Overview

Dataset statistics

Number of variables13
Number of observations126
Missing cells27
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.9 KiB
Average record size in memory113.0 B

Variable types

Numeric8
Categorical2
Text3

Dataset

Description부산광역시 사상구 관내 교통섬, 화단 및 쌈지공원 조성 현황(명칭, 위치, 면적, 수목 수량, 시설물 등)에 대한 정보를 상세히 제공합니다.
Author부산광역시 사상구
URLhttps://www.data.go.kr/data/15025908/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 편의시설(종) and 2 other fieldsHigh correlation
면적(제곱미터) is highly overall correlated with 수목High correlation
수목 is highly overall correlated with 면적(제곱미터) and 1 other fieldsHigh correlation
교목 is highly overall correlated with 관목High correlation
관목 is highly overall correlated with 수목 and 1 other fieldsHigh correlation
편의시설(종) is highly overall correlated with 연번 and 2 other fieldsHigh correlation
편의시설(점) is highly overall correlated with 연번 and 1 other fieldsHigh correlation
구분 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
면적(제곱미터) has 3 (2.4%) missing valuesMissing
수목 has 2 (1.6%) missing valuesMissing
수종 has 16 (12.7%) missing valuesMissing
교목 has 2 (1.6%) missing valuesMissing
관목 has 2 (1.6%) missing valuesMissing
연번 has unique valuesUnique
수목 has 7 (5.6%) zerosZeros
교목 has 38 (30.2%) zerosZeros
관목 has 29 (23.0%) zerosZeros
편의시설(종) has 64 (50.8%) zerosZeros
편의시설(점) has 64 (50.8%) zerosZeros

Reproduction

Analysis started2024-05-04 08:15:39.797798
Analysis finished2024-05-04 08:16:02.118108
Duration22.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct126
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.5
Minimum1
Maximum126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-04T08:16:02.355596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.25
Q132.25
median63.5
Q394.75
95-th percentile119.75
Maximum126
Range125
Interquartile range (IQR)62.5

Descriptive statistics

Standard deviation36.517119
Coefficient of variation (CV)0.57507274
Kurtosis-1.2
Mean63.5
Median Absolute Deviation (MAD)31.5
Skewness0
Sum8001
Variance1333.5
MonotonicityStrictly increasing
2024-05-04T08:16:02.927621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
81 1
 
0.8%
94 1
 
0.8%
93 1
 
0.8%
92 1
 
0.8%
91 1
 
0.8%
90 1
 
0.8%
89 1
 
0.8%
88 1
 
0.8%
87 1
 
0.8%
Other values (116) 116
92.1%
ValueCountFrequency (%)
1 1
0.8%
2 1
0.8%
3 1
0.8%
4 1
0.8%
5 1
0.8%
6 1
0.8%
7 1
0.8%
8 1
0.8%
9 1
0.8%
10 1
0.8%
ValueCountFrequency (%)
126 1
0.8%
125 1
0.8%
124 1
0.8%
123 1
0.8%
122 1
0.8%
121 1
0.8%
120 1
0.8%
119 1
0.8%
118 1
0.8%
117 1
0.8%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
쌈지공원
75 
화단
51 

Length

Max length4
Median length4
Mean length3.1904762
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
쌈지공원 75
59.5%
화단 51
40.5%

Length

2024-05-04T08:16:03.951220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T08:16:04.478009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
쌈지공원 75
59.5%
화단 51
40.5%

명칭
Text

Distinct125
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-05-04T08:16:05.339360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length20
Mean length13.293651
Min length5

Characters and Unicode

Total characters1675
Distinct characters218
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)98.4%

Sample

1st row능인사 입구 쌈지공원
2nd row모라 희망 쌈지공원
3rd row모덕역 쌈지공원
4th row모라역 쌈지공원
5th row덕포역 쌈지공원
ValueCountFrequency (%)
쌈지공원 43
 
10.4%
화단 33
 
8.0%
옆 17
 
4.1%
앞 13
 
3.1%
주민쉼터 8
 
1.9%
기찻길 7
 
1.7%
입구 7
 
1.7%
조성 6
 
1.4%
가로화단 6
 
1.4%
주례 5
 
1.2%
Other values (189) 269
65.0%
2024-05-04T08:16:06.687859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
289
 
