Overview

Dataset statistics

Number of variables4
Number of observations92
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory35.4 B

Variable types

Categorical2
Text1
Numeric1

Dataset

Description2020년도 양천구 도시공원(근린공원, 어린이공원) 이용자 수 현황
Author서울특별시 양천구
URLhttps://www.data.go.kr/data/15074361/fileData.do

Alerts

연도 has constant value ""Constant

Reproduction

Analysis started2023-12-12 01:30:27.762446
Analysis finished2023-12-12 01:30:28.246602
Duration0.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct2
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size868.0 B
어린이공원
74 
근린공원
18 

Length

Max length5
Median length5
Mean length4.8043478
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
어린이공원 74
80.4%
근린공원 18
 
19.6%

Length

2023-12-12T10:30:28.316623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:30:28.424295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
어린이공원 74
80.4%
근린공원 18
 
19.6%

공원명
Text

Distinct90
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size868.0 B
2023-12-12T10:30:28.729247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.3695652
Min length2

Characters and Unicode

Total characters218
Distinct characters115
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)95.7%

Sample

1st row목마
2nd row파리
3rd row오목
4th row양천
5th row신트리
ValueCountFrequency (%)
곰달래 2
 
2.2%
신월 2
 
2.2%
개나리 1
 
1.1%
정목 1
 
1.1%
모세미 1
 
1.1%
은하수 1
 
1.1%
진달래 1
 
1.1%
영도 1
 
1.1%
별님 1
 
1.1%
햇님 1
 
1.1%
Other values (80) 80
87.0%
2023-12-12T10:30:29.201294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
신 12
 
5.5%
동 6
 
2.8%
리 6
 
2.8%
목 6
 
2.8%
산 6
 
2.8%
달 5
 
2.3%
양 5
 
2.3%
마 5
 
2.3%
래 4
 
1.8%
오 4
 
1.8%
Other values (105) 159
72.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 218
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
신 12
 
5.5%
동 6
 
2.8%
리 6
 
2.8%
목 6
 
2.8%
산 6
 
2.8%
달 5
 
2.3%
양 5
 
2.3%
마 5
 
2.3%
래 4
 
1.8%
오 4
 
1.8%
Other values (105) 159
72.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 218
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
신 12
 
5.5%
동 6
 
2.8%
리 6
 
2.8%
목 6
 
2.8%
산 6
 
2.8%
달 5
 
2.3%
양 5
 
2.3%
마 5
 
2.3%
래 4
 
1.8%
오 4
 
1.8%
Other values (105) 159
72.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 218
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
신 12
 
5.5%
동 6
 
2.8%
리 6
 
2.8%
목 6
 
2.8%
산 6
 
2.8%
달 5
 
2.3%
양 5
 
2.3%
마 5
 
2.3%
래 4
 
1.8%
오 4
 
1.8%
Other values (105) 159
72.9%

이용자수(천명)
Real number (ℝ)

Distinct48
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.032609
Minimum10
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2023-12-12T10:30:29.356858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q128.75
median65.5
Q385.75
95-th percentile114.5
Maximum150
Range140
Interquartile range (IQR)57

Descriptive statistics

Standard deviation36.482858
Coefficient of variation (CV)0.60771735
Kurtosis-0.56226929
Mean60.032609
Median Absolute Deviation (MAD)26.5
Skewness0.24097535
Sum5523
Variance1330.9989
MonotonicityNot monotonic
2023-12-12T10:30:29.517687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
12 8
 
8.7%
10 8
 
8.7%
88 4
 
4.3%
50 3
 
3.3%
30 3
 
3.3%
150 3
 
3.3%
37 3
 
3.3%
100 3
 
3.3%
80 3
 
3.3%
76 3
 
3.3%
Other values (38) 51
55.4%
ValueCountFrequency (%)
10 8
8.7%
11 1
 
1.1%
12 8
8.7%
13 1
 
1.1%
15 1
 
1.1%
17 1
 
1.1%
20 1
 
1.1%
21 1
 
1.1%
25 1
 
1.1%
30 3
 
3.3%
ValueCountFrequency (%)
150 3
3.3%
120 2
2.2%
110 2
2.2%
100 3
3.3%
99 2
2.2%
98 1
 
1.1%
97 1
 
1.1%
93 1
 
1.1%
91 2
2.2%
90 2
2.2%

연도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size868.0 B
2020
92 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 92
100.0%

Length

2023-12-12T10:30:29.671703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:30:29.785668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 92
100.0%

Interactions

2023-12-12T10:30:27.988124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:30:29.848892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분공원명이용자수(천명)
구분1.0000.0000.527
공원명0.0001.0000.639
이용자수(천명)0.5270.6391.000
2023-12-12T10:30:29.967225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이용자수(천명)구분
이용자수(천명)1.0000.500
구분0.5001.000

Missing values

2023-12-12T10:30:28.120856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:30:28.211767image/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

구분공원명이용자수(천명)연도
0근린공원목마502020
1근린공원파리1502020
2근린공원오목802020
3근린공원양천802020
4근린공원신트리902020
5근린공원신월1202020
6근린공원용왕산1202020
7근린공원계남1502020
8근린공원독서852020
9근린공원오솔길762020
구분공원명이용자수(천명)연도
82어린이공원사슴572020
83어린이공원신기122020
84어린이공원푸른122020
85어린이공원신이122020
86어린이공원학마을122020
87어린이공원넓은들122020
88어린이공원숲속102020
89어린이공원하늘마루102020
90어린이공원단지102020
91어린이공원목동누리102020