Overview

Dataset statistics

Number of variables3
Number of observations113
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory26.2 B

Variable types

Categorical1
Text1
Numeric1

Dataset

Description한국지역난방공사의 지사별 난방공급지역 및 건물개수에 대한 정보로 23.09월 기준으로 작성되었으며 지역난방을 사용하는 지역 분석 등에 활용하실 수 있습니다.
Author한국지역난방공사
URLhttps://www.data.go.kr/data/15124155/fileData.do

Alerts

건물수 is highly overall correlated with 지사High correlation
지사 is highly overall correlated with 건물수High correlation

Reproduction

Analysis started2023-12-12 03:44:03.203326
Analysis finished2023-12-12 03:44:03.675490
Duration0.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지사
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
고양사업소
18 
수원사업소
12 
중앙지사
11 
강남지사
10 
용인지사
10 
Other values (14)
52 

Length

Max length6
Median length4
Mean length4.3451327
Min length4

Unique

Unique5 ?
Unique (%)4.4%

Sample

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

Common Values

ValueCountFrequency (%)
고양사업소 18
15.9%
수원사업소 12
10.6%
중앙지사 11
9.7%
강남지사 10
8.8%
용인지사 10
8.8%
청주지사 9
8.0%
대구지사 7
 
6.2%
판교지사 7
 
6.2%
화성지사 6
 
5.3%
분당사업소 5
 
4.4%
Other values (9) 18
15.9%

Length

2023-12-12T12:44:03.806304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고양사업소 18
15.9%
수원사업소 12
10.6%
중앙지사 11
9.7%
강남지사 10
8.8%
용인지사 10
8.8%
청주지사 9
8.0%
대구지사 7
 
6.2%
판교지사 7
 
6.2%
화성지사 6
 
5.3%
삼송지사 5
 
4.4%
Other values (9) 18
15.9%

난방지역
Text

Distinct112
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-12T12:44:04.151429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length4.8849558
Min length2

Characters and Unicode

Total characters552
Distinct characters133
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique111 ?
Unique (%)98.2%

Sample

1st row여의도
2nd row반포
3rd row용산지구
4th row마포지구
5th row상암1지구
ValueCountFrequency (%)
용산지구 2
 
1.8%
흥덕지구 1
 
0.9%
능동지구 1
 
0.9%
부지 1
 
0.9%
한국식품연구원 1
 
0.9%
성남고등지구 1
 
0.9%
판교창조경제벨리 1
 
0.9%
동판교(중심상업지역 1
 
0.9%
동판교(테크노벨리 1
 
0.9%
동판교(기타 1
 
0.9%
Other values (103) 103
90.4%
2023-12-12T12:44:04.635840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 96
 
17.4%
구 94
 
17.0%
동 12
 
2.2%
2 12
 
2.2%
남 11
 
2.0%
기 11
 
2.0%
존 9
 
1.6%
성 8
 
1.4%
산 8
 
1.4%
정 8
 
1.4%
Other values (123) 283
51.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 520
94.2%
Decimal Number 20
 
3.6%
Close Punctuation 4
 
0.7%
Open Punctuation 4
 
0.7%
Uppercase Letter 3
 
0.5%
Space Separator 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 96
 
18.5%
구 94
 
18.1%
동 12
 
2.3%
남 11
 
2.1%
기 11
 
2.1%
존 9
 
1.7%
성 8
 
1.5%
산 8
 
1.5%
정 8
 
1.5%
교 7
 
1.3%
Other values (113) 256
49.2%
Decimal Number
ValueCountFrequency (%)
2 12
60.0%
1 3
 
15.0%
3 3
 
15.0%
4 2
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
D 1
33.3%
M 1
33.3%
C 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 520
94.2%
Common 29
 
5.3%
Latin 3
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 96
 
18.5%
구 94
 
18.1%
동 12
 
2.3%
남 11
 
2.1%
기 11
 
2.1%
존 9
 
1.7%
성 8
 
1.5%
산 8
 
1.5%
정 8
 
1.5%
교 7
 
1.3%
Other values (113) 256
49.2%
Common
ValueCountFrequency (%)
2 12
41.4%
) 4
 
13.8%
( 4
 
13.8%
1 3
 
10.3%
3 3
 
10.3%
4 2
 
6.9%
1
 
3.4%
Latin
ValueCountFrequency (%)
D 1
33.3%
M 1
33.3%
C 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 520
94.2%
ASCII 32
 
5.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 96
 
18.5%
구 94
 
18.1%
동 12
 
2.3%
남 11
 
2.1%
기 11
 
2.1%
존 9
 
1.7%
성 8
 
1.5%
산 8
 
1.5%
정 8
 
1.5%
교 7
 
1.3%
Other values (113) 256
49.2%
ASCII
ValueCountFrequency (%)
2 12
37.5%
) 4
 
12.5%
( 4
 
12.5%
1 3
 
9.4%
3 3
 
9.4%
4 2
 
6.2%
1
 
3.1%
D 1
 
3.1%
M 1
 
3.1%
C 1
 
3.1%

건물수
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)56.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.132743
Minimum1
Maximum712
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T12:44:04.815400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q111
median20
Q351
95-th percentile146.4
Maximum712
Range711
Interquartile range (IQR)40

Descriptive statistics

Standard deviation94.213616
Coefficient of variation (CV)1.9573706
Kurtosis26.154954
Mean48.132743
Median Absolute Deviation (MAD)13
Skewness4.7096566
Sum5439
Variance8876.2054
MonotonicityNot monotonic
2023-12-12T12:44:05.041317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 8
 
7.1%
13 7
 
6.2%
7 5
 
4.4%
1 4
 
3.5%
23 4
 
3.5%
20 3
 
2.7%
12 3
 
2.7%
8 3
 
2.7%
15 3
 
2.7%
4 3
 
2.7%
Other values (54) 70
61.9%
ValueCountFrequency (%)
1 4
3.5%
2 3
2.7%
3 1
 
0.9%
4 3
2.7%
5 1
 
0.9%
6 2
 
1.8%
7 5
4.4%
8 3
2.7%
9 3
2.7%
10 2
 
1.8%
ValueCountFrequency (%)
712 1
0.9%
453 1
0.9%
385 1
0.9%
351 1
0.9%
250 1
0.9%
162 1
0.9%
136 1
0.9%
133 1
0.9%
123 1
0.9%
109 1
0.9%

Interactions

2023-12-12T12:44:03.360364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:44:05.195028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지사건물수
지사1.0000.886
건물수0.8861.000
2023-12-12T12:44:05.337048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건물수지사
건물수1.0000.606
지사0.6061.000

Missing values

2023-12-12T12:44:03.522754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:44:03.642900image/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

지사난방지역건물수
0중앙지사여의도109
1중앙지사반포53
2중앙지사용산지구40
3중앙지사마포지구13
4중앙지사상암1지구13
5중앙지사성산지구11
6중앙지사가재울뉴타운9
7중앙지사상암DMC지구39
8중앙지사상암2지구6
9중앙지사상암기존지구2
지사난방지역건물수
103청주지사가경4지구11
104청주지사분평지구23
105청주지사용암2지구24
106청주지사하복대지구19
107청주지사산남3지구11
108청주지사청주기존지구65
109청주지사충북대4
110청주지사청주동남지구16
111세종지사행정중심복합도시385
112광주전남지사광주전남혁신도시57