Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells71
Missing cells (%)13.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory156.4 B

Variable types

DateTime2
Text4
Numeric8
Categorical3
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/8de762e5-4049-4ccb-972f-78f7fb324f1f

Alerts

정책주간결제시작일자 has constant value ""Constant
정책주간결제종료일자 has constant value ""Constant
사용여부 is highly overall correlated with 가맹점번호 and 6 other fieldsHigh correlation
가맹점업종명 is highly overall correlated with 가맹점번호 and 2 other fieldsHigh correlation
시도명 is highly overall correlated with 회원코드 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 가맹점우편번호 and 1 other fieldsHigh correlation
가맹점우편번호 is highly overall correlated with 연령대코드 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
경도 is highly overall correlated with 가맹점번호 and 3 other fieldsHigh correlation
결제금액 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
성별코드 is highly overall correlated with 가맹점업종명High correlation
가맹점우편번호 has 23 (76.7%) missing valuesMissing
시군구명 has 24 (80.0%) missing valuesMissing
읍면동명 has 24 (80.0%) missing valuesMissing
카드번호 has unique valuesUnique
회원코드 has unique valuesUnique
연령대코드 has 5 (16.7%) zerosZeros
위도 has 24 (80.0%) zerosZeros
경도 has 24 (80.0%) zerosZeros
결제금액 has 23 (76.7%) zerosZeros

Reproduction

Analysis started2024-04-17 14:46:41.589598
Analysis finished2024-04-17 14:46:47.097632
Duration5.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정책주간결제시작일자
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2021-12-13 00:00:00
Maximum2021-12-13 00:00:00
2024-04-17T23:46:47.132234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:47.443738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

정책주간결제종료일자
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2021-12-19 00:00:00
Maximum2021-12-19 00:00:00
2024-04-17T23:46:47.510465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:47.581254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

카드번호
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-04-17T23:46:47.756245image/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++/rhHaxN0ALpUWFHKmptuH/5bc7F1Lt+gRjoq3AAfA=
2nd rowzzf/j2wNQJ2QBQz642n+7YdAwdsaYoMC5j+72e5IIg8=
3rd rowHLPQwCVi9D3k/rE9rOzAMDceBshpLMGZnGaoG+fn1qM=
4th rowEn47czByzs5XqjJZsYs4c2ZolsURN/NwBel8Iix1SdA=
5th rowlrWaesxFFiITfKpZhCU0PUkIVNS6aR8pOzYVxTn3Bk8=
ValueCountFrequency (%)
rhhaxn0alpuwfhkmptuh/5bc7f1lt+grjoq3aafa 1
 
3.3%
zzf/j2wnqj2qbqz642n+7ydawdsayomc5j+72e5iig8 1
 
3.3%
8szuri6glmyazdkssdk2n8w/f6i6ghg1rgx415/ohsc 1
 
3.3%
6iloejy65ggn+qgb/sgdwiems2uavkaw712dmurbvxw 1
 
3.3%
4ijffv9qlfdtpmjj94cnuzkslq8jwwy53cfzacq8wsm 1
 
3.3%
2epnb4sml8uvg7kxogcmy/tsecfe2apfbplkeblhhne 1
 
3.3%
0b3djvnbciv1+ukrkgulrod14o/uhshllqbeoy5c3rg 1
 
3.3%
2ligfwm3xobkriindij3dzp2dgizubsfjyy/ugxcm 1
 
3.3%
8o9uzm5ozvhuoyakwayjjchlb5jhcjlm+sd76/wobbq 1
 
3.3%
0w4l+hycu0dvq235katvm8glymrwjsm6a+v3er1hnlo 1
 
3.3%
Other values (20) 20
66.7%
2024-04-17T23:46:48.063900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
= 30
 
2.3%
g 29
 
2.2%
V 29
 
2.2%
/ 29
 
2.2%
4 29
 
2.2%
U 28
 
2.1%
+ 26
 
2.0%
2 26
 
2.0%
I 26
 
2.0%
A 25
 
1.9%
Other values (55) 1043
79.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 525
39.8%
Lowercase Letter 493
37.3%
Decimal Number 217
16.4%
Math Symbol 56
 
4.2%
Other Punctuation 29
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
g 29
 
