Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells53
Missing cells (%)9.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory156.4 B

Variable types

Categorical6
Text3
Numeric8
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/cec846af-9891-4be8-ac1c-9466adbc93ba

Alerts

정책주간결제시작일자 has constant value ""Constant
정책주간결제종료일자 has constant value ""Constant
시도명 is highly overall correlated with 가맹점번호 and 5 other fieldsHigh correlation
가맹점업종명 is highly overall correlated with 가맹점번호 and 5 other fieldsHigh correlation
사용여부 is highly overall correlated with 가맹점번호 and 5 other fieldsHigh correlation
가맹점번호 is highly overall correlated with 위도 and 5 other fieldsHigh correlation
결제상품ID is highly overall correlated with 가맹점우편번호 and 2 other fieldsHigh correlation
가맹점우편번호 is highly overall correlated with 결제상품ID and 5 other fieldsHigh correlation
위도 is highly overall correlated with 가맹점번호 and 5 other fieldsHigh correlation
경도 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
결제금액 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
성별코드 is highly overall correlated with 가맹점우편번호High correlation
결제상품명 is highly overall correlated with 결제상품ID and 3 other fieldsHigh correlation
가맹점우편번호 has 17 (56.7%) missing valuesMissing
시군구명 has 18 (60.0%) missing valuesMissing
읍면동명 has 18 (60.0%) missing valuesMissing
카드번호 has unique valuesUnique
회원코드 has unique valuesUnique
연령대코드 has 3 (10.0%) zerosZeros
위도 has 18 (60.0%) zerosZeros
경도 has 18 (60.0%) zerosZeros
결제금액 has 17 (56.7%) zerosZeros

Reproduction

Analysis started2023-12-10 13:55:43.994961
Analysis finished2023-12-10 13:55:56.929803
Duration12.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정책주간결제시작일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2021-07-05
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-07-05
2nd row2021-07-05
3rd row2021-07-05
4th row2021-07-05
5th row2021-07-05

Common Values

ValueCountFrequency (%)
2021-07-05 30
100.0%

Length

2023-12-10T22:55:57.028415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:55:57.225746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-07-05 30
100.0%

정책주간결제종료일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2021-07-11
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-07-11
2nd row2021-07-11
3rd row2021-07-11
4th row2021-07-11
5th row2021-07-11

Common Values

ValueCountFrequency (%)
2021-07-11 30
100.0%

Length

2023-12-10T22:55:57.405843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:55:57.550793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-07-11 30
100.0%

카드번호
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:55:57.918661image/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 row1Nq4OJcYXVAOL1c1VIB8kWJCUv6Ub5hS0+mz7FXJUes=
2nd row4Dyo/2nNH1QmCeno8VFCqUpb4n/ryvh+d7wFLrJSigQ=
3rd rowSK53XWBT/8O2r++PmSj5yR8kVcaptGxIYXNSB+feDj8=
4th rowOwQlmVHDs7r35Qo6hw3nTyKq7EKT9VjasEa31i21nrk=
5th rowYvNd/wi01Y4cnX3p1vcZ3jMh5TpZxsvaPYszKXNUIGo=
ValueCountFrequency (%)
1nq4ojcyxvaol1c1vib8kwjcuv6ub5hs0+mz7fxjues 1
 
3.3%
4dyo/2nnh1qmceno8vfcqupb4n/ryvh+d7wflrjsigq 1
 
3.3%
gxw1r5c20kpzmlq/j/5pvoonlttyzbrjnk/l9ho6iu4 1
 
3.3%
e9yynrtehjjm4z8jzylezhlmaiuwlrdptodtsycymka 1
 
3.3%
qrzpa4yeik94ug9c7tsptjqliczq+bizsmjtxxrxi/o 1
 
3.3%
ashejzzba1p8ygd0mvajiv2efsld0xw1nyw19q8uu7e 1
 
3.3%
qwvdxfmrojttbqid8ccmonegtofdvpyp+si+z8mfay8 1
 
3.3%
ezwn5ldteak51gkw0qbcuqmmbasex70nta/r+bygbkc 1
 
3.3%
wvs3r+xpktg4is+bqo9gz199fbvjc0pn7qhevl4jjfa 1
 
3.3%
abghyxclrnenaunirlolxoyewqxzatpo8/ucd6q8myw 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T22:55:58.535155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 30
 
2.3%
= 30
 
2.3%
/ 29
 
2.2%
e 28
 
2.1%
Q 28
 
2.1%
8 26
 
2.0%
q 26
 
2.0%
9 26
 
2.0%
y 26
 
2.0%
5 25
 
1.9%
Other values (55) 1046
79.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 524
39.7%
Uppercase Letter 512
38.8%
Decimal Number 204
 
