Overview

Dataset statistics

Number of variables13
Number of observations30
Missing cells24
Missing cells (%)6.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory114.4 B

Variable types

Categorical3
Numeric6
Text3
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/7190ec01-bf5f-4d11-a9d0-d999cc275cd3

Alerts

정책일간결제일자 has constant value ""Constant
시도명 has constant value ""Constant
결제상품ID is highly overall correlated with 결제금액 and 1 other fieldsHigh correlation
가맹점우편번호 is highly overall correlated with 위도High correlation
위도 is highly overall correlated with 가맹점우편번호 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 위도High correlation
결제금액 is highly overall correlated with 결제상품ID and 1 other fieldsHigh correlation
사용여부 is highly overall correlated with 결제상품ID and 1 other fieldsHigh correlation
결제상품명 has 24 (80.0%) missing valuesMissing
가맹점번호 has unique valuesUnique
결제금액 has 24 (80.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:46:56.810444
Analysis finished2023-12-10 13:47:04.227802
Duration7.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정책일간결제일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2021-06-01
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-06-01
2nd row2021-06-01
3rd row2021-06-01
4th row2021-06-01
5th row2021-06-01

Common Values

ValueCountFrequency (%)
2021-06-01 30
100.0%

Length

2023-12-10T22:47:04.451479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:47:04.695177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-06-01 30
100.0%

가맹점번호
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0001338 × 108
Minimum7.0000231 × 108
Maximum7.0002557 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:47:04.870507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0000231 × 108
5-th percentile7.0000326 × 108
Q17.0000938 × 108
median7.0001438 × 108
Q37.0001741 × 108
95-th percentile7.0002424 × 108
Maximum7.0002557 × 108
Range23254
Interquartile range (IQR)8033.25

Descriptive statistics

Standard deviation6462.7026
Coefficient of variation (CV)9.2322559 × 10-6
Kurtosis-0.66655244
Mean7.0001338 × 108
Median Absolute Deviation (MAD)4005
Skewness0.067254654
Sum2.1000401 × 1010
Variance41766525
MonotonicityNot monotonic
2023-12-10T22:47:05.080731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
700002555 1
 
3.3%
700014489 1
 
3.3%
700025566 1
 
3.3%
700024781 1
 
3.3%
700023573 1
 
3.3%
700018504 1
 
3.3%
700022894 1
 
3.3%
700018221 1
 
3.3%
700017640 1
 
3.3%
700017543 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
700002312 1
3.3%
700002555 1
3.3%
700004118 1
3.3%
700004564 1
3.3%
700005739 1
3.3%
700006648 1
3.3%
700007588 1
3.3%
700009352 1
3.3%
700009454 1
3.3%
700009530 1
3.3%
ValueCountFrequency (%)
700025566 1
3.3%
700024781 1
3.3%
700023573 1
3.3%
700022894 1
3.3%
700018504 1
3.3%
700018221 1
3.3%
700017640 1
3.3%
700017543 1
3.3%
700017014 1
3.3%
700016755 1
3.3%

결제상품ID
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.00028 × 1014
Minimum1.4000002 × 1011
Maximum1 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:47:05.257072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000002 × 1011
5-th percentile1.4000003 × 1011
Q11 × 1015
median1 × 1015
Q31 × 1015
95-th percentile1 × 1015
Maximum1 × 1015
Range9.9986 × 1014
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.0678114 × 1014
Coefficient of variation (CV)0.50845863
Kurtosis0.52744709
Mean8.00028 × 1014
Median Absolute Deviation (MAD)0
Skewness-1.5801301
Sum2.400084 × 1016
Variance1.654709 × 1029
MonotonicityNot monotonic
2023-12-10T22:47:05.419785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
999999999999999 24
80.0%
140000124000 2
 
6.7%
140000116000 1
 
3.3%
140000032000 1
 
3.3%
140000024000 1
 
3.3%
140000018000 1
 
3.3%
ValueCountFrequency (%)
140000018000 1
 
3.3%
140000024000 1
 
3.3%
140000032000 1
 
3.3%
140000116000 1
 
3.3%
140000124000 2
 
6.7%
999999999999999 24
80.0%
ValueCountFrequency (%)
999999999999999 24
80.0%
140000124000 2
 
6.7%
140000116000 1
 
3.3%
140000032000 1
 
3.3%
140000024000 1
 
3.3%
140000018000 1
 
3.3%

가맹점업종명
Categorical

Distinct11
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
일반휴게음식
10 
음료식품
4 
유통업 영리
4 
의류
2 
숙박업
2 
Other values (6)
8 