5.9%
s 24
 
4.9%
d 24
 
4.9%
l 22
 
4.5%
y 22
 
4.5%
n 22
 
4.5%
v 22
 
4.5%
c 22
 
4.5%
b 22
 
4.5%
z 21
 
4.3%
Other values (16) 263
53.3%
Uppercase Letter
ValueCountFrequency (%)
V 29
 
5.5%
U 28
 
5.3%
I 26
 
5.0%
A 25
 
4.8%
Z 25
 
4.8%
L 24
 
4.6%
H 24
 
4.6%
J 23
 
4.4%
G 22
 
4.2%
B 22
 
4.2%
Other values (16) 277
52.8%
Decimal Number
ValueCountFrequency (%)
4 29
13.4%
2 26
12.0%
5 25
11.5%
3 23
10.6%
6 23
10.6%
8 22
10.1%
0 21
9.7%
7 19
8.8%
1 19
8.8%
9 10
 
4.6%
Math Symbol
ValueCountFrequency (%)
= 30
53.6%
+ 26
46.4%
Other Punctuation
ValueCountFrequency (%)
/ 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1018
77.1%
Common 302
 
22.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
g 29
 
2.8%
V 29
 
2.8%
U 28
 
2.8%
I 26
 
2.6%
A 25
 
2.5%
Z 25
 
2.5%
L 24
 
2.4%
s 24
 
2.4%
d 24
 
2.4%
H 24
 
2.4%
Other values (42) 760
74.7%
Common
ValueCountFrequency (%)
= 30
9.9%
/ 29
9.6%
4 29
9.6%
+ 26
8.6%
2 26
8.6%
5 25
8.3%
3 23
7.6%
6 23
7.6%
8 22
7.3%
0 21
7.0%
Other values (3) 48
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
= 30
 
2.3%
g 29
 
2.2%
V 29
 
2.2%
/ 29
 
2.2%
4 29
 
2.2%
U 28
 
2.1%
+ 26
 
2.0%
2 26
 
2.0%
I 26
 
2.0%
A 25
 
1.9%
Other values (55) 1043
79.0%

회원코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0205525 × 109
Minimum3.0018916 × 109
Maximum3.0598598 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-17T23:46:48.177992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0018916 × 109
5-th percentile3.0034203 × 109
Q13.0171762 × 109
median3.019511 × 109
Q33.0208221 × 109
95-th percentile3.0380139 × 109
Maximum3.0598598 × 109
Range57968206
Interquartile range (IQR)3645896.5

Descriptive statistics

Standard deviation11572242
Coefficient of variation (CV)0.0038311673
Kurtosis3.7220818
Mean3.0205525 × 109
Median Absolute Deviation (MAD)1656304.5
Skewness1.3061095
Sum9.0616574 × 1010
Variance1.3391678 × 1014
MonotonicityNot monotonic
2024-04-17T23:46:48.290072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3020044641 1
 
3.3%
3019530392 1
 
3.3%
3005006443 1
 
3.3%
3006156158 1
 
3.3%
3020422371 1
 
3.3%
3019356518 1
 
3.3%
3034004175 1
 
3.3%
3018464358 1
 
3.3%
3020249654 1
 
3.3%
3001891559 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3001891559 1
3.3%
3002122625 1
3.3%
3005006443 1
3.3%
3006156158 1
3.3%
3009524109 1
3.3%
3015293144 1
3.3%
3016530735 1
3.3%
3016965891 1
3.3%
3017807231 1
3.3%
3017902109 1
3.3%
ValueCountFrequency (%)
3059859765 1
3.3%
3039064306 1
3.3%
3036730158 1
3.3%
3034004175 1
3.3%
3032546227 1
3.3%
3028396205 1
3.3%
3021107148 1
3.3%
3020861146 1
3.3%
3020705052 1
3.3%
3020422371 1
3.3%

가맹점번호
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6666684 × 1014
Minimum7.0195544 × 108
Maximum1 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-17T23:46:48.392219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0195544 × 108
5-th percentile7.2163791 × 108
Q11 × 1015
median1 × 1015
Q31 × 1015
95-th percentile1 × 1015
Maximum1 × 1015
Range9.999993 × 1014
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.3018275 × 1014
Coefficient of variation (CV)0.56110781
Kurtosis-0.25732032
Mean7.6666684 × 1014
Median Absolute Deviation (MAD)0
Skewness-1.3283381
Sum2.3000005 × 1016
Variance1.850572 × 1029
MonotonicityNot monotonic
2024-04-17T23:46:48.480846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
999999999999999 23
76.7%
779936893 1
 