17.3%
지 67
 
4.0%
공 67
 
4.0%
원 60
 
3.6%
주 50
 
3.0%
화 47
 
2.8%
쌈 47
 
2.8%
단 46
 
2.7%
동 44
 
2.6%
터 31
 
1.9%
Other values (208) 927
55.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1343
80.2%
Space Separator 289
 
17.3%
Decimal Number 28
 
1.7%
Open Punctuation 6
 
0.4%
Close Punctuation 6
 
0.4%
Uppercase Letter 2
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 67
 
5.0%
공 67
 
5.0%
원 60
 
4.5%
주 50
 
3.7%
화 47
 
3.5%
쌈 47
 
3.5%
단 46
 
3.4%
동 44
 
3.3%
터 31
 
2.3%
로 30
 
2.2%
Other values (193) 854
63.6%
Decimal Number
ValueCountFrequency (%)
1 12
42.9%
2 7
25.0%
7 2
 
7.1%
3 2
 
7.1%
6 1
 
3.6%
0 1
 
3.6%
4 1
 
3.6%
8 1
 
3.6%
5 1
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
H 1
50.0%
L 1
50.0%
Space Separator
ValueCountFrequency (%)
289
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1343
80.2%
Common 330
 
19.7%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 67
 
5.0%
공 67
 
5.0%
원 60
 
4.5%
주 50
 
3.7%
화 47
 
3.5%
쌈 47
 
3.5%
단 46
 
3.4%
동 44
 
3.3%
터 31
 
2.3%
로 30
 
2.2%
Other values (193) 854
63.6%
Common
ValueCountFrequency (%)
289
87.6%
1 12
 
3.6%
2 7
 
2.1%
( 6
 
1.8%
) 6
 
1.8%
7 2
 
0.6%
3 2
 
0.6%
6 1
 
0.3%
0 1
 
0.3%
4 1
 
0.3%
Other values (3) 3
 
0.9%
Latin
ValueCountFrequency (%)
H 1
50.0%
L 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1343
80.2%
ASCII 332
 
19.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
289
87.0%
1 12
 
3.6%
2 7
 
2.1%
( 6
 
1.8%
) 6
 
1.8%
7 2
 
0.6%
3 2
 
0.6%
6 1
 
0.3%
0 1
 
0.3%
4 1
 
0.3%
Other values (5) 5
 
1.5%
Hangul
ValueCountFrequency (%)
지 67
 
5.0%
공 67
 
5.0%
원 60
 
4.5%
주 50
 
3.7%
화 47
 
3.5%
쌈 47
 
3.5%
단 46
 
3.4%
동 44
 
3.3%
터 31
 
2.3%
로 30
 
2.2%
Other values (193) 854
63.6%

위치(도로명 주소)
Text

Distinct124
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-05-04T08:16:07.147353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length32
Mean length20.563492
Min length7

Characters and Unicode

Total characters2591
Distinct characters169
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

Unique122 ?
Unique (%)96.8%

Sample

1st row괘법동 228-13번지
2nd row모라1동 943-2번지(모라동원@ 108동 뒤 ~ 모라동 387-1 앞)
3rd row덕포2동 257-11번지 앞(모덕역 1번출구 일원)
4th row모라1동 1002-1 ~ 991-2번지(모라역 2번 ~ 4번출구)
5th row덕포2동 403-6번지(덕포역 3번 출구)
ValueCountFrequency (%)
부산광역시 51
 
10.4%
사상구 51
 
10.4%
일원 27
 
5.5%
앞 18
 
3.7%
감전동 15
 
3.1%
괘법동 12
 
2.4%
학장동 11
 
2.2%
주례동 10
 
2.0%
주례2동 9
 
1.8%
주례1동 7
 
1.4%
Other values (217) 280
57.0%
2024-05-04T08:16:08.032760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
365
 
14.1%
동 125
 
4.8%
1 120
 
4.6%
번 87
 
3.4%
지 84
 
3.2%
- 80
 
3.1%
2 80
 
3.1%
3 78
 
3.0%
구 67
 
2.6%
역 60
 
2.3%
Other values (159) 1445
55.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1497
57.8%
Decimal Number 523
 
