Overview

Dataset statistics

Number of variables18
Number of observations10000
Missing cells24638
Missing cells (%)13.7%
Duplicate rows1066
Duplicate rows (%)10.7%
Total size in memory1.5 MiB
Average record size in memory154.0 B

Variable types

Categorical12
Text4
Numeric2

Dataset

Description부산광역시_부산광역시_도시공간정보시스템_도로관리(가로수)_20230717
Author부산광역시
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15080570

Alerts

지형지물명 has constant value ""Constant
Dataset has 1066 (10.7%) duplicate rowsDuplicates
관리기관 is highly overall correlated with 등록구 and 8 other fieldsHigh correlation
등록구 is highly overall correlated with 관리기관 and 2 other fieldsHigh correlation
규격(수고) is highly overall correlated with 규격(흉고)High correlation
규격(흉고) is highly overall correlated with 규격(수고)High correlation
지주목유무 is highly overall correlated with 관리기관 and 3 other fieldsHigh correlation
보호덥개유무 is highly overall correlated with 관리기관 and 4 other fieldsHigh correlation
보호대유무 is highly overall correlated with 관리기관 and 2 other fieldsHigh correlation
생육상태 is highly overall correlated with 관리기관 and 2 other fieldsHigh correlation
병충해 is highly overall correlated with 등록구 and 5 other fieldsHigh correlation
식수대종류 is highly overall correlated with 관리기관 and 3 other fieldsHigh correlation
기타수종 is highly overall correlated with 등록구 and 5 other fieldsHigh correlation
보호틀유무 is highly overall correlated with 관리기관High correlation
관리기관 is highly imbalanced (68.6%)Imbalance
생육상태 is highly imbalanced (52.5%)Imbalance
병충해 is highly imbalanced (59.8%)Imbalance
기타수종 is highly imbalanced (75.4%)Imbalance
보호틀유무 is highly imbalanced (61.9%)Imbalance
도로구간명 has 2709 (27.1%) missing valuesMissing
행정동 has 1862 (18.6%) missing valuesMissing
식재위치 has 3683 (36.8%) missing valuesMissing
규격(수고) has 4355 (43.5%) missing valuesMissing
규격(흉고) has 4244 (42.4%) missing valuesMissing
특기사항 has 7785 (77.8%) missing valuesMissing
규격(수고) has 390 (3.9%) zerosZeros
규격(흉고) has 783 (7.8%) zerosZeros

Reproduction

Analysis started2023-12-10 16:26:25.093815
Analysis finished2023-12-10 16:26:28.702147
Duration3.61 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

2023-12-11T01:26:28.753796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:26:28.824540image/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
3090 
부산진구
846 
북구
805 
사하구
717 
사상구
714 
Other values (11)
3828 

Length

Max length4
Median length3
Mean length2.924
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기장군
2nd row동래구
3rd row사하구
4th row연제구
5th row강서구

Common Values

ValueCountFrequency (%)
강서구 3090
30.9%
부산진구 846
 
8.5%
북구 805
 
8.1%
사하구 717
 
7.2%
사상구 714
 
7.1%
금정구 638
 
6.4%
연제구 549
 
5.5%
남구 489
 
4.9%
동래구 488
 
4.9%
해운대구 340
 
3.4%
Other values (6) 1324
13.2%

Length

2023-12-11T01:26:28.911808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강서구 3090
30.9%
부산진구 846
 
8.5%
북구 805
 
8.1%
사하구 717
 
7.2%
사상구 714
 
7.1%
금정구 638
 
6.4%
연제구 549
 
5.5%
남구 489
 
4.9%
동래구 488
 
4.9%
해운대구 340
 
3.4%
Other values (6) 1324
13.2%

도로구간명
Text

MISSING 

Distinct383
Distinct (%)5.3%
Missing2709
Missing (%)27.1%
Memory size156.2 KiB
2023-12-11T01:26:29.183511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length6.153614
Min length3

Characters and Unicode

Total characters44866
Distinct characters255
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67 ?
Unique (%)0.9%

Sample

1st row미남로, 우장춘로,금샘길
2nd row대영로, 낙동로,금곡로
3rd row외부순환도로
4th row녹산산업17길
5th row하장안1길
ValueCountFrequency (%)
대영로 344
 
3.9%
낙동로,금곡로 344
 
3.9%
외부순환도로 330
 
3.7%
구덕로 311
 
3.5%
중앙로,금정로 279
 
3.1%
내부순환도로 273
 
3.1%
공항로 257
 
2.9%
낙동남로 231
 
2.6%
만덕로 143
 
1.6%
충렬로,해운로 143
 
1.6%
Other values (388) 6238
70.1%
2023-12-11T01:26:29.593304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 7389
 
16.5%
, 2575
 
5.7%
산 2546
 
5.7%
길 2455
 
5.5%
1602
 
3.6%
업 1236
 
2.8%
녹 1075
 
2.4%
대 1035
 
2.3%
동 945
 
2.1%
도 900
 
2.0%
Other values (245) 23108
51.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 38030
84.8%
Other Punctuation 2575
 
