Overview

Dataset statistics

Number of variables18
Number of observations10000
Missing cells24710
Missing cells (%)13.7%
Duplicate rows1082
Duplicate rows (%)10.8%
Total size in memory1.5 MiB
Average record size in memory154.0 B

Variable types

Categorical12
Text4
Numeric2

Dataset

Description부산광역시 도시공간정보시스템 내 도로 안내 시설 중 가로수에 대한 현황입니다.(도로 안내 시설이란 도로표지판, 안내표지판, 노선현황 등 입니다)
URLhttps://www.data.go.kr/data/15080570/fileData.do

Alerts

지형지물명 has constant value ""Constant
Dataset has 1082 (10.8%) duplicate rowsDuplicates
등록구 is highly overall correlated with 관리기관 and 4 other fieldsHigh correlation
관리기관 is highly overall correlated with 규격(흉고) and 9 other fieldsHigh correlation
규격(수고) is highly overall correlated with 규격(흉고)High correlation
규격(흉고) is highly overall correlated with 규격(수고) and 1 other fieldsHigh correlation
지주목유무 is highly overall correlated with 등록구 and 3 other fieldsHigh correlation
보호덥개유무 is highly overall correlated with 관리기관 and 2 other fieldsHigh correlation
보호대유무 is highly overall correlated with 관리기관High correlation
생육상태 is highly overall correlated with 관리기관 and 3 other fieldsHigh correlation
병충해 is highly overall correlated with 등록구 and 4 other fieldsHigh correlation
식수대종류 is highly overall correlated with 관리기관 and 2 other fieldsHigh correlation
기타수종 is highly overall correlated with 등록구 and 5 other fieldsHigh correlation
보호틀유무 is highly overall correlated with 등록구 and 1 other fieldsHigh correlation
관리기관 is highly imbalanced (69.6%)Imbalance
생육상태 is highly imbalanced (53.6%)Imbalance
병충해 is highly imbalanced (56.9%)Imbalance
기타수종 is highly imbalanced (74.7%)Imbalance
보호틀유무 is highly imbalanced (61.8%)Imbalance
도로구간명 has 2710 (27.1%) missing valuesMissing
행정동 has 1874 (18.7%) missing valuesMissing
식재위치 has 3702 (37.0%) missing valuesMissing
규격(수고) has 4431 (44.3%) missing valuesMissing
규격(흉고) has 4231 (42.3%) missing valuesMissing
특기사항 has 7762 (77.6%) missing valuesMissing
규격(수고) has 399 (4.0%) zerosZeros
규격(흉고) has 788 (7.9%) zerosZeros

Reproduction

Analysis started2023-12-12 14:44:09.377047
Analysis finished2023-12-12 14:44:13.315934
Duration3.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지형지물명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
가로수
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가로수 10000
100.0%

Length

2023-12-12T23:44:13.367335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:44:13.446945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가로수 10000
100.0%

등록구
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
강서구
3185 
사하구
798 
부산진구
797 
북구
758 
사상구
705 
Other values (11)
3757 

Length

Max length4
Median length3
Mean length2.9201
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row북구
2nd row강서구
3rd row연제구
4th row동구
5th row사하구

Common Values

ValueCountFrequency (%)
강서구 3185
31.9%
사하구 798
 
8.0%
부산진구 797
 
8.0%
북구 758
 
7.6%
사상구 705
 
7.0%
금정구 607
 
6.1%
연제구 516
 
5.2%
남구 508
 
5.1%
동래구 493
 
4.9%
해운대구 318
 
3.2%
Other values (6) 1315
13.2%

Length

2023-12-12T23:44:13.532302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강서구 3185
31.9%
사하구 798
 
8.0%
부산진구 797
 
8.0%
북구 758
 
7.6%
사상구 705
 
7.0%
금정구 607
 
6.1%
연제구 516
 
5.2%
남구 508
 
5.1%
동래구 493
 
4.9%
해운대구 318
 
3.2%
Other values (6) 1315
13.2%

도로구간명
Text

MISSING 

Distinct373
Distinct (%)5.1%
Missing2710
Missing (%)27.1%
Memory size156.2 KiB
2023-12-12T23:44:13.761834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length6.037037
Min length3

Characters and Unicode

Total characters44010
Distinct characters248
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

Unique61 ?
Unique (%)0.8%

Sample

1st row도서관길
2nd row다대산업로
3rd row대영로, 낙동로,금곡로
4th row하신번영로
5th row사상로
ValueCountFrequency (%)
외부순환도로 343
 
3.9%
낙동로,금곡로 331
 
3.8%
대영로 331
 
3.8%
공항로 299
 
3.4%
구덕로 292
 
3.3%
중앙로,금정로 256
 
2.9%
내부순환도로 229
 
2.6%
낙동남로 221
 
2.5%
순환로 135
 
1.5%
백양로 131
 
1.5%
Other values (379) 6216
70.8%
2023-12-12T23:44:14.142790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 7224
 