15.5%
Math Symbol 51
 
3.9%
Other Punctuation 29
 
2.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 30
 
5.9%
Q 28
 
5.5%
K 24
 
4.7%
A 24
 
4.7%
T 23
 
4.5%
P 23
 
4.5%
X 23
 
4.5%
V 23
 
4.5%
D 22
 
4.3%
C 22
 
4.3%
Other values (16) 270
52.7%
Lowercase Letter
ValueCountFrequency (%)
e 28
 
5.3%
q 26
 
5.0%
y 26
 
5.0%
l 24
 
4.6%
m 24
 
4.6%
t 22
 
4.2%
o 22
 
4.2%
c 21
 
4.0%
k 21
 
4.0%
r 21
 
4.0%
Other values (16) 289
55.2%
Decimal Number
ValueCountFrequency (%)
8 26
12.7%
9 26
12.7%
5 25
12.3%
1 23
11.3%
0 21
10.3%
3 19
9.3%
2 18
8.8%
4 17
8.3%
7 15
7.4%
6 14
6.9%
Math Symbol
ValueCountFrequency (%)
= 30
58.8%
+ 21
41.2%
Other Punctuation
ValueCountFrequency (%)
/ 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1036
78.5%
Common 284
 
21.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 30
 
2.9%
e 28
 
2.7%
Q 28
 
2.7%
q 26
 
2.5%
y 26
 
2.5%
K 24
 
2.3%
l 24
 
2.3%
m 24
 
2.3%
A 24
 
2.3%
T 23
 
2.2%
Other values (42) 779
75.2%
Common
ValueCountFrequency (%)
= 30
10.6%
/ 29
10.2%
8 26
9.2%
9 26
9.2%
5 25
8.8%
1 23
8.1%
+ 21
7.4%
0 21
7.4%
3 19
6.7%
2 18
 
6.3%
Other values (3) 46
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 30
 
2.3%
= 30
 
2.3%
/ 29
 
2.2%
e 28
 
2.1%
Q 28
 
2.1%
8 26
 
2.0%
q 26
 
2.0%
9 26
 
2.0%
y 26
 
2.0%
5 25
 
1.9%
Other values (55) 1046
79.2%

회원코드
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0192062 × 109
Minimum3.0040441 × 109
Maximum3.0484934 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:55:58.749218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0040441 × 109
5-th percentile3.0055528 × 109
Q13.0162978 × 109
median3.017389 × 109
Q33.0196194 × 109
95-th percentile3.0393481 × 109
Maximum3.0484934 × 109
Range44449285
Interquartile range (IQR)3321581.5

Descriptive statistics

Standard deviation9380746
Coefficient of variation (CV)0.003107024
Kurtosis4.9482611
Mean3.0192062 × 109
Median Absolute Deviation (MAD)2119134.5
Skewness1.8064288
Sum9.0576186 × 1010
Variance8.7998396 × 1013
MonotonicityNot monotonic
2023-12-10T22:55:58.951426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3048493407 1
 
3.3%
3017329164 1
 
3.3%
3015050142 1
 
3.3%
3018641783 1
 
3.3%
3019594719 1
 
3.3%
3018506992 1
 
3.3%
3019421636 1
 
3.3%
3004846437 1
 
3.3%
3027275258 1
 
3.3%
3017318997 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3004044122 1
3.3%
3004846437 1
3.3%
3006416217 1
3.3%
3014579113 1
3.3%
3014869181 1
3.3%
3015050142 1
3.3%
3015761125 1
3.3%
3016183145 1
3.3%
3016641662 1
3.3%
3016683729 1
3.3%
ValueCountFrequency (%)
3048493407 1
3.3%
3047468447 1
3.3%
3029423212 1
3.3%
3027275258 1
3.3%
3021618133 1
3.3%
3020424321 1
3.3%
3020154310 1
3.3%
3019627568 1
3.3%
3019594719 1
3.3%
3019421636 1
3.3%

가맹점번호
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6666699 × 1014
Minimum7.0733254 × 108
Maximum1 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:55:59.136576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0733254 × 108
5-th percentile7.119942 × 108
Q17.5046563 × 108
median1 × 1015
Q31 × 1015
95-th percentile1 × 1015
Maximum1 × 1015
Range9.9999929 × 1014
Interquartile range (IQR)9.9999925 × 1014