5.7%
Decimal Number 2443
 
5.4%
Space Separator 1602
 
3.6%
Open Punctuation 74
 
0.2%
Close Punctuation 74
 
0.2%
Connector Punctuation 64
 
0.1%
Uppercase Letter 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 7389
 
19.4%
산 2546
 
6.7%
길 2455
 
6.5%
업 1236
 
3.3%
녹 1075
 
2.8%
대 1035
 
2.7%
동 945
 
2.5%
도 900
 
2.4%
금 861
 
2.3%
순 773
 
2.0%
Other values (226) 18815
49.5%
Decimal Number
ValueCountFrequency (%)
1 706
28.9%
2 539
22.1%
6 241
 
9.9%
5 227
 
9.3%
4 180
 
7.4%
3 176
 
7.2%
7 145
 
5.9%
8 121
 
5.0%
9 86
 
3.5%
0 22
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
A 1
25.0%
C 1
25.0%
P 1
25.0%
E 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 2575
100.0%
Space Separator
ValueCountFrequency (%)
1602
100.0%
Open Punctuation
ValueCountFrequency (%)
( 74
100.0%
Close Punctuation
ValueCountFrequency (%)
) 74
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 38030
84.8%
Common 6832
 
15.2%
Latin 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 7389
 
19.4%
산 2546
 
6.7%
길 2455
 
6.5%
업 1236
 
3.3%
녹 1075
 
2.8%
대 1035
 
2.7%
동 945
 
2.5%
도 900
 
2.4%
금 861
 
2.3%
순 773
 
2.0%
Other values (226) 18815
49.5%
Common
ValueCountFrequency (%)
, 2575
37.7%
1602
23.4%
1 706
 
10.3%
2 539
 
7.9%
6 241
 
3.5%
5 227
 
3.3%
4 180
 
2.6%
3 176
 
2.6%
7 145
 
2.1%
8 121
 
1.8%
Other values (5) 320
 
4.7%
Latin
ValueCountFrequency (%)
A 1
25.0%
C 1
25.0%
P 1
25.0%
E 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 38030
84.8%
ASCII 6836
 
15.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 7389
 
19.4%
산 2546
 
6.7%
길 2455
 
6.5%
업 1236
 
3.3%
녹 1075
 
2.8%
대 1035
 
2.7%
동 945
 
2.5%
도 900
 
2.4%
금 861
 
2.3%
순 773
 
2.0%
Other values (226) 18815
49.5%
ASCII
ValueCountFrequency (%)
, 2575
37.7%
1602
23.4%
1 706
 
10.3%
2 539
 
7.9%
6 241
 
3.5%
5 227
 
3.3%
4 180
 
2.6%
3 176
 
2.6%
7 145
 
2.1%
8 121
 
1.8%
Other values (9) 324
 
4.7%

관리기관
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7869 
금정구 지역경제과
 
356
강서구 산업단지관리사업소
 
349
동래구 복지환경국 경제녹지과
 
266
수영구 주민생활지원국 지역경제과
 
260
Other values (22)
900 

Length

Max length17
Median length4
Mean length5.7907
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row기장군
2nd row동래구 복지환경국 경제녹지과
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 7869
78.7%
금정구 지역경제과 356
 
3.6%
강서구 산업단지관리사업소 349
 
3.5%
동래구 복지환경국 경제녹지과 266
 
2.7%
수영구 주민생활지원국 지역경제과 260
 
2.6%
해운대구 관광경제국 늘푸른과 214
 
2.1%
사상구 주민생활지원국 지역경제과 200
 
2.0%
북구 안전도시국 청정녹지과 138
 
1.4%
강서구 70
 
0.7%
기장군 60
 
0.6%
Other values (17) 218
 
2.2%

Length

2023-12-11T01:26:29.740720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 7869
60.5%
지역경제과 816
 
6.3%
주민생활지원국 460
 
3.5%
강서구 424
 
3.3%
금정구 381
 
2.9%
산업단지관리사업소 349
 
2.7%
동래구 274
 
2.1%
복지환경국 266
 
2.0%
경제녹지과 266
 
2.0%
수영구 264
 
2.0%
Other values (21) 1630
 
12.5%

행정동
Text

MISSING 

Distinct191
Distinct (%)2.3%
Missing1862
Missing (%)18.6%
Memory size156.2 KiB
2023-12-11T01:26:30.024679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.5829442
Min length3

Characters and Unicode

Total characters29158
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

Unique2 ?
Unique (%)< 0.1%

Sample

1st row정관읍
2nd row온천1동
3rd row괴정2동
4th row녹산동
5th row만덕2동
ValueCountFrequency (%)
녹산동 1838
22.6%
명지동 431
 
5.3%
대저2동 348
 
4.3%
대저1동 325
 
4.0%
금곡동 230
 
2.8%
다대1동 140
 
1.7%
화명3동 115
 
1.4%
신평2동 109
 
1.3%
청룡노포동 97
 
1.2%
민락동 95
 
1.2%
Other values (181) 4410
54.2%
2023-12-11T01:26:30.489172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 8213
28.2%
산 1986
 
