Overview

Dataset statistics

Number of variables3
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory726.0 B
Average record size in memory31.6 B

Variable types

Text1
Numeric2

Dataset

Description익산시에서 발행한 다이로춤 지역화폐 카드를 사용하여 업종별 년도별로 결재 건수 및 결재 금액을 제공하는 데이터입니다.
Author전북특별자치도 익산시
URLhttps://www.data.go.kr/data/15101810/fileData.do

Alerts

결제건수 is highly overall correlated with 결제금액High correlation
결제금액 is highly overall correlated with 결제건수High correlation
카테고리별 has unique valuesUnique
결제건수 has 4 (17.4%) zerosZeros
결제금액 has 4 (17.4%) zerosZeros

Reproduction

Analysis started2024-03-14 18:58:54.591474
Analysis finished2024-03-14 18:58:56.021102
Duration1.43 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

카테고리별
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
2024-03-15T03:58:56.643841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.173913
Min length2

Characters and Unicode

Total characters119
Distinct characters83
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

Unique23 ?
Unique (%)100.0%

Sample

1st row합계
2nd row병원/약국
3rd row슈퍼/마트
4th row스포츠/헬스
5th row미용/뷰티/위생
ValueCountFrequency (%)
합계 1
 
4.3%
분식 1
 
4.3%
로컬친환경 1
 
4.3%
가전/통신 1
 
4.3%
주유소 1
 
4.3%
자동차/자전거 1
 
4.3%
의류/잡화/안경 1
 
4.3%
산모/육아 1
 
4.3%
전통시장/상점가 1
 
4.3%
기타 1
 
4.3%
Other values (13) 13
56.5%
2024-03-15T03:58:57.636544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 18
 
15.1%
리 3
 
2.5%
전 3
 
2.5%
육 2
 
1.7%
경 2
 
1.7%
자 2
 
1.7%
통 2
 
1.7%
동 2
 
1.7%
가 2
 
1.7%
스 2
 
1.7%
Other values (73) 81
68.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 101
84.9%
Other Punctuation 18
 
15.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 3
 
3.0%
전 3
 
3.0%
육 2
 
2.0%
경 2
 
2.0%
자 2
 
2.0%
통 2
 
2.0%
동 2
 
2.0%
가 2
 
2.0%
스 2
 
2.0%
산 2
 
2.0%
Other values (72) 79
78.2%
Other Punctuation
ValueCountFrequency (%)
/ 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 101
84.9%
Common 18
 
15.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 3
 
3.0%
전 3
 
3.0%
육 2
 
2.0%
경 2
 
2.0%
자 2
 
2.0%
통 2
 
2.0%
동 2
 
2.0%
가 2
 
2.0%
스 2
 
2.0%
산 2
 
2.0%
Other values (72) 79
78.2%
Common
ValueCountFrequency (%)
/ 18
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 101
84.9%
ASCII 18
 
15.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 18
100.0%
Hangul
ValueCountFrequency (%)
리 3
 
3.0%
전 3
 
3.0%
육 2
 
2.0%
경 2
 
2.0%
자 2
 
2.0%
통 2
 
2.0%
동 2
 
2.0%
가 2
 
2.0%
스 2
 
2.0%
산 2
 
2.0%
Other values (72) 79
78.2%

결제건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean503432.52
Minimum0
Maximum5789474
Zeros4
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-15T03:58:57.936148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14067
median49678
Q3329367.5
95-th percentile1845454.1
Maximum5789474
Range5789474
Interquartile range (IQR)325300.5

Descriptive statistics

Standard deviation1246637.7
Coefficient of variation (CV)2.4762757
Kurtosis15.980395
Mean503432.52
Median Absolute Deviation (MAD)49678
Skewness3.836081
Sum11578948
Variance1.5541056 × 1012
MonotonicityNot monotonic
2024-03-15T03:58:58.256543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 4
 