3.3%
726987173 1
 
3.3%
701955437 1
 
3.3%
723853472 1
 
3.3%
719825180 1
 
3.3%
725519061 1
 
3.3%
726585274 1
 
3.3%
ValueCountFrequency (%)
701955437 1
 
3.3%
719825180 1
 
3.3%
723853472 1
 
3.3%
725519061 1
 
3.3%
726585274 1
 
3.3%
726987173 1
 
3.3%
779936893 1
 
3.3%
999999999999999 23
76.7%
ValueCountFrequency (%)
999999999999999 23
76.7%
779936893 1
 
3.3%
726987173 1
 
3.3%
726585274 1
 
3.3%
725519061 1
 
3.3%
723853472 1
 
3.3%
719825180 1
 
3.3%
701955437 1
 
3.3%

성별코드
Categorical

HIGH CORRELATION 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2024-04-17T23:46:48.575674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:46:48.661824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 16
53.3%
m 14
46.7%

연령대코드
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29
Minimum0
Maximum60
Zeros5
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-17T23:46:48.731500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120
median30
Q340
95-th percentile50
Maximum60
Range60
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.473594
Coefficient of variation (CV)0.56805498
Kurtosis-0.34604317
Mean29
Median Absolute Deviation (MAD)10
Skewness-0.47403375
Sum870
Variance271.37931
MonotonicityNot monotonic
2024-04-17T23:46:48.811378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
40 8
26.7%
30 8
26.7%
20 5
16.7%
0 5
16.7%
50 3
 
10.0%
60 1
 
3.3%
ValueCountFrequency (%)
0 5
16.7%
20 5
16.7%
30 8
26.7%
40 8
26.7%
50 3
 
10.0%
60 1
 
3.3%
ValueCountFrequency (%)
60 1
 
3.3%
50 3
 
10.0%
40 8
26.7%
30 8
26.7%
20 5
16.7%
0 5
16.7%

결제상품ID
Real number (ℝ)

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4000007 × 1011
Minimum1.4000002 × 1011
Maximum1.4000013 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-17T23:46:48.912225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000002 × 1011
5-th percentile1.4000002 × 1011
Q11.4000003 × 1011
median1.4000008 × 1011
Q31.4000012 × 1011
95-th percentile1.4000012 × 1011
Maximum1.4000013 × 1011
Range108000
Interquartile range (IQR)85000

Descriptive statistics

Standard deviation40827.982
Coefficient of variation (CV)2.9162829 × 10-7
Kurtosis-1.7556
Mean1.4000007 × 1011
Median Absolute Deviation (MAD)37000
Skewness-0.10361399
Sum4.2000022 × 1012
Variance1.6669241 × 109
MonotonicityNot monotonic
2024-04-17T23:46:49.010081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
140000030000 4
 
13.3%
140000116000 3
 
10.0%
140000046000 2
 
6.7%
140000044000 2
 
6.7%
140000018000 2
 
6.7%
140000124000 2
 
6.7%
140000118000 2
 
6.7%
140000078000 1
 
3.3%
140000120000 1
 
3.3%
140000088000 1
 
3.3%
Other values (10) 10
33.3%
ValueCountFrequency (%)
140000018000 2
6.7%
140000020000 1
 
3.3%
140000024000 1
 
3.3%
140000030000 4
13.3%
140000034000 1
 
3.3%
140000044000 2
6.7%
140000046000 2
6.7%
140000074000 1
 
3.3%
140000078000 1
 
3.3%
140000084000 1
 
3.3%
ValueCountFrequency (%)
140000126000 1
 
3.3%
140000124000 2
6.7%
140000122000 1
 
3.3%
140000120000 1
 
3.3%
140000118000 2
6.7%
140000116000 3
10.0%
140000104000 1
 
3.3%
140000102000 1
 
3.3%
140000100000 1
 
3.3%
140000088000 1
 
3.3%

결제상품명
Text

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-04-17T23:46:49.171901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length12
Mean length7.7333333
Min length4

Characters and Unicode

Total characters232
Distinct characters59
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

Unique13 ?
Unique (%)43.3%

Sample

1st row안성사랑카드(통합)
2nd row하남하머니
3rd row오산화폐 오색전
4th row부천페이
5th row행복화성지역화폐
ValueCountFrequency (%)
부천페이 4
 
10.5%
안산사랑상품권 3
 
7.9%
행복화성지역화폐 3
 
7.9%
오산화폐 2
 
5.3%
오색전 2
 
5.3%
고양페이카드 2
 
5.3%
다온 2
 
5.3%
하남하머니 2
 
5.3%
용인와이페이 2
 
5.3%
you 1
 
2.6%
Other values (15) 15
39.5%
2024-04-17T23:46:49.432465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 14
 
