 30
57.7%
+ 22
42.3%
Other Punctuation
ValueCountFrequency (%)
/ 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1034
78.3%
Common 286
 
21.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 30
 
2.9%
z 29
 
2.8%
M 27
 
2.6%
V 27
 
2.6%
F 26
 
2.5%
J 26
 
2.5%
i 25
 
2.4%
I 25
 
2.4%
p 25
 
2.4%
d 24
 
2.3%
Other values (42) 770
74.5%
Common
ValueCountFrequency (%)
5 32
11.2%
= 30
10.5%
9 28
9.8%
2 26
9.1%
8 22
7.7%
+ 22
7.7%
0 21
7.3%
4 20
7.0%
1 18
 
6.3%
/ 18
 
6.3%
Other values (3) 49
17.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 32
 
2.4%
= 30
 
2.3%
w 30
 
2.3%
z 29
 
2.2%
9 28
 
2.1%
M 27
 
2.0%
V 27
 
2.0%
F 26
 
2.0%
J 26
 
2.0%
2 26
 
2.0%
Other values (55) 1039
78.7%

회원코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0327282 × 109
Minimum3.0019757 × 109
Maximum3.1007966 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:54:54.220737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0019757 × 109
5-th percentile3.0050422 × 109
Q13.0172731 × 109
median3.020337 × 109
Q33.0377273 × 109
95-th percentile3.0910328 × 109
Maximum3.1007966 × 109
Range98820945
Interquartile range (IQR)20454184

Descriptive statistics

Standard deviation26162038
Coefficient of variation (CV)0.0086265687
Kurtosis1.4369977
Mean3.0327282 × 109
Median Absolute Deviation (MAD)10736038
Skewness1.4257065
Sum9.0981845 × 1010
Variance6.8445223 × 1014
MonotonicityNot monotonic
2024-03-13T20:54:54.358509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3020704503 1
 
3.3%
3019969474 1
 
3.3%
3018347148 1
 
3.3%
3016578213 1
 
3.3%
3041835228 1
 
3.3%
3079491573 1
 
3.3%
3019589377 1
 
3.3%
3017513460 1
 
3.3%
3017469096 1
 
3.3%
3031730216 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3001975692 1
3.3%
3004178239 1
3.3%
3006098235 1
3.3%
3010258140 1
3.3%
3016578213 1
3.3%
3017009676 1
3.3%
3017072117 1
3.3%
3017207759 1
3.3%
3017469096 1
3.3%
3017513460 1
3.3%
ValueCountFrequency (%)
3100796637 1
3.3%
3100475571 1
3.3%
3079491573 1
3.3%
3066279529 1
3.3%
3062393520 1
3.3%
3059075536 1
3.3%
3041835228 1
3.3%
3037918311 1
3.3%
3037154175 1
3.3%
3036063155 1
3.3%

가맹점번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
999999999999999
28 
8106335569
 
1
714625160
 
1

Length

Max length15
Median length15
Mean length14.633333
Min length9

Unique

Unique2 ?
Unique (%)6.7%

Sample

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

Common Values

ValueCountFrequency (%)
999999999999999 28
93.3%
8106335569 1
 
3.3%
714625160 1
 
3.3%

Length

2024-03-13T20:54:54.496717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:54:54.648517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
999999999999999 28
93.3%
8106335569 1
 
3.3%
714625160 1
 
3.3%

성별코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
F
21 
M
9 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
F 21
70.0%
M 9
30.0%

Length

2024-03-13T20:54:54.770228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:54:54.886235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 21
70.0%
m 9
30.0%

연령대코드
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.333333
Minimum0
Maximum60
Zeros11
Zeros (%)36.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:54:54.985154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q340
95-th percentile60
Maximum60
Range60
Interquartile range (IQR)40

Descriptive statistics

Standard deviation21.063669
Coefficient of variation (CV)0.90272868
Kurtosis-1.2393871
Mean23.333333
Median Absolute Deviation (MAD)20
Skewness0.23807836
Sum700
Variance443.67816
MonotonicityNot monotonic
2024-03-13T20:54:55.079400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 11
36.7%
40 7
23.3%
20 5
16.7%
30 3
 
10.0%
60 3
 
10.0%
50 1
 
3.3%
ValueCountFrequency (%)
0 11
36.7%
20 5
16.7%
30 3
 
10.0%
40 7
23.3%
50 1
 
3.3%
60 3
 
10.0%
ValueCountFrequency (%)
60 3
 
10.0%
50 1
 
3.3%
40 7
23.3%
30 3
 
10.0%
20 5
16.7%
0 11
36.7%

결제상품ID
Real number (ℝ)

