Overview

Dataset statistics

Number of variables17
Number of observations603
Missing cells1835
Missing cells (%)17.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.7 KiB
Average record size in memory147.2 B

Variable types

Numeric9
Categorical2
Text4
DateTime1
Unsupported1

Dataset

Description고유번호,구명,법정동명,산지여부,주지번,부지번,새주소명,학교명,조성년도,조성면적,교목수,관목수,초화류수,생성일,사진파일명,Y 좌표,X 좌표
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-363/S/1/datasetView.do

Alerts

새주소명 has constant value ""Constant
생성일 has constant value ""Constant
Y 좌표 is highly overall correlated with 구명High correlation
X 좌표 is highly overall correlated with 구명High correlation
구명 is highly overall correlated with Y 좌표 and 1 other fieldsHigh correlation
산지여부 is highly imbalanced (95.5%)Imbalance
새주소명 has 602 (99.8%) missing valuesMissing
교목수 has 7 (1.2%) missing valuesMissing
초화류수 has 11 (1.8%) missing valuesMissing
생성일 has 602 (99.8%) missing valuesMissing
사진파일명 has 603 (100.0%) missing valuesMissing
고유번호 has unique valuesUnique
사진파일명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
교목수 has 17 (2.8%) zerosZeros
관목수 has 8 (1.3%) zerosZeros
초화류수 has 74 (12.3%) zerosZeros

Reproduction

Analysis started2023-12-11 05:04:18.290821
Analysis finished2023-12-11 05:04:35.988570
Duration17.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고유번호
Real number (ℝ)

UNIQUE 

Distinct603
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean302.63847
Minimum1
Maximum641
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-11T14:04:36.127794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile31.1
Q1151.5
median303
Q3453.5
95-th percentile573.9
Maximum641
Range640
Interquartile range (IQR)302

Descriptive statistics

Standard deviation174.75118
Coefficient of variation (CV)0.57742553
Kurtosis-1.1985039
Mean302.63847
Median Absolute Deviation (MAD)151
Skewness0.0012000607
Sum182491
Variance30537.975
MonotonicityNot monotonic
2023-12-11T14:04:36.416489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
303 1
 
0.2%
176 1
 
0.2%
556 1
 
0.2%
174 1
 
0.2%
567 1
 
0.2%
568 1
 
0.2%
233 1
 
0.2%
166 1
 
0.2%
157 1
 
0.2%
154 1
 
0.2%
Other values (593) 593
98.3%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
641 1
0.2%
603 1
0.2%
602 1
0.2%
601 1
0.2%
600 1
0.2%
599 1
0.2%
598 1
0.2%
597 1
0.2%
596 1
0.2%
595 1
0.2%

구명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
노원구
44 
강남구
 
39
양천구
 
39
송파구
 
32
강서구
 
31
Other values (20)
418 

Length

Max length4
Median length3
Mean length3.0845771
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row노원구
2nd row동대문구
3rd row노원구
4th row강서구
5th row강서구

Common Values

ValueCountFrequency (%)
노원구 44
 
7.3%
강남구 39
 
6.5%
양천구 39
 
6.5%
송파구 32
 
5.3%
강서구 31
 
5.1%
중랑구 29
 
4.8%
관악구 27
 
4.5%
서초구 27
 
4.5%
마포구 27
 
4.5%
영등포구 27
 
4.5%
Other values (15) 281
46.6%

Length

2023-12-11T14:04:36.709620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
노원구 44
 
7.3%
양천구 39
 
6.5%
강남구 39
 
6.5%
송파구 32
 
5.3%
강서구 31
 
5.1%
중랑구 29
 
4.8%
관악구 27
 
4.5%
서초구 27
 
4.5%
마포구 27
 
4.5%
영등포구 27
 
4.5%
Other values (15) 281
46.6%

법정동명
Text

Distinct189
Distinct (%)31.3%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2023-12-11T14:04:37.203824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0912106
Min length2

Characters and Unicode

Total characters1864
Distinct characters165
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)11.3%