Descriptive statistics

Standard deviation5.0400656 × 1014
Coefficient of variation (CV)0.88942284
Kurtosis-2.0620556
Mean5.6666699 × 1014
Median Absolute Deviation (MAD)0
Skewness-0.28344281
Sum1.700001 × 1016
Variance2.5402261 × 1029
MonotonicityNot monotonic
2023-12-10T22:55:59.317100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
999999999999999 17
56.7%
768903473 1
 
3.3%
712127472 1
 
3.3%
723055286 1
 
3.3%
747104751 1
 
3.3%
761754500 1
 
3.3%
768487038 1
 
3.3%
760548271 1
 
3.3%
707332537 1
 
3.3%
715449222 1
 
3.3%
Other values (4) 4
 
13.3%
ValueCountFrequency (%)
707332537 1
3.3%
711885155 1
3.3%
712127472 1
3.3%
715449222 1
3.3%
716991804 1
3.3%
723055286 1
3.3%
723435102 1
3.3%
747104751 1
3.3%
760548271 1
3.3%
761754500 1
3.3%
ValueCountFrequency (%)
999999999999999 17
56.7%
783060618 1
 
3.3%
768903473 1
 
3.3%
768487038 1
 
3.3%
761754500 1
 
3.3%
760548271 1
 
3.3%
747104751 1
 
3.3%
723435102 1
 
3.3%
723055286 1
 
3.3%
716991804 1
 
3.3%

성별코드
Categorical

HIGH CORRELATION 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2023-12-10T22:55:59.535864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:55:59.702308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 17
56.7%
f 13
43.3%

연령대코드
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.666667
Minimum0
Maximum60
Zeros3
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:55:59.830112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120
median30
Q340
95-th percentile55.5
Maximum60
Range60
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.554871
Coefficient of variation (CV)0.49120646
Kurtosis0.13664526
Mean31.666667
Median Absolute Deviation (MAD)10
Skewness-0.35558305
Sum950
Variance241.95402
MonotonicityNot monotonic
2023-12-10T22:55:59.996907image/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 6
20.0%
0 3
 
10.0%
50 3
 
10.0%
60 2
 
6.7%
ValueCountFrequency (%)
0 3
 
10.0%
20 6
20.0%
30 8
26.7%
40 8
26.7%
50 3
 
10.0%
60 2
 
6.7%
ValueCountFrequency (%)
60 2
 
6.7%
50 3
 
10.0%
40 8
26.7%
30 8
26.7%
20 6
20.0%
0 3
 
10.0%

결제상품ID
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4000006 × 1011
Minimum1.4000002 × 1011
Maximum1.4000012 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:56:00.177975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000002 × 1011
5-th percentile1.4000002 × 1011
Q11.4000002 × 1011
median1.4000005 × 1011
Q31.4000012 × 1011
95-th percentile1.4000012 × 1011
Maximum1.4000012 × 1011
Range106000
Interquartile range (IQR)92000

Descriptive statistics

Standard deviation42230.675
Coefficient of variation (CV)3.0164754 × 10-7
Kurtosis-1.5634532
Mean1.4000006 × 1011
Median Absolute Deviation (MAD)29000
Skewness0.39141782
Sum4.2000019 × 1012
Variance1.7834299 × 109
MonotonicityNot monotonic
2023-12-10T22:56:00.366415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
140000024000 5
16.7%
140000124000 4
13.3%
140000018000 4
13.3%
140000116000 3
10.0%
140000046000 2
 
6.7%
140000084000 2
 
6.7%
140000122000 2
 
6.7%
140000064000 2
 
6.7%
140000076000 1
 
3.3%
140000030000 1
 
3.3%
Other values (4) 4
13.3%
ValueCountFrequency (%)
140000018000 4
13.3%
140000020000 1
 
3.3%
140000024000 5
16.7%
140000030000 1
 
3.3%
140000038000 1
 
3.3%
140000044000 1
 
3.3%
140000046000 2
 
6.7%
140000048000 1
 
3.3%
140000064000 2
 
6.7%
140000076000 1
 
3.3%
ValueCountFrequency (%)
140000124000 4
13.3%
140000122000 2
6.7%
140000116000 3
10.0%
140000084000 2
6.7%
140000076000 1
 
3.3%
140000064000 2
6.7%
140000048000 1
 
3.3%
140000046000 2
6.7%
140000044000 1
 
3.3%
140000038000 1
 
3.3%

결제상품명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
광주사랑카드
5 
안산사랑상품권 다온
4 
고양페이카드
4 
행복화성지역화폐
3 
용인와이페이
2 
Other values (9)
12 

Length

Max length11
Median length10
Mean length7.5
Min length4

Unique

Unique6 ?
Unique (%)20.0%

Sample

1st row안양사랑상품권(통합)
2nd row용인와이페이
3rd row안산사랑상품권 다온
4th row광주사랑카드
5th row안산사랑상품권 다온

