Overview

Dataset statistics

Number of variables7
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 KiB
Average record size in memory65.0 B

Variable types

Categorical3
Text2
Numeric2

Dataset

Description야외(실외) 운동기구 설치주소, 설치기구종류, 담당부서, 설치현황, 운동기구 설치업체, 연도별고장현황 등을 공공데이터로 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=418&beforeMenuCd=DOM_000000201001001000&publicdatapk=15038428

Alerts

구분 has constant value ""Constant
비고 has constant value ""Constant
헬스기구 is highly overall correlated with 설치년도High correlation
설치년도 is highly overall correlated with 헬스기구High correlation
운동장 종류 has unique valuesUnique

Reproduction

Analysis started2024-01-09 20:29:17.823990
Analysis finished2024-01-09 20:29:18.779041
Duration0.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
1
22 

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 22
100.0%

Length

2024-01-10T05:29:18.874659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:29:19.124772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 22
100.0%

소재지
Text

Distinct19
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-01-10T05:29:19.395343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length9.2272727
Min length3

Characters and Unicode

Total characters203
Distinct characters35
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)81.8%

Sample

1st row금암동 189
2nd row신도안면 남선리 1286
3rd row금암동 69-2
4th row금암동 22-1
5th row두마면 두계리 62-31
ValueCountFrequency (%)
금암동 8
15.4%
두마면 8
15.4%
신도안면 4
 
7.7%
두계리 4
 
7.7%
남선리 2
 
3.8%
왕대리 2
 
3.8%
입암리 2
 
3.8%
엄사면 2
 
3.8%
76 1
 
1.9%
110-6 1
 
1.9%
Other values (18) 18
34.6%
2024-01-10T05:29:19.696662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
30
 
14.8%
면 14
 
6.9%
1 14
 
6.9%
두 12
 
5.9%
리 12
 
5.9%
2 11
 
5.4%
암 10
 
4.9%
6 9
 
4.4%
마 8
 
3.9%
- 8
 
3.9%
Other values (25) 75
36.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 110
54.2%
Decimal Number 55
27.1%
Space Separator 30
 
14.8%
Dash Punctuation 8
 
3.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 14
12.7%
두 12
10.9%
리 12
10.9%
암 10
 
9.1%
마 8
 
7.3%
금 8
 
7.3%
동 8
 
7.3%
안 4
 
3.6%
도 4
 
3.6%
신 4
 
3.6%
Other values (14) 26
23.6%
Decimal Number
ValueCountFrequency (%)
1 14
25.5%
2 11
20.0%
6 9
16.4%
3 6
10.9%
9 4
 
7.3%
0 4
 
7.3%
8 3
 
5.5%
4 3
 
5.5%
7 1
 
1.8%
Space Separator
ValueCountFrequency (%)
30
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 110
54.2%
Common 93
45.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 14
12.7%
두 12
10.9%
리 12
10.9%
암 10
 
9.1%
마 8
 
7.3%
금 8
 
7.3%
동 8
 
7.3%
안 4
 
3.6%
도 4
 
3.6%
신 4
 
3.6%
Other values (14) 26
23.6%
Common
ValueCountFrequency (%)
30
32.3%
1 14
15.1%
2 11
 
11.8%
6 9
 
9.7%
- 8
 
8.6%
3 6
 
6.5%
9 4
 
4.3%
0 4
 
4.3%
8 3
 
3.2%
4 3
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 110
54.2%
ASCII 93
45.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30
32.3%
1 14
15.1%
2 11
 
11.8%
6 9
 
9.7%
- 8
 
8.6%
3 6
 
6.5%
9 4
 
4.3%
0 4
 
4.3%
8 3
 
3.2%
4 3
 
3.2%
Hangul
ValueCountFrequency (%)
면 14
12.7%
두 12
10.9%
리 12
10.9%
암 10
 
9.1%
마 8
 
7.3%
금 8
 
7.3%
동 8
 
7.3%
안 4
 
3.6%
도 4
 
3.6%
신 4
 
3.6%
Other values (14) 26
23.6%

운동장 종류
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-01-10T05:29:19.877640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length7.5454545
Min length3