Sample

1st row중계동
2nd row장안동
3rd row월계동
4th row가양동
5th row화곡동
ValueCountFrequency (%)
상계동 18
 
3.0%
신정동 14
 
2.3%
신림동 14
 
2.3%
신월동 13
 
2.2%
봉천동 13
 
2.2%
목동 12
 
2.0%
개포동 9
 
1.5%
창동 9
 
1.5%
서초동 9
 
1.5%
중계동 9
 
1.5%
Other values (179) 483
80.1%
2023-12-11T14:04:38.006029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 605
32.5%
신 70
 
3.8%
가 48
 
2.6%
계 39
 
2.1%
방 31
 
1.7%
상 29
 
1.6%
정 26
 
1.4%
수 24
 
1.3%
양 23
 
1.2%
산 21
 
1.1%
Other values (155) 948
50.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1830
98.2%
Decimal Number 34
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 605
33.1%
신 70
 
3.8%
가 48
 
2.6%
계 39
 
2.1%
방 31
 
1.7%
상 29
 
1.6%
정 26
 
1.4%
수 24
 
1.3%
양 23
 
1.3%
산 21
 
1.1%
Other values (148) 914
49.9%
Decimal Number
ValueCountFrequency (%)
2 16
47.1%
3 8
23.5%
1 4
 
11.8%
6 2
 
5.9%
7 2
 
5.9%
4 1
 
2.9%
5 1
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1830
98.2%
Common 34
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 605
33.1%
신 70
 
3.8%
가 48
 
2.6%
계 39
 
2.1%
방 31
 
1.7%
상 29
 
1.6%
정 26
 
1.4%
수 24
 
1.3%
양 23
 
1.3%
산 21
 
1.1%
Other values (148) 914
49.9%
Common
ValueCountFrequency (%)
2 16
47.1%
3 8
23.5%
1 4
 
11.8%
6 2
 
5.9%
7 2
 
5.9%
4 1
 
2.9%
5 1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1830
98.2%
ASCII 34
 
1.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 605
33.1%
신 70
 
3.8%
가 48
 
2.6%
계 39
 
2.1%
방 31
 
1.7%
상 29
 
1.6%
정 26
 
1.4%
수 24
 
1.3%
양 23
 
1.3%
산 21
 
1.1%
Other values (148) 914
49.9%
ASCII
ValueCountFrequency (%)
2 16
47.1%
3 8
23.5%
1 4
 
11.8%
6 2
 
5.9%
7 2
 
5.9%
4 1
 
2.9%
5 1
 
2.9%

산지여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
1
600 
2
 
3

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 (%)
1 600
99.5%
2 3
 
0.5%

Length

2023-12-11T14:04:38.222474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:04:38.399451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 600
99.5%
2 3
 
0.5%

주지번
Real number (ℝ)

Distinct390
Distinct (%)64.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean398.05307
Minimum1
Maximum4656
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-11T14:04:38.622379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.1
Q198
median272
Q3573
95-th percentile1089.9
Maximum4656
Range4655
Interquartile range (IQR)475

Descriptive statistics

Standard deviation440.89091
Coefficient of variation (CV)1.1076184
Kurtosis27.224962
Mean398.05307
Median Absolute Deviation (MAD)202
Skewness3.7242335
Sum240026
Variance194384.79
MonotonicityNot monotonic
2023-12-11T14:04:38.851111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 11
 
1.8%
89 6
 
1.0%
94 6
 
1.0%
150 6
 
1.0%
220 5
 
0.8%
43 5
 
0.8%
267 5
 
0.8%
90 5
 
0.8%
7 5
 
0.8%
3 5
 
0.8%
Other values (380) 544
90.2%
ValueCountFrequency (%)
1 11
1.8%
2 3
 
0.5%
3 5
0.8%
4 2
 
0.3%
5 2
 
0.3%
6 2
 
0.3%
7 5
0.8%
8 1
 
0.2%
9 1
 
0.2%
11 2
 
0.3%
ValueCountFrequency (%)
4656 1
0.2%
4482 1
0.2%
2727 1
0.2%
1704 1
0.2%
1694 1
0.2%
1690 1
0.2%
1685 1
0.2%
1647 1
0.2%
1635 1
0.2%
1531 1
0.2%