16.4%
산 2668
 
6.1%
길 2442
 
5.5%
, 2403
 
5.5%
1494
 
3.4%
업 1326
 
3.0%
녹 1108
 
2.5%
대 1064
 
2.4%
동 919
 
2.1%
도 832
 
1.9%
Other values (238) 22530
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 37497
85.2%
Decimal Number 2419
 
5.5%
Other Punctuation 2403
 
5.5%
Space Separator 1494
 
3.4%
Connector Punctuation 81
 
0.2%
Open Punctuation 58
 
0.1%
Close Punctuation 58
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 7224
 
19.3%
산 2668
 
7.1%
길 2442
 
6.5%
업 1326
 
3.5%
녹 1108
 
3.0%
대 1064
 
2.8%
동 919
 
2.5%
도 832
 
2.2%
금 788
 
2.1%
순 735
 
2.0%
Other values (223) 18391
49.0%
Decimal Number
ValueCountFrequency (%)
1 703
29.1%
2 545
22.5%
5 224
 
9.3%
6 206
 
8.5%
4 189
 
7.8%
3 162
 
6.7%
7 142
 
5.9%
8 134
 
5.5%
9 81
 
3.3%
0 33
 
1.4%
Other Punctuation
ValueCountFrequency (%)
, 2403
100.0%
Space Separator
ValueCountFrequency (%)
1494
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 81
100.0%
Open Punctuation
ValueCountFrequency (%)
( 58
100.0%
Close Punctuation
ValueCountFrequency (%)
) 58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 37497
85.2%
Common 6513
 
14.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 7224
 
19.3%
산 2668
 
7.1%
길 2442
 
6.5%
업 1326
 
3.5%
녹 1108
 
3.0%
대 1064
 
2.8%
동 919
 
2.5%
도 832
 
2.2%
금 788
 
2.1%
순 735
 
2.0%
Other values (223) 18391
49.0%
Common
ValueCountFrequency (%)
, 2403
36.9%
1494
22.9%
1 703
 
10.8%
2 545
 
8.4%
5 224
 
3.4%
6 206
 
3.2%
4 189
 
2.9%
3 162
 
2.5%
7 142
 
2.2%
8 134
 
2.1%
Other values (5) 311
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 37497
85.2%
ASCII 6513
 
14.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 7224
 
19.3%
산 2668
 
7.1%
길 2442
 
6.5%
업 1326
 
3.5%
녹 1108
 
3.0%
대 1064
 
2.8%
동 919
 
2.5%
도 832
 
2.2%
금 788
 
2.1%
순 735
 
2.0%
Other values (223) 18391
49.0%
ASCII
ValueCountFrequency (%)
, 2403
36.9%
1494
22.9%
1 703
 
10.8%
2 545
 
8.4%
5 224
 
3.4%
6 206
 
3.2%
4 189
 
2.9%
3 162
 
2.5%
7 142
 
2.2%
8 134
 
2.1%
Other values (5) 311
 
4.8%

관리기관
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7940 
강서구 산업단지관리사업소
 
363
금정구 지역경제과
 
320
동래구 복지환경국 경제녹지과
 
273
수영구 주민생활지원국 지역경제과
 
251
Other values (22)
853 

Length

Max length17
Median length4
Mean length5.7566
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 7940
79.4%
강서구 산업단지관리사업소 363
 
3.6%
금정구 지역경제과 320
 
3.2%
동래구 복지환경국 경제녹지과 273
 
2.7%
수영구 주민생활지원국 지역경제과 251
 
2.5%
해운대구 관광경제국 늘푸른과 207
 
2.1%
사상구 주민생활지원국 지역경제과 191
 
1.9%
북구 안전도시국 청정녹지과 137
 
1.4%
강서구 68
 
0.7%
기장군 62
 
0.6%
Other values (17) 188
 
1.9%

Length

2023-12-12T23:44:14.296206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 7940
61.4%
지역경제과 762
 
5.9%
주민생활지원국 443
 
3.4%
강서구 436
 
3.4%
산업단지관리사업소 363
 
2.8%
금정구 339
 
2.6%
동래구 279
 
2.2%
복지환경국 273
 
2.1%
경제녹지과 273
 
2.1%
수영구 258
 
2.0%
Other values (20) 1557
 
12.0%

행정동
Text

MISSING 

Distinct188
Distinct (%)2.3%
Missing1874
Missing (%)18.7%
Memory size156.2 KiB
2023-12-12T23:44:14.638963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.5794979
Min length3

Characters and Unicode

Total characters29087
Distinct characters105
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

Unique3 ?
Unique (%)< 0.1%

Sample

1st row만덕2동
2nd row대저2동
3rd row초량6동
4th row장림2동
5th row감전동
ValueCountFrequency (%)
녹산동 1908
23.5%
명지동 424
 
5.2%
대저2동 385
 
4.7%
대저1동 310
 
3.8%
금곡동 182
 
2.2%
다대1동 162
 
2.0%
화명3동 120
 
1.5%
신평2동 111
 
1.4%
대연3동 108
 
1.3%
다대2동 104
 
1.3%
Other values (178) 4312
53.1%
2023-12-12T23:44:15.146174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 8193
28.2%
산 2062
 
