Overview

Dataset statistics

Number of variables8
Number of observations2048
Missing cells1611
Missing cells (%)9.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory130.1 KiB
Average record size in memory65.1 B

Variable types

Numeric1
Categorical3
Text2
DateTime2

Dataset

Description노원구에 위치한 근린공원, 하천, 산, 놀이터, 에 설치된 운동기구에 대한 데이터로 위치, 설치기구명, 관리부서, 설치년도 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15080676/fileData.do

Alerts

기준일시 has constant value ""Constant
설치장소 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
상세구분 is highly overall correlated with 설치장소 and 1 other fieldsHigh correlation
관리부서 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
연번 is highly overall correlated with 설치장소 and 1 other fieldsHigh correlation
상세구분 is highly imbalanced (50.3%)Imbalance
설치연도 has 1611 (78.7%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 07:36:26.288671
Analysis finished2023-12-12 07:36:27.254691
Duration0.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct2048
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1024.5
Minimum1
Maximum2048
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-12-12T16:36:27.341367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile103.35
Q1512.75
median1024.5
Q31536.25
95-th percentile1945.65
Maximum2048
Range2047
Interquartile range (IQR)1023.5

Descriptive statistics

Standard deviation591.351
Coefficient of variation (CV)0.57720937
Kurtosis-1.2
Mean1024.5
Median Absolute Deviation (MAD)512
Skewness0
Sum2098176
Variance349696
MonotonicityStrictly increasing
2023-12-12T16:36:27.517125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
1026 1
 
< 0.1%
1376 1
 
< 0.1%
1375 1
 
< 0.1%
1374 1
 
< 0.1%
1373 1
 
< 0.1%
1372 1
 
< 0.1%
1371 1
 
< 0.1%
1370 1
 
< 0.1%
1369 1
 
< 0.1%
Other values (2038) 2038
99.5%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
2048 1
< 0.1%
2047 1
< 0.1%
2046 1
< 0.1%
2045 1
< 0.1%
2044 1
< 0.1%
2043 1
< 0.1%
2042 1
< 0.1%
2041 1
< 0.1%
2040 1
< 0.1%
2039 1
< 0.1%

설치장소
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
상계동
572 
월계동
256 
중랑천
196 
공릉동
176 
불암산
172 
Other values (9)
676 

Length

Max length4
Median length3
Mean length3.0107422
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상계동 572
27.9%
월계동 256
12.5%
중랑천 196
 
9.6%
공릉동 176
 
8.6%
불암산 172
 
8.4%
중계동 148
 
7.2%
수락산 140
 
6.8%
초안산 94
 
4.6%
하계동 74
 
3.6%
우이천 66
 
3.2%
Other values (4) 154
 
7.5%

Length

2023-12-12T16:36:27.681696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
상계동 572
27.9%
월계동 256
12.5%
중랑천 196
 
9.6%
공릉동 176
 
8.6%
불암산 172
 
8.4%
중계동 170
 
8.3%
수락산 140
 
6.8%
초안산 94
 
4.6%
하계동 74
 
3.6%
우이천 66
 
3.2%
Other values (3) 132
 
6.4%

상세구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
<NA>
1710 
좌안
244 
우안
 
94

Length

Max length4
Median length4
Mean length3.6699219
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1710
83.5%
좌안 244
 
11.9%
우안 94
 
4.6%

Length

2023-12-12T16:36:27.844518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:36:27.984437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1710
83.5%
좌안 244
 
11.9%
우안 94
 
4.6%

위치
Text

Distinct426
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
2023-12-12T16:36:28.279323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length24
Mean length15.59668
Min length3

Characters and Unicode

Total characters31942
Distinct characters293
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique198 ?
Unique (%)9.7%

Sample

1st row상계역 하부 교각P-10
2nd row상계역 하부 교각P-10
3rd row상계역 하부 교각P-10
4th row상계역 하부 교각P-10
5th row상계역 하부 교각P-10
ValueCountFrequency (%)
하부 121
 
2.7%
공릉동 71
 
1.6%
앞 64
 
1.4%
부근 50
 
1.1%
706 43
 
1.0%
초안산 43
 
1.0%
솔밭길(공릉동 43
 
1.0%
진입경사로 42
 
0.9%
반디어린이공원(상계동 39
 
0.9%
665 39
 
0.9%
Other values (593) 3963
87.7%
2023-12-12T16:36:28.819885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2484
 