부지번
Text

Distinct68
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2023-12-11T14:04:39.126604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length1
Mean length1.2139303
Min length1

Characters and Unicode

Total characters732
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)6.1%

Sample

1st row
2nd row2
3rd row
4th row
5th row42
ValueCountFrequency (%)
1 110
31.5%
2 34
 
9.7%
3 30
 
8.6%
4 22
 
6.3%
5 17
 
4.9%
6 12
 
3.4%
7 10
 
2.9%
10 7
 
2.0%
16 5
 
1.4%
28 5
 
1.4%
Other values (57) 97
27.8%
2023-12-11T14:04:39.600814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
254
34.7%
1 162
22.1%
2 78
 
10.7%
3 55
 
7.5%
6 38
 
5.2%
4 34
 
4.6%
5 32
 
4.4%
7 28
 
3.8%
8 19
 
2.6%
9 18
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 478
65.3%
Space Separator 254
34.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 162
33.9%
2 78
16.3%
3 55
 
11.5%
6 38
 
7.9%
4 34
 
7.1%
5 32
 
6.7%
7 28
 
5.9%
8 19
 
4.0%
9 18
 
3.8%
0 14
 
2.9%
Space Separator
ValueCountFrequency (%)
254
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 732
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
254
34.7%
1 162
22.1%
2 78
 
10.7%
3 55
 
7.5%
6 38
 
5.2%
4 34
 
4.6%
5 32
 
4.4%
7 28
 
3.8%
8 19
 
2.6%
9 18
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 732
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
254
34.7%
1 162
22.1%
2 78
 
10.7%
3 55
 
7.5%
6 38
 
5.2%
4 34
 
4.6%
5 32
 
4.4%
7 28
 
3.8%
8 19
 
2.6%
9 18
 
2.5%

새주소명
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing602
Missing (%)99.8%
Memory size4.8 KiB
2023-12-11T14:04:39.862452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters15
Distinct characters13
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

Unique1 ?
Unique (%)100.0%

Sample

1st row현석동 토정로16길 42-4
ValueCountFrequency (%)
현석동 1
33.3%
토정로16길 1
33.3%
42-4 1
33.3%
2023-12-11T14:04:40.299171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2
13.3%
4 2
13.3%
현 1
 
6.7%
석 1
 
6.7%
동 1
 
6.7%
토 1
 
6.7%
정 1
 
6.7%
로 1
 
6.7%
1 1
 
6.7%
6 1
 
6.7%
Other values (3) 3
20.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7
46.7%
Decimal Number 5
33.3%
Space Separator 2
 
13.3%
Dash Punctuation 1
 
6.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
현 1
14.3%
석 1
14.3%
동 1
14.3%
토 1
14.3%
정 1
14.3%
로 1
14.3%
길 1
14.3%
Decimal Number
ValueCountFrequency (%)
4 2
40.0%
1 1
20.0%
6 1
20.0%
2 1
20.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8
53.3%
Hangul 7
46.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
현 1
14.3%
석 1
14.3%
동 1
14.3%
토 1
14.3%
정 1
14.3%
로 1
14.3%
길 1
14.3%
Common
ValueCountFrequency (%)
2
25.0%
4 2
25.0%
1 1
12.5%
6 1
12.5%
2 1
12.5%
- 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8
53.3%
Hangul 7
46.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2
25.0%
4 2
25.0%
1 1
12.5%
6 1
12.5%
2 1
12.5%
- 1
12.5%
Hangul
ValueCountFrequency (%)
현 1
14.3%
석 1
14.3%
동 1
14.3%
토 1
14.3%
정 1
14.3%
로 1
14.3%
길 1
14.3%

학교명
Text

Distinct577
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2023-12-11T14:04:40.815280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length6
Mean length5.9369818
Min length3

Characters and Unicode

Total characters3580
Distinct characters200
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

