Overview

Dataset statistics

Number of variables6
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory56.0 B

Variable types

Numeric2
Text2
Categorical2

Dataset

Description서울특별시 영등포구 관내 건물 옥상녹화현황 정보입니다. 시행연도, 대상지(건물), 건물위치 주소, 면적 등의 정보를 제공합니다.
Author서울특별시 영등포구
URLhttps://www.data.go.kr/data/15035583/fileData.do

Alerts

데이터기준일 has constant value ""Constant
대상지 has unique valuesUnique
위치 has unique valuesUnique

Reproduction

Analysis started2023-12-12 04:01:59.689283
Analysis finished2023-12-12 04:02:00.885092
Duration1.2 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시행 연도
Real number (ℝ)

Distinct13
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011
Minimum2002
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T13:02:00.948585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2002
5-th percentile2003
Q12009
median2011
Q32013
95-th percentile2018.9
Maximum2020
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.9473418
Coefficient of variation (CV)0.0024601401
Kurtosis-0.35097029
Mean2011
Median Absolute Deviation (MAD)2
Skewness-0.06229026
Sum44242
Variance24.47619
MonotonicityIncreasing
2023-12-12T13:02:01.109865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2010 3
13.6%
2012 3
13.6%
2003 2
9.1%
2009 2
9.1%
2011 2
9.1%
2013 2
9.1%
2017 2
9.1%
2002 1
 
4.5%
2006 1
 
4.5%
2007 1
 
4.5%
Other values (3) 3
13.6%
ValueCountFrequency (%)
2002 1
 
4.5%
2003 2
9.1%
2006 1
 
4.5%
2007 1
 
4.5%
2009 2
9.1%
2010 3
13.6%
2011 2
9.1%
2012 3
13.6%
2013 2
9.1%
2016 1
 
4.5%
ValueCountFrequency (%)
2020 1
 
4.5%
2019 1
 
4.5%
2017 2
9.1%
2016 1
 
4.5%
2013 2
9.1%
2012 3
13.6%
2011 2
9.1%
2010 3
13.6%
2009 2
9.1%
2007 1
 
4.5%

대상지
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T13:02:01.387251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9.5
Mean length7.7272727
Min length4

Characters and Unicode

Total characters170
Distinct characters81
Distinct categories3 ?
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 row영등포병원
2nd row에이스테크노타워
3rd row한국화학시험연구원
4th row대성공업사
5th row중소기업중앙회
ValueCountFrequency (%)
영등포구청 2
 
6.9%
영등포병원 1
 
3.4%
에이스테크노타워 1
 
3.4%
신길4동 1
 
3.4%
영등포소방서 1
 
3.4%
주민센터 1
 
3.4%
문래동 1
 
3.4%
직장어린이집 1
 
3.4%
별관 1
 
3.4%
유치원 1
 
3.4%
Other values (18) 18
62.1%
2023-12-12T13:02:01.840738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 7
 
4.1%
7
 
4.1%
영 6
 
3.5%
포 5
 
2.9%
공 5
 
2.9%
회 5
 
2.9%
등 5
 
2.9%
한 4
 
2.4%
사 4
 
2.4%
서 4
 
2.4%
Other values (71) 118
69.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 162
95.3%
Space Separator 7
 
4.1%
Decimal Number 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 7
 
4.3%
영 6
 
3.7%
포 5
 
3.1%
공 5
 
3.1%
회 5
 
3.1%
등 5
 
3.1%
한 4
 
2.5%
사 4
 
2.5%
서 4
 
2.5%
복 4
 
2.5%
Other values (69) 113
69.8%
Space Separator
ValueCountFrequency (%)
7
100.0%
Decimal Number
ValueCountFrequency (%)
4 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 162
95.3%
Common 8
 
4.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 7
 
4.3%
영 6
 
3.7%
포 5
 
3.1%
공 5
 
3.1%
회 5
 
3.1%
등 5
 
3.1%
한 4
 
2.5%
사 4
 
2.5%
서 4
 
2.5%
복 4
 
2.5%
Other values (69) 113
69.8%
Common
ValueCountFrequency (%)
7
87.5%
4 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 162
95.3%
ASCII 8
 