Length

Max length8
Median length6
Mean length4.9
Min length2

Unique

Unique4 ?
Unique (%)13.3%

Sample

1st row일반휴게음식
2nd row음료식품
3rd row일반휴게음식
4th row일반휴게음식
5th row일반휴게음식

Common Values

ValueCountFrequency (%)
일반휴게음식 10
33.3%
음료식품 4
 
13.3%
유통업 영리 4
 
13.3%
의류 2
 
6.7%
숙박업 2
 
6.7%
학원 2
 
6.7%
자동차정비 유지 2
 
6.7%
사무통신 1
 
3.3%
레져용품 1
 
3.3%
보건위생 1
 
3.3%

Length

2023-12-10T22:47:05.635644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반휴게음식 10
27.8%
음료식품 4
 
11.1%
유통업 4
 
11.1%
영리 4
 
11.1%
의류 2
 
5.6%
숙박업 2
 
5.6%
학원 2
 
5.6%
자동차정비 2
 
5.6%
유지 2
 
5.6%
사무통신 1
 
2.8%
Other values (3) 3
 
8.3%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15404.633
Minimum10017
Maximum18477
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:47:05.957082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10017
5-th percentile10223.65
Q114095
median16169.5
Q317589.25
95-th percentile18407.6
Maximum18477
Range8460
Interquartile range (IQR)3494.25

Descriptive statistics

Standard deviation2669.9262
Coefficient of variation (CV)0.17331969
Kurtosis-0.5496184
Mean15404.633
Median Absolute Deviation (MAD)1588
Skewness-0.76321549
Sum462139
Variance7128506.2
MonotonicityNot monotonic
2023-12-10T22:47:06.195537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
17052 2
 
6.7%
17757 1
 
3.3%
12139 1
 
3.3%
14733 1
 
3.3%
18477 1
 
3.3%
10017 1
 
3.3%
16253 1
 
3.3%
17758 1
 
3.3%
17581 1
 
3.3%
12271 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
10017 1
3.3%
10126 1
3.3%
10343 1
3.3%
11340 1
3.3%
12139 1
3.3%
12271 1
3.3%
12760 1
3.3%
13991 1
3.3%
14407 1
3.3%
14733 1
3.3%
ValueCountFrequency (%)
18477 1
3.3%
18413 1
3.3%
18401 1
3.3%
18379 1
3.3%
18258 1
3.3%
17758 1
3.3%
17757 1
3.3%
17592 1
3.3%
17581 1
3.3%
17052 2
6.7%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
30 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 30
100.0%

Length

2023-12-10T22:47:06.484513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:47:06.694125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Text

Distinct17
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:47:06.996243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length4.6
Min length3

Characters and Unicode

Total characters138
Distinct characters39
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

Unique8 ?
Unique (%)26.7%

Sample

1st row평택시
2nd row김포시
3rd row수원시 영통구
4th row의왕시
5th row안산시 상록구
ValueCountFrequency (%)
화성시 5
 
12.2%
수원시 3
 
7.3%
부천시 3
 
7.3%
용인시 3
 
7.3%
안산시 3
 
7.3%
상록구 2
 
4.9%
김포시 2
 
4.9%
남양주시 2
 
4.9%
평택시 2
 
4.9%
처인구 2
 
4.9%
Other values (12) 14
34.1%
2023-12-10T22:47:07.578503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 30
21.7%
구 11
 
8.0%
11
 
8.0%
성 7
 
5.1%
안 7
 
5.1%
화 5
 
3.6%
인 5
 
3.6%
산 4
 
2.9%
양 4
 
2.9%
원 4
 
2.9%
Other values (29) 50
36.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 127
92.0%
Space Separator 11
 
8.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 30
23.6%
구 11
 
8.7%
성 7
 
5.5%
안 7
 
5.5%
화 5
 
3.9%
인 5
 
3.9%
산 4
 
3.1%
양 4
 
3.1%
원 4
 
3.1%
천 4
 
3.1%
Other values (28) 46
36.2%
Space Separator
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 127
92.0%
Common 11
 
8.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 30
23.6%
구 11
 
8.7%
성 7
 
5.5%
안 7
 
5.5%
화 5
 
3.9%
인 5
 
3.9%
산 4
 
3.1%
양 4
 
3.1%
원 4
 
3.1%
천 4
 
3.1%
Other values (28) 46
36.2%
Common
ValueCountFrequency (%)
11
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 127
92.0%
ASCII 11
 