7.1%
녹 1908
 
6.6%
2 1811
 
6.2%
1 1776
 
6.1%
대 1221
 
4.2%
3 768
 
2.6%
명 706
 
2.4%
저 695
 
2.4%
지 440
 
1.5%
Other values (95) 9507
32.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24476
84.1%
Decimal Number 4611
 
15.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 8193
33.5%
산 2062
 
8.4%
녹 1908
 
7.8%
대 1221
 
5.0%
명 706
 
2.9%
저 695
 
2.8%
지 440
 
1.8%
천 396
 
1.6%
부 322
 
1.3%
금 298
 
1.2%
Other values (87) 8235
33.6%
Decimal Number
ValueCountFrequency (%)
2 1811
39.3%
1 1776
38.5%
3 768
16.7%
4 183
 
4.0%
6 26
 
0.6%
5 25
 
0.5%
8 13
 
0.3%
9 9
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24476
84.1%
Common 4611
 
15.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 8193
33.5%
산 2062
 
8.4%
녹 1908
 
7.8%
대 1221
 
5.0%
명 706
 
2.9%
저 695
 
2.8%
지 440
 
1.8%
천 396
 
1.6%
부 322
 
1.3%
금 298
 
1.2%
Other values (87) 8235
33.6%
Common
ValueCountFrequency (%)
2 1811
39.3%
1 1776
38.5%
3 768
16.7%
4 183
 
4.0%
6 26
 
0.6%
5 25
 
0.5%
8 13
 
0.3%
9 9
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24476
84.1%
ASCII 4611
 
15.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 8193
33.5%
산 2062
 
8.4%
녹 1908
 
7.8%
대 1221
 
5.0%
명 706
 
2.9%
저 695
 
2.8%
지 440
 
1.8%
천 396
 
1.6%
부 322
 
1.3%
금 298
 
1.2%
Other values (87) 8235
33.6%
ASCII
ValueCountFrequency (%)
2 1811
39.3%
1 1776
38.5%
3 768
16.7%
4 183
 
4.0%
6 26
 
0.6%
5 25
 
0.5%
8 13
 
0.3%
9 9
 
0.2%

식재위치
Text

MISSING 

Distinct2168
Distinct (%)34.4%
Missing3702
Missing (%)37.0%
Memory size156.2 KiB
2023-12-12T23:44:15.411680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length27
Mean length10.935059
Min length1

Characters and Unicode

Total characters68869
Distinct characters541
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1275 ?
Unique (%)20.2%

Sample

1st row예림슈퍼부터
2nd row서부산IC(강서오리마을 입구)~금호마을 입구
3rd row엄궁다리~(주)샤니
4th row신선로(메가마트앞~영남제분)
5th row강서구 송정동 1521
ValueCountFrequency (%)
강서구 2071
 
15.5%
송정동 1325
 
9.9%
부산진구 714
 
5.3%
앞 570
 
4.3%
명지동 287
 
2.1%
신호동 185
 
1.4%
대저1동 161
 
1.2%
부전동 150
 
1.1%
입구 138
 
1.0%
당감동 131
 
1.0%
Other values (2286) 7661
57.2%
2023-12-12T23:44:15.787436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7911
 
11.5%
구 3440
 
5.0%
동 3430
 
5.0%
1 2896
 
4.2%
서 2336
 
3.4%
강 2282
 
3.3%
정 1581
 
2.3%
송 1579
 
2.3%
5 1502
 
2.2%
2 1447
 
2.1%
Other values (531) 40465
58.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 43849
63.7%
Decimal Number 12791
 
18.6%
Space Separator 7911
 
11.5%
Math Symbol 1392
 
2.0%
Open Punctuation 809
 
1.2%
Close Punctuation 809
 
1.2%
Dash Punctuation 716
 
1.0%
Uppercase Letter 489
 
0.7%
Other Punctuation 85
 
0.1%
Lowercase Letter 16
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3440
 
7.8%
동 3430
 
7.8%
서 2336
 
5.3%
강 2282
 
5.2%
정 1581
 
3.6%
송 1579
 
3.6%
부 1328
 
3.0%
산 1128
 
2.6%
대 837
 
1.9%
진 809
 
1.8%
Other values (493) 25099
57.2%
Uppercase Letter
ValueCountFrequency (%)
R 90
18.4%
C 73
14.9%
I 61
12.5%
K 49
10.0%
G 41
8.4%
S 41
8.4%
L 36
 
7.4%
T 27
 
5.5%
P 20
 
4.1%
E 18
 
3.7%
Other values (7) 33
 
6.7%
Decimal Number
ValueCountFrequency (%)
1 2896
22.6%
5 1502
11.7%
2 1447
11.3%
3 1260
9.9%
6 1147
 
9.0%
7 1123
 
8.8%
4 1083
 
8.5%
9 852
 
6.7%
8 757
 
5.9%
0 724
 
5.7%
Lowercase Letter
ValueCountFrequency (%)
k 8
50.0%
s 7
43.8%
t 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 84
98.8%
! 1
 
