Overview

Dataset statistics

Number of variables5
Number of observations237
Missing cells185
Missing cells (%)15.6%
Duplicate rows7
Duplicate rows (%)3.0%
Total size in memory9.6 KiB
Average record size in memory41.6 B

Variable types

Numeric1
Text2
Categorical2

Dataset

Description서울특별시_송파구_실외운동기구 현황에 대한 데이터로 설치연도, 최근5년간의 실외 운동기구 설치주소, 실외운동기구종류, 담당부서, 설치업체, 운동기구 업체 관리현황, 고장난 실외운동기구 등에 항목으로 제공합니다.
URLhttps://www.data.go.kr/data/15038015/fileData.do

Alerts

Dataset has 7 (3.0%) duplicate rowsDuplicates
설치업체 is highly overall correlated with 설치연도 and 1 other fieldsHigh correlation
담당부서 is highly overall correlated with 설치연도 and 1 other fieldsHigh correlation
설치연도 is highly overall correlated with 담당부서 and 1 other fieldsHigh correlation
설치업체 is highly imbalanced (64.0%)Imbalance
설치연도 has 185 (78.1%) missing valuesMissing

Reproduction

Analysis started2023-12-12 10:15:30.604193
Analysis finished2023-12-12 10:15:31.399453
Duration0.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

설치연도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)11.5%
Missing185
Missing (%)78.1%
Infinite0
Infinite (%)0.0%
Mean2020
Minimum2018
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T19:15:31.457977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2018
5-th percentile2018
Q12019
median2020
Q32021
95-th percentile2021
Maximum2023
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2524486
Coefficient of variation (CV)0.00062002405
Kurtosis-0.74668163
Mean2020
Median Absolute Deviation (MAD)1
Skewness-0.24911202
Sum105040
Variance1.5686275
MonotonicityNot monotonic
2023-12-12T19:15:31.588799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2021 22
 
9.3%
2020 10
 
4.2%
2018 9
 
3.8%
2019 9
 
3.8%
2022 1
 
0.4%
2023 1
 
0.4%
(Missing) 185
78.1%
ValueCountFrequency (%)
2018 9
3.8%
2019 9
3.8%
2020 10
4.2%
2021 22
9.3%
2022 1
 
0.4%
2023 1
 
0.4%
ValueCountFrequency (%)
2023 1
 
0.4%
2022 1
 
0.4%
2021 22
9.3%
2020 10
4.2%
2019 9
3.8%
2018 9
3.8%

최근5년간의 실외 운동기구 설치주소
Text

Distinct54
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-12T19:15:31.819176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length29
Mean length21.514768
Min length15

Characters and Unicode

Total characters5099
Distinct characters71
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)7.2%

Sample

1st row서울특별시 송파구 풍납동 183-5
2nd row서울특별시 송파구 풍납동 183-5
3rd row서울특별시 송파구 풍납동 183-5
4th row서울특별시 송파구 풍납동 183-5
5th row서울특별시 송파구 풍납동 183-5
ValueCountFrequency (%)
서울특별시 237
22.2%
송파구 237
22.2%
오금동 38
 
3.6%
좌안 37
 
3.5%
풍납동 33
 
3.1%
하부 32
 
3.0%
우안 32
 
3.0%
잠실동 30
 
2.8%
방이동 24
 
2.2%
신천동 22
 
2.1%
Other values (65) 347
32.5%
2023-12-12T19:15:32.233325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
832
 
16.3%
송 248
 
4.9%
파 248
 
4.9%
동 242
 
4.7%
서 237
 
4.6%
구 237
 
4.6%
울 237
 
4.6%
시 237
 
4.6%
별 237
 
4.6%
특 237
 
4.6%
Other values (61) 2107
41.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3189
62.5%
Space Separator 832
 
16.3%
Decimal Number 765
 
15.0%
Close Punctuation 105
 
2.1%
Open Punctuation 105
 
2.1%
Dash Punctuation 97
 
1.9%
Other Punctuation 6
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
송 248
 
7.8%
파 248
 
7.8%
동 242
 
7.6%
서 237
 
7.4%
구 237
 
7.4%
울 237
 
7.4%
시 237
 
7.4%
별 237
 
7.4%
특 237
 
7.4%
안 83
 
2.6%
Other values (46) 946
29.7%
Decimal Number
ValueCountFrequency (%)
1 165
21.6%
2 123
16.1%
4 99
12.9%
8 93
12.2%
3 58
 
7.6%
0 56
 
7.3%
7 51
 
6.7%
5 41
 
5.4%
6 40
 
5.2%
9 39
 
5.1%
Space Separator
ValueCountFrequency (%)
832
100.0%
Close Punctuation
ValueCountFrequency (%)
) 105
100.0%
Open Punctuation
ValueCountFrequency (%)
( 105
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 97
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3189
62.5%
Common 1910
37.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
송 248
 