6.8%
녹 1838
 
6.3%
2 1817
 
6.2%
1 1800
 
6.2%
대 1124
 
3.9%
3 756
 
2.6%
명 711
 
2.4%
저 673
 
2.3%
지 450
 
1.5%
Other values (95) 9790
33.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24531
84.1%
Decimal Number 4627
 
15.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 8213
33.5%
산 1986
 
8.1%
녹 1838
 
7.5%
대 1124
 
4.6%
명 711
 
2.9%
저 673
 
2.7%
지 450
 
1.8%
천 398
 
1.6%
금 372
 
1.5%
곡 302
 
1.2%
Other values (87) 8464
34.5%
Decimal Number
ValueCountFrequency (%)
2 1817
39.3%
1 1800
38.9%
3 756
16.3%
4 169
 
3.7%
5 33
 
0.7%
6 31
 
0.7%
8 17
 
0.4%
9 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24531
84.1%
Common 4627
 
15.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 8213
33.5%
산 1986
 
8.1%
녹 1838
 
7.5%
대 1124
 
4.6%
명 711
 
2.9%
저 673
 
2.7%
지 450
 
1.8%
천 398
 
1.6%
금 372
 
1.5%
곡 302
 
1.2%
Other values (87) 8464
34.5%
Common
ValueCountFrequency (%)
2 1817
39.3%
1 1800
38.9%
3 756
16.3%
4 169
 
3.7%
5 33
 
0.7%
6 31
 
0.7%
8 17
 
0.4%
9 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24531
84.1%
ASCII 4627
 
15.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 8213
33.5%
산 1986
 
8.1%
녹 1838
 
7.5%
대 1124
 
4.6%
명 711
 
2.9%
저 673
 
2.7%
지 450
 
1.8%
천 398
 
1.6%
금 372
 
1.5%
곡 302
 
1.2%
Other values (87) 8464
34.5%
ASCII
ValueCountFrequency (%)
2 1817
39.3%
1 1800
38.9%
3 756
16.3%
4 169
 
3.7%
5 33
 
0.7%
6 31
 
0.7%
8 17
 
0.4%
9 4
 
0.1%

식재위치
Text

MISSING 

Distinct2213
Distinct (%)35.0%
Missing3683
Missing (%)36.8%
Memory size156.2 KiB
2023-12-11T01:26:30.706830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length93
Median length32
Mean length10.904227
Min length1

Characters and Unicode

Total characters68882
Distinct characters538
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

Unique1349 ?
Unique (%)21.4%

Sample

1st row3-6답
2nd row강서구 송정동 1535
3rd row
4th row강서구 송정동 1570
5th row신성산업~명지시장(좌측)
ValueCountFrequency (%)
강서구 2050
 
15.2%
송정동 1269
 
9.4%
부산진구 751
 
5.6%
앞 554
 
4.1%
명지동 301
 
2.2%
대저1동 190
 
1.4%
신호동 159
 
1.2%
당감동 143
 
1.1%
좌동 135
 
1.0%
개금동 124
 
0.9%
Other values (2326) 7796
57.9%
2023-12-11T01:26:31.124202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8142
 
11.8%
동 3500
 
5.1%
구 3418
 
5.0%
1 3022
 
4.4%
서 2274
 
3.3%
강 2223
 
3.2%
정 1554
 
2.3%
송 1515
 
2.2%
2 1466
 
2.1%
5 1426
 
2.1%
Other values (528) 40342
58.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 43590
63.3%
Decimal Number 13021
 
18.9%
Space Separator 8142
 
11.8%
Math Symbol 1340
 
1.9%
Close Punctuation 774
 
1.1%
Open Punctuation 773
 
1.1%
Dash Punctuation 740
 
1.1%
Uppercase Letter 412
 
0.6%
Other Punctuation 62
 
0.1%
Lowercase Letter 26
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 3500
 
8.0%
구 3418
 
7.8%
서 2274
 
5.2%
강 2223
 
5.1%
정 1554
 
3.6%
송 1515
 
3.5%
부 1308
 
3.0%
산 1148
 
2.6%
진 847
 
1.9%
대 796
 
1.8%
Other values (489) 25007
57.4%
Uppercase Letter
ValueCountFrequency (%)
R 66
16.0%
C 60
14.6%
K 52
12.6%
I 50
12.1%
T 33
8.0%
S 32
7.8%
G 29
7.0%
L 27
6.6%
E 15
 
3.6%
P 14
 
3.4%
Other values (8) 34
8.3%
Decimal Number
ValueCountFrequency (%)
1 3022
23.2%
2 1466
11.3%
5 1426
11.0%
3 1294
9.9%
7 1180
 