6.0%
페 12
 
5.2%
랑 11
 
4.7%
사 11
 
4.7%
화 10
 
4.3%
( 8
 
3.4%
) 8
 
3.4%
8
 
3.4%
카 7
 
3.0%
드 7
 
3.0%
Other values (49) 136
58.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 192
82.8%
Lowercase Letter 10
 
4.3%
Open Punctuation 8
 
3.4%
Close Punctuation 8
 
3.4%
Space Separator 8
 
3.4%
Uppercase Letter 5
 
2.2%
Dash Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 14
 
7.3%
페 12
 
6.2%
랑 11
 
5.7%
사 11
 
5.7%
화 10
 
5.2%
카 7
 
3.6%
드 7
 
3.6%
통 7
 
3.6%
합 7
 
3.6%
폐 7
 
3.6%
Other values (34) 99
51.6%
Lowercase Letter
ValueCountFrequency (%)
a 3
30.0%
y 2
20.0%
h 1
 
10.0%
n 1
 
10.0%
k 1
 
10.0%
o 1
 
10.0%
u 1
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
P 2
40.0%
T 1
20.0%
Y 1
20.0%
N 1
20.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 192
82.8%
Common 25
 
10.8%
Latin 15
 
6.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 14
 
7.3%
페 12
 
6.2%
랑 11
 
5.7%
사 11
 
5.7%
화 10
 
5.2%
카 7
 
3.6%
드 7
 
3.6%
통 7
 
3.6%
합 7
 
3.6%
폐 7
 
3.6%
Other values (34) 99
51.6%
Latin
ValueCountFrequency (%)
a 3
20.0%
y 2
13.3%
P 2
13.3%
T 1
 
6.7%
h 1
 
6.7%
n 1
 
6.7%
k 1
 
6.7%
Y 1
 
6.7%
o 1
 
6.7%
u 1
 
6.7%
Common
ValueCountFrequency (%)
( 8
32.0%
) 8
32.0%
8
32.0%
- 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 192
82.8%
ASCII 40
 
17.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 14
 
7.3%
페 12
 
6.2%
랑 11
 
5.7%
사 11
 
5.7%
화 10
 
5.2%
카 7
 
3.6%
드 7
 
3.6%
통 7
 
3.6%
합 7
 
3.6%
폐 7
 
3.6%
Other values (34) 99
51.6%
ASCII
ValueCountFrequency (%)
( 8
20.0%
) 8
20.0%
8
20.0%
a 3
 
7.5%
y 2
 
5.0%
P 2
 
5.0%
T 1
 
2.5%
h 1
 
2.5%
n 1
 
2.5%
k 1
 
2.5%
Other values (5) 5
12.5%

가맹점업종명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
23 
일반휴게음식
4 
음료식품
 
2
유통업 영리
 
1

Length

Max length6
Median length4
Mean length4.3333333
Min length4

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 23
76.7%
일반휴게음식 4
 
13.3%
음료식품 2
 
6.7%
유통업 영리 1
 
3.3%

Length

2024-04-17T23:46:49.559565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:46:49.651732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 23
74.2%
일반휴게음식 4
 
12.9%
음료식품 2
 
6.5%
유통업 1
 
3.2%
영리 1
 
3.2%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)100.0%
Missing23
Missing (%)76.7%
Infinite0
Infinite (%)0.0%
Mean12556.286
Minimum10486
Maximum14759
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-17T23:46:49.726809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10486
5-th percentile10777
Q111575.5
median12119
Q313689.5
95-th percentile14710.7
Maximum14759
Range4273
Interquartile range (IQR)2114

Descriptive statistics

Standard deviation1607.316
Coefficient of variation (CV)0.12800887
Kurtosis-1.1108134
Mean12556.286
Median Absolute Deviation (MAD)663
Skewness0.46537539
Sum87894
Variance2583464.6
MonotonicityNot monotonic
2024-04-17T23:46:49.808213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
14598 1
 
3.3%
10486 1
 
3.3%
11695 1
 
3.3%
14759 1
 
3.3%
12119 1
 
3.3%
11456 1
 
3.3%
12781 1
 
3.3%
(Missing) 23
76.7%
ValueCountFrequency (%)
10486 1
3.3%
11456 1
3.3%
11695 1
3.3%
12119 1
3.3%
12781 1
3.3%
14598 1
3.3%
14759 1
3.3%
ValueCountFrequency (%)
14759 1
3.3%
14598 1
3.3%
12781 1
3.3%
12119 1
3.3%
11695 1
3.3%
11456 1
3.3%
10486 1
3.3%