8.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 30
23.6%
구 11
 
8.7%
성 7
 
5.5%
안 7
 
5.5%
화 5
 
3.9%
인 5
 
3.9%
산 4
 
3.1%
양 4
 
3.1%
원 4
 
3.1%
천 4
 
3.1%
Other values (28) 46
36.2%
ASCII
ValueCountFrequency (%)
11
100.0%

읍면동명
Text

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:47:07.955321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9333333
Min length2

Characters and Unicode

Total characters88
Distinct characters45
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

Unique28 ?
Unique (%)93.3%

Sample

1st row지산동
2nd row고촌읍
3rd row신동
4th row삼동
5th row사동
ValueCountFrequency (%)
김량장동 2
 
6.7%
지산동 1
 
3.3%
남수동 1
 
3.3%
청계동 1
 
3.3%
통진읍 1
 
3.3%
북수동 1
 
3.3%
신장동 1
 
3.3%
신흥동 1
 
3.3%
와부읍 1
 
3.3%
상패동 1
 
3.3%
Other values (19) 19
63.3%
2023-12-10T22:47:08.928590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 25
28.4%
읍 5
 
5.7%
장 3
 
3.4%
진 3
 
3.4%
신 3
 
3.4%
김 2
 
2.3%
양 2
 
2.3%
수 2
 
2.3%
량 2
 
2.3%
곡 2
 
2.3%
Other values (35) 39
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 88
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 25
28.4%
읍 5
 
5.7%
장 3
 
3.4%
진 3
 
3.4%
신 3
 
3.4%
김 2
 
2.3%
양 2
 
2.3%
수 2
 
2.3%
량 2
 
2.3%
곡 2
 
2.3%
Other values (35) 39
44.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 88
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 25
28.4%
읍 5
 
5.7%
장 3
 
3.4%
진 3
 
3.4%
신 3
 
3.4%
김 2
 
2.3%
양 2
 
2.3%
수 2
 
2.3%
량 2
 
2.3%
곡 2
 
2.3%
Other values (35) 39
44.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 88
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 25
28.4%
읍 5
 
5.7%
장 3
 
3.4%
진 3
 
3.4%
신 3
 
3.4%
김 2
 
2.3%
양 2
 
2.3%
수 2
 
2.3%
량 2
 
2.3%
곡 2
 
2.3%
Other values (35) 39
44.3%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.3496
Minimum37.002
Maximum37.899
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:47:09.168766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.002
5-th percentile37.0403
Q137.21775
median37.3045
Q337.478
95-th percentile37.6863
Maximum37.899
Range0.897
Interquartile range (IQR)0.26025

Descriptive statistics

Standard deviation0.21420915
Coefficient of variation (CV)0.0057352461
Kurtosis0.16160093
Mean37.3496
Median Absolute Deviation (MAD)0.1015
Skewness0.62579339
Sum1120.488
Variance0.045885559
MonotonicityNot monotonic
2023-12-10T22:47:09.390900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
37.081 2
 
6.7%
37.321 2
 
6.7%
37.236 2
 
6.7%
37.279 1
 
3.3%
37.48 1
 
3.3%
37.201 1
 
3.3%
37.683 1
 
3.3%
37.282 1
 
3.3%
37.002 1
 
3.3%
37.58 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
37.002 1
3.3%
37.007 1
3.3%
37.081 2
6.7%
37.201 1
3.3%
37.204 1
3.3%
37.21 1
3.3%
37.212 1
3.3%
37.235 1
3.3%
37.236 2
6.7%
37.247 1
3.3%
ValueCountFrequency (%)
37.899 1
3.3%
37.689 1
3.3%
37.683 1
3.3%
37.64 1
3.3%
37.606 1
3.3%
37.58 1
3.3%
37.528 1
3.3%
37.48 1
3.3%
37.472 1
3.3%
37.407 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.9967
Minimum126.62
Maximum127.269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:47:09.575904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.62
5-th percentile126.76325
Q1126.83075
median127.0375
Q3127.1
95-th percentile127.2617
Maximum127.269
Range0.649
Interquartile range (IQR)0.26925

Descriptive statistics

Standard deviation0.17638519
Coefficient of variation (CV)0.0013888958
Kurtosis-0.85514982
Mean126.9967
Median Absolute Deviation (MAD)0.1685
Skewness-0.18349593
Sum3809.901
Variance0.031111734
MonotonicityNot monotonic
2023-12-10T22:47:09.814627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
127.038 2
 