7.8%
파 248
 
7.8%
동 242
 
7.6%
서 237
 
7.4%
구 237
 
7.4%
울 237
 
7.4%
시 237
 
7.4%
별 237
 
7.4%
특 237
 
7.4%
안 83
 
2.6%
Other values (46) 946
29.7%
Common
ValueCountFrequency (%)
832
43.6%
1 165
 
8.6%
2 123
 
6.4%
) 105
 
5.5%
( 105
 
5.5%
4 99
 
5.2%
- 97
 
5.1%
8 93
 
4.9%
3 58
 
3.0%
0 56
 
2.9%
Other values (5) 177
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3189
62.5%
ASCII 1910
37.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
832
43.6%
1 165
 
8.6%
2 123
 
6.4%
) 105
 
5.5%
( 105
 
5.5%
4 99
 
5.2%
- 97
 
5.1%
8 93
 
4.9%
3 58
 
3.0%
0 56
 
2.9%
Other values (5) 177
 
9.3%
Hangul
ValueCountFrequency (%)
송 248
 
7.8%
파 248
 
7.8%
동 242
 
7.6%
서 237
 
7.4%
구 237
 
7.4%
울 237
 
7.4%
시 237
 
7.4%
별 237
 
7.4%
특 237
 
7.4%
안 83
 
2.6%
Other values (46) 946
29.7%

실외운동기구종류
Text

Distinct98
Distinct (%)41.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-12T19:15:32.474651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length6.2658228
Min length2

Characters and Unicode

Total characters1485
Distinct characters137
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)26.2%

Sample

1st row큰활차+작은활차
2nd row다리밀기+양팔줄당기기
3rd row역기올리기+역기내리기
4th row거꾸리머신
5th row자전거타기+허리돌리기
ValueCountFrequency (%)
크로스컨트리 15
 
6.0%
롤링웨이스트 14
 
5.6%
스트레칭로라 12
 
4.8%
오버턴스트레칭 11
 
4.4%
윗몸일으키기 10
 
4.0%
풀웨이트 9
 
3.6%
양팔줄당기기 9
 
3.6%
워밍암 9
 
3.6%
스텝사이클 9
 
3.6%
레그프레스 8
 
3.2%
Other values (86) 144
57.6%
2023-12-12T19:15:32.881575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
트 151
 
10.2%
스 117
 
7.9%
기 112
 
7.5%
리 68
 
4.6%
레 51
 
3.4%
이 50
 
3.4%
웨 38
 
2.6%
로 30
 
2.0%
칭 27
 
1.8%
위 26
 
1.8%
Other values (127) 815
54.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1417
95.4%
Math Symbol 18
 
1.2%
Space Separator 13
 
0.9%
Close Punctuation 13
 
0.9%
Open Punctuation 13
 
0.9%
Other Punctuation 11
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
트 151
 
10.7%
스 117
 
8.3%
기 112
 
7.9%
리 68
 
4.8%
레 51
 
3.6%
이 50
 
3.5%
웨 38
 
2.7%
로 30
 
2.1%
칭 27
 
1.9%
위 26
 
1.8%
Other values (122) 747
52.7%
Math Symbol
ValueCountFrequency (%)
+ 18
100.0%
Space Separator
ValueCountFrequency (%)
13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Other Punctuation
ValueCountFrequency (%)
, 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1417
95.4%
Common 68
 
4.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
트 151
 
10.7%
스 117
 
8.3%
기 112
 
7.9%
리 68
 
4.8%
레 51
 
3.6%
이 50
 
3.5%
웨 38
 
2.7%
로 30
 
2.1%
칭 27
 
1.9%
위 26
 
1.8%
Other values (122) 747
52.7%
Common
ValueCountFrequency (%)
+ 18
26.5%
13
19.1%
) 13
19.1%
( 13
19.1%
, 11
16.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1417
95.4%
ASCII 68
 
4.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
트 151
 
10.7%
스 117
 
8.3%
기 112
 
7.9%
리 68
 
4.8%
레 51
 
3.6%
이 50
 
3.5%
웨 38
 
2.7%
로 30
 
2.1%
칭 27
 
1.9%
위 26
 
1.8%
Other values (122) 747
52.7%
ASCII
ValueCountFrequency (%)
+ 18
26.5%
13
19.1%
) 13
19.1%
( 13
19.1%
, 11
16.2%

담당부서
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
치수과
105 
공원녹지과
94 
생활체육과
32 
문화재과
 
6

Length

Max length5
Median length5
Mean length4.0886076
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
치수과 105
44.3%
공원녹지과 94
39.7%
생활체육과 32
 
13.5%
문화재과 6
 
2.5%

Length

2023-12-12T19:15:33.069484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:15:33.235320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
치수과 105
44.3%
공원녹지과 94
39.7%
생활체육과 32
 