Common Values

ValueCountFrequency (%)
광주사랑카드 5
16.7%
안산사랑상품권 다온 4
13.3%
고양페이카드 4
13.3%
행복화성지역화폐 3
10.0%
용인와이페이 2
 
6.7%
광주사랑카드(통합) 2
 
6.7%
의정부사랑카드 2
 
6.7%
용인와이페이(통합) 2
 
6.7%
안양사랑상품권(통합) 1
 
3.3%
부천페이 1
 
3.3%
Other values (4) 4
13.3%

Length

2023-12-10T22:56:00.694399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
광주사랑카드 5
14.3%
안산사랑상품권 4
11.4%
다온 4
11.4%
고양페이카드 4
11.4%
행복화성지역화폐 3
8.6%
용인와이페이 2
 
5.7%
광주사랑카드(통합 2
 
5.7%
의정부사랑카드 2
 
5.7%
용인와이페이(통합 2
 
5.7%
안양사랑상품권(통합 1
 
2.9%
Other values (6) 6
17.1%

가맹점업종명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
17 
일반휴게음식
5 
유통업 영리
3 
음료식품
3 
보건위생
 
1

Length

Max length6
Median length4
Mean length4.5333333
Min length4

Unique

Unique2 ?
Unique (%)6.7%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 17
56.7%
일반휴게음식 5
 
16.7%
유통업 영리 3
 
10.0%
음료식품 3
 
10.0%
보건위생 1
 
3.3%
레져용품 1
 
3.3%

Length

2023-12-10T22:56:01.025470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:56:01.214997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
51.5%
일반휴게음식 5
 
15.2%
유통업 3
 
9.1%
영리 3
 
9.1%
음료식품 3
 
9.1%
보건위생 1
 
3.0%
레져용품 1
 
3.0%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)100.0%
Missing17
Missing (%)56.7%
Infinite0
Infinite (%)0.0%
Mean13559.769
Minimum10417
Maximum18473
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:56:01.391580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10417
5-th percentile10452.4
Q111612
median12785
Q315445
95-th percentile17769.2
Maximum18473
Range8056
Interquartile range (IQR)3833

Descriptive statistics

Standard deviation2717.4668
Coefficient of variation (CV)0.20040657
Kurtosis-0.85645723
Mean13559.769
Median Absolute Deviation (MAD)2196
Skewness0.60016108
Sum176277
Variance7384625.7
MonotonicityNot monotonic
2023-12-10T22:56:01.916268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
14049 1
 
3.3%
17006 1
 
3.3%
15445 1
 
3.3%
12777 1
 
3.3%
10476 1
 
3.3%
12793 1
 
3.3%
12785 1
 
3.3%
18473 1
 
3.3%
10417 1
 
3.3%
17300 1
 
3.3%
Other values (3) 3
 
10.0%
(Missing) 17
56.7%
ValueCountFrequency (%)
10417 1
3.3%
10476 1
3.3%
10589 1
3.3%
11612 1
3.3%
12555 1
3.3%
12777 1
3.3%
12785 1
3.3%
12793 1
3.3%
14049 1
3.3%
15445 1
3.3%
ValueCountFrequency (%)
18473 1
3.3%
17300 1
3.3%
17006 1
3.3%
15445 1
3.3%
14049 1
3.3%
12793 1
3.3%
12785 1
3.3%
12777 1
3.3%
12555 1
3.3%
11612 1
3.3%

시도명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
17 
경기도
12 
NONE
 
1

Length

Max length4
Median length4
Mean length3.6
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 17
56.7%
경기도 12
40.0%
NONE 1
 
3.3%

Length

2023-12-10T22:56:02.150143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:56:02.354336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
56.7%
경기도 12
40.0%
none 1
 
3.3%

시군구명
Text

MISSING 

Distinct9
Distinct (%)75.0%
Missing18
Missing (%)60.0%
Memory size372.0 B
2023-12-10T22:56:02.571392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7.5
Mean length4.8333333
Min length3

Characters and Unicode

Total characters58
Distinct characters27
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

Unique7 ?
Unique (%)58.3%

Sample

1st row용인시 기흥구
2nd row안산시 단원구
3rd row광주시
4th row고양시 덕양구
5th row광주시
ValueCountFrequency (%)
광주시 3
17.6%
고양시 3
17.6%
덕양구 2
11.8%
용인시 1
 
5.9%
기흥구 1
 
5.9%
안산시 1
 
5.9%
단원구 1
 
5.9%
화성시 1
 
5.9%
일산동구 1
 
5.9%
이천시 1
 
5.9%
Other values (2) 2
11.8%
2023-12-10T22:56:03.150954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 11
19.0%
양 6
 