4.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 7
 
4.3%
영 6
 
3.7%
포 5
 
3.1%
공 5
 
3.1%
회 5
 
3.1%
등 5
 
3.1%
한 4
 
2.5%
사 4
 
2.5%
서 4
 
2.5%
복 4
 
2.5%
Other values (69) 113
69.8%
ASCII
ValueCountFrequency (%)
7
87.5%
4 1
 
12.5%

위치
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T13:02:02.131642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length20.772727
Min length17

Characters and Unicode

Total characters457
Distinct characters42
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

Unique22 ?
Unique (%)100.0%

Sample

1st row서울특별시 영등포구 당산3가 386-3
2nd row서울특별시 영등포구 양평동3가 40-2
3rd row서울특별시 영등포구 영등포동8가 88-2
4th row서울특별시 영등포구 문래동5가 5-7
5th row서울특별시 영등포구 여의동 16-2
ValueCountFrequency (%)
서울특별시 22
25.3%
영등포구 22
25.3%
여의동 2
 
2.3%
대림3동 2
 
2.3%
도림동 1
 
1.1%
93 1
 
1.1%
양평2동(5가 1
 
1.1%
76 1
 
1.1%
영등포동(4가 1
 
1.1%
134-3 1
 
1.1%
Other values (33) 33
37.9%
2023-12-12T13:02:02.613759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
65
 
14.2%
영 28
 
6.1%
등 28
 
6.1%
포 28
 
6.1%
서 22
 
4.8%
특 22
 
4.8%
별 22
 
4.8%
시 22
 
4.8%
구 22
 
4.8%
울 22
 
4.8%
Other values (32) 176
38.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 284
62.1%
Decimal Number 88
 
19.3%
Space Separator 65
 
14.2%
Dash Punctuation 14
 
3.1%
Close Punctuation 3
 
0.7%
Open Punctuation 3
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 28
9.9%
등 28
9.9%
포 28
9.9%
서 22
7.7%
특 22
7.7%
별 22
7.7%
시 22
7.7%
구 22
7.7%
울 22
7.7%
동 19
 
6.7%
Other values (18) 49
17.3%
Decimal Number
ValueCountFrequency (%)
1 15
17.0%
3 12
13.6%
6 11
12.5%
5 11
12.5%
4 9
10.2%
8 8
9.1%
2 7
8.0%
7 7
8.0%
0 5
 
5.7%
9 3
 
3.4%
Space Separator
ValueCountFrequency (%)
65
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 284
62.1%
Common 173
37.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 28
9.9%
등 28
9.9%
포 28
9.9%
서 22
7.7%
특 22
7.7%
별 22
7.7%
시 22
7.7%
구 22
7.7%
울 22
7.7%
동 19
 
6.7%
Other values (18) 49
17.3%
Common
ValueCountFrequency (%)
65
37.6%
1 15
 
8.7%
- 14
 
8.1%
3 12
 
6.9%
6 11
 
6.4%
5 11
 
6.4%
4 9
 
5.2%
8 8
 
4.6%
2 7
 
4.0%
7 7
 
4.0%
Other values (4) 14
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 284
62.1%
ASCII 173
37.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
65
37.6%
1 15
 
8.7%
- 14
 
8.1%
3 12
 
6.9%
6 11
 
6.4%
5 11
 
6.4%
4 9
 
5.2%
8 8
 
4.6%
2 7
 
4.0%
7 7
 
4.0%
Other values (4) 14
 
8.1%
Hangul
ValueCountFrequency (%)
영 28
9.9%
등 28
9.9%
포 28
9.9%
서 22
7.7%
특 22
7.7%
별 22
7.7%
시 22
7.7%
구 22
7.7%
울 22
7.7%
동 19
 
6.7%
Other values (18) 49
17.3%

면적(제곱미터)
Real number (ℝ)

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean414.05818
Minimum66.07
Maximum1867
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T13:02:02.761289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum66.07
5-th percentile92.1
Q1167.25
median336.46
Q3521.6425
95-th percentile1003.85
Maximum1867
Range1800.93
Interquartile range (IQR)354.3925

Descriptive statistics

Standard deviation393.59251
Coefficient of variation (CV)0.95057296
Kurtosis8.8047872
Mean414.05818
Median Absolute Deviation (MAD)174.96
Skewness2.6884769
Sum9109.28
Variance154915.06
MonotonicityNot monotonic
2023-12-12T13:02:02.937601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
213.0 2
 