6.7%
127.1 2
 
6.7%
127.207 1
 
3.3%
126.768 1
 
3.3%
126.62 1
 
3.3%
127.016 1
 
3.3%
127.051 1
 
3.3%
127.268 1
 
3.3%
127.217 1
 
3.3%
126.813 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
126.62 1
3.3%
126.761 1
3.3%
126.766 1
3.3%
126.768 1
3.3%
126.801 1
3.3%
126.813 1
3.3%
126.819 1
3.3%
126.824 1
3.3%
126.851 1
3.3%
126.863 1
3.3%
ValueCountFrequency (%)
127.269 1
3.3%
127.268 1
3.3%
127.254 1
3.3%
127.217 1
3.3%
127.207 1
3.3%
127.205 1
3.3%
127.163 1
3.3%
127.1 2
6.7%
127.066 1
3.3%
127.056 1
3.3%

결제상품명
Text

MISSING 

Distinct5
Distinct (%)83.3%
Missing24
Missing (%)80.0%
Memory size372.0 B
2023-12-10T22:47:10.193761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length7.6666667
Min length6

Characters and Unicode

Total characters46
Distinct characters25
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

Unique4 ?
Unique (%)66.7%

Sample

1st row안산사랑상품권 다온
2nd row행복화성지역화폐
3rd row안성사랑카드
4th row안산사랑상품권 다온
5th row광주사랑카드
ValueCountFrequency (%)
안산사랑상품권 2
25.0%
다온 2
25.0%
행복화성지역화폐 1
12.5%
안성사랑카드 1
12.5%
광주사랑카드 1
12.5%
고양페이카드 1
12.5%
2023-12-10T22:47:10.854271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 4
 
8.7%
랑 4
 
8.7%
안 3
 
6.5%
드 3
 
6.5%
카 3
 
6.5%
온 2
 
4.3%
성 2
 
4.3%
산 2
 
4.3%
화 2
 
4.3%
다 2
 
4.3%
Other values (15) 19
41.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 44
95.7%
Space Separator 2
 
4.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 4
 
9.1%
랑 4
 
9.1%
안 3
 
6.8%
드 3
 
6.8%
카 3
 
6.8%
온 2
 
4.5%
성 2
 
4.5%
산 2
 
4.5%
화 2
 
4.5%
다 2
 
4.5%
Other values (14) 17
38.6%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 44
95.7%
Common 2
 
4.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 4
 
9.1%
랑 4
 
9.1%
안 3
 
6.8%
드 3
 
6.8%
카 3
 
6.8%
온 2
 
4.5%
성 2
 
4.5%
산 2
 
4.5%
화 2
 
4.5%
다 2
 
4.5%
Other values (14) 17
38.6%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 44
95.7%
ASCII 2
 
4.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 4
 
9.1%
랑 4
 
9.1%
안 3
 
6.8%
드 3
 
6.8%
카 3
 
6.8%
온 2
 
4.5%
성 2
 
4.5%
산 2
 
4.5%
화 2
 
4.5%
다 2
 
4.5%
Other values (14) 17
38.6%
ASCII
ValueCountFrequency (%)
2
100.0%

사용여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
24 
True
6 
ValueCountFrequency (%)
False 24
80.0%
True 6
 
20.0%
2023-12-10T22:47:11.049678image/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%
Mean7844.6667
Minimum0
Maximum128340
Zeros24
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:47:11.267529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile34075
Maximum128340
Range128340
Interquartile range (IQR)0

Descriptive statistics

Standard deviation24695.729
Coefficient of variation (CV)3.1480916
Kurtosis20.834759
Mean7844.6667
Median Absolute Deviation (MAD)0
Skewness4.3627056
Sum235340
Variance6.0987903 × 108
MonotonicityNot monotonic
2023-12-10T22:47:11.475771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 24
80.0%
24000 1
 