10.3%
구 5
 
8.6%
5
 
8.6%
광 3
 
5.2%
고 3
 
5.2%
주 3
 
5.2%
산 2
 
3.4%
덕 2
 
3.4%
용 1
 
1.7%
Other values (17) 17
29.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 53
91.4%
Space Separator 5
 
8.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 11
20.8%
양 6
 
11.3%
구 5
 
9.4%
광 3
 
5.7%
고 3
 
5.7%
주 3
 
5.7%
산 2
 
3.8%
덕 2
 
3.8%
용 1
 
1.9%
일 1
 
1.9%
Other values (16) 16
30.2%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 53
91.4%
Common 5
 
8.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 11
20.8%
양 6
 
11.3%
구 5
 
9.4%
광 3
 
5.7%
고 3
 
5.7%
주 3
 
5.7%
산 2
 
3.8%
덕 2
 
3.8%
용 1
 
1.9%
일 1
 
1.9%
Other values (16) 16
30.2%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 53
91.4%
ASCII 5
 
8.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 11
20.8%
양 6
 
11.3%
구 5
 
9.4%
광 3
 
5.7%
고 3
 
5.7%
주 3
 
5.7%
산 2
 
3.8%
덕 2
 
3.8%
용 1
 
1.9%
일 1
 
1.9%
Other values (16) 16
30.2%
ASCII
ValueCountFrequency (%)
5
100.0%

읍면동명
Text

MISSING 

Distinct11
Distinct (%)91.7%
Missing18
Missing (%)60.0%
Memory size372.0 B
2023-12-10T22:56:03.411380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9166667
Min length2

Characters and Unicode

Total characters35
Distinct characters22
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

Unique10 ?
Unique (%)83.3%

Sample

1st row중동
2nd row초지동
3rd row장지동
4th row화정동
5th row오포읍
ValueCountFrequency (%)
장지동 2
16.7%
중동 1
8.3%
초지동 1
8.3%
화정동 1
8.3%
오포읍 1
8.3%
영천동 1
8.3%
마두동 1
8.3%
신둔면 1
8.3%
삼송동 1
8.3%
양평읍 1
8.3%
2023-12-10T22:56:03.867021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 9
25.7%
지 3
 
8.6%
장 2
 
5.7%
양 2
 
5.7%
읍 2
 
5.7%
두 1
 
2.9%
평 1
 
2.9%
송 1
 
2.9%
삼 1
 
2.9%
면 1
 
2.9%
Other values (12) 12
34.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 35
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 9
25.7%
지 3
 
8.6%
장 2
 
5.7%
양 2
 
5.7%
읍 2
 
5.7%
두 1
 
2.9%
평 1
 
2.9%
송 1
 
2.9%
삼 1
 
2.9%
면 1
 
2.9%
Other values (12) 12
34.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 35
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 9
25.7%
지 3
 
8.6%
장 2
 
5.7%
양 2
 
5.7%
읍 2
 
5.7%
두 1
 
2.9%
평 1
 
2.9%
송 1
 
2.9%
삼 1
 
2.9%
면 1
 
2.9%
Other values (12) 12
34.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 35
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 9
25.7%
지 3
 
8.6%
장 2
 
5.7%
양 2
 
5.7%
읍 2
 
5.7%
두 1
 
2.9%
평 1
 
2.9%
송 1
 
2.9%
삼 1
 
2.9%
면 1
 
2.9%
Other values (12) 12
34.3%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.981767
Minimum0
Maximum37.758
Zeros18
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:56:04.150545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q337.36525
95-th percentile37.65385
Maximum37.758
Range37.758
Interquartile range (IQR)37.36525

Descriptive statistics

Standard deviation18.66285
Coefficient of variation (CV)1.2457042
Kurtosis-1.9496748
Mean14.981767
Median Absolute Deviation (MAD)0
Skewness0.43017116
Sum449.453
Variance348.30197
MonotonicityNot monotonic
2023-12-10T22:56:04.342964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.0 18
60.0%
37.272 1
 