9.1%
403.0 1
 
4.5%
533.0 1
 
4.5%
90.0 1
 
4.5%
307.53 1
 
4.5%
174.0 1
 
4.5%
372.0 1
 
4.5%
532.19 1
 
4.5%
337.8 1
 
4.5%
158.0 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
66.07 1
4.5%
90.0 1
4.5%
132.0 1
4.5%
151.7 1
4.5%
158.0 1
4.5%
165.0 1
4.5%
174.0 1
4.5%
213.0 2
9.1%
307.53 1
4.5%
335.12 1
4.5%
ValueCountFrequency (%)
1867.0 1
4.5%
1025.0 1
4.5%
602.0 1
4.5%
588.0 1
4.5%
533.0 1
4.5%
532.19 1
4.5%
490.0 1
4.5%
403.0 1
4.5%
372.0 1
4.5%
353.87 1
4.5%

비고
Categorical

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size308.0 B
민간
12 
공공
10 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row민간
2nd row민간
3rd row민간
4th row민간
5th row공공

Common Values

ValueCountFrequency (%)
민간 12
54.5%
공공 10
45.5%

Length

2023-12-12T13:02:03.111108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:02:03.241380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
민간 12
54.5%
공공 10
45.5%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-11-09
22 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-11-09
2nd row2023-11-09
3rd row2023-11-09
4th row2023-11-09
5th row2023-11-09

Common Values

ValueCountFrequency (%)
2023-11-09 22
100.0%

Length

2023-12-12T13:02:03.377204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:02:03.515024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-11-09 22
100.0%

Interactions

2023-12-12T13:02:00.236629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:59.946869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:02:00.363540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:02:00.087242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:02:03.611041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시행 연도대상지위치면적(제곱미터)비고
시행 연도1.0001.0001.0000.0280.564
대상지1.0001.0001.0001.0001.000
위치1.0001.0001.0001.0001.000
면적(제곱미터)0.0281.0001.0001.0000.000
비고0.5641.0001.0000.0001.000
2023-12-12T13:02:03.735899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시행 연도면적(제곱미터)비고
시행 연도1.000-0.4520.476
면적(제곱미터)-0.4521.0000.000
비고0.4760.0001.000

Missing values

2023-12-12T13:02:00.597675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:02:00.829277image/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

시행 연도대상지위치면적(제곱미터)비고데이터기준일
02002영등포병원서울특별시 영등포구 당산3가 386-3403.0민간2023-11-09
12003에이스테크노타워서울특별시 영등포구 양평동3가 40-2533.0민간2023-11-09
22003한국화학시험연구원서울특별시 영등포구 영등포동8가 88-2602.0민간2023-11-09
32006대성공업사서울특별시 영등포구 문래동5가 5-7132.0민간2023-11-09
42007중소기업중앙회서울특별시 영등포구 여의동 16-21025.0공공2023-11-09
52009한국스카우트연맹회관서울특별시 영등포구 여의동 18-3588.0민간2023-11-09
62009성일빌딩서울특별시 영등포구 대림3동 651-5353.87민간2023-11-09
72010영등포본동 주민자치회관서울특별시 영등포구 신길1동 166-41151.7공공2023-11-09
82010근로복지공단서울특별시 영등포구 영등포동2가 94-267490.0공공2023-11-09
92010한국정책금융공사서울특별시 영등포구 여의도동 161867.0공공2023-11-09
시행 연도대상지위치면적(제곱미터)비고데이터기준일
122012충무병원서울특별시 영등포구 영등포동4가 93213.0민간2023-11-09
132012한신혜원유치원서울특별시 영등포구 양평2동(5가) 76213.0민간2023-11-09
142012서울사회복지대학원대학교서울특별시 영등포구 영등포동(4가) 134-366.07민간2023-11-09
152013서울시 근로자복지관서울특별시 영등포구 영등포동(7가) 57158.0공공2023-11-09
162013돈보스꼬 유치원서울특별시 영등포구 도림동 144-10337.8민간2023-11-09
172016영등포구청 별관서울특별시 영등포구 선유동1로 80532.19공공2023-11-09
182017영등포구청 직장어린이집서울특별시 영등포구 당산동3가 560372.0공공2023-11-09
192017문래동 주민센터서울특별시 영등포구 문래동3가 55-16174.0공공2023-11-09
202019영등포소방서서울특별시 영등포구 문래로197307.53공공2023-11-09
212020신길4동 공공복합청사서울특별시 영등포구 신길로42길 190.0공공2023-11-09