1.2%
Space Separator
ValueCountFrequency (%)
7911
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1392
100.0%
Open Punctuation
ValueCountFrequency (%)
( 809
100.0%
Close Punctuation
ValueCountFrequency (%)
) 809
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 716
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 43849
63.7%
Common 24515
35.6%
Latin 505
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3440
 
7.8%
동 3430
 
7.8%
서 2336
 
5.3%
강 2282
 
5.2%
정 1581
 
3.6%
송 1579
 
3.6%
부 1328
 
3.0%
산 1128
 
2.6%
대 837
 
1.9%
진 809
 
1.8%
Other values (493) 25099
57.2%
Latin
ValueCountFrequency (%)
R 90
17.8%
C 73
14.5%
I 61
12.1%
K 49
9.7%
G 41
8.1%
S 41
8.1%
L 36
 
7.1%
T 27
 
5.3%
P 20
 
4.0%
E 18
 
3.6%
Other values (10) 49
9.7%
Common
ValueCountFrequency (%)
7911
32.3%
1 2896
 
11.8%
5 1502
 
6.1%
2 1447
 
5.9%
~ 1392
 
5.7%
3 1260
 
5.1%
6 1147
 
4.7%
7 1123
 
4.6%
4 1083
 
4.4%
9 852
 
3.5%
Other values (8) 3902
15.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 43849
63.7%
ASCII 25020
36.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7911
31.6%
1 2896
 
11.6%
5 1502
 
6.0%
2 1447
 
5.8%
~ 1392
 
5.6%
3 1260
 
5.0%
6 1147
 
4.6%
7 1123
 
4.5%
4 1083
 
4.3%
9 852
 
3.4%
Other values (28) 4407
17.6%
Hangul
ValueCountFrequency (%)
구 3440
 
7.8%
동 3430
 
7.8%
서 2336
 
5.3%
강 2282
 
5.2%
정 1581
 
3.6%
송 1579
 
3.6%
부 1328
 
3.0%
산 1128
 
2.6%
대 837
 
1.9%
진 809
 
1.8%
Other values (493) 25099
57.2%

규격(수고)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct60
Distinct (%)1.1%
Missing4431
Missing (%)44.3%
Infinite0
Infinite (%)0.0%
Mean5.1153349
Minimum0
Maximum63.5
Zeros399
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:44:16.238731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median5
Q36
95-th percentile9
Maximum63.5
Range63.5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.6237261
Coefficient of variation (CV)0.51291385
Kurtosis103.12054
Mean5.1153349
Median Absolute Deviation (MAD)1
Skewness5.0714915
Sum28487.3
Variance6.8839386
MonotonicityNot monotonic
2023-12-12T23:44:16.394048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.0 1543
 
15.4%
6.0 729
 
7.3%
5.0 628
 
6.3%
7.0 412
 
4.1%
4.5 401
 
4.0%
0.0 399
 
4.0%
8.0 267
 
2.7%
5.5 223
 
2.2%
3.5 190
 
1.9%
6.5 172
 
1.7%
Other values (50) 605
 
6.0%
(Missing) 4431
44.3%
ValueCountFrequency (%)
0.0 399
4.0%
0.1 3
 
< 0.1%
0.5 1
 
< 0.1%
1.0 1
 
< 0.1%
1.3 2
 
< 0.1%
1.4 1
 
< 0.1%
1.5 1
 
< 0.1%
1.6 1
 
< 0.1%
1.8 2
 
< 0.1%
1.9 1
 
< 0.1%
ValueCountFrequency (%)
63.5 1
 
< 0.1%
58.0 2
 
< 0.1%
15.0 2
 
< 0.1%
14.0 4
 
< 0.1%
13.0 8
 
0.1%
12.0 76
0.8%
11.0 67
0.7%
10.0 90
0.9%
9.5 1
 
< 0.1%
9.0 68
0.7%

규격(흉고)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct105
Distinct (%)1.8%
Missing4231
Missing (%)42.3%
Infinite0
Infinite (%)0.0%
Mean0.22516554
Minimum0
Maximum2.4
Zeros788
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:44:16.654461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.12
median0.16
Q30.25
95-th percentile0.78
Maximum2.4
Range2.4
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.2293089
Coefficient of variation (CV)1.0184014
Kurtosis8.0675792
Mean0.22516554
Median Absolute Deviation (MAD)0.06
Skewness2.4241002
Sum1298.98
Variance0.052582571
MonotonicityNot monotonic
2023-12-12T23:44:16.807139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.12 1063
 
10.6%
0.0 788
 
7.9%
0.1 344
 
3.4%
0.2 283
 
2.8%
0.16 232
 
2.3%
0.15 217
 
2.2%
0.18 215
 
2.1%
0.14 176
 
1.8%
0.24 163
 
1.6%
0.22 155
 
1.6%
Other values (95) 2133
21.3%
(Missing) 4231
42.3%
ValueCountFrequency (%)
0.0 788
7.9%
0.02 1
 