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4000008 × 1011
Minimum1.4000002 × 1011
Maximum1.4000013 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:54:55.212655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000002 × 1011
5-th percentile1.4000002 × 1011
Q11.4000005 × 1011
median1.4000008 × 1011
Q31.4000012 × 1011
95-th percentile1.4000013 × 1011
Maximum1.4000013 × 1011
Range110000
Interquartile range (IQR)68500

Descriptive statistics

Standard deviation38197.852
Coefficient of variation (CV)2.7284164 × 10-7
Kurtosis-1.3699207
Mean1.4000008 × 1011
Median Absolute Deviation (MAD)36000
Skewness-0.23685025
Sum4.2000024 × 1012
Variance1.4590759 × 109
MonotonicityNot monotonic
2024-03-13T20:54:55.364228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
140000126000 3
 
10.0%
140000122000 2
 
6.7%
140000090000 2
 
6.7%
140000034000 2
 
6.7%
140000116000 2
 
6.7%
140000018000 2
 
6.7%
140000076000 1
 
3.3%
140000080000 1
 
3.3%
140000114000 1
 
3.3%
140000058000 1
 
3.3%
Other values (13) 13
43.3%
ValueCountFrequency (%)
140000016000 1
3.3%
140000018000 2
6.7%
140000030000 1
3.3%
140000034000 2
6.7%
140000038000 1
3.3%
140000048000 1
3.3%
140000052000 1
3.3%
140000056000 1
3.3%
140000058000 1
3.3%
140000068000 1
3.3%
ValueCountFrequency (%)
140000126000 3
10.0%
140000124000 1
 
3.3%
140000122000 2
6.7%
140000120000 1
 
3.3%
140000118000 1
 
3.3%
140000116000 2
6.7%
140000114000 1
 
3.3%
140000092000 1
 
3.3%
140000090000 2
6.7%
140000082000 1
 
3.3%

결제상품명
Text

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:54:55.584441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length10.5
Mean length7.9333333
Min length4

Characters and Unicode

Total characters238
Distinct characters60
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)56.7%

Sample

1st row의정부사랑카드
2nd row수원페이
3rd row부천페이
4th row하남하머니
5th row이천사랑지역화폐
ValueCountFrequency (%)
수원페이 3
 
8.8%
안양사랑페이 2
 
5.9%
행복화성지역화폐 2
 
5.9%
고양페이카드 2
 
5.9%
의정부사랑카드 2
 
5.9%
고양페이카드(통합 2
 
5.9%
구리사랑카드(통합 1
 
2.9%
가평gp페이 1
 
2.9%
pay-n 1
 
2.9%
you 1
 
2.9%
Other values (17) 17
50.0%
2024-03-13T20:54:55.904968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 14
 
5.9%
페 13
 
5.5%
통 12
 
5.0%
랑 12
 
5.0%
사 12
 
5.0%
) 11
 
4.6%
( 11
 
4.6%
합 10
 
4.2%
양 9
 
3.8%
카 8
 
3.4%
Other values (50) 126
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 194
81.5%
Close Punctuation 11
 
4.6%
Open Punctuation 11
 
4.6%
Lowercase Letter 10
 
4.2%
Uppercase Letter 7
 
2.9%
Space Separator 4
 
1.7%
Dash Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 14
 
7.2%
페 13
 
6.7%
통 12
 
6.2%
랑 12
 
6.2%
사 12
 
6.2%
합 10
 
5.2%
양 9
 
4.6%
카 8
 
4.1%
드 8
 
4.1%
화 7
 
3.6%
Other values (34) 89
45.9%
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 3
42.9%
T 1
 
14.3%
N 1
 
14.3%
Y 1
 
14.3%
G 1
 
14.3%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 194
81.5%
Common 27
 
11.3%
Latin 17
 
7.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 14
 
7.2%
페 13
 
6.7%
통 12
 
6.2%
랑 12
 
6.2%
사 12
 
6.2%
합 10
 
5.2%
양 9
 
4.6%
카 8
 
4.1%
드 8
 
4.1%
화 7
 
3.6%
Other values (34) 89
45.9%
Latin
ValueCountFrequency (%)
a 3
17.6%
P 3
17.6%
y 2
11.8%
T 1
 
5.9%
N 1
 
5.9%
u 1
 
5.9%
o 1
 
5.9%
Y 1
 
5.9%
k 1
 
5.9%
n 1
 
5.9%
Other values (2) 2
11.8%
Common
ValueCountFrequency (%)
) 11
40.7%
( 11
40.7%
4
 