9.1%
6 1180
 
9.1%
4 1094
 
8.4%
9 847
 
6.5%
8 819
 
6.3%
0 693
 
5.3%
Lowercase Letter
ValueCountFrequency (%)
k 13
50.0%
s 12
46.2%
t 1
 
3.8%
Other Punctuation
ValueCountFrequency (%)
, 61
98.4%
/ 1
 
1.6%
Space Separator
ValueCountFrequency (%)
8142
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1340
100.0%
Close Punctuation
ValueCountFrequency (%)
) 774
100.0%
Open Punctuation
ValueCountFrequency (%)
( 773
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 740
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 43590
63.3%
Common 24854
36.1%
Latin 438
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 3500
 
8.0%
구 3418
 
7.8%
서 2274
 
5.2%
강 2223
 
5.1%
정 1554
 
3.6%
송 1515
 
3.5%
부 1308
 
3.0%
산 1148
 
2.6%
진 847
 
1.9%
대 796
 
1.8%
Other values (489) 25007
57.4%
Latin
ValueCountFrequency (%)
R 66
15.1%
C 60
13.7%
K 52
11.9%
I 50
11.4%
T 33
7.5%
S 32
7.3%
G 29
6.6%
L 27
6.2%
E 15
 
3.4%
P 14
 
3.2%
Other values (11) 60
13.7%
Common
ValueCountFrequency (%)
8142
32.8%
1 3022
 
12.2%
2 1466
 
5.9%
5 1426
 
5.7%
~ 1340
 
5.4%
3 1294
 
5.2%
7 1180
 
4.7%
6 1180
 
4.7%
4 1094
 
4.4%
9 847
 
3.4%
Other values (8) 3863
15.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 43590
63.3%
ASCII 25292
36.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8142
32.2%
1 3022
 
11.9%
2 1466
 
5.8%
5 1426
 
5.6%
~ 1340
 
5.3%
3 1294
 
5.1%
7 1180
 
4.7%
6 1180
 
4.7%
4 1094
 
4.3%
9 847
 
3.3%
Other values (29) 4301
17.0%
Hangul
ValueCountFrequency (%)
동 3500
 
8.0%
구 3418
 
7.8%
서 2274
 
5.2%
강 2223
 
5.1%
정 1554
 
3.6%
송 1515
 
3.5%
부 1308
 
3.0%
산 1148
 
2.6%
진 847
 
1.9%
대 796
 
1.8%
Other values (489) 25007
57.4%

규격(수고)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct63
Distinct (%)1.1%
Missing4355
Missing (%)43.5%
Infinite0
Infinite (%)0.0%
Mean5.2078477
Minimum0
Maximum60.5
Zeros390
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:26:31.304146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median5
Q36
95-th percentile9
Maximum60.5
Range60.5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.9411889
Coefficient of variation (CV)0.56476093
Kurtosis106.81228
Mean5.2078477
Median Absolute Deviation (MAD)1
Skewness6.5085398
Sum29398.3
Variance8.6505922
MonotonicityNot monotonic
2023-12-11T01:26:31.487515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.0 1499
 
15.0%
6.0 789
 
7.9%
5.0 639
 
6.4%
7.0 400
 
4.0%
0.0 390
 
3.9%
4.5 387
 
3.9%
8.0 288
 
2.9%
5.5 239
 
2.4%
6.5 185
 
1.8%
3.5 176
 
1.8%
Other values (53) 653
 
6.5%
(Missing) 4355
43.5%
ValueCountFrequency (%)
0.0 390
3.9%
0.1 2
 
< 0.1%
1.0 1
 
< 0.1%
1.2 1
 
< 0.1%
1.4 2
 
< 0.1%
1.5 1
 
< 0.1%
2.0 12
 
0.1%
2.5 30
 
0.3%
3.0 113
 
1.1%
3.3 1
 
< 0.1%
ValueCountFrequency (%)
60.5 1
 
< 0.1%
58.0 1
 
< 0.1%
56.0 1
 
< 0.1%
55.0 1
 
< 0.1%
54.0 1
 
< 0.1%
50.0 1
 
< 0.1%
47.5 1
 
< 0.1%
46.0 1
 
< 0.1%
19.0 1
 
< 0.1%
15.0 4
< 0.1%

규격(흉고)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct102
Distinct (%)1.8%
Missing4244
Missing (%)42.4%
Infinite0
Infinite (%)0.0%
Mean0.23561327
Minimum0
Maximum6
Zeros783
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:26:31.675374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.12
median0.16
Q30.26
95-th percentile0.78
Maximum6
Range6
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.29631456
Coefficient of variation (CV)1.257631
Kurtosis149.9825
Mean0.23561327
Median Absolute Deviation (MAD)0.06
Skewness8.7139917
Sum1356.19
Variance0.087802317
MonotonicityNot monotonic
2023-12-11T01:26:31.828725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.12 1003
 
10.0%
0.0 783
 
7.8%
0.1 362
 
3.6%
0.2 268
 
2.7%
0.16 264
 
2.6%
0.18 228
 
2.3%
0.15 208
 
2.1%
0.22 160
 
1.6%
0.24 157
 
1.6%
0.14 146
 
1.5%
Other values (92) 2177
21.8%
(Missing) 4244
42.4%
ValueCountFrequency (%)
0.0 783
7.8%
0.01 1
 