20.2%
Space Separator 365
 
14.1%
Dash Punctuation 80
 
3.1%
Close Punctuation 54
 
2.1%
Open Punctuation 54
 
2.1%
Math Symbol 12
 
0.5%
Other Punctuation 6
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 125
 
8.4%
번 87
 
5.8%
지 84
 
5.6%
구 67
 
4.5%
역 60
 
4.0%
시 58
 
3.9%
부 55
 
3.7%
사 55
 
3.7%
상 55
 
3.7%
산 54
 
3.6%
Other values (142) 797
53.2%
Decimal Number
ValueCountFrequency (%)
1 120
22.9%
2 80
15.3%
3 78
14.9%
6 46
 
8.8%
4 39
 
7.5%
7 35
 
6.7%
8 34
 
6.5%
9 32
 
6.1%
5 30
 
5.7%
0 29
 
5.5%
Other Punctuation
ValueCountFrequency (%)
, 4
66.7%
@ 2
33.3%
Space Separator
ValueCountFrequency (%)
365
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 80
100.0%
Close Punctuation
ValueCountFrequency (%)
) 54
100.0%
Open Punctuation
ValueCountFrequency (%)
( 54
100.0%
Math Symbol
ValueCountFrequency (%)
~ 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1497
57.8%
Common 1094
42.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 125
 
8.4%
번 87
 
5.8%
지 84
 
5.6%
구 67
 
4.5%
역 60
 
4.0%
시 58
 
3.9%
부 55
 
3.7%
사 55
 
3.7%
상 55
 
3.7%
산 54
 
3.6%
Other values (142) 797
53.2%
Common
ValueCountFrequency (%)
365
33.4%
1 120
 
11.0%
- 80
 
7.3%
2 80
 
7.3%
3 78
 
7.1%
) 54
 
4.9%
( 54
 
4.9%
6 46
 
4.2%
4 39
 
3.6%
7 35
 
3.2%
Other values (7) 143
 
13.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1497
57.8%
ASCII 1094
42.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
365
33.4%
1 120
 
11.0%
- 80
 
7.3%
2 80
 
7.3%
3 78
 
7.1%
) 54
 
4.9%
( 54
 
4.9%
6 46
 
4.2%
4 39
 
3.6%
7 35
 
3.2%
Other values (7) 143
 
13.1%
Hangul
ValueCountFrequency (%)
동 125
 
8.4%
번 87
 
5.8%
지 84
 
5.6%
구 67
 
4.5%
역 60
 
4.0%
시 58
 
3.9%
부 55
 
3.7%
사 55
 
3.7%
상 55
 
3.7%
산 54
 
3.6%
Other values (142) 797
53.2%

면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct99
Distinct (%)80.5%
Missing3
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean751.34959
Minimum10
Maximum21000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-04T08:16:08.351516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile20.1
Q177
median203
Q3844.5
95-th percentile2813.1
Maximum21000
Range20990
Interquartile range (IQR)767.5

Descriptive statistics

Standard deviation2028.2146
Coefficient of variation (CV)2.6994286
Kurtosis82.674258
Mean751.34959
Median Absolute Deviation (MAD)173
Skewness8.4168504
Sum92416
Variance4113654.4
MonotonicityNot monotonic
2024-05-04T08:16:08.775971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 5
 
4.0%
1000 4
 
3.2%
100 4
 
3.2%
200 4
 
3.2%
20 3
 
2.4%
90 3
 
2.4%
120 2
 
1.6%
50 2
 
1.6%
25 2
 
1.6%
239 2
 
1.6%
Other values (89) 92
73.0%
(Missing) 3
 
2.4%
ValueCountFrequency (%)
10 1
 
0.8%
12 1
 
0.8%
16 1
 
0.8%
19 1
 
0.8%
20 3
2.4%
21 1
 
0.8%
23 1
 
0.8%
25 2
1.6%
26 1
 
0.8%
27 1
 
0.8%
ValueCountFrequency (%)
21000 1
0.8%
4485 1
0.8%
3577 1
0.8%
3250 1
0.8%
3213 1
0.8%
3150 1
0.8%
2819 1
0.8%
2760 1
0.8%
2074 1
0.8%
2000 1
0.8%