3.3%
30500 1
 
3.3%
37000 1
 
3.3%
2000 1
 
3.3%
128340 1
 
3.3%
13500 1
 
3.3%
ValueCountFrequency (%)
0 24
80.0%
2000 1
 
3.3%
13500 1
 
3.3%
24000 1
 
3.3%
30500 1
 
3.3%
37000 1
 
3.3%
128340 1
 
3.3%
ValueCountFrequency (%)
128340 1
 
3.3%
37000 1
 
3.3%
30500 1
 
3.3%
24000 1
 
3.3%
13500 1
 
3.3%
2000 1
 
3.3%
0 24
80.0%

Interactions

2023-12-10T22:47:02.596140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:57.569306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:58.482687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:59.450009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:00.402331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:01.290632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:02.736425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:57.694194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:58.635040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:59.579401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:00.532489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:01.787569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:02.947231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:57.826977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:58.792192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:59.788371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:00.673992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:01.991290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:03.115782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:57.976691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:58.944896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:59.936574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:00.809277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:02.145301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:03.259400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:58.111248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:59.140403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:00.088238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:00.931263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:02.294388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:03.484991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:58.273027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:59.284831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:00.249808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:01.091831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:02.454113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:47:11.676654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호결제상품ID가맹점업종명가맹점우편번호시군구명읍면동명위도경도결제상품명사용여부결제금액
가맹점번호1.0000.0000.5290.0000.0000.9390.0000.0000.0000.0000.000
결제상품ID0.0001.0000.0000.5290.4001.0000.0000.000NaN0.9810.976
가맹점업종명0.5290.0001.0000.4580.0000.9530.1690.7080.8590.0000.000
가맹점우편번호0.0000.5290.4581.0001.0001.0000.8990.8021.0000.4850.708
시군구명0.0000.4000.0001.0001.0001.0000.9690.8951.0000.4920.771
읍면동명0.9391.0000.9531.0001.0001.0001.0001.0001.0001.0001.000
위도0.0000.0000.1690.8990.9691.0001.0000.3971.0000.0000.397
경도0.0000.0000.7080.8020.8951.0000.3971.0000.5730.0000.000
결제상품명0.000NaN0.8591.0001.0001.0001.0000.5731.000NaN0.573
사용여부0.0000.9810.0000.4850.4921.0000.0000.000NaN1.0000.983
결제금액0.0000.9760.0000.7080.7711.0000.3970.0000.5730.9831.000
2023-12-10T22:47:11.942706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점업종명사용여부
가맹점업종명1.0000.000
사용여부0.0001.000
2023-12-10T22:47:12.102447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호결제상품ID가맹점우편번호위도경도결제금액가맹점업종명사용여부
가맹점번호1.0000.131-0.1520.1750.082-0.1600.2370.000
결제상품ID0.1311.0000.082-0.032-0.034-0.9900.0000.892
가맹점우편번호-0.1520.0821.000-0.9370.432-0.0440.2030.263
위도0.175-0.032-0.9371.000-0.503-0.0130.0000.000
경도0.082-0.0340.432-0.5031.0000.0860.3820.000
결제금액-0.160-0.990-0.044-0.0130.0861.0000.0000.849
가맹점업종명0.2370.0000.2030.0000.3820.0001.0000.000
사용여부0.0000.8920.2630.0000.0000.8490.0001.000

Missing values

2023-12-10T22:47:03.758131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:47:04.039687image/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.

Sample

정책일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
02021-06-01700002555999999999999999일반휴게음식17757경기도평택시지산동37.081127.056<NA>N0
12021-06-01700002312999999999999999음료식품10126경기도김포시고촌읍37.606126.766<NA>N0
22021-06-01700004118999999999999999일반휴게음식16683경기도수원시 영통구신동37.247127.044<NA>N0
32021-06-01700005739999999999999999일반휴게음식16086경기도의왕시삼동37.321126.951<NA>N0
42021-06-01700004564140000124000일반휴게음식15622경기도안산시 상록구사동37.288126.851안산사랑상품권 다온Y24000
52021-06-01700006648999999999999999사무통신18413경기도화성시병점동37.204127.037<NA>N0
62021-06-01700007588999999999999999일반휴게음식14407경기도부천시고강동37.528126.819<NA>N0
72021-06-01700009352140000116000음료식품18379경기도화성시반월동37.235127.066행복화성지역화폐Y30500
82021-06-01700009454999999999999999레져용품13991경기도안양시 만안구안양동37.402126.918<NA>N0
92021-06-01700009530140000032000의류17592경기도안성시서인동37.007127.269안성사랑카드Y37000
정책일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
202021-06-01700016755140000018000유통업 영리10343경기도고양시 일산서구탄현동37.689126.761고양페이카드Y13500
212021-06-01700017014999999999999999보건위생14783경기도부천시범박동37.472126.813<NA>N0
222021-06-01700017543999999999999999음료식품11340경기도동두천시상패동37.899127.038<NA>N0
232021-06-01700017640999999999999999유통업 영리12271경기도남양주시와부읍37.58127.217<NA>N0
242021-06-01700018221999999999999999유통업 영리17581경기도안성시신흥동37.002127.268<NA>N0
252021-06-01700022894999999999999999숙박업17758경기도평택시신장동37.081127.051<NA>N0
262021-06-01700018504999999999999999학원16253경기도수원시 팔달구북수동37.282127.016<NA>N0
272021-06-01700023573999999999999999자동차정비 유지10017경기도김포시통진읍37.683126.62<NA>N0
282021-06-01700024781999999999999999수리서비스18477경기도화성시청계동37.201127.1<NA>N0
292021-06-01700025566999999999999999일반휴게음식14733경기도부천시송내동37.48126.768<NA>N0