< 0.1%
0.05 9
 
0.1%
0.06 5
 
0.1%
0.07 7
 
0.1%
0.08 38
 
0.4%
0.09 16
 
0.2%
0.1 344
 
3.4%
0.11 55
 
0.5%
0.12 1063
10.6%
ValueCountFrequency (%)
2.4 1
 
< 0.1%
2.2 1
 
< 0.1%
2.0 2
 
< 0.1%
1.6 2
 
< 0.1%
1.5 2
 
< 0.1%
1.4 3
< 0.1%
1.31 1
 
< 0.1%
1.3 3
< 0.1%
1.28 1
 
< 0.1%
1.25 5
0.1%

지주목유무
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
X
4495 
O
3102 
<NA>
2403 

Length

Max length4
Median length1
Mean length1.7209
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowX
2nd rowO
3rd row<NA>
4th rowX
5th row<NA>

Common Values

ValueCountFrequency (%)
X 4495
45.0%
O 3102
31.0%
<NA> 2403
24.0%

Length

2023-12-12T23:44:16.985890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:44:17.109011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
x 4495
45.0%
o 3102
31.0%
na 2403
24.0%

보호덥개유무
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
O
4691 
X
2981 
<NA>
2328 

Length

Max length4
Median length1
Mean length1.6984
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowX
2nd rowX
3rd row<NA>
4th rowX
5th row<NA>

Common Values

ValueCountFrequency (%)
O 4691
46.9%
X 2981
29.8%
<NA> 2328
23.3%

Length

2023-12-12T23:44:17.249074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:44:17.355674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
o 4691
46.9%
x 2981
29.8%
na 2328
23.3%

보호대유무
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
4728 
X
3237 
O
2026 
 
9

Length

Max length4
Median length1
Mean length2.4184
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowX
2nd rowX
3rd row<NA>
4th rowX
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 4728
47.3%
X 3237
32.4%
O 2026
20.3%
9
 
0.1%

Length

2023-12-12T23:44:17.471017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:44:17.582196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 4728
47.3%
x 3237
32.4%
o 2026
20.3%

생육상태
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5006 
양호
2257 
보통
1606 
 
406
좋음
 
189
Other values (17)
536 

Length

Max length48
Median length4
Mean length3.5832
Min length1

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st row양호
2nd row보통
3rd row<NA>
4th row양호
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 5006
50.1%
양호 2257
22.6%
보통 1606
 
16.1%
406
 
4.1%
좋음 189
 
1.9%
X 167
 
1.7%
양호 142
 
1.4%
O 72
 
0.7%
양 69
 
0.7%
불량 46
 
0.5%
Other values (12) 40
 
0.4%

Length

2023-12-12T23:44:17.730808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 5006
52.2%
양호 2403
25.0%
보통 1606
 
16.7%
좋음 189
 
2.0%
x 167
 
1.7%
o 72
 
0.8%
양 69
 
0.7%
불량 46
 
0.5%
0 24
 
0.3%
중 2
 
< 0.1%
Other values (8) 10
 
0.1%

병충해
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
6326 
없음
1850 
양호
693 
 
441
무
 
243
Other values (12)
 
447

Length

Max length50
Median length4
Mean length3.8644
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row양호
2nd row없음
3rd row<NA>
4th row없음
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 6326
63.3%
없음 1850
 
18.5%
양호 693
 
6.9%
441
 
4.4%
무 243
 
2.4%
O 217
 
2.2%
없음 108
 
1.1%
진딧물 37
 
0.4%
djqtdma 32
 
0.3%
X 28
 
0.3%
Other values (7) 25
 
0.2%

Length

2023-12-12T23:44:17.864530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 6326
65.9%
없음 1961
 
20.4%
양호 693
 
7.2%
무 245
 
2.6%
o 217
 
2.3%
진딧물 37
 
0.4%
djqtdma 32
 
0.3%
x 28
 
0.3%
초봄에 14
 
0.1%
진딧물류의 14
 
0.1%
Other values (5) 34
 
0.4%

식수대종류
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
4519 
SSD101
1412 
SSD103
1229 
SSD102
1175 
SSD999
805 
Other values (10)
860 

Length

Max length6
Median length6
Mean length4.9698
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSSD103
2nd rowSSD999
3rd row<NA>
4th rowSSD999
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 4519
45.2%
SSD101 1412
 
14.1%
SSD103 1229
 
12.3%
SSD102 1175
 
11.8%
SSD999 805
 
8.1%
SSD001 184
 
1.8%
SSD002 179
 
1.8%
136
 
1.4%
SSD006 128
 
1.3%
X 116
 
1.2%
Other values (5) 117
 
1.2%

Length

2023-12-12T23:44:18.004355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 4519
45.8%
ssd101 1412
 
14.3%
ssd103 1232
 
12.5%
ssd102 1175
 
11.9%
ssd999 805
 
8.2%
ssd001 184
 
1.9%
ssd002 179
 
1.8%
ssd006 128
 
1.3%
x 116
 
1.2%
ssd003 78
 
0.8%
Other values (3) 36
 
0.4%

수종
Categorical

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
은행나무
2442 
벚나무
2242 
미분류
1904 
기타낙엽
939 
버즘나무
808 
Other values (8)
1665 