시도명
Categorical

HIGH CORRELATION 

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

Length

Max length4
Median length4
Mean length3.8
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

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

Length

2024-04-17T23:46:49.898985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:46:49.980973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 23
76.7%
경기도 6
 
20.0%
none 1
 
3.3%

시군구명
Text

MISSING 

Distinct6
Distinct (%)100.0%
Missing24
Missing (%)80.0%
Memory size372.0 B
2024-04-17T23:46:50.099820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5.5
Mean length4
Min length3

Characters and Unicode

Total characters24
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)100.0%

Sample

1st row고양시 덕양구
2nd row의정부시
3rd row부천시
4th row남양주시
5th row양주시
ValueCountFrequency (%)
고양시 1
14.3%
덕양구 1
14.3%
의정부시 1
14.3%
부천시 1
14.3%
남양주시 1
14.3%
양주시 1
14.3%
광주시 1
14.3%
2024-04-17T23:46:50.352236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 6
25.0%
양 4
16.7%
주 3
12.5%
부 2
 
8.3%
고 1
 
4.2%
1
 
4.2%
덕 1
 
4.2%
구 1
 
4.2%
의 1
 
4.2%
정 1
 
4.2%
Other values (3) 3
12.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 23
95.8%
Space Separator 1
 
4.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 6
26.1%
양 4
17.4%
주 3
13.0%
부 2
 
8.7%
고 1
 
4.3%
덕 1
 
4.3%
구 1
 
4.3%
의 1
 
4.3%
정 1
 
4.3%
천 1
 
4.3%
Other values (2) 2
 
8.7%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 23
95.8%
Common 1
 
4.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 6
26.1%
양 4
17.4%
주 3
13.0%
부 2
 
8.7%
고 1
 
4.3%
덕 1
 
4.3%
구 1
 
4.3%
의 1
 
4.3%
정 1
 
4.3%
천 1
 
4.3%
Other values (2) 2
 
8.7%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 23
95.8%
ASCII 1
 
4.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 6
26.1%
양 4
17.4%
주 3
13.0%
부 2
 
8.7%
고 1
 
4.3%
덕 1
 
4.3%
구 1
 
4.3%
의 1
 
4.3%
정 1
 
4.3%
천 1
 
4.3%
Other values (2) 2
 
8.7%
ASCII
ValueCountFrequency (%)
1
100.0%

읍면동명
Text

MISSING 

Distinct6
Distinct (%)100.0%
Missing24
Missing (%)80.0%
Memory size372.0 B
2024-04-17T23:46:50.483237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3.5
Mean length3.5
Min length3

Characters and Unicode

Total characters21
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)100.0%

Sample

1st row행신동
2nd row의정부동
3rd row소사본동
4th row퇴계원읍
5th row덕정동
ValueCountFrequency (%)
행신동 1
16.7%
의정부동 1
16.7%
소사본동 1
16.7%
퇴계원읍 1
16.7%
덕정동 1
16.7%
태전동 1
16.7%
2024-04-17T23:46:50.738476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 5
23.8%
정 2
 
9.5%
행 1
 
4.8%
신 1
 
4.8%
의 1
 
4.8%
부 1
 
4.8%
소 1
 
4.8%
사 1
 
4.8%
본 1
 
4.8%
퇴 1
 
4.8%
Other values (6) 6
28.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 5
23.8%
정 2
 
9.5%
행 1
 
4.8%
신 1
 
4.8%
의 1
 
4.8%
부 1
 
4.8%
소 1
 
4.8%
사 1
 
4.8%
본 1
 
4.8%
퇴 1
 
4.8%
Other values (6) 6
28.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 5
23.8%
정 2
 
9.5%
행 1
 
4.8%
신 1
 
4.8%
의 1
 
4.8%
부 1
 
4.8%
소 1
 
4.8%
사 1
 
4.8%
본 1
 
4.8%
퇴 1
 
4.8%
Other values (6) 6
28.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 5
23.8%
정 2
 