14.8%
- 1
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 194
81.5%
ASCII 44
 
18.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 14
 
7.2%
페 13
 
6.7%
통 12
 
6.2%
랑 12
 
6.2%
사 12
 
6.2%
합 10
 
5.2%
양 9
 
4.6%
카 8
 
4.1%
드 8
 
4.1%
화 7
 
3.6%
Other values (34) 89
45.9%
ASCII
ValueCountFrequency (%)
) 11
25.0%
( 11
25.0%
4
 
9.1%
a 3
 
6.8%
P 3
 
6.8%
y 2
 
4.5%
T 1
 
2.3%
N 1
 
2.3%
- 1
 
2.3%
u 1
 
2.3%
Other values (6) 6
13.6%

가맹점업종명
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing29
Missing (%)96.7%
Memory size372.0 B
2024-03-13T20:54:56.051386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8
Distinct characters8
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

Unique1 ?
Unique (%)100.0%

Sample

1st row일반/휴게 음식
ValueCountFrequency (%)
일반/휴게 1
50.0%
음식 1
50.0%
2024-03-13T20:54:56.286785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
일 1
12.5%
반 1
12.5%
/ 1
12.5%
휴 1
12.5%
게 1
12.5%
1
12.5%
음 1
12.5%
식 1
12.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6
75.0%
Other Punctuation 1
 
12.5%
Space Separator 1
 
12.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
일 1
16.7%
반 1
16.7%
휴 1
16.7%
게 1
16.7%
음 1
16.7%
식 1
16.7%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6
75.0%
Common 2
 
25.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
일 1
16.7%
반 1
16.7%
휴 1
16.7%
게 1
16.7%
음 1
16.7%
식 1
16.7%
Common
ValueCountFrequency (%)
/ 1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6
75.0%
ASCII 2
 
25.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
일 1
16.7%
반 1
16.7%
휴 1
16.7%
게 1
16.7%
음 1
16.7%
식 1
16.7%
ASCII
ValueCountFrequency (%)
/ 1
50.0%
1
50.0%

가맹점우편번호
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
29 
14048
 
1

Length

Max length5
Median length4
Mean length4.0333333
Min length4

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 29
96.7%
14048 1
 
3.3%

Length

2024-03-13T20:54:56.416239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:54:56.510077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 29
96.7%
14048 1
 
3.3%

시도명
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing29
Missing (%)96.7%
Memory size372.0 B
2024-03-13T20:54:56.593165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
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

Unique1 ?
Unique (%)100.0%

Sample

1st row경기도
ValueCountFrequency (%)
경기도 1
100.0%
2024-03-13T20:54:56.862551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 1
33.3%
기 1
33.3%
도 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 1
33.3%
기 1
33.3%
도 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 1
33.3%
기 1
33.3%
도 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 1
33.3%
기 1
33.3%
도 1
33.3%

시군구명
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing29
Missing (%)96.7%
Memory size372.0 B
2024-03-13T20:54:57.003527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
Distinct characters6
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

Unique1 ?
Unique (%)100.0%

Sample

1st row안양시 동안구
ValueCountFrequency (%)
안양시 1
50.0%
동안구 1
50.0%
2024-03-13T20:54:57.245975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
안 2
28.6%
양 1
14.3%
시 1
14.3%
1
14.3%
동 1
14.3%
구 1
14.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6
85.7%
Space Separator 1
 
14.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
안 2
33.3%
양 1
16.7%
시 1
16.7%
동 1
16.7%
구 1
16.7%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6
85.7%
Common 1
 
14.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
안 2
33.3%
양 1
16.7%
시 1
16.7%
동 1
16.7%
구 1
16.7%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6
85.7%
ASCII 1
 
14.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
안 2
33.3%
양 1
16.7%
시 1
16.7%
동 1
16.7%
구 1
16.7%
ASCII
ValueCountFrequency (%)
1
100.0%

읍면동명
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing29
Missing (%)96.7%
Memory size372.0 B
2024-03-13T20:54:57.356794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
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

Unique1 ?
Unique (%)100.0%

Sample

1st row비산동
ValueCountFrequency (%)
비산동 1
100.0%
2024-03-13T20:54:57.568770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
비 1
33.3%
산 1
33.3%
동 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
비 1
33.3%
산 1
33.3%
동 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
비 1
33.3%
산 1
33.3%
동 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
비 1
33.3%
산 1
33.3%
동 1
33.3%