Characters and Unicode

Total characters166
Distinct characters73
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row천마산 입구
2nd row생활체육공원
3rd row수변공원 1
4th row수변공원 2
5th row두계근린공원
ValueCountFrequency (%)
수변공원 3
 
8.1%
옆 3
 
8.1%
공원 2
 
5.4%
입구 2
 
5.4%
정자 2
 
5.4%
두계천생태공원 1
 
2.7%
왕대2리 1
 
2.7%
정자나무 1
 
2.7%
입암저수지 1
 
2.7%
천마산 1
 
2.7%
Other values (20) 20
54.1%
2024-01-10T05:29:20.168490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15
 
9.0%
공 14
 
8.4%
원 13
 
7.8%
수 6
 
3.6%
대 5
 
3.0%
변 4
 
2.4%
지 4
 
2.4%
입 4
 
2.4%
2 4
 
2.4%
왕 4
 
2.4%
Other values (63) 93
56.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 143
86.1%
Space Separator 15
 
9.0%
Decimal Number 6
 
3.6%
Open Punctuation 1
 
0.6%
Close Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 14
 
9.8%
원 13
 
9.1%
수 6
 
4.2%
대 5
 
3.5%
변 4
 
2.8%
지 4
 
2.8%
입 4
 
2.8%
왕 4
 
2.8%
옆 4
 
2.8%
정 4
 
2.8%
Other values (58) 81
56.6%
Decimal Number
ValueCountFrequency (%)
2 4
66.7%
1 2
33.3%
Space Separator
ValueCountFrequency (%)
15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 143
86.1%
Common 23
 
13.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 14
 
9.8%
원 13
 
9.1%
수 6
 
4.2%
대 5
 
3.5%
변 4
 
2.8%
지 4
 
2.8%
입 4
 
2.8%
왕 4
 
2.8%
옆 4
 
2.8%
정 4
 
2.8%
Other values (58) 81
56.6%
Common
ValueCountFrequency (%)
15
65.2%
2 4
 
17.4%
1 2
 
8.7%
( 1
 
4.3%
) 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 143
86.1%
ASCII 23
 
13.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15
65.2%
2 4
 
17.4%
1 2
 
8.7%
( 1
 
4.3%
) 1
 
4.3%
Hangul
ValueCountFrequency (%)
공 14
 
9.8%
원 13
 
9.1%
수 6
 
4.2%
대 5
 
3.5%
변 4
 
2.8%
지 4
 
2.8%
입 4
 
2.8%
왕 4
 
2.8%
옆 4
 
2.8%
정 4
 
2.8%
Other values (58) 81
56.6%

헬스기구
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6363636
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-01-10T05:29:20.276672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q36
95-th percentile10.85
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.9039457
Coefficient of variation (CV)0.62634122
Kurtosis1.27514
Mean4.6363636
Median Absolute Deviation (MAD)2
Skewness1.117081
Sum102
Variance8.4329004
MonotonicityNot monotonic
2024-01-10T05:29:20.373231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
6 5
22.7%
3 5
22.7%
4 4
18.2%
1 3
13.6%
5 1
 
4.5%
12 1
 
4.5%
11 1
 
4.5%
8 1
 
4.5%
2 1
 
4.5%
ValueCountFrequency (%)
1 3
13.6%
2 1
 
4.5%
3 5
22.7%
4 4
18.2%
5 1
 
4.5%
6 5
22.7%
8 1
 
4.5%
11 1
 
4.5%
12 1
 
4.5%
ValueCountFrequency (%)
12 1
 
4.5%
11 1
 
4.5%
8 1
 
4.5%
6 5
22.7%
5 1
 
4.5%
4 4
18.2%
3 5
22.7%
2 1
 
4.5%
1 3
13.6%

설치년도
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.9545
Minimum2005
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-01-10T05:29:20.474728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2015
Q12015.25
median2016
Q32017
95-th percentile2018
Maximum2019
Range14
Interquartile range (IQR)1.75