Unique551 ?
Unique (%)91.4%

Sample

1st row서라벌고
2nd row군자초등학교
3rd row염광여고
4th row탑산초
5th row신정초
ValueCountFrequency (%)
수락초등학교 2
 
0.3%
양진초등학교 2
 
0.3%
당산중학교 2
 
0.3%
신도봉중학교 2
 
0.3%
서울사대부속초등학교 2
 
0.3%
성원중학교 2
 
0.3%
동구로초등학교 2
 
0.3%
대모초등학교 2
 
0.3%
갈현초등학교 2
 
0.3%
신봉초등학교 2
 
0.3%
Other values (571) 587
96.7%
2023-12-11T14:04:41.530508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 572
16.0%
교 566
15.8%
등 410
 
11.5%
초 336
 
9.4%
중 203
 
5.7%
고 99
 
2.8%
신 59
 
1.6%
동 47
 
1.3%
여 45
 
1.3%
서 39
 
1.1%
Other values (190) 1204
33.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3565
99.6%
Space Separator 5
 
0.1%
Close Punctuation 4
 
0.1%
Open Punctuation 4
 
0.1%
Decimal Number 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 572
16.0%
교 566
15.9%
등 410
 
11.5%
초 336
 
9.4%
중 203
 
5.7%
고 99
 
2.8%
신 59
 
1.7%
동 47
 
1.3%
여 45
 
1.3%
서 39
 
1.1%
Other values (185) 1189
33.4%
Space Separator
ValueCountFrequency (%)
5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3565
99.6%
Common 15
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 572
16.0%
교 566
15.9%
등 410
 
11.5%
초 336
 
9.4%
중 203
 
5.7%
고 99
 
2.8%
신 59
 
1.7%
동 47
 
1.3%
여 45
 
1.3%
서 39
 
1.1%
Other values (185) 1189
33.4%
Common
ValueCountFrequency (%)
5
33.3%
) 4
26.7%
( 4
26.7%
2 1
 
6.7%
. 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3565
99.6%
ASCII 15
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 572
16.0%
교 566
15.9%
등 410
 
11.5%
초 336
 
9.4%
중 203
 
5.7%
고 99
 
2.8%
신 59
 
1.7%
동 47
 
1.3%
여 45
 
1.3%
서 39
 
1.1%
Other values (185) 1189
33.4%
ASCII
ValueCountFrequency (%)
5
33.3%
) 4
26.7%
( 4
26.7%
2 1
 
6.7%
. 1
 
6.7%

조성년도
Real number (ℝ)

Distinct7
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2004.2803
Minimum2001
Maximum2007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-11T14:04:41.757670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2001
Q12003
median2005
Q32006
95-th percentile2007
Maximum2007
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8922974
Coefficient of variation (CV)0.00094412812
Kurtosis-1.1402046
Mean2004.2803
Median Absolute Deviation (MAD)2
Skewness-0.19055718
Sum1208581
Variance3.5807893
MonotonicityDecreasing
2023-12-11T14:04:41.952953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2005 118
19.6%
2006 102
16.9%
2002 87
14.4%
2007 86
14.3%
2004 79
13.1%
2003 78
12.9%
2001 53
8.8%
ValueCountFrequency (%)
2001 53
8.8%
2002 87
14.4%
2003 78
12.9%
2004 79
13.1%
2005 118
19.6%
2006 102
16.9%
2007 86
14.3%
ValueCountFrequency (%)
2007 86
14.3%
2006 102
16.9%
2005 118
19.6%
2004 79
13.1%
2003 78
12.9%
2002 87
14.4%
2001 53
8.8%

조성면적
Real number (ℝ)

Distinct213
Distinct (%)35.6%
Missing4
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean1114.7796
Minimum0
Maximum7246
Zeros2
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-11T14:04:42.202270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile253.6
Q1620
median1000
Q31338.5
95-th percentile2500
Maximum7246
Range7246
Interquartile range (IQR)718.5