17.4%
1294070 1
 
4.3%
11216 1
 
4.3%
258991 1
 
4.3%
33366 1
 
4.3%
100942 1
 
4.3%
813 1
 
4.3%
724754 1
 
4.3%
399744 1
 
4.3%
104243 1
 
4.3%
Other values (10) 10
43.5%
ValueCountFrequency (%)
0 4
17.4%
813 1
 
4.3%
2462 1
 
4.3%
5672 1
 
4.3%
11216 1
 
4.3%
23362 1
 
4.3%
28545 1
 
4.3%
33366 1
 
4.3%
49678 1
 
4.3%
93639 1
 
4.3%
ValueCountFrequency (%)
5789474 1
4.3%
1906719 1
4.3%
1294070 1
4.3%
724754 1
4.3%
622685 1
4.3%
399744 1
4.3%
258991 1
4.3%
128573 1
4.3%
104243 1
4.3%
100942 1
4.3%

결제금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4208977 × 1010
Minimum0
Maximum1.6340323 × 1011
Zeros4
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-15T03:58:58.478388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.5346625 × 108
median3.7512351 × 109
Q31.315335 × 1010
95-th percentile3.4015559 × 1010
Maximum1.6340323 × 1011
Range1.6340323 × 1011
Interquartile range (IQR)1.2499884 × 1010

Descriptive statistics

Standard deviation3.4025433 × 1010
Coefficient of variation (CV)2.3946434
Kurtosis18.65498
Mean1.4208977 × 1010
Median Absolute Deviation (MAD)3.7129893 × 109
Skewness4.1728474
Sum3.2680646 × 1011
Variance1.1577301 × 1021
MonotonicityNot monotonic
2024-03-15T03:58:58.710764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 4
 
17.4%
34471902006 1
 
4.3%
1130864581 1
 
4.3%
12837059610 1
 
4.3%
4043171060 1
 
4.3%
5739368671 1
 
4.3%
38245836 1
 
4.3%
19698445618 1
 
4.3%
3751235140 1
 
4.3%
1243690999 1
 
4.3%
Other values (10) 10
43.5%
ValueCountFrequency (%)
0 4
17.4%
38245836 1
 
4.3%
176067920 1
 
4.3%
1130864581 1
 
4.3%
1184997700 1
 
4.3%
1243690999 1
 
4.3%
1517461516 1
 
4.3%
2279965971 1
 
4.3%
3751235140 1
 
4.3%
4008227059 1
 
4.3%
ValueCountFrequency (%)
163403231670 1
4.3%
34471902006 1
4.3%
29908467794 1
4.3%
21549428584 1
4.3%
19698445618 1
4.3%
13469640050 1
4.3%
12837059610 1
4.3%
6354991555 1
4.3%
5739368671 1
4.3%
4043171060 1
4.3%

Interactions

2024-03-15T03:58:55.246593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:58:54.730443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:58:55.523526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:58:54.978767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T03:58:59.033411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
카테고리별결제건수결제금액
카테고리별1.0001.0001.000
결제건수1.0001.0001.000
결제금액1.0001.0001.000
2024-03-15T03:58:59.185001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
결제건수결제금액
결제건수1.0000.930
결제금액0.9301.000

Missing values

2024-03-15T03:58:55.835739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T03:58:55.968219image/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

카테고리별결제건수결제금액
0합계5789474163403231670
1병원/약국62268521549428584
2슈퍼/마트190671929908467794
3스포츠/헬스233622279965971
4미용/뷰티/위생936394008227059
5레저1285736354991555
6학원/교육4967813469640050
7부동산/인테리어56721517461516
8숙박/캠핑2462176067920
9도서/문화/공연285451184997700
카테고리별결제건수결제금액
13카페/베이커리3997443751235140
14기타72475419698445618
15전통시장/상점가00
16산모/육아81338245836
17의류/잡화/안경1009425739368671
18자동차/자전거333664043171060
19주유소25899112837059610
20가전/통신112161130864581
21로컬친환경00
22귀금속00