위도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
0.0
29 
37.391
 
1

Length

Max length6
Median length3
Mean length3.1
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
0.0 29
96.7%
37.391 1
 
3.3%

Length

2024-03-13T20:54:57.694084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:54:57.834149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 29
96.7%
37.391 1
 
3.3%

경도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
0.0
29 
126.949
 
1

Length

Max length7
Median length3
Mean length3.1333333
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
0.0 29
96.7%
126.949 1
 
3.3%

Length

2024-03-13T20:54:57.974830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:54:58.105291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 29
96.7%
126.949 1
 
3.3%

사용여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
28 
True
 
2
ValueCountFrequency (%)
False 28
93.3%
True 2
 
6.7%
2024-03-13T20:54:58.237670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
28 
3900
 
1
2000
 
1

Length

Max length4
Median length1
Mean length1.2
Min length1

Unique

Unique2 ?
Unique (%)6.7%

Sample

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

Common Values

ValueCountFrequency (%)
0 28
93.3%
3900 1
 
3.3%
2000 1
 
3.3%

Length

2024-03-13T20:54:58.378926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:54:58.476115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28
93.3%
3900 1
 
3.3%
2000 1
 
3.3%

Interactions

2024-03-13T20:54:51.856414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:51.223069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:51.536157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:51.961581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:51.322920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:51.635442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:52.043216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:51.436963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:51.745735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:54:58.565984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명위도경도사용여부결제금액
카드번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
회원코드1.0001.0000.0000.6350.4600.3170.9480.0000.0000.0000.000
가맹점번호1.0000.0001.0000.1010.4870.0000.3131.0001.0001.0001.000
성별코드1.0000.6350.1011.0000.6010.0000.7140.0000.0000.0000.101
연령대코드1.0000.4600.4870.6011.0000.0000.6710.0000.0000.1990.487
결제상품ID1.0000.3170.0000.0000.0001.0001.0000.0000.0000.0000.000
결제상품명1.0000.9480.3130.7140.6711.0001.0000.0000.0000.0000.313
위도1.0000.0001.0000.0000.0000.0000.0001.0000.6550.4121.000
경도1.0000.0001.0000.0000.0000.0000.0000.6551.0000.4121.000
사용여부1.0000.0001.0000.0000.1990.0000.0000.4120.4121.0001.000
결제금액1.0000.0001.0000.1010.4870.0000.3131.0001.0001.0001.000
2024-03-13T20:54:58.709198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도가맹점우편번호성별코드가맹점번호결제금액경도사용여부
위도1.000NaN0.0000.9820.9820.4540.268
가맹점우편번호NaN1.000NaNNaNNaNNaNNaN
성별코드0.000NaN1.0000.1580.1580.0000.000
가맹점번호0.982NaN0.1581.0001.0000.9820.982
결제금액0.982NaN0.1581.0001.0000.9820.982
경도0.454NaN0.0000.9820.9821.0000.268
사용여부0.268NaN0.0000.9820.9820.2681.000
2024-03-13T20:54:58.832792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회원코드연령대코드결제상품ID가맹점번호성별코드가맹점우편번호위도경도사용여부결제금액
회원코드1.000-0.039-0.0740.0000.587NaN0.0000.0000.0000.000
연령대코드-0.0391.000-0.1440.2030.399NaN0.0000.0000.1080.203
결제상품ID-0.074-0.1441.0000.0000.000NaN0.0000.0000.0000.000
가맹점번호0.0000.2030.0001.0000.158NaN0.9820.9820.9821.000
성별코드0.5870.3990.0000.1581.000NaN0.0000.0000.0000.158
가맹점우편번호NaNNaNNaNNaNNaN1.000NaNNaNNaNNaN
위도0.0000.0000.0000.9820.000NaN1.0000.4540.2680.982
경도0.0000.0000.0000.9820.000NaN0.4541.0000.2680.982
사용여부0.0000.1080.0000.9820.000NaN0.2680.2681.0000.982
결제금액0.0000.2030.0001.0000.158NaN0.9820.9820.9821.000