3.3%
37.309 1
 
3.3%
37.393 1
 
3.3%
37.638 1
 
3.3%
37.384 1
 
3.3%
37.39 1
 
3.3%
37.205 1
 
3.3%
37.657 1
 
3.3%
37.308 1
 
3.3%
Other values (3) 3
 
10.0%
ValueCountFrequency (%)
0.0 18
60.0%
37.205 1
 
3.3%
37.272 1
 
3.3%
37.308 1
 
3.3%
37.309 1
 
3.3%
37.384 1
 
3.3%
37.39 1
 
3.3%
37.393 1
 
3.3%
37.489 1
 
3.3%
37.638 1
 
3.3%
ValueCountFrequency (%)
37.758 1
3.3%
37.657 1
3.3%
37.65 1
3.3%
37.638 1
3.3%
37.489 1
3.3%
37.393 1
3.3%
37.39 1
3.3%
37.384 1
3.3%
37.309 1
3.3%
37.308 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.8424
Minimum0
Maximum127.491
Zeros18
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:56:04.518969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3126.9965
95-th percentile127.3552
Maximum127.491
Range127.491
Interquartile range (IQR)126.9965

Descriptive statistics

Standard deviation63.333645
Coefficient of variation (CV)1.2456856
Kurtosis-1.9499128
Mean50.8424
Median Absolute Deviation (MAD)0
Skewness0.43007447
Sum1525.272
Variance4011.1505
MonotonicityNot monotonic
2023-12-10T22:56:04.706369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.0 18
60.0%
127.151 1
 
3.3%
126.815 1
 
3.3%
127.238 1
 
3.3%
126.831 1
 
3.3%
127.254 1
 
3.3%
127.233 1
 
3.3%
127.115 1
 
3.3%
126.788 1
 
3.3%
127.438 1
 
3.3%
Other values (3) 3
 
10.0%
ValueCountFrequency (%)
0.0 18
60.0%
126.788 1
 
3.3%
126.815 1
 
3.3%
126.831 1
 
3.3%
126.884 1
 
3.3%
127.034 1
 
3.3%
127.115 1
 
3.3%
127.151 1
 
3.3%
127.233 1
 
3.3%
127.238 1
 
3.3%
ValueCountFrequency (%)
127.491 1
3.3%
127.438 1
3.3%
127.254 1
3.3%
127.238 1
3.3%
127.233 1
3.3%
127.151 1
3.3%
127.115 1
3.3%
127.034 1
3.3%
126.884 1
3.3%
126.831 1
3.3%

사용여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
17 
True
13 
ValueCountFrequency (%)
False 17
56.7%
True 13
43.3%
2023-12-10T22:56:04.868011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30369.333
Minimum0
Maximum740000
Zeros17
Zeros (%)56.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:56:05.024828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q311125
95-th percentile31725
Maximum740000
Range740000
Interquartile range (IQR)11125

Descriptive statistics

Standard deviation134346.6
Coefficient of variation (CV)4.4237587
Kurtosis29.685528
Mean30369.333
Median Absolute Deviation (MAD)0
Skewness5.4361579
Sum911080
Variance1.8049009 × 1010
MonotonicityNot monotonic
2023-12-10T22:56:05.338523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 17
56.7%
7000 1
 
3.3%
10000 1
 
3.3%
11580 1
 
3.3%
23000 1
 
3.3%
26500 1
 
3.3%
11500 1
 
3.3%
8000 1
 
3.3%
36000 1
 
3.3%
13400 1
 
3.3%
Other values (4) 4
 
13.3%
ValueCountFrequency (%)
0 17
56.7%
2600 1
 
3.3%
5000 1
 
3.3%
7000 1
 
3.3%
8000 1
 
3.3%
10000 1
 
3.3%
11500 1
 
3.3%
11580 1
 
3.3%
13400 1
 
3.3%
16500 1
 
3.3%
ValueCountFrequency (%)
740000 1
3.3%
36000 1
3.3%
26500 1
3.3%
23000 1
3.3%
16500 1
3.3%
13400 1
3.3%
11580 1
3.3%
11500 1
3.3%
10000 1
3.3%
8000 1
3.3%