13.5%
문화재과 6
 
2.5%

설치업체
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
<NA>
199 
㈜디자인파크개발
 
19
(주)카이로스
 
9
월드스포츠산업주식회사
 
6
프로테우스㈜
 
3

Length

Max length11
Median length4
Mean length4.6413502
Min length4

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row(주)카이로스
2nd row(주)카이로스
3rd row(주)카이로스
4th row(주)카이로스
5th row(주)카이로스

Common Values

ValueCountFrequency (%)
<NA> 199
84.0%
㈜디자인파크개발 19
 
8.0%
(주)카이로스 9
 
3.8%
월드스포츠산업주식회사 6
 
2.5%
프로테우스㈜ 3
 
1.3%
대명ENG 1
 
0.4%

Length

2023-12-12T19:15:33.366399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:15:33.504069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 199
84.0%
㈜디자인파크개발 19
 
8.0%
주)카이로스 9
 
3.8%
월드스포츠산업주식회사 6
 
2.5%
프로테우스㈜ 3
 
1.3%
대명eng 1
 
0.4%

Interactions

2023-12-12T19:15:31.115651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:15:33.620073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치연도최근5년간의 실외 운동기구 설치주소실외운동기구종류담당부서설치업체
설치연도1.0000.6760.8710.6340.860
최근5년간의 실외 운동기구 설치주소0.6761.0000.9021.0000.784
실외운동기구종류0.8710.9021.0000.9160.985
담당부서0.6341.0000.9161.0001.000
설치업체0.8600.7840.9851.0001.000
2023-12-12T19:15:33.744158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치업체담당부서
설치업체1.0000.957
담당부서0.9571.000
2023-12-12T19:15:33.854063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치연도담당부서설치업체
설치연도1.0000.5840.871
담당부서0.5841.0000.957
설치업체0.8710.9571.000

Missing values

2023-12-12T19:15:31.266217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:15:31.359255image/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

설치연도최근5년간의 실외 운동기구 설치주소실외운동기구종류담당부서설치업체
02018서울특별시 송파구 풍납동 183-5큰활차+작은활차생활체육과(주)카이로스
12018서울특별시 송파구 풍납동 183-5다리밀기+양팔줄당기기생활체육과(주)카이로스
22018서울특별시 송파구 풍납동 183-5역기올리기+역기내리기생활체육과(주)카이로스
32018서울특별시 송파구 풍납동 183-5거꾸리머신생활체육과(주)카이로스
42018서울특별시 송파구 풍납동 183-5자전거타기+허리돌리기생활체육과(주)카이로스
52018서울특별시 송파구 풍납동 121-26옆파도타기+등허리지압기생활체육과(주)카이로스
62018서울특별시 송파구 오금동 163큰활차+양팔줄당기기생활체육과(주)카이로스
72018서울특별시 송파구 송파동 106달리기+하늘걷기생활체육과(주)카이로스
82018서울특별시 송파구 신천동 9큰활차+양팔줄당기기생활체육과(주)카이로스
92019서울특별시 송파구 풍납동 108-7트리플트위스트생활체육과㈜디자인파크개발
설치연도최근5년간의 실외 운동기구 설치주소실외운동기구종류담당부서설치업체
2272021서울특별시 송파구 신천동 21(좌안)스트레칭로라치수과<NA>
2282021서울특별시 송파구 신천동 21(좌안)양팔줄당기기치수과<NA>
2292021서울특별시 송파구 신천동 21(좌안)오버턴스트레칭치수과<NA>
2302021서울특별시 송파구 신천동 21(좌안)워밍암치수과<NA>
2312021서울특별시 송파구 신천동 21(좌안)워밍암(트윈워밍암)치수과<NA>
2322021서울특별시 송파구 신천동 21(좌안)윗몸일으키기, 바디싣업치수과<NA>
2332021서울특별시 송파구 신천동 21(좌안)체어웨이트치수과<NA>
2342021서울특별시 송파구 신천동 21(좌안)크로스컨트리치수과<NA>
2352021서울특별시 송파구 신천동 21(좌안)트위스트(트리플트위스트)치수과<NA>
2362021서울특별시 송파구 신천동 21(좌안)풀웨이트치수과<NA>

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설치연도최근5년간의 실외 운동기구 설치주소실외운동기구종류담당부서설치업체# duplicates
02021서울특별시 송파구 신천동 21(좌안)스트레칭로라치수과<NA>2
1<NA>서울특별시 송파구 잠실동 84공중걷기공원녹지과<NA>2
2<NA>서울특별시 송파구 잠실동 84양팔줄당기기공원녹지과<NA>2
3<NA>서울특별시 송파구 잠실동 84온몸근육풀기공원녹지과<NA>2
4<NA>서울특별시 송파구 잠실동 84윗몸일으키기공원녹지과<NA>2
5<NA>서울특별시 송파구 잠실동 84하체흔들기공원녹지과<NA>2
6<NA>서울특별시 송파구 잠실동 84허리돌리기공원녹지과<NA>2