Overview

Dataset statistics

Number of variables4
Number of observations1394
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory46.4 KiB
Average record size in memory34.1 B

Variable types

Numeric1
Categorical2
Text1

Dataset

Description제주특별자치도 총대출금 현황
Author제주데이터허브
URLhttps://www.jejudatahub.net/data/view/data/550

Reproduction

Analysis started2024-04-21 09:51:59.529423
Analysis finished2024-04-21 09:52:00.363798
Duration0.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.7647
Minimum2015
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2024-04-21T18:52:00.529818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12017
median2019
Q32021
95-th percentile2023
Maximum2023
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4630541
Coefficient of variation (CV)0.0012200799
Kurtosis-1.1846328
Mean2018.7647
Median Absolute Deviation (MAD)2
Skewness0.035383562
Sum2814158
Variance6.0666357
MonotonicityIncreasing
2024-04-21T18:52:00.902013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2015 164
11.8%
2016 164
11.8%
2017 164
11.8%
2018 164
11.8%
2019 164
11.8%
2020 164
11.8%
2021 164
11.8%
2022 164
11.8%
2023 82
5.9%
ValueCountFrequency (%)
2015 164
11.8%
2016 164
11.8%
2017 164
11.8%
2018 164
11.8%
2019 164
11.8%
2020 164
11.8%
2021 164
11.8%
2022 164
11.8%
2023 82
5.9%
ValueCountFrequency (%)
2023 82
5.9%
2022 164
11.8%
2021 164
11.8%
2020 164
11.8%
2019 164
11.8%
2018 164
11.8%
2017 164
11.8%
2016 164
11.8%
2015 164
11.8%

분기
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
1
369 
2
369 
3
328 
4
328 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 369
26.5%
2 369
26.5%
3 328
23.5%
4 328
23.5%

Length

2024-04-21T18:52:01.316514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:52:01.639770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 369
26.5%
2 369
26.5%
3 328
23.5%
4 328
23.5%

산업별
Categorical

Distinct41
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
농업, 임업 및 어업
 
34
광업
 
34
제조업
 
34
식료품 및 음료
 
34
담배
 
34
Other values (36)
1224 

Length

Max length25
Median length16
Mean length10.97561
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row농업, 임업 및 어업
2nd row광업
3rd row제조업
4th row식료품 및 음료
5th row담배

Common Values

ValueCountFrequency (%)
농업, 임업 및 어업 34
 
2.4%
광업 34
 
2.4%
제조업 34
 
2.4%
식료품 및 음료 34
 
2.4%
담배 34
 
2.4%
섬유제품 34
 
2.4%
의복, 의복액세서리 및 모피제품 34
 
2.4%
가죽, 가방 및 신발 34
 
2.4%
목재 및 나무제품 34
 
2.4%
펄프, 종이 및 종이제품 34
 
2.4%
Other values (31) 1054
75.6%

Length

2024-04-21T18:52:02.011875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
및 918
 
21.3%
서비스업 170
 
3.9%
기타 102
 
2.4%
제조 68
 
1.6%
정보통신업 68
 
1.6%
여가관련 68
 
1.6%
도매 34
 
0.8%
건설업 34
 
0.8%
원료재생업 34
 
0.8%
폐기물처리 34
 
0.8%
Other values (82) 2788
64.6%

십억원
Text

Distinct896
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
2024-04-21T18:52:03.282098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.8558106
Min length1

Characters and Unicode

Total characters5375
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique704 ?
Unique (%)50.5%

Sample

1st row1283.6
2nd row9.3
3rd row390.5
4th row159.4
5th row0
ValueCountFrequency (%)
51
 
3.7%
0.2 14
 
1.0%
0 12
 
0.9%
0.4 10
 
0.7%
2 9
 
0.6%
3.6 9
 
0.6%
3.7 9
 
0.6%
5.6 9
 
0.6%
5 8
 
0.6%
4.9 7
 
0.5%
Other values (886) 1256
90.1%
2024-04-21T18:52:04.695367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1183
22.0%
1 677
12.6%
2 539
10.0%
3 500
9.3%
4 458
 
8.5%
5 389
 
7.2%
7 369
 
6.9%
6 336
 
6.3%
8 321
 
6.0%
9 311
 
5.8%
Other values (2) 292
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4141
77.0%
Other Punctuation 1183
 
22.0%
Dash Punctuation 51
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 677
16.3%
2 539
13.0%
3 500
12.1%
4 458
11.1%
5 389
9.4%
7 369
8.9%
6 336
8.1%
8 321
7.8%
9 311
7.5%
0 241
 
5.8%
Other Punctuation
ValueCountFrequency (%)
. 1183
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 51
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5375
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1183
22.0%
1 677
12.6%
2 539
10.0%
3 500
9.3%
4 458
 
8.5%
5 389
 
7.2%
7 369
 
6.9%
6 336
 
6.3%
8 321
 
6.0%
9 311
 
5.8%
Other values (2) 292
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5375
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1183
22.0%
1 677
12.6%
2 539
10.0%
3 500
9.3%
4 458
 
8.5%
5 389
 
7.2%
7 369
 
6.9%
6 336
 
6.3%
8 321
 
6.0%
9 311
 
5.8%
Other values (2) 292
 
5.4%

Interactions

2024-04-21T18:51:59.758527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T18:52:05.064460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도분기산업별
연도1.0000.2690.000
분기0.2691.0000.000
산업별0.0000.0001.000
2024-04-21T18:52:05.214116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분기산업별
분기1.0000.000
산업별0.0001.000
2024-04-21T18:52:05.359106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도분기산업별
연도1.0000.1130.000
분기0.1131.0000.000
산업별0.0000.0001.000

Missing values

2024-04-21T18:51:59.987173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T18:52:00.254343image/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

연도분기산업별십억원
020151농업, 임업 및 어업1283.6
120151광업9.3
220151제조업390.5
320151식료품 및 음료159.4
420151담배0
520151섬유제품3.2
620151의복, 의복액세서리 및 모피제품2.4
720151가죽, 가방 및 신발0.2
820151목재 및 나무제품4.2
920151펄프, 종이 및 종이제품11.2
연도분기산업별십억원
138420232정보통신업119.2
138520232금융 및 보험업255.2
138620232부동산업1874.5
138720232전문, 과학 및 기술 서비스업178.3
138820232사업시설관리 및 사업지원 및 임대서비스업264.5
138920232교육 서비스업143
139020232보건업 및 사회복지 서비스업402.2
139120232예술, 스포츠 및 여가관련 서비스업517.4
139220232공공행정 등 기타 서비스853.4
139320232정보통신업, 예술,스포츠 및 여가관련 서비스업-