< 0.1%
0.04 1
 
< 0.1%
0.05 13
 
0.1%
0.06 9
 
0.1%
0.07 3
 
< 0.1%
0.08 29
 
0.3%
0.09 11
 
0.1%
0.1 362
3.6%
0.11 63
 
0.6%
ValueCountFrequency (%)
6.0 6
0.1%
2.0 4
< 0.1%
1.9 1
 
< 0.1%
1.5 5
0.1%
1.4 3
< 0.1%
1.31 2
 
< 0.1%
1.25 2
 
< 0.1%
1.2 1
 
< 0.1%
1.19 4
< 0.1%
1.16 5
0.1%

지주목유무
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
X
4540 
O
3094 
<NA>
2365 
0
 
1

Length

Max length4
Median length1
Mean length1.7095
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
X 4540
45.4%
O 3094
30.9%
<NA> 2365
23.6%
0 1
 
< 0.1%

Length

2023-12-11T01:26:31.973258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:26:32.080301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
x 4540
45.4%
o 3094
30.9%
na 2365
23.6%
0 1
 
< 0.1%

보호덥개유무
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
O
4675 
X
2997 
<NA>
2327 
0
 
1

Length

Max length4
Median length1
Mean length1.6981
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
O 4675
46.8%
X 2997
30.0%
<NA> 2327
23.3%
0 1
 
< 0.1%

Length

2023-12-11T01:26:32.222690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:26:32.374888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
o 4675
46.8%
x 2997
30.0%
na 2327
23.3%
0 1
 
< 0.1%

보호대유무
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
4658 
X
3232 
O
2093 
 
16
0
 
1

Length

Max length4
Median length1
Mean length2.3974
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 4658
46.6%
X 3232
32.3%
O 2093
20.9%
16
 
0.2%
0 1
 
< 0.1%

Length

2023-12-11T01:26:32.556596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:26:32.712951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 4658
46.7%
x 3232
32.4%
o 2093
21.0%
0 1
 
< 0.1%

생육상태
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
4915 
양호
2392 
보통
1531 
 
406
좋음
 
193
Other values (16)
563 

Length

Max length48
Median length5
Mean length3.5524
Min length1

Unique

Unique6 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 4915
49.1%
양호 2392
23.9%
보통 1531
 
15.3%
406
 
4.1%
좋음 193
 
1.9%
X 181
 
1.8%
양호 140
 
1.4%
O 80
 
0.8%
양 70
 
0.7%
불량 46
 
0.5%
Other values (11) 46
 
0.5%

Length

2023-12-11T01:26:32.846894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 4915
51.2%
양호 2536
26.4%
보통 1531
 
16.0%
좋음 193
 
2.0%
x 181
 
1.9%
o 80
 
0.8%
양 70
 
0.7%
불량 46
 
0.5%
0 30
 
0.3%
야호 3
 
< 0.1%
Other values (7) 9
 
0.1%

병충해
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
6258 
없음
1839 
양호
746 
 
436
무
 
250
Other values (17)
 
471

Length

Max length50
Median length4
Mean length3.8384
Min length1

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 6258
62.6%
없음 1839
 
18.4%
양호 746
 
7.5%
436
 
4.4%
무 250
 
2.5%
O 240
 
2.4%
없음 115
 
1.1%
진딧물 28
 
0.3%
X 28
 
0.3%
djqtdma 17
 
0.2%
Other values (12) 43
 
0.4%

Length

2023-12-11T01:26:32.974069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 6258
65.1%
없음 1957
 
20.4%
양호 748
 
7.8%
무 257
 
2.7%
o 240
 
2.5%
진딧물 28
 
0.3%
x 28
 
0.3%
djqtdma 17
 
0.2%
피해(증상:잎이 15
 
0.2%
말림 15
 
0.2%
Other values (8) 46
 
0.5%

식수대종류
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
4450 
SSD101
1430 
SSD102
1219 
SSD103
1219 
SSD999
803 
Other values (10)
879 

Length

Max length6
Median length6
Mean length4.9716
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd rowSSD102
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 4450
44.5%
SSD101 1430
 
14.3%
SSD102 1219
 
12.2%
SSD103 1219
 
12.2%
SSD999 803
 
8.0%
SSD002 189
 
1.9%
SSD001 177
 
1.8%
151
 
1.5%
X 125
 
1.2%
SSD006 118
 
1.2%
Other values (5) 119
 
1.2%

Length

2023-12-11T01:26:33.097558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 4450
45.2%
ssd101 1430
 
14.5%
ssd103 1222
 
12.4%
ssd102 1219
 
12.4%
ssd999 803
 
8.2%
ssd002 189
 
1.9%
ssd001 177
 
1.8%
x 125
 
1.3%
ssd006 118
 
1.2%
ssd003 90
 
0.9%
Other values (3) 26
 
0.3%

수종
Categorical

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
은행나무
2498 
벚나무
2236 
미분류
1895 
기타낙엽
978 
버즘나무
782 
Other values (8)
1611 