9.5%
행 1
 
4.8%
신 1
 
4.8%
의 1
 
4.8%
부 1
 
4.8%
소 1
 
4.8%
사 1
 
4.8%
본 1
 
4.8%
퇴 1
 
4.8%
Other values (6) 6
28.6%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5236333
Minimum0
Maximum37.836
Zeros24
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-17T23:46:50.829947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile37.6994
Maximum37.836
Range37.836
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.304656
Coefficient of variation (CV)2.0342108
Kurtosis0.52786104
Mean7.5236333
Median Absolute Deviation (MAD)0
Skewness1.5802087
Sum225.709
Variance234.23249
MonotonicityNot monotonic
2024-04-17T23:46:50.925934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.0 24
80.0%
37.618 1
 
3.3%
37.739 1
 
3.3%
37.472 1
 
3.3%
37.651 1
 
3.3%
37.836 1
 
3.3%
37.393 1
 
3.3%
ValueCountFrequency (%)
0.0 24
80.0%
37.393 1
 
3.3%
37.472 1
 
3.3%
37.618 1
 
3.3%
37.651 1
 
3.3%
37.739 1
 
3.3%
37.836 1
 
3.3%
ValueCountFrequency (%)
37.836 1
 
3.3%
37.739 1
 
3.3%
37.651 1
 
3.3%
37.618 1
 
3.3%
37.472 1
 
3.3%
37.393 1
 
3.3%
0.0 24
80.0%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.404133
Minimum0
Maximum127.226
Zeros24
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-17T23:46:51.023105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile127.1087
Maximum127.226
Range127.226
Interquartile range (IQR)0

Descriptive statistics

Standard deviation51.676894
Coefficient of variation (CV)2.0341924
Kurtosis0.52748514
Mean25.404133
Median Absolute Deviation (MAD)0
Skewness1.5801374
Sum762.124
Variance2670.5014
MonotonicityNot monotonic
2024-04-17T23:46:51.129264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.0 24
80.0%
126.845 1
 
3.3%
127.048 1
 
3.3%
126.795 1
 
3.3%
127.142 1
 
3.3%
127.068 1
 
3.3%
127.226 1
 
3.3%
ValueCountFrequency (%)
0.0 24
80.0%
126.795 1
 
3.3%
126.845 1
 
3.3%
127.048 1
 
3.3%
127.068 1
 
3.3%
127.142 1
 
3.3%
127.226 1
 
3.3%
ValueCountFrequency (%)
127.226 1
 
3.3%
127.142 1
 
3.3%
127.068 1
 
3.3%
127.048 1
 
3.3%
126.845 1
 
3.3%
126.795 1
 
3.3%
0.0 24
80.0%

사용여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
23 
True
7 
ValueCountFrequency (%)
False 23
76.7%
True 7
 
23.3%
2024-04-17T23:46:51.227507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3051.6667
Minimum0
Maximum23850
Zeros23
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-17T23:46:51.303209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile22000
Maximum23850
Range23850
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7016.2678
Coefficient of variation (CV)2.2991593
Kurtosis4.5291693
Mean3051.6667
Median Absolute Deviation (MAD)0
Skewness2.3885121
Sum91550
Variance49228014
MonotonicityNot monotonic
2024-04-17T23:46:51.386060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 23
76.7%
22000 2
 