7.8%
동 1625
 
5.1%
) 1553
 
4.9%
( 1553
 
4.9%
공 1482
 
4.6%
계 1402
 
4.4%
1 1362
 
4.3%
원 1308
 
4.1%
- 1012
 
3.2%
린 1005
 
3.1%
Other values (283) 17156
53.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19130
59.9%
Decimal Number 6092
 
19.1%
Space Separator 2484
 
7.8%
Close Punctuation 1553
 
4.9%
Open Punctuation 1553
 
4.9%
Dash Punctuation 1012
 
3.2%
Other Punctuation 84
 
0.3%
Lowercase Letter 23
 
0.1%
Uppercase Letter 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1625
 
8.5%
공 1482
 
7.7%
계 1402
 
7.3%
원 1308
 
6.8%
린 1005
 
5.3%
이 766
 
4.0%
상 761
 
4.0%
어 720
 
3.8%
근 372
 
1.9%
월 358
 
1.9%
Other values (265) 9331
48.8%
Decimal Number
ValueCountFrequency (%)
1 1362
22.4%
6 853
14.0%
2 732
12.0%
5 607
10.0%
3 596
9.8%
7 578
9.5%
0 496
 
8.1%
4 436
 
7.2%
8 222
 
3.6%
9 210
 
3.4%
Other Punctuation
ValueCountFrequency (%)
· 79
94.0%
. 5
 
6.0%
Space Separator
ValueCountFrequency (%)
2484
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1553
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1553
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1012
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 23
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19130
59.9%
Common 12778
40.0%
Latin 34
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1625
 
8.5%
공 1482
 
7.7%
계 1402
 
7.3%
원 1308
 
6.8%
린 1005
 
5.3%
이 766
 
4.0%
상 761
 
4.0%
어 720
 
3.8%
근 372
 
1.9%
월 358
 
1.9%
Other values (265) 9331
48.8%
Common
ValueCountFrequency (%)
2484
19.4%
) 1553
12.2%
( 1553
12.2%
1 1362
10.7%
- 1012
7.9%
6 853
 
6.7%
2 732
 
5.7%
5 607
 
4.8%
3 596
 
4.7%
7 578
 
4.5%
Other values (6) 1448
11.3%
Latin
ValueCountFrequency (%)
m 23
67.6%
P 11
32.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 19130
59.9%
ASCII 12733
39.9%
None 79
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2484
19.5%
) 1553
12.2%
( 1553
12.2%
1 1362
10.7%
- 1012
7.9%
6 853
 
6.7%
2 732
 
5.7%
5 607
 
4.8%
3 596
 
4.7%
7 578
 
4.5%
Other values (7) 1403
11.0%
Hangul
ValueCountFrequency (%)
동 1625
 
8.5%
공 1482
 
7.7%
계 1402
 
7.3%
원 1308
 
6.8%
린 1005
 
5.3%
이 766
 
4.0%
상 761
 
4.0%
어 720
 
3.8%
근 372
 
1.9%
월 358
 
1.9%
Other values (265) 9331
48.8%
None
ValueCountFrequency (%)
· 79
100.0%

기구명
Text

Distinct390
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
2023-12-12T16:36:29.096780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length5.4770508
Min length2

Characters and Unicode

Total characters11217
Distinct characters210
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique198 ?
Unique (%)9.7%

Sample

1st row윗몸일으키기
2nd row트윈트위스트
3rd row역기내리기
4th row달리기운동
5th row공중걷기
ValueCountFrequency (%)
허리돌리기 241
 
11.3%
공중걷기 108
 
5.0%
파도타기 102
 
4.8%
윗몸일으키기 99
 
4.6%
철봉 59
 
2.8%
평행봉 55
 
2.6%
역기올리기 54
 
2.5%
달리기운동 50
 
2.3%
거꾸로매달리기 39
 
1.8%
달리기 35
 
1.6%
Other values (386) 1297
60.6%
2023-12-12T16:36:29.544773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 1587
 
14.1%
리 1063
 
9.5%
허 360
 
3.2%
트 316
 
2.8%
스 314
 
2.8%
돌 302
 
2.7%
이 224
 
2.0%
몸 191
 
1.7%
풀 167
 
1.5%
타 158
 
1.4%
Other values (200) 6535
58.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10799
96.3%
Math Symbol 148
 