Length

Max length6
Median length4
Mean length3.5635
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row은행나무
2nd row벚나무
3rd row미분류
4th row벚나무
5th row미분류

Common Values

ValueCountFrequency (%)
은행나무 2442
24.4%
벚나무 2242
22.4%
미분류 1904
19.0%
기타낙엽 939
 
9.4%
버즘나무 808
 
8.1%
느티나무 596
 
6.0%
후박나무 509
 
5.1%
기타상록 188
 
1.9%
해송 130
 
1.3%
구실잦밤나무 75
 
0.8%
Other values (3) 167
 
1.7%

Length

2023-12-12T23:44:18.161643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
은행나무 2442
24.4%
벚나무 2242
22.4%
미분류 1904
19.0%
기타낙엽 939
 
9.4%
버즘나무 808
 
8.1%
느티나무 596
 
6.0%
후박나무 509
 
5.1%
기타상록 188
 
1.9%
해송 130
 
1.3%
구실잦밤나무 75
 
0.8%
Other values (3) 167
 
1.7%

기타수종
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct40
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7815 
1185 
중국단풍
 
286
팽나무(포구나무)
 
150
회화
 
125
Other values (35)
 
439

Length

Max length17
Median length4
Mean length3.7462
Min length1

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 7815
78.1%
1185
 
11.8%
중국단풍 286
 
2.9%
팽나무(포구나무) 150
 
1.5%
회화 125
 
1.2%
회화나무 69
 
0.7%
벽오동 62
 
0.6%
먼나무 38
 
0.4%
향나무 26
 
0.3%
청오동나무 26
 
0.3%
Other values (30) 218
 
2.2%

Length

2023-12-12T23:44:18.327487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 7815
88.7%
중국단풍 286
 
3.2%
팽나무(포구나무 150
 
1.7%
회화 125
 
1.4%
회화나무 78
 
0.9%
벽오동 62
 
0.7%
먼나무 54
 
0.6%
향나무 26
 
0.3%
청오동나무 26
 
0.3%
소나무 25
 
0.3%
Other values (24) 168
 
1.9%

특기사항
Text

MISSING 

Distinct54
Distinct (%)2.4%
Missing7762
Missing (%)77.6%
Memory size156.2 KiB
2023-12-12T23:44:18.573956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length2
Mean length2.5263628
Min length1

Characters and Unicode

Total characters5654
Distinct characters129
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

Unique27 ?
Unique (%)1.2%

Sample

1st row없음
2nd row없음
3rd row포장
4th row없음
5th row없음
ValueCountFrequency (%)
없음 1333
71.1%
무 214
 
11.4%
수벽내 80
 
4.3%
보호덮개(주철 25
 
1.3%
덮개 25
 
1.3%
보도블럭 19
 
1.0%
보호덮개(칼콘 18
 
1.0%
칼콘 12
 
0.6%
수벽 11
 
0.6%
내 11
 
0.6%
Other values (63) 126
 
6.7%
2023-12-12T23:44:18.929486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
없 1333
23.6%
음 1333
23.6%
1228
21.7%
무 235
 
4.2%
수 114
 
2.0%
내 92
 
1.6%
벽 91
 
1.6%
0 78
 
1.4%
개 72
 
1.3%
덮 70
 
1.2%
Other values (119) 1008
17.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4039
71.4%
Space Separator 1228
 
21.7%
Decimal Number 200
 
3.5%
Other Punctuation 82
 
1.5%
Open Punctuation 49
 
0.9%
Close Punctuation 49
 
0.9%
Math Symbol 6
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
없 1333
33.0%
음 1333
33.0%
무 235
 
5.8%
수 114
 
2.8%
내 92
 
2.3%
벽 91
 
2.3%
개 72
 
1.8%
덮 70
 
1.7%
보 69
 
1.7%
호 43
 
1.1%
Other values (103) 587
14.5%
Decimal Number
ValueCountFrequency (%)
0 78
39.0%
2 41
20.5%
7 24
 
12.0%
1 22
 
11.0%
5 19
 
9.5%
6 7
 
3.5%
8 4
 
2.0%
4 3
 
1.5%
9 2
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 43
52.4%
/ 39
47.6%
Space Separator
ValueCountFrequency (%)
1228
100.0%
Open Punctuation
ValueCountFrequency (%)
( 49
100.0%
Close Punctuation
ValueCountFrequency (%)
) 49
100.0%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4039
71.4%
Common 1615
 
28.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
없 1333
33.0%
음 1333
33.0%
무 235
 
5.8%
수 114
 
2.8%
내 92
 
2.3%
벽 91
 
2.3%
개 72
 
1.8%
덮 70
 
1.7%
보 69
 
1.7%
호 43
 
1.1%
Other values (103) 587
14.5%
Common
ValueCountFrequency (%)
1228
76.0%
0 78
 
4.8%
( 49
 
3.0%
) 49
 
3.0%
. 43
 
2.7%
2 41
 
2.5%
/ 39
 
2.4%
7 24
 
1.5%
1 22
 
1.4%
5 19
 
1.2%
Other values (6) 23
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4039
71.4%
ASCII 1615
 