Length

Max length6
Median length4
Mean length3.568
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
은행나무 2498
25.0%
벚나무 2236
22.4%
미분류 1895
18.9%
기타낙엽 978
 
9.8%
버즘나무 782
 
7.8%
느티나무 609
 
6.1%
후박나무 464
 
4.6%
기타상록 180
 
1.8%
해송 131
 
1.3%
구실잦밤나무 81
 
0.8%
Other values (3) 146
 
1.5%

Length

2023-12-11T01:26:33.285381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
은행나무 2498
25.0%
벚나무 2236
22.4%
미분류 1895
18.9%
기타낙엽 978
 
9.8%
버즘나무 782
 
7.8%
느티나무 609
 
6.1%
후박나무 464
 
4.6%
기타상록 180
 
1.8%
해송 131
 
1.3%
구실잦밤나무 81
 
0.8%
Other values (3) 146
 
1.5%

기타수종
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct43
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7897 
1106 
중국단풍
 
251
팽나무(포구나무)
 
146
회화
 
137
Other values (38)
 
463

Length

Max length17
Median length4
Mean length3.7851
Min length1

Unique

Unique8 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 7897
79.0%
1106
 
11.1%
중국단풍 251
 
2.5%
팽나무(포구나무) 146
 
1.5%
회화 137
 
1.4%
회화나무 76
 
0.8%
벽오동 65
 
0.7%
먼나무 34
 
0.3%
메타세콰이어 27
 
0.3%
이팝나무 22
 
0.2%
Other values (33) 239
 
2.4%

Length

2023-12-11T01:26:33.479300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 7897
88.8%
중국단풍 251
 
2.8%
팽나무(포구나무 146
 
1.6%
회화 137
 
1.5%
회화나무 89
 
1.0%
벽오동 65
 
0.7%
먼나무 49
 
0.6%
이팝나무 40
 
0.4%
메타세콰이어 27
 
0.3%
소나무 22
 
0.2%
Other values (28) 171
 
1.9%

특기사항
Text

MISSING 

Distinct53
Distinct (%)2.4%
Missing7785
Missing (%)77.8%
Memory size156.2 KiB
2023-12-11T01:26:33.720869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length2
Mean length2.8623025
Min length1

Characters and Unicode

Total characters6340
Distinct characters139
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)1.0%

Sample

1st row
2nd row없음
3rd row무
4th row없음
5th row없음
ValueCountFrequency (%)
없음 1270
68.9%
무 220
 
11.9%
수벽내 74
 
4.0%
보호덮개(주철 27
 
1.5%
덮개 19
 
1.0%
보호덮개(칼콘 18
 
1.0%
수벽 17
 
0.9%
내 17
 
0.9%
보도블럭 17
 
0.9%
칼콘 15
 
0.8%
Other values (64) 148
 
8.0%
2023-12-11T01:26:34.218184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1775
28.0%
음 1272
20.1%
없 1271
20.0%
무 259
 
4.1%
수 123
 
1.9%
0 97
 
1.5%
내 93
 
1.5%
벽 91
 
1.4%
보 75
 
1.2%
개 72
 
1.1%
Other values (129) 1212
19.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4071
64.2%
Space Separator 1775
28.0%
Decimal Number 250
 
3.9%
Other Punctuation 128
 
2.0%
Close Punctuation 51
 
0.8%
Open Punctuation 51
 
0.8%
Math Symbol 7
 
0.1%
Uppercase Letter 4
 
0.1%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
음 1272
31.2%
없 1271
31.2%
무 259
 
6.4%
수 123
 
3.0%
내 93
 
2.3%
벽 91
 
2.2%
보 75
 
1.8%
개 72
 
1.8%
덮 68
 
1.7%
호 46
 
1.1%
Other values (107) 701
17.2%
Decimal Number
ValueCountFrequency (%)
0 97
38.8%
2 51
20.4%
1 35
 
14.0%
7 27
 
10.8%
5 20
 
8.0%
6 7
 
2.8%
4 6
 
2.4%
9 3
 
1.2%
8 3
 
1.2%
3 1
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
C 1
25.0%
E 1
25.0%
P 1
25.0%
A 1
25.0%
Other Punctuation
ValueCountFrequency (%)
/ 65
50.8%
. 61
47.7%
, 2
 
1.6%
Space Separator
ValueCountFrequency (%)
1775
100.0%
Close Punctuation
ValueCountFrequency (%)
) 51
100.0%
Open Punctuation
ValueCountFrequency (%)
( 51
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4071
64.2%
Common 2265
35.7%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
음 1272
31.2%
없 1271
31.2%
무 259
 