1.3%
Space Separator 112
 
1.0%
Open Punctuation 55
 
0.5%
Close Punctuation 55
 
0.5%
Decimal Number 30
 
0.3%
Other Punctuation 16
 
0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 1587
 
14.7%
리 1063
 
9.8%
허 360
 
3.3%
트 316
 
2.9%
스 314
 
2.9%
돌 302
 
2.8%
이 224
 
2.1%
몸 191
 
1.8%
풀 167
 
1.5%
타 158
 
1.5%
Other values (187) 6117
56.6%
Decimal Number
ValueCountFrequency (%)
2 17
56.7%
3 5
 
16.7%
1 5
 
16.7%
5 1
 
3.3%
8 1
 
3.3%
4 1
 
3.3%
Other Punctuation
ValueCountFrequency (%)
, 12
75.0%
/ 4
 
25.0%
Math Symbol
ValueCountFrequency (%)
+ 148
100.0%
Space Separator
ValueCountFrequency (%)
112
100.0%
Open Punctuation
ValueCountFrequency (%)
( 55
100.0%
Close Punctuation
ValueCountFrequency (%)
) 55
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10799
96.3%
Common 418
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 1587
 
14.7%
리 1063
 
9.8%
허 360
 
3.3%
트 316
 
2.9%
스 314
 
2.9%
돌 302
 
2.8%
이 224
 
2.1%
몸 191
 
1.8%
풀 167
 
1.5%
타 158
 
1.5%
Other values (187) 6117
56.6%
Common
ValueCountFrequency (%)
+ 148
35.4%
112
26.8%
( 55
 
13.2%
) 55
 
13.2%
2 17
 
4.1%
, 12
 
2.9%
3 5
 
1.2%
1 5
 
1.2%
/ 4
 
1.0%
- 2
 
0.5%
Other values (3) 3
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10799
96.3%
ASCII 418
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 1587
 
14.7%
리 1063
 
9.8%
허 360
 
3.3%
트 316
 
2.9%
스 314
 
2.9%
돌 302
 
2.8%
이 224
 
2.1%
몸 191
 
1.8%
풀 167
 
1.5%
타 158
 
1.5%
Other values (187) 6117
56.6%
ASCII
ValueCountFrequency (%)
+ 148
35.4%
112
26.8%
( 55
 
13.2%
) 55
 
13.2%
2 17
 
4.1%
, 12
 
2.9%
3 5
 
1.2%
1 5
 
1.2%
/ 4
 
1.0%
- 2
 
0.5%
Other values (3) 3
 
0.7%

설치연도
Date

MISSING 

Distinct15
Distinct (%)3.4%
Missing1611
Missing (%)78.7%
Memory size16.1 KiB
Minimum2008-01-01 00:00:00
Maximum2023-01-01 00:00:00
2023-12-12T16:36:29.670970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:29.778844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)

관리부서
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
푸른도시과
1710 
치수과
338 

Length

Max length5
Median length5
Mean length4.6699219
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
푸른도시과 1710
83.5%
치수과 338
 
16.5%

Length

2023-12-12T16:36:29.942779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:36:30.080034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
푸른도시과 1710
83.5%
치수과 338
 
16.5%

기준일시
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
Minimum2023-08-09 00:00:00
Maximum2023-08-09 00:00:00
2023-12-12T16:36:30.168187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:30.249694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T16:36:26.816042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:36:30.325474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번설치장소상세구분설치연도관리부서
연번1.0000.8750.6970.8260.990
설치장소0.8751.0000.8880.7991.000
상세구분0.6970.8881.0000.591NaN
설치연도0.8260.7990.5911.0000.625
관리부서0.9901.000NaN0.6251.000
2023-12-12T16:36:30.438135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치장소상세구분관리부서
설치장소1.0000.6940.997
상세구분0.6941.0001.000
관리부서0.9971.0001.000
2023-12-12T16:36:30.530135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번설치장소상세구분관리부서
연번1.0000.6050.4910.911
설치장소0.6051.0000.6940.997
상세구분0.4910.6941.0001.000
관리부서0.9110.9971.0001.000

Missing values

2023-12-12T16:36:26.987826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:36:27.202333image/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

연번설치장소상세구분위치기구명설치연도관리부서기준일시
01당현천좌안상계역 하부 교각P-10윗몸일으키기2017치수과2023-08-09
12당현천좌안상계역 하부 교각P-10트윈트위스트2015치수과2023-08-09
23당현천좌안상계역 하부 교각P-10역기내리기2020치수과2023-08-09
34당현천좌안상계역 하부 교각P-10달리기운동2020치수과2023-08-09
45당현천좌안상계역 하부 교각P-10공중걷기2020치수과2023-08-09
56당현천좌안상계역 하부 교각P-10벤치프레스2020치수과2023-08-09
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