Missing values

2024-03-13T20:54:52.194308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:54:52.510884image/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.
2024-03-13T20:54:52.690336image/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
02023-08-072023-08-13G5waM21XUUZQVW9KRCRHz58mpFwPEKbDJ0/XqPcaRfQ=3020704503999999999999999F0140000122000의정부사랑카드<NA><NA><NA><NA><NA>0.00.0N0
12023-08-072023-08-13HrgEkSP3kxQnjjNkwE22PJr9rzzu5KGGiKjl5jj2s2w=3017072117999999999999999F30140000126000수원페이<NA><NA><NA><NA><NA>0.00.0N0
22023-08-072023-08-132ZpiIyaxIgTwkdsWXyOQdFaja6pbTDjQw8mrAXVvGik=3010258140999999999999999M40140000030000부천페이<NA><NA><NA><NA><NA>0.00.0N0
32023-08-072023-08-13G5zy+uQHJio+fyXkvBIuXo5AI2zyzqpNfdEZgBiLGFU=3037154175999999999999999F40140000118000하남하머니<NA><NA><NA><NA><NA>0.00.0N0
42023-08-072023-08-13LsDV5WVnl5UYO9MH9Hh3z4BlmsiWVAsOsb5ICwd8PLI=3017009676999999999999999F0140000048000이천사랑지역화폐<NA><NA><NA><NA><NA>0.00.0N0
52023-08-072023-08-13rMUdFJYcJ1mPVPkU1EljS0ykBy4JZSnrk1vNKsa06Kg=3004178239999999999999999M40140000120000파주 Pay(파주페이)<NA><NA><NA><NA><NA>0.00.0N0
62023-08-072023-08-13W2g9BDyqVD1xH24WQndE0pIc5l9PIiw0MaDk3+pG39Q=3032997163999999999999999F0140000034000안양사랑페이<NA><NA><NA><NA><NA>0.00.0N0
72023-08-072023-08-13IpdU+gRTcS1v+EFnmOdNpZ+gtueMWOf9Coz0dI5A700=3025241178999999999999999F0140000124000안산사랑상품권 다온<NA><NA><NA><NA><NA>0.00.0N0
82023-08-072023-08-13JA0RNPuCJ7SBwXhwkz9ndLe4erJSb3D70sYH9K/sPRg=3017961390999999999999999F40140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
92023-08-072023-08-13MJ1xSj7A8Z4ZybhNQZyxyM9f9snfKNvwWtGIDwj1iAQ=3100796637999999999999999M40140000072000양평통보(통합)<NA><NA><NA><NA><NA>0.00.0N0
정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
202023-08-072023-08-13NR7uqaIhfB6V+vaDwr8h8UaZq92tJd7H+NLMcVoftHA=3066279529999999999999999F0140000056000포천사랑상품권(통합)<NA><NA><NA><NA><NA>0.00.0N0
212023-08-072023-08-13OzrhMWAF62VOIZtsjFCtPsgdkJiwewY7tmnn1BaVBlI=3062393520999999999999999F0140000090000고양페이카드(통합)<NA><NA><NA><NA><NA>0.00.0N0
222023-08-072023-08-1329da8Ffl6i4cSBRfSY+2gUW3kVqmpoVxz2X/Iz8MOpg=3031730216999999999999999F40140000058000평택사랑카드(통합)<NA><NA><NA><NA><NA>0.00.0N0
232023-08-072023-08-13tHjQ4n9zJAufRoTLm/lYI5j055lXndVT6n8R/5lDbOY=3017469096999999999999999F0140000018000고양페이카드<NA><NA><NA><NA><NA>0.00.0N0
242023-08-072023-08-132z+6JsUU/zT++Kz1PZ8pi4e57uWEJpb7TAXvCN0UrPE=3017513460999999999999999F30140000114000Thank You Pay-N<NA><NA><NA><NA><NA>0.00.0N0
252023-08-072023-08-133lebEswLYRZR/m9a0F/hEyWUibic2qUSV9f2l98Td4Y=3019589377999999999999999M30140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
262023-08-072023-08-13VHvLQKFiDyopaYfRDM624GygiV1F6B+iKoesQL4BQiM=3079491573999999999999999F60140000090000고양페이카드(통합)<NA><NA><NA><NA><NA>0.00.0N0
272023-08-072023-08-135JCqTRaTMFhztFdLnyNdXjc7DW2JiciAc5FvELsBvh8=3041835228999999999999999F0140000080000부천페이(통합)<NA><NA><NA><NA><NA>0.00.0N0
282023-08-072023-08-13WpTZi2LwUkG5d+PAuiwQYxvruXbeRcz1/dtvHA6p5y4=3016578213714625160F40140000034000안양사랑페이일반/휴게 음식14048경기도안양시 동안구비산동37.391126.949Y2000
292023-08-072023-08-13XcKwNElZJuMAeM4FwsVMVtFWGp+5bnuW+D3fbD/rJ9c=3018347148999999999999999F20140000122000의정부사랑카드<NA><NA><NA><NA><NA>0.00.0N0