6.7%
6500 1
 
3.3%
2600 1
 
3.3%
4600 1
 
3.3%
10000 1
 
3.3%
23850 1
 
3.3%
ValueCountFrequency (%)
0 23
76.7%
2600 1
 
3.3%
4600 1
 
3.3%
6500 1
 
3.3%
10000 1
 
3.3%
22000 2
 
6.7%
23850 1
 
3.3%
ValueCountFrequency (%)
23850 1
 
3.3%
22000 2
 
6.7%
10000 1
 
3.3%
6500 1
 
3.3%
4600 1
 
3.3%
2600 1
 
3.3%
0 23
76.7%

Interactions

2024-04-17T23:46:46.178322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:42.105605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:42.639803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:43.381720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:43.992019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:44.539316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:45.061332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:45.616994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:46.251255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:42.170273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:42.708499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:43.454328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:44.071639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:44.601081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:45.131028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:45.687694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:46.320278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:42.235976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:42.771799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:43.520031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:44.139297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:44.669067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:45.213912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:45.752680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:46.386361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:42.303488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:42.839506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:43.588303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:44.207860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:44.748668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:45.281981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:45.822168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:46.452523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:42.370407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:42.905553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:43.670201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:44.275831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:44.816362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:45.351559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:45.894910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:46.507360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:42.427405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:43.185036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:43.738772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:44.335738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:44.875172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:45.409509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:45.968452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:46.575485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:42.499697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:43.250711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:43.815063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:44.405791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:44.939928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:45.484127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:46.037108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:46.641543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:42.571484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:43.315958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:43.903816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:44.474991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:45.002233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:45.550591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:46:46.116719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T23:46:51.463120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
카드번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
회원코드1.0001.0000.0000.5340.1810.6530.8530.0000.7711.0001.0001.0000.0000.0000.0000.000
가맹점번호1.0000.0001.0000.0000.3650.2880.462NaNNaNNaNNaNNaN0.9370.9370.9851.000
성별코드1.0000.5340.0001.0000.7050.3830.0000.3940.0000.0001.0001.0000.0000.0000.0000.000
연령대코드1.0000.1810.3650.7051.0000.0000.0000.0000.5731.0001.0001.0000.3110.3110.0490.412
결제상품ID1.0000.6530.2880.3830.0001.0001.0000.6471.0000.0001.0001.0000.0000.0000.0000.431
결제상품명1.0000.8530.4620.0000.0001.0001.0000.8791.0000.0001.0001.0000.6700.6700.6100.626
가맹점업종명1.0000.000NaN0.3940.0000.6470.8791.0000.7220.0001.0001.0000.0000.000NaN0.000
가맹점우편번호1.0000.771NaN0.0000.5731.0001.0000.7221.0000.0001.0001.0000.0000.000NaN0.688
시도명1.0001.000NaN0.0001.0000.0000.0000.0000.0001.000NaNNaN0.2930.293NaN1.000
시군구명1.0001.000NaN1.0001.0001.0001.0001.0001.000NaN1.0001.000NaNNaNNaN1.000
읍면동명1.0001.000NaN1.0001.0001.0001.0001.0001.000NaN1.0001.000NaNNaNNaN1.000
위도1.0000.0000.9370.0000.3110.0000.6700.0000.0000.293NaNNaN1.0000.9860.9511.000
경도1.0000.0000.9370.0000.3110.0000.6700.0000.0000.293NaNNaN0.9861.0000.9511.000
사용여부1.0000.0000.9850.0000.0490.0000.610NaNNaNNaNNaNNaN0.9510.9511.0001.000
결제금액1.0000.0001.0000.0000.4120.4310.6260.0000.6881.0001.0001.0001.0001.0001.0001.000
2024-04-17T23:46:51.595692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용여부가맹점업종명시도명성별코드
사용여부1.0001.0001.0000.000
가맹점업종명1.0001.0000.0000.524
시도명1.0000.0001.0000.000
성별코드0.0000.5240.0001.000
2024-04-17T23:46:51.676176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회원코드가맹점번호연령대코드결제상품ID가맹점우편번호위도경도결제금액성별코드가맹점업종명시도명사용여부
회원코드1.0000.0810.078-0.3050.286-0.039-0.031-0.0530.3840.0000.6320.000
가맹점번호0.0811.000-0.2580.1000.036-0.934-0.927-0.9840.0001.0001.0000.903
연령대코드0.078-0.2581.000-0.1280.7270.1640.1780.3050.4770.0000.6320.000
결제상품ID-0.3050.100-0.1281.000-0.018-0.054-0.048-0.1250.2940.0000.0000.000
가맹점우편번호0.2860.0360.727-0.0181.000-0.679-0.2860.4680.0000.4680.0001.000
위도-0.039-0.9340.164-0.054-0.6791.0000.9840.8960.0000.0000.0910.800
경도-0.031-0.9270.178-0.048-0.2860.9841.0000.9060.0000.0000.0910.800
결제금액-0.053-0.9840.305-0.1250.4680.8960.9061.0000.0000.0000.7750.945
성별코드0.3840.0000.4770.2940.0000.0000.0000.0001.0000.5240.0000.000
가맹점업종명0.0001.0000.0000.0000.4680.0000.0000.0000.5241.0000.0001.000
시도명0.6321.0000.6320.0000.0000.0910.0910.7750.0000.0001.0001.000
사용여부0.0000.9030.0000.0001.0000.8000.8000.9450.0001.0001.0001.000