6.4%
수 123
 
3.0%
내 93
 
2.3%
벽 91
 
2.2%
보 75
 
1.8%
개 72
 
1.8%
덮 68
 
1.7%
호 46
 
1.1%
Other values (107) 701
17.2%
Common
ValueCountFrequency (%)
1775
78.4%
0 97
 
4.3%
/ 65
 
2.9%
. 61
 
2.7%
) 51
 
2.3%
2 51
 
2.3%
( 51
 
2.3%
1 35
 
1.5%
7 27
 
1.2%
5 20
 
0.9%
Other values (8) 32
 
1.4%
Latin
ValueCountFrequency (%)
C 1
25.0%
E 1
25.0%
P 1
25.0%
A 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4071
64.2%
ASCII 2269
35.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1775
78.2%
0 97
 
4.3%
/ 65
 
2.9%
. 61
 
2.7%
) 51
 
2.2%
2 51
 
2.2%
( 51
 
2.2%
1 35
 
1.5%
7 27
 
1.2%
5 20
 
0.9%
Other values (12) 36
 
1.6%
Hangul
ValueCountFrequency (%)
음 1272
31.2%
없 1271
31.2%
무 259
 
6.4%
수 123
 
3.0%
내 93
 
2.3%
벽 91
 
2.2%
보 75
 
1.8%
개 72
 
1.8%
덮 68
 
1.7%
호 46
 
1.1%
Other values (107) 701
17.2%

보호틀유무
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8444 
O
1135 
X
 
411
 
10

Length

Max length4
Median length4
Mean length3.5332
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 8444
84.4%
O 1135
 
11.3%
X 411
 
4.1%
10
 
0.1%

Length

2023-12-11T01:26:34.419874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:26:34.562924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 8444
84.5%
o 1135
 
11.4%
x 411
 
4.1%

Interactions

2023-12-11T01:26:27.746979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:26:27.565145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:26:27.854732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:26:27.649397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:26:34.994162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록구관리기관규격(수고)규격(흉고)지주목유무보호덥개유무보호대유무생육상태병충해식수대종류수종기타수종특기사항보호틀유무
등록구1.0000.9990.4970.6090.5710.3960.6620.8560.9080.7950.6240.9090.9490.673
관리기관0.9991.0000.5430.7760.7620.6110.9550.8720.9360.8920.7560.9010.9560.854
규격(수고)0.4970.5431.0000.2260.2180.0250.2170.2810.1630.3340.3010.1330.8030.150
규격(흉고)0.6090.7760.2261.0000.1110.0210.1820.4430.6430.5690.1550.6470.8100.000
지주목유무0.5710.7620.2180.1111.0000.9470.6840.6050.6430.4040.4760.6030.6670.225
보호덥개유무0.3960.6110.0250.0210.9471.0000.7150.3510.3930.6850.3280.6630.4850.272
보호대유무0.6620.9550.2170.1820.6840.7151.0000.4860.6070.5510.3610.6400.6200.307
생육상태0.8560.8720.2810.4430.6050.3510.4861.0000.9460.7200.5690.8880.9580.412
병충해0.9080.9360.1630.6430.6430.3930.6070.9461.0000.8930.6930.8920.9660.735
식수대종류0.7950.8920.3340.5690.4040.6850.5510.7200.8931.0000.5740.8620.8830.532
수종0.6240.7560.3010.1550.4760.3280.3610.5690.6930.5741.0000.8230.8930.256
기타수종0.9090.9010.1330.6470.6030.6630.6400.8880.8920.8620.8231.0000.8150.615
특기사항0.9490.9560.8030.8100.6670.4850.6200.9580.9660.8830.8930.8151.0000.815
보호틀유무0.6730.8540.1500.0000.2250.2720.3070.4120.7350.5320.2560.6150.8151.000
2023-12-11T01:26:35.188269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
보호덥개유무관리기관수종기타수종보호대유무생육상태보호틀유무병충해지주목유무식수대종류등록구
보호덥개유무1.0000.5310.1560.5320.7550.2040.4410.3480.7170.5030.236
관리기관0.5311.0000.3650.5080.7770.5010.6840.6760.6760.6070.990
수종0.1560.3651.0000.4150.1740.2320.1190.3170.2450.2570.276
기타수종0.5320.5080.4151.0000.3760.5680.4120.5770.4800.5000.521
보호대유무0.7550.7770.1740.3761.0000.2460.1040.3470.7160.3540.368
생육상태0.2040.5010.2320.5680.2461.0000.3420.6830.3970.3360.463
보호틀유무0.4410.6840.1190.4120.1040.3421.0000.4140.3670.3960.488
병충해0.3480.6760.3170.5770.3470.6830.4141.0000.5700.5190.568
지주목유무0.7170.6760.2450.4800.7160.3970.3670.5701.0000.2460.378
식수대종류0.5030.6070.2570.5000.3540.3360.3960.5190.2461.0000.421
등록구0.2360.9900.2760.5210.3680.4630.4880.5680.3780.4211.000
2023-12-11T01:26:35.391129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
규격(수고)규격(흉고)등록구관리기관지주목유무보호덥개유무보호대유무생육상태병충해식수대종류수종기타수종보호틀유무
규격(수고)1.0000.6060.2540.2650.2340.0270.1490.1250.0710.1290.1510.0670.142
규격(흉고)0.6061.0000.3640.4730.1340.0290.1730.1480.4120.3520.0860.3480.027
등록구0.2540.3641.0000.9900.3780.2360.3680.4630.5680.4210.2760.5210.488
관리기관0.2650.4730.9901.0000.6760.5310.7770.5010.6760.6070.3650.5080.684
지주목유무0.2340.1340.3780.6761.0000.7170.7160.3970.5700.2460.2450.4800.367
보호덥개유무0.0270.0290.2360.5310.7171.0000.7550.2040.3480.5030.1560.5320.441
보호대유무0.1490.1730.3680.7770.7160.7551.0000.2460.3470.3540.1740.3760.104
생육상태0.1250.1480.4630.5010.3970.2040.2461.0000.6830.3360.2320.5680.342
병충해0.0710.4120.5680.6760.5700.3480.3470.6831.0000.5190.3170.5770.414
식수대종류0.1290.3520.4210.6070.2460.5030.3540.3360.5191.0000.2570.5000.396
수종0.1510.0860.2760.3650.2450.1560.1740.2320.3170.2571.0000.4150.119
기타수종0.0670.3480.5210.5080.4800.5320.3760.5680.5770.5000.4151.0000.412
보호틀유무0.1420.0270.4880.6840.3670.4410.1040.3420.4140.3960.1190.4121.000