28.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
없 1333
33.0%
음 1333
33.0%
무 235
 
5.8%
수 114
 
2.8%
내 92
 
2.3%
벽 91
 
2.3%
개 72
 
1.8%
덮 70
 
1.7%
보 69
 
1.7%
호 43
 
1.1%
Other values (103) 587
14.5%
ASCII
ValueCountFrequency (%)
1228
76.0%
0 78
 
4.8%
( 49
 
3.0%
) 49
 
3.0%
. 43
 
2.7%
2 41
 
2.5%
/ 39
 
2.4%
7 24
 
1.5%
1 22
 
1.4%
5 19
 
1.2%
Other values (6) 23
 
1.4%

보호틀유무
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8431 
O
1154 
X
 
404
 
11

Length

Max length4
Median length4
Mean length3.5293
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowO
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 8431
84.3%
O 1154
 
11.5%
X 404
 
4.0%
11
 
0.1%

Length

2023-12-12T23:44:19.077434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:44:19.187797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 8431
84.4%
o 1154
 
11.6%
x 404
 
4.0%

Interactions

2023-12-12T23:44:12.418231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:44:12.196520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:44:12.571296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:44:12.294672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:44:19.278134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록구관리기관규격(수고)규격(흉고)지주목유무보호덥개유무보호대유무생육상태병충해식수대종류수종기타수종특기사항보호틀유무
등록구1.0000.9990.6550.6310.6710.4130.6350.8690.9540.7950.6340.9120.9490.811
관리기관0.9991.0000.7060.8300.8120.6710.9090.8880.9540.8930.7550.9230.9530.876
규격(수고)0.6550.7061.0000.3730.3600.0470.1780.3330.2170.3200.4400.3630.7600.114
규격(흉고)0.6310.8300.3731.0000.2610.0430.4570.4500.6470.5780.2710.7030.7940.236
지주목유무0.6710.8120.3600.2611.0000.2530.1170.6000.7020.4300.4350.5620.5860.203
보호덥개유무0.4130.6710.0470.0430.2531.0000.2160.2980.3970.8670.3200.6350.4870.271
보호대유무0.6350.9090.1780.4570.1170.2161.0000.4240.4990.5890.4110.6000.6370.286
생육상태0.8690.8880.3330.4500.6000.2980.4241.0000.9280.7350.5880.9240.9670.671
병충해0.9540.9540.2170.6470.7020.3970.4990.9281.0000.7470.7040.9020.9790.771
식수대종류0.7950.8930.3200.5780.4300.8670.5890.7350.7471.0000.5910.8650.7310.519
수종0.6340.7550.4400.2710.4350.3200.4110.5880.7040.5911.0000.8320.8980.211
기타수종0.9120.9230.3630.7030.5620.6350.6000.9240.9020.8650.8321.0000.6800.556
특기사항0.9490.9530.7600.7940.5860.4870.6370.9670.9790.7310.8980.6801.0000.885
보호틀유무0.8110.8760.1140.2360.2030.2710.2860.6710.7710.5190.2110.5560.8851.000
2023-12-12T23:44:19.420437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지주목유무보호틀유무보호덥개유무등록구수종식수대종류기타수종생육상태관리기관병충해보호대유무
지주목유무1.0000.3330.1630.5350.3380.3370.4730.5310.6670.5620.193
보호틀유무0.3331.0000.4400.5420.0960.3840.3520.3570.7190.4490.095
보호덥개유무0.1630.4401.0000.3250.2490.7150.5080.2340.5380.3620.354
등록구0.5350.5420.3251.0000.2820.4210.5330.4880.9890.5670.440
수종0.3380.0960.2490.2821.0000.2690.4360.2440.3640.3360.204
식수대종류0.3370.3840.7150.4210.2691.0000.5220.3500.5810.4100.408
기타수종0.4730.3520.5080.5330.4360.5221.0000.6430.5550.6010.375
생육상태0.5310.3570.2340.4880.2440.3500.6431.0000.5180.6820.256
관리기관0.6670.7190.5380.9890.3640.5810.5550.5181.0000.7720.691
병충해0.5620.4490.3620.5670.3360.4100.6010.6820.7721.0000.321
보호대유무0.1930.0950.3540.4400.2040.4080.3750.2560.6910.3211.000
2023-12-12T23:44:19.577150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
규격(수고)규격(흉고)등록구관리기관지주목유무보호덥개유무보호대유무생육상태병충해식수대종류수종기타수종보호틀유무
규격(수고)1.0000.6110.3620.4850.2400.0310.1680.1850.1250.1860.2160.1780.107
규격(흉고)0.6111.0000.3150.5490.2460.0440.2290.1730.3230.2930.1190.3360.104
등록구0.3620.3151.0000.9890.5350.3250.4400.4880.5670.4210.2820.5330.542
관리기관0.4850.5490.9891.0000.6670.5380.6910.5180.7720.5810.3640.5550.719
지주목유무0.2400.2460.5350.6671.0000.1630.1930.5310.5620.3370.3380.4730.333
보호덥개유무0.0310.0440.3250.5380.1631.0000.3540.2340.3620.7150.2490.5080.440
보호대유무0.1680.2290.4400.6910.1930.3541.0000.2560.3210.4080.2040.3750.095
생육상태0.1850.1730.4880.5180.5310.2340.2561.0000.6820.3500.2440.6430.357
병충해0.1250.3230.5670.7720.5620.3620.3210.6821.0000.4100.3360.6010.449
식수대종류0.1860.2930.4210.5810.3370.7150.4080.3500.4101.0000.2690.5220.384
수종0.2160.1190.2820.3640.3380.2490.2040.2440.3360.2691.0000.4360.096
기타수종0.1780.3360.5330.5550.4730.5080.3750.6430.6010.5220.4361.0000.352
보호틀유무0.1070.1040.5420.7190.3330.4400.0950.3570.4490.3840.0960.3521.000