Descriptive statistics

Standard deviation802.58735
Coefficient of variation (CV)0.71995157
Kurtosis16.006052
Mean1114.7796
Median Absolute Deviation (MAD)363
Skewness2.98535
Sum667753
Variance644146.45
MonotonicityNot monotonic
2023-12-11T14:04:42.951047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 43
 
7.1%
1200 42
 
7.0%
1500 31
 
5.1%
500 28
 
4.6%
800 22
 
3.6%
1100 17
 
2.8%
2000 16
 
2.7%
600 16
 
2.7%
900 15
 
2.5%
700 14
 
2.3%
Other values (203) 355
58.9%
ValueCountFrequency (%)
0 2
 
0.3%
26 1
 
0.2%
49 1
 
0.2%
73 1
 
0.2%
75 1
 
0.2%
87 1
 
0.2%
100 6
1.0%
107 1
 
0.2%
120 1
 
0.2%
140 1
 
0.2%
ValueCountFrequency (%)
7246 1
0.2%
7000 1
0.2%
6320 1
0.2%
6265 1
0.2%
4500 1
0.2%
3700 1
0.2%
3480 1
0.2%
3448 1
0.2%
3324 1
0.2%
3300 2
0.3%

교목수
Real number (ℝ)

MISSING  ZEROS 

Distinct172
Distinct (%)28.9%
Missing7
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean68.09396
Minimum0
Maximum378
Zeros17
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-11T14:04:43.218160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q132
median56
Q391
95-th percentile155.5
Maximum378
Range378
Interquartile range (IQR)59

Descriptive statistics

Standard deviation53.17649
Coefficient of variation (CV)0.78092815
Kurtosis5.895616
Mean68.09396
Median Absolute Deviation (MAD)29
Skewness1.8582626
Sum40584
Variance2827.7391
MonotonicityNot monotonic
2023-12-11T14:04:43.483727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
2.8%
48 11
 
1.8%
38 11
 
1.8%
41 11
 
1.8%
24 9
 
1.5%
21 9
 
1.5%
28 9
 
1.5%
52 8
 
1.3%
57 8
 
1.3%
39 8
 
1.3%
Other values (162) 495
82.1%
ValueCountFrequency (%)
0 17
2.8%
2 1
 
0.2%
3 2
 
0.3%
4 3
 
0.5%
5 3
 
0.5%
6 5
 
0.8%
7 5
 
0.8%
8 6
 
1.0%
9 4
 
0.7%
10 3
 
0.5%
ValueCountFrequency (%)
378 1
0.2%
373 1
0.2%
306 2
0.3%
303 1
0.2%
292 1
0.2%
271 1
0.2%
262 1
0.2%
246 1
0.2%
240 1
0.2%
228 1
0.2%

관목수
Real number (ℝ)

ZEROS 

Distinct529
Distinct (%)88.6%
Missing6
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean3491.5042
Minimum0
Maximum27540
Zeros8
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-11T14:04:43.756792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile590
Q11729
median3073
Q34750
95-th percentile8201.2
Maximum27540
Range27540
Interquartile range (IQR)3021

Descriptive statistics

Standard deviation2523.8478
Coefficient of variation (CV)0.72285401
Kurtosis14.149213
Mean3491.5042
Median Absolute Deviation (MAD)1478
Skewness2.2854176
Sum2084428
Variance6369807.7
MonotonicityNot monotonic
2023-12-11T14:04:44.038633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
1.3%
1800 4
 
0.7%
4190 3
 
0.5%
2950 3
 
0.5%
1580 3
 
0.5%
3430 3
 
0.5%
2570 3
 
0.5%
1442 3
 
0.5%
1894 2
 
0.3%
3590 2
 
0.3%
Other values (519) 563
93.4%
(Missing) 6
 
1.0%
ValueCountFrequency (%)
0 8
1.3%
30 1
 
0.2%
80 1
 
0.2%
141 1
 
0.2%
150 2
 
0.3%
210 1
 
0.2%
244 1
 
0.2%
295 1
 
0.2%
310 1
 
0.2%
313 1
 
0.2%
ValueCountFrequency (%)
27540 1
0.2%
13621 1
0.2%
13235 1
0.2%
11800 1
0.2%
11470 1
0.2%
10864 1
0.2%
10778 1
0.2%
10639 1
0.2%
10612 1
0.2%
9785 1
0.2%