Interactions

2023-12-10T22:55:54.932382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:45.414510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:46.595155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:47.791911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:49.175833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:50.322033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:52.056317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:53.713626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:55.103623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:45.528759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:46.737298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:47.949086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:49.300401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:50.454791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:52.283842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:53.862466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:55.248090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:45.697872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:46.911389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:48.107620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:49.430051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:50.640640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:52.425657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:54.026673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:55.410895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:45.830946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:47.045742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:48.270735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:49.577235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:50.799601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:52.614580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:54.173210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:55.587694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:45.973371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:47.189980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:48.446526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:49.719133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:50.967865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:52.883488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:54.343391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:55.731229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:46.129069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:47.350827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:48.649546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:49.881853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:51.594386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:53.108853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:54.506236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:55.862249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:46.286564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:47.492074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:48.845889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:50.042982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:51.735047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:53.333945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:54.644354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:56.004061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:46.433075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:47.623923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:48.989001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:50.180899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:51.878005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:53.514582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:54.781981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:56:05.600509image/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
회원코드1.0001.0000.3180.0000.0000.6800.7560.6320.4240.3500.0000.0000.0650.0650.2810.521
가맹점번호1.0000.3181.0000.3650.6370.3290.493NaNNaNNaNNaNNaN0.9730.9730.9930.000
성별코드1.0000.0000.3651.0000.6330.3460.2850.2880.6460.0001.0001.0000.4000.4000.2770.000
연령대코드1.0000.0000.6370.6331.0000.0000.0000.3660.4040.7520.0000.0000.7580.7580.6860.000
결제상품ID1.0000.6800.3290.3460.0001.0001.0000.7260.8321.0000.6571.0000.1930.1930.2720.000
결제상품명1.0000.7560.4930.2850.0001.0001.0000.9611.0001.0000.9851.0000.2130.2130.2851.000
가맹점업종명1.0000.632NaN0.2880.3660.7260.9611.0000.7011.0000.7201.0001.0001.000NaN1.000
가맹점우편번호1.0000.424NaN0.6460.4040.8321.0000.7011.0001.0001.0001.0001.0001.000NaN0.000
시도명1.0000.350NaN0.0000.7521.0001.0001.0001.0001.000NaNNaN0.5620.562NaN0.000
시군구명1.0000.000NaN1.0000.0000.6570.9850.7201.000NaN1.0001.000NaNNaNNaN1.000
읍면동명1.0000.000NaN1.0000.0001.0001.0001.0001.000NaN1.0001.000NaNNaNNaN1.000
위도1.0000.0650.9730.4000.7580.1930.2131.0001.0000.562NaNNaN1.0000.9940.9760.000
경도1.0000.0650.9730.4000.7580.1930.2131.0001.0000.562NaNNaN0.9941.0000.9760.000
사용여부1.0000.2810.9930.2770.6860.2720.285NaNNaNNaNNaNNaN0.9760.9761.0000.000
결제금액1.0000.5210.0000.0000.0000.0001.0001.0000.0000.0001.0001.0000.0000.0000.0001.000
2023-12-10T22:56:05.925761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별코드결제상품명시도명가맹점업종명사용여부
성별코드1.0000.1170.0000.2560.175
결제상품명0.1171.0000.5220.4330.117
시도명0.0000.5221.0000.8531.000
가맹점업종명0.2560.4330.8531.0001.000
사용여부0.1750.1171.0001.0001.000
2023-12-10T22:56:06.098521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회원코드가맹점번호연령대코드결제상품ID가맹점우편번호위도경도결제금액성별코드결제상품명가맹점업종명시도명사용여부
회원코드1.000-0.1300.0750.0660.3300.0150.1900.0680.0000.4360.1290.3690.343
가맹점번호-0.1301.000-0.2920.061-0.313-0.850-0.899-0.9350.1750.1171.0001.0000.930
연령대코드0.075-0.2921.0000.031-0.1360.3150.3590.2440.4210.0000.2280.4770.461
결제상품ID0.0660.0610.0311.0000.545-0.150-0.051-0.0960.3740.8340.4560.7980.000
가맹점우편번호0.330-0.313-0.1360.5451.000-0.9010.2750.1870.5290.8160.3980.7391.000
위도0.015-0.8500.315-0.150-0.9011.0000.8900.8530.2600.0290.8530.3720.860
경도0.190-0.8990.359-0.0510.2750.8901.0000.8720.2600.0290.8530.3720.860
결제금액0.068-0.9350.244-0.0960.1870.8530.8721.0000.0000.7560.8530.0000.000
성별코드0.0000.1750.4210.3740.5290.2600.2600.0001.0000.1170.2560.0000.175
결제상품명0.4360.1170.0000.8340.8160.0290.0290.7560.1171.0000.4330.5220.117
가맹점업종명0.1291.0000.2280.4560.3980.8530.8530.8530.2560.4331.0000.8531.000
시도명0.3691.0000.4770.7980.7390.3720.3720.0000.0000.5220.8531.0001.000
사용여부0.3430.9300.4610.0001.0000.8600.8600.0000.1750.1171.0001.0001.000