Descriptive statistics

Standard deviation2.6988053
Coefficient of variation (CV)0.0013387233
Kurtosis13.909836
Mean2015.9545
Median Absolute Deviation (MAD)1
Skewness-3.3526475
Sum44351
Variance7.2835498
MonotonicityNot monotonic
2024-01-10T05:29:20.593845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2016 6
27.3%
2017 6
27.3%
2015 5
22.7%
2018 3
13.6%
2005 1
 
4.5%
2019 1
 
4.5%
ValueCountFrequency (%)
2005 1
 
4.5%
2015 5
22.7%
2016 6
27.3%
2017 6
27.3%
2018 3
13.6%
2019 1
 
4.5%
ValueCountFrequency (%)
2019 1
 
4.5%
2018 3
13.6%
2017 6
27.3%
2016 6
27.3%
2015 5
22.7%
2005 1
 
4.5%

관리책임자
Categorical

Distinct6
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Memory size308.0 B
농림과
6 
건설교통과
6 
금암동
5 
문화체육과
3 
공공시설사업소
1 

Length

Max length7
Median length6
Mean length4.0454545
Min length3

Unique

Unique2 ?
Unique (%)9.1%

Sample

1st row문화체육과
2nd row문화체육과
3rd row농림과
4th row농림과
5th row농림과

Common Values

ValueCountFrequency (%)
농림과 6
27.3%
건설교통과 6
27.3%
금암동 5
22.7%
문화체육과 3
13.6%
공공시설사업소 1
 
4.5%
신도안면 1
 
4.5%

Length

2024-01-10T05:29:20.744555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:29:20.884450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농림과 6
27.3%
건설교통과 6
27.3%
금암동 5
22.7%
문화체육과 3
13.6%
공공시설사업소 1
 
4.5%
신도안면 1
 
4.5%

비고
Categorical

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
?
22 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row?
2nd row?
3rd row?
4th row?
5th row?

Common Values

ValueCountFrequency (%)
? 22
100.0%

Length

2024-01-10T05:29:21.022495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:29:21.132479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
22
100.0%

Interactions

2024-01-10T05:29:18.174768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:29:18.036047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:29:18.241958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:29:18.101052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:29:21.210852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지운동장 종류헬스기구설치년도관리책임자
소재지1.0001.0000.9540.5781.000
운동장 종류1.0001.0001.0001.0001.000
헬스기구0.9541.0001.0000.4660.700
설치년도0.5781.0000.4661.0000.573
관리책임자1.0001.0000.7000.5731.000
2024-01-10T05:29:21.325086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
헬스기구설치년도관리책임자
헬스기구1.000-0.5010.483
설치년도-0.5011.0000.000
관리책임자0.4830.0001.000

Missing values

2024-01-10T05:29:18.609901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:29:18.726008image/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

구분소재지운동장 종류헬스기구설치년도관리책임자비고
01금암동 189천마산 입구62015문화체육과?
11신도안면 남선리 1286생활체육공원52018문화체육과?
21금암동 69-2수변공원 1122005농림과?
31금암동 22-1수변공원 262015농림과?
41두마면 두계리 62-31두계근린공원112016농림과?
51엄사면 엄사리 226-28양정어린이공원42017농림과?
61두마면 입암리 630소공원32017농림과?
71신도안면 남선리 1296신도안전통경관사업지62017농림과?
81엄사면 문화로 31종합문화체육단지82015공공시설사업소?
91두마면 두계리 46-1아랫장터 정자 옆42017건설교통과?
구분소재지운동장 종류헬스기구설치년도관리책임자비고
121두마면 왕대리 76왕대1리 마을입구 정자62016건설교통과?
131두마면 왕대리 203왕대2리 정자나무 옆32016건설교통과?
141두마면 입암리 414-1입암저수지42016건설교통과?
151신도안면 정장리 113두계천생태공원42017문화체육과?
161신도안면신도안문화공원22016신도안면?
171금암동 2통보호수옆(계룡대반점)32019금암동?
181금암동신성2차 아파트앞 공원12018금암동?
191금암동주공2단지 옆 수변공원12018금암동?
201금암동수변공원내32016금암동?
211금암동뱃골공원12015금암동?