초화류수
Real number (ℝ)

MISSING  ZEROS 

Distinct396
Distinct (%)66.9%
Missing11
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean3583.3176
Minimum0
Maximum40034
Zeros74
Zeros (%)12.3%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-11T14:04:44.308611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1845
median2400
Q35019
95-th percentile10822.5
Maximum40034
Range40034
Interquartile range (IQR)4174

Descriptive statistics

Standard deviation4127.3727
Coefficient of variation (CV)1.15183
Kurtosis15.529266
Mean3583.3176
Median Absolute Deviation (MAD)1800
Skewness2.9659228
Sum2121324
Variance17035205
MonotonicityNot monotonic
2023-12-11T14:04:44.574942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 74
 
12.3%
1500 9
 
1.5%
1000 5
 
0.8%
800 5
 
0.8%
600 5
 
0.8%
3250 5
 
0.8%
750 5
 
0.8%
1400 5
 
0.8%
300 4
 
0.7%
2000 4
 
0.7%
Other values (386) 471
78.1%
(Missing) 11
 
1.8%
ValueCountFrequency (%)
0 74
12.3%
35 1
 
0.2%
75 1
 
0.2%
76 1
 
0.2%
89 1
 
0.2%
100 1
 
0.2%
120 2
 
0.3%
180 1
 
0.2%
190 1
 
0.2%
200 2
 
0.3%
ValueCountFrequency (%)
40034 1
0.2%
27905 1
0.2%
26434 1
0.2%
25540 1
0.2%
22860 1
0.2%
17490 1
0.2%
17200 1
0.2%
17000 2
0.3%
16150 1
0.2%
15700 1
0.2%

생성일
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing602
Missing (%)99.8%
Memory size4.8 KiB
Minimum2012-02-02 00:00:00
Maximum2012-02-02 00:00:00
2023-12-11T14:04:44.774028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:44.971801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

사진파일명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing603
Missing (%)100.0%
Memory size5.4 KiB

Y 좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct561
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean450439.47
Minimum438895.78
Maximum465230.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-11T14:04:45.182455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum438895.78
5-th percentile442341.17
Q1445306.59
median449786.83
Q3454510.79
95-th percentile461303.37
Maximum465230.76
Range26334.978
Interquartile range (IQR)9204.2015

Descriptive statistics

Standard deviation5973.3775
Coefficient of variation (CV)0.013261221
Kurtosis-0.71615294
Mean450439.47
Median Absolute Deviation (MAD)4633.926
Skewness0.39519235
Sum2.71615 × 108
Variance35681239
MonotonicityNot monotonic
2023-12-11T14:04:45.445643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
451901.932 2
 
0.3%
464928.7 2
 
0.3%
461408.363 2
 
0.3%
444576.76 2
 
0.3%
444695.256 2
 
0.3%
448858.319 2
 
0.3%
443083.87 2
 
0.3%
443942.867 2
 
0.3%
461483.339 2
 
0.3%
453190.139 2
 
0.3%
Other values (551) 583
96.7%
ValueCountFrequency (%)
438895.783 1
0.2%
439491.114 1
0.2%
439870.39 1
0.2%
439983.294 1
0.2%
440306.811 1
0.2%
440381.665 1
0.2%
440487.665 1
0.2%
440842.561 1
0.2%
440866.439 1
0.2%
440937.23 1
0.2%
ValueCountFrequency (%)
465230.761 1
0.2%
464928.7 2
0.3%
463707.736 1
0.2%
463705.437 1
0.2%
463556.736 1
0.2%
463498.636 1
0.2%
463412.971 1
0.2%
463108.148 1
0.2%
463086.141 1
0.2%
462931.23 1
0.2%