Missing values

2023-12-12T23:44:12.703742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:44:12.911503image/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.
2023-12-12T23:44:13.125744image/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

지형지물명등록구도로구간명관리기관행정동식재위치규격(수고)규격(흉고)지주목유무보호덥개유무보호대유무생육상태병충해식수대종류수종기타수종특기사항보호틀유무
86987가로수북구도서관길<NA>만덕2동예림슈퍼부터6.00.47XXX양호양호SSD103은행나무<NA><NA>O
11593가로수강서구<NA><NA>대저2동서부산IC(강서오리마을 입구)~금호마을 입구4.00.12OXX보통없음SSD999벚나무<NA>없음<NA>
26549가로수연제구<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>
40470가로수동구<NA><NA>초량6동<NA>7.00.12XXX양호없음SSD999벚나무<NA>없음<NA>
32581가로수사하구다대산업로<NA>장림2동<NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>
43317가로수사상구대영로, 낙동로,금곡로사상구 주민생활지원국 지역경제과감전동엄궁다리~(주)샤니5.50.16XXX양호<NA>SSD103기타낙엽메타세콰이어<NA><NA>
42633가로수사하구하신번영로<NA>신평2동<NA><NA>0.15XXX<NA><NA><NA>벚나무<NA>X
59134가로수사상구사상로<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>
61218가로수사하구다대로,_강변대로<NA>당리동<NA><NA>0.15XOX<NA><NA><NA>벚나무<NA>O
25617가로수남구내부순환도로<NA>대연3동신선로(메가마트앞~영남제분)5.50.13OXO보통<NA>SSD999벚나무<NA><NA><NA>
지형지물명등록구도로구간명관리기관행정동식재위치규격(수고)규격(흉고)지주목유무보호덥개유무보호대유무생육상태병충해식수대종류수종기타수종특기사항보호틀유무
72600가로수부산진구<NA><NA>연지동부산진구 연지동 687.00.69OOO양호<NA>SSD001은행나무<NA><NA><NA>
40024가로수동래구만덕로, 충렬로,해운로<NA>온천3동제일화재보험5.70.12XOO<NA><NA>SSD102은행나무<NA><NA><NA>
55050가로수금정구구덕로, 중앙로,금정로금정구 지역경제과장전2동<NA>4.00.28XXX양호없음SSD102벚나무<NA><NA><NA>
2399가로수강서구녹산산업13길<NA>녹산동강서구 송정동 1595<NA><NA>XO<NA><NA><NA><NA>버즘나무<NA><NA><NA>
61195가로수연제구<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>
17746가로수강서구공항로<NA>대저2동염막길(태영기계,한국음향)~염막1구입구4.00.12XOO보통없음SSD999벚나무<NA>없음<NA>
47664가로수연제구<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>
56618가로수사하구공단로<NA>신평1동<NA><NA>0.21XXX<NA><NA><NA>버즘나무<NA>O
30954가로수금정구<NA>금정구 지역경제과구서2동<NA>3.00.14XOX양없음SSD102벚나무<NA><NA><NA>
7784가로수강서구낙동북로<NA>대저1동강서구 대저1동 117975<NA><NA>XO<NA><NA><NA><NA>은행나무<NA><NA><NA>

Duplicate rows

Most frequently occurring

지형지물명등록구도로구간명관리기관행정동식재위치규격(수고)규격(흉고)지주목유무보호덥개유무보호대유무생육상태병충해식수대종류수종기타수종특기사항보호틀유무# duplicates
1000가로수연제구<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>358
1052가로수중구<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>147
675가로수동래구<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>109
512가로수금정구금정도서관길<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>85
638가로수남구<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>82
765가로수사상구사상로<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>81
756가로수사상구대영로, 낙동로,금곡로<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>69
698가로수부산진구<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>68
843가로수사하구다대산업로<NA>다대1동<NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>64
317가로수강서구외부순환도로<NA>녹산동0.00.0OOXSSD101벚나무<NA>59