Missing values

2024-04-17T23:46:46.748850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T23:46:46.925626image/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.
2024-04-17T23:46:47.040573image/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
02021-12-132021-12-19++/rhHaxN0ALpUWFHKmptuH/5bc7F1Lt+gRjoq3AAfA=3020044641999999999999999M20140000078000안성사랑카드(통합)<NA><NA><NA><NA><NA>0.00.0N0
12021-12-132021-12-19zzf/j2wNQJ2QBQz642n+7YdAwdsaYoMC5j+72e5IIg8=3020705052999999999999999M40140000118000하남하머니<NA><NA><NA><NA><NA>0.00.0N0
22021-12-132021-12-19HLPQwCVi9D3k/rE9rOzAMDceBshpLMGZnGaoG+fn1qM=3019728578999999999999999F0140000044000오산화폐 오색전<NA><NA><NA><NA><NA>0.00.0N0
32021-12-132021-12-19En47czByzs5XqjJZsYs4c2ZolsURN/NwBel8Iix1SdA=3015293144779936893M50140000030000부천페이일반휴게음식14598NONE<NA><NA>0.00.0Y6500
42021-12-132021-12-19lrWaesxFFiITfKpZhCU0PUkIVNS6aR8pOzYVxTn3Bk8=3018440832999999999999999F40140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
52021-12-132021-12-19++14qB6tOQKQ7Yf+A3/W454J/ud5RvUiS8KiNJH0Onw=3017807231999999999999999F30140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
62021-12-132021-12-196I7zF83s27TUPQLS3xqJ6JH+/+qVJYGheWo74Pln2aE=3018705315999999999999999M30140000020000광명사랑화폐<NA><NA><NA><NA><NA>0.00.0N0
72021-12-132021-12-19zzlIOI4MPROV6XdVr/e2LI2RKLQax60wD4Xv0Buukmc=3016530735999999999999999M20140000024000광주사랑카드<NA><NA><NA><NA><NA>0.00.0N0
82021-12-132021-12-19VMsVy65R2DbHvr6YSUfRAVNbb6HGpMZlmY2XayFglXE=3032546227726987173M30140000018000고양페이카드일반휴게음식10486경기도고양시 덕양구행신동37.618126.845Y2600
92021-12-132021-12-19ls3QxmKZ6Ky+umunyz+w1brkV7wBgy8zVb2GeRd/Jdc=3021107148999999999999999F0140000124000안산사랑상품권 다온<NA><NA><NA><NA><NA>0.00.0N0
정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
202021-12-132021-12-19uAVS/I/nSlD35vAyZdTzUlqYEMuz5/k46f5to+BG5t0=3059859765999999999999999F40140000018000고양페이카드<NA><NA><NA><NA><NA>0.00.0N0
212021-12-132021-12-190W4L+hyCu0dvq235KAtvm8glyMrWJsm6a+V3ER1HNLo=3020861146999999999999999M50140000044000오산화폐 오색전<NA><NA><NA><NA><NA>0.00.0N0
222021-12-132021-12-198o9uZM5oZVHuoyAkWaYjJchLb5JHcJlm+sD76/woBbQ=3001891559725519061F40140000074000양주사랑카드(통합)일반휴게음식11456경기도양주시덕정동37.836127.068Y10000
232021-12-132021-12-19++2liGFwM3xObKRiINdIj3dZp2DgIZubSFJyY/UGXCM=3020249654726585274M40140000084000광주사랑카드(통합)유통업 영리12781경기도광주시태전동37.393127.226Y23850
242021-12-132021-12-190B3djvnBcIv1+UKRkGuLROD14O/uHSHlLQBeoY5c3Rg=3018464358999999999999999M30140000046000용인와이페이<NA><NA><NA><NA><NA>0.00.0N0
252021-12-132021-12-192EPNb4sMl8UVG7kXoGcMY/TseCfe2aPFbpLKEBLHHnE=3034004175999999999999999M40140000088000광명사랑화폐(통합)<NA><NA><NA><NA><NA>0.00.0N0
262021-12-132021-12-194IJfFV9QLFDTpmJj94cnuZkSLQ8jWwY53CfZacq8wsM=3019356518999999999999999M30140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
272021-12-132021-12-196ILoEJy65ggN+qgB/sgdwIems2UAVKAW712DMURbVxw=3020422371999999999999999F0140000124000안산사랑상품권 다온<NA><NA><NA><NA><NA>0.00.0N0
282021-12-132021-12-198SzURi6GLmyAZdkSSDk2n8W/F6i6GhG1rgx415/OHsc=3006156158999999999999999F40140000120000파주 Pay(파주페이)<NA><NA><NA><NA><NA>0.00.0N0
292021-12-132021-12-19AdUBb51eV6dC0F1ibUvxVGPy4KtH8P9mV+5MvCZqck0=3005006443999999999999999F0140000118000하남하머니<NA><NA><NA><NA><NA>0.00.0N0