X 좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct561
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199475.65
Minimum182952.77
Maximum213548.57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-11T14:04:45.659598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum182952.77
5-th percentile186058.46
Q1192893.01
median200989.3
Q3205718.01
95-th percentile211409.72
Maximum213548.57
Range30595.795
Interquartile range (IQR)12824.998

Descriptive statistics

Standard deviation7877.7803
Coefficient of variation (CV)0.039492441
Kurtosis-1.0008295
Mean199475.65
Median Absolute Deviation (MAD)6118.017
Skewness-0.24125415
Sum1.2028381 × 108
Variance62059422
MonotonicityNot monotonic
2023-12-11T14:04:45.894711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192173.426 2
 
0.3%
204812.823 2
 
0.3%
204938.214 2
 
0.3%
193431.178 2
 
0.3%
204207.68 2
 
0.3%
204610.95 2
 
0.3%
191411.143 2
 
0.3%
190504.811 2
 
0.3%
202807.399 2
 
0.3%
200324.681 2
 
0.3%
Other values (551) 583
96.7%
ValueCountFrequency (%)
182952.771 1
0.2%
183076.351 1
0.2%
183115.015 1
0.2%
183249.428 1
0.2%
183285.191 1
0.2%
183621.909 1
0.2%
183691.331 1
0.2%
183879.419 1
0.2%
184009.347 1
0.2%
184193.132 1
0.2%
ValueCountFrequency (%)
213548.566 1
0.2%
213542.797 1
0.2%
213472.142 1
0.2%
213341.59 1
0.2%
213336.744 1
0.2%
213295.979 1
0.2%
213215.752 1
0.2%
213073.267 1
0.2%
212924.844 1
0.2%
212905.255 1
0.2%

Interactions

2023-12-11T14:04:33.463110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:20.173694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:21.553338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:22.876849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:24.392781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:25.879351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:27.711533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:29.439352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:31.664124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:33.627612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:20.323233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:21.686549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:23.048772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:24.568228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:26.050700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:27.935347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:29.626539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:31.850306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:33.795998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:20.451722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:21.820187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:23.216179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:24.706451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:26.229484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:28.121427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:29.818007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:32.033347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:33.957412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:20.610964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:21.969512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:23.373193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:24.869226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:26.434360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:28.337253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:30.006005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:32.229083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:34.145908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:20.785377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:22.134002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:23.543470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:25.045041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:26.617088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:28.527210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:30.691899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:32.432112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:34.324258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:20.943012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:22.273838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:23.745318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:25.228309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:26.787925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:28.713155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:30.884360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:32.652399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:34.494104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:21.080159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:22.418723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:23.922399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:25.374141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:26.944283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:28.888889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:31.055225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:32.835083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:34.703688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:21.254976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:22.573724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:24.090631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:25.537355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:27.181309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:29.101650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:31.272070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:33.085608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:34.887455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:21.414622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:22.725084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:24.243104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:25.690561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:27.468668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:29.265992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:31.464686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:04:33.286606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T14:04:46.091387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호구명산지여부주지번부지번조성년도조성면적교목수관목수초화류수Y 좌표X 좌표
고유번호1.0000.8520.0000.2610.0000.0710.0970.1230.1140.0870.6780.753
구명0.8521.0000.0680.5630.6770.0000.3950.2130.1230.2490.9170.939
산지여부0.0000.0681.0000.0000.6330.0120.0000.0000.0000.0000.0000.000
주지번0.2610.5630.0001.0000.0000.2030.0000.0000.2100.0000.3370.262
부지번0.0000.6770.6330.0001.0000.1200.4780.4950.0370.0000.0000.219
조성년도0.0710.0000.0120.2030.1201.0000.0000.0790.4120.0720.0000.000
조성면적0.0970.3950.0000.0000.4780.0001.0000.5760.3540.2430.1050.131
교목수0.1230.2130.0000.0000.4950.0790.5761.0000.4670.0000.0000.194
관목수0.1140.1230.0000.2100.0370.4120.3540.4671.0000.1260.0300.074
초화류수0.0870.2490.0000.0000.0000.0720.2430.0000.1261.0000.1350.000
Y 좌표0.6780.9170.0000.3370.0000.0000.1050.0000.0300.1351.0000.597
X 좌표0.7530.9390.0000.2620.2190.0000.1310.1940.0740.0000.5971.000
2023-12-11T14:04:46.306342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구명산지여부
구명1.0000.057
산지여부0.0571.000
2023-12-11T14:04:46.499321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호주지번조성년도조성면적교목수관목수초화류수Y 좌표X 좌표구명산지여부
고유번호1.0000.098-0.090-0.0390.010-0.107-0.010-0.289-0.3750.4970.000
주지번0.0981.000-0.0030.011-0.0270.0100.009-0.083-0.0950.2870.000
조성년도-0.090-0.0031.0000.079-0.0540.3530.2090.0200.0400.0000.000
조성면적-0.0390.0110.0791.0000.3890.3710.159-0.0280.0040.1600.000
교목수0.010-0.027-0.0540.3891.0000.3070.1000.0130.0010.0750.000
관목수-0.1070.0100.3530.3710.3071.0000.200-0.0200.1250.0540.000
초화류수-0.0100.0090.2090.1590.1000.2001.0000.029-0.0130.0990.000
Y 좌표-0.289-0.0830.020-0.0280.013-0.0200.0291.0000.1930.6290.000
X 좌표-0.375-0.0950.0400.0040.0010.125-0.0130.1931.0000.6850.000
구명0.4970.2870.0000.1600.0750.0540.0990.6290.6851.0000.057
산지여부0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0571.000