Missing values

2023-12-10T22:55:56.212557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:55:56.564337image/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.
2023-12-10T22:55:56.820042image/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
02021-07-052021-07-111Nq4OJcYXVAOL1c1VIB8kWJCUv6Ub5hS0+mz7FXJUes=3048493407768903473M20140000076000안양사랑상품권(통합)보건위생14049NONE<NA><NA>0.00.0Y7000
12021-07-052021-07-114Dyo/2nNH1QmCeno8VFCqUpb4n/ryvh+d7wFLrJSigQ=3019627568712127472M40140000046000용인와이페이일반휴게음식17006경기도용인시 기흥구중동37.272127.151Y10000
22021-07-052021-07-11SK53XWBT/8O2r++PmSj5yR8kVcaptGxIYXNSB+feDj8=3014869181999999999999999M40140000124000안산사랑상품권 다온<NA><NA><NA><NA><NA>0.00.0N0
32021-07-052021-07-11OwQlmVHDs7r35Qo6hw3nTyKq7EKT9VjasEa31i21nrk=3016683729999999999999999F20140000024000광주사랑카드<NA><NA><NA><NA><NA>0.00.0N0
42021-07-052021-07-11YvNd/wi01Y4cnX3p1vcZ3jMh5TpZxsvaPYszKXNUIGo=3015761125723055286F20140000124000안산사랑상품권 다온유통업 영리15445경기도안산시 단원구초지동37.309126.815Y11580
52021-07-052021-07-115KSezZ20scJIultP/aeMOldDm8D/29UETPHLP/v8/Io=3029423212747104751F40140000084000광주사랑카드(통합)일반휴게음식12777경기도광주시장지동37.393127.238Y23000
62021-07-052021-07-11/qUhaM1dGYAkAFbynzrPCdPTG+r/5MeH9e9jWwGbQ94=3017374368999999999999999M30140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
72021-07-052021-07-11bv2D3/YB3Yk+1Jw/xDqfHOPyFUiI8HrcOYV3luus/jQ=3020424321999999999999999M20140000030000부천페이<NA><NA><NA><NA><NA>0.00.0N0
82021-07-052021-07-11KwKAV5OCjR+It82eYBTA1sihiS9R6W2CNMTr/mfh2oI=3016891679999999999999999F0140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
92021-07-052021-07-115tmBdGaKexYX5y+i0nL3N6kPGBNmt9lv6jTwgyuklDQ=3017620060999999999999999M30140000122000의정부사랑카드<NA><NA><NA><NA><NA>0.00.0N0
정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
202021-07-052021-07-11yIgu8ga71vSEIeHgcbVVmXb1VKtK2rD3YN5I8GbAHMs=3021618133999999999999999M50140000044000오산화폐 오색전<NA><NA><NA><NA><NA>0.00.0N0
212021-07-052021-07-11AbgHYxClRneNAuNIrlolxoYeWqXzATPo8/Ucd6Q8Myw=3016183145707332537M40140000116000행복화성지역화폐음료식품18473경기도화성시영천동37.205127.115Y36000
222021-07-052021-07-11Wvs3R+XpKTg4iS+bQO9Gz199FbvJC0Pn7qHeVL4JJFA=3017318997715449222F40140000018000고양페이카드일반휴게음식10417경기도고양시 일산동구마두동37.657126.788Y13400
232021-07-052021-07-11ezwn5lDteak51GKW0qBcUQMMbAseX70nta/R+BYGbkc=3027275258716991804M30140000048000이천사랑지역화폐레져용품17300경기도이천시신둔면37.308127.438Y740000
242021-07-052021-07-11qwVdxFmrOjtTBQid8CCmoNEgtOFDVpyP+SI+z8mfaY8=3004846437723435102F40140000018000고양페이카드음료식품10589경기도고양시 덕양구삼송동37.65126.884Y16500
252021-07-052021-07-11AsHEJzzBA1P8YgD0mVaJIv2efSld0XW1nYw19Q8Uu7E=3019421636999999999999999M30140000024000광주사랑카드<NA><NA><NA><NA><NA>0.00.0N0
262021-07-052021-07-11QrZPA4yEiK94ug9c7TSPTjQliczq+bIzsMjTxxRXi/o=3018506992999999999999999F60140000024000광주사랑카드<NA><NA><NA><NA><NA>0.00.0N0
272021-07-052021-07-11e9yYnRTeHJjm4Z8JZylezhLmAiUWLRDpTODtsYcYmKA=3019594719711885155M40140000038000양평통보일반휴게음식12555경기도양평군양평읍37.489127.491Y5000
282021-07-052021-07-11gxW1r5C20kpzmlQ/j/5PvOonltTYZBrJnK/L9ho6iu4=3018641783783060618F30140000122000의정부사랑카드음료식품11612경기도의정부시녹양동37.758127.034Y2600
292021-07-052021-07-11CCVl6gyg7aeLVHkZuPylx1A01fqrBfRvtoqqC2jIgD4=3015050142999999999999999M50140000064000용인와이페이(통합)<NA><NA><NA><NA><NA>0.00.0N0