Missing values

2023-12-11T01:26:28.027644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:26:28.269746image/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.
2023-12-11T01:26:28.504994image/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
73294가로수기장군<NA>기장군정관읍3-6답6.00.1OX<NA>양호없음<NA>느티나무느티나무<NA><NA>
42860가로수동래구미남로, 우장춘로,금샘길동래구 복지환경국 경제녹지과온천1동<NA>11.0<NA>XOO<NA><NA>SSD102벚나무<NA><NA><NA>
42044가로수사하구대영로, 낙동로,금곡로<NA>괴정2동<NA><NA>0.18XOX<NA><NA><NA>은행나무<NA>O
67484가로수연제구<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>
10482가로수강서구외부순환도로<NA>녹산동강서구 송정동 1535<NA><NA>OO<NA><NA><NA><NA>후박나무<NA><NA><NA>
73480가로수북구<NA><NA>만덕2동0.00.0OXX기타낙엽<NA>
10221가로수강서구녹산산업17길<NA>녹산동강서구 송정동 1570<NA><NA>XO<NA><NA><NA><NA>버즘나무<NA><NA><NA>
90058가로수금정구하장안1길금정구 지역경제과금사동<NA>4.00.08OXX양호없음SSD103벚나무<NA><NA><NA>
31381가로수강서구공항로<NA>대저2동신성산업~명지시장(좌측)4.00.12OXX보통없음SSD103벚나무<NA>없음<NA>
78520가로수부산진구진경1로<NA>부전1동부산진구 부전동 417-36.00.77XXX양호<NA><NA>기타낙엽벽오동<NA><NA>
지형지물명등록구도로구간명관리기관행정동식재위치규격(수고)규격(흉고)지주목유무보호덥개유무보호대유무생육상태병충해식수대종류수종기타수종특기사항보호틀유무
87386가로수연제구내부순환도로<NA>거제1동0.00.0OXX기타낙엽<NA>
69630가로수사상구<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>
72154가로수남구<NA>남구용호1동<NA>6.00.17OO<NA><NA><NA><NA>벚나무<NA><NA><NA>
37909가로수금정구부금로금정구 지역경제과부곡4동<NA>5.00.14XOX양호없음SSD102은행나무<NA><NA><NA>
46657가로수사하구하신번영로<NA>하단2동<NA><NA>0.11XXX<NA><NA><NA>은행나무<NA>O
15966가로수강서구공항로<NA>대저2동송백마을입구~전천후컨테이너~송백마을4.00.12XOO보통없음SSD999벚나무<NA>없음<NA>
87768가로수연제구내부순환도로<NA>거제2동0.00.0OOXSSD101기타낙엽<NA>
37583가로수사하구다대로,_강변대로<NA>다대2동<NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>
31275가로수강서구과학로<NA>녹산동0.00.0OOXSSD101벚나무<NA>
27896가로수동래구<NA>동래구 복지환경국 경제녹지과온천3동동래지하철역7.00.1XOO<NA><NA>SSD101은행나무<NA><NA>

Duplicate rows

Most frequently occurring

지형지물명등록구도로구간명관리기관행정동식재위치규격(수고)규격(흉고)지주목유무보호덥개유무보호대유무생육상태병충해식수대종류수종기타수종특기사항보호틀유무# duplicates
972가로수연제구<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>376
1025가로수중구<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>148
672가로수동래구<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>101
504가로수금정구금정도서관길<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>89
633가로수남구<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>80
699가로수부산진구<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>74
769가로수사상구사상로<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>73
659가로수동구<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>68
761가로수사상구대영로, 낙동로,금곡로<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>67
788가로수사상구학장로<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>미분류<NA><NA><NA>56