Missing values

2023-12-11T14:04:35.167123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T14:04:35.564479image/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.
2023-12-11T14:04:35.845474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

고유번호구명법정동명산지여부주지번부지번새주소명학교명조성년도조성면적교목수관목수초화류수생성일사진파일명Y 좌표X 좌표
0303노원구중계동1313<NA>서라벌고20071500953210350<NA><NA>460613.741206670.617
1185동대문구장안동12392<NA>군자초등학교2007130010458506980<NA><NA>451632.578205419.138
2206노원구월계동1820<NA>염광여고2007300030627540950<NA><NA>459022.204204458.413
3379강서구가양동11473<NA>탑산초2007300<NA><NA><NA><NA><NA>451925.832186958.768
4380강서구화곡동186942<NA>신정초20077000<NA><NA><NA><NA><NA>448149.416186968.241
553중랑구망우동1216<NA>동원초등학교20071200544615400<NA><NA>456152.658209267.485
636동대문구답십리동1274<NA>답십리초등학교20076302952809610<NA><NA>452148.793204918.041
724광진구자양동16741<NA>광양고등학교200780043586012436<NA><NA>447764.612207624.101
822광진구중곡동119179<NA>중마초등학교2007850123596910403<NA><NA>451856.851207111.28
9189동작구흑석동171<NA>흑석초등학교20077004532604620<NA><NA>445459.778196919.87
고유번호구명법정동명산지여부주지번부지번새주소명학교명조성년도조성면적교목수관목수초화류수생성일사진파일명Y 좌표X 좌표
593283노원구하계동1152<NA>연촌초등학교20014763011205120<NA><NA>459405.895206444.078
594546강남구청담동1142<NA>청담초등학교20018002418963830<NA><NA>447611.205203982.583
595308은평구불광동1731<NA>연신초등학교200118001572620900<NA><NA>458527.145193678.682
596213양천구목동1909<NA>월촌중학교200110001432532250<NA><NA>448668.359189662.971
59725동대문구전농동1581<NA>전농중학교2001200013226760<NA><NA>453530.991205273.451
598109강동구암사동11861<NA>선사초등학교2001220020647500<NA><NA>450730.826211378.84
599431용산구효창동11264<NA>금양초등학교20019352411970<NA><NA>449017.278196570.706
600448마포구합정동13651<NA>성산중학교20011209602520570<NA><NA>449747.328192564.062
601554동작구상도동15011<NA>강남초등학교2001100015410581595<NA><NA>443986.276196048.281
60290송파구방이동1167<NA>방이초등학교20011120111510510<NA><NA>445998.738210695.242