수목
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct108
Distinct (%)87.1%
Missing2
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean2265.4113
Minimum0
Maximum42507
Zeros7
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-04T08:16:09.193305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.15
Q1161.75
median643
Q32584.75
95-th percentile8867.45
Maximum42507
Range42507
Interquartile range (IQR)2423

Descriptive statistics

Standard deviation4981.0559
Coefficient of variation (CV)2.1987424
Kurtosis39.021389
Mean2265.4113
Median Absolute Deviation (MAD)639
Skewness5.5814431
Sum280911
Variance24810918
MonotonicityNot monotonic
2024-05-04T08:16:09.741900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7
 
5.6%
1 4
 
3.2%
2 3
 
2.4%
3 2
 
1.6%
152 2
 
1.6%
200 2
 
1.6%
4 2
 
1.6%
283 2
 
1.6%
1272 1
 
0.8%
4308 1
 
0.8%
Other values (98) 98
77.8%
(Missing) 2
 
1.6%
ValueCountFrequency (%)
0 7
5.6%
1 4
3.2%
2 3
2.4%
3 2
 
1.6%
4 2
 
1.6%
13 1
 
0.8%
45 1
 
0.8%
46 1
 
0.8%
63 1
 
0.8%
72 1
 
0.8%
ValueCountFrequency (%)
42507 1
0.8%
27170 1
0.8%
12190 1
0.8%
10990 1
0.8%
10220 1
0.8%
9374 1
0.8%
9026 1
0.8%
7969 1
0.8%
7450 1
0.8%
6952 1
0.8%

수종
Text

MISSING 

Distinct53
Distinct (%)48.2%
Missing16
Missing (%)12.7%
Memory size1.1 KiB
2024-05-04T08:16:10.418987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length5.7636364
Min length2

Characters and Unicode

Total characters634
Distinct characters79
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)30.9%

Sample

1st row느티나무 등
2nd row왕벚나무 등
3rd row느티나무
4th row느티나무
5th row후박나무 등
ValueCountFrequency (%)
등 90
41.9%
느티나무 25
 
11.6%
왕벚나무 7
 
3.3%
외 6
 
2.8%
배롱나무 5
 
2.3%
동백나무 5
 
2.3%
선주목 4
 
1.9%
아왜나무 4
 
1.9%
금목서 4
 
1.9%
홍단풍 3
 
1.4%
Other values (44) 62
28.8%
2024-05-04T08:16:11.601439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
105
16.6%
등 94
14.8%
무 75
11.8%
나 74
 
11.7%
느 28
 
4.4%
티 28
 
4.4%
백 12
 
1.9%
홍 11
 
1.7%
목 11
 
1.7%
동 9
 
1.4%
Other values (69) 187
29.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 515
81.2%
Space Separator 105
 
16.6%
Decimal Number 14
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
등 94
18.3%
무 75
14.6%
나 74
14.4%
느 28
 
5.4%
티 28
 
5.4%
백 12
 
2.3%
홍 11
 
2.1%
목 11
 
2.1%
동 9
 
1.7%
종 9
 
1.7%
Other values (62) 164
31.8%
Decimal Number
ValueCountFrequency (%)
1 5
35.7%
2 3
21.4%
8 2
 
14.3%
4 2
 
14.3%
7 1
 
7.1%
0 1
 
7.1%
Space Separator
ValueCountFrequency (%)
105
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 515
81.2%
Common 119
 
18.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
등 94
18.3%
무 75
14.6%
나 74
14.4%
느 28
 
5.4%
티 28
 
5.4%
백 12
 
2.3%
홍 11
 
2.1%
목 11
 
2.1%
동 9
 
1.7%
종 9
 
1.7%
Other values (62) 164
31.8%
Common
ValueCountFrequency (%)
105
88.2%
1 5
 
4.2%
2 3
 
2.5%
8 2
 
1.7%
4 2
 
1.7%
7 1
 
0.8%
0 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 515
81.2%
ASCII 119
 
18.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
105
88.2%
1 5
 
4.2%
2 3
 
2.5%
8 2
 
1.7%
4 2
 
1.7%
7 1
 
0.8%
0 1
 
0.8%
Hangul
ValueCountFrequency (%)
등 94
18.3%
무 75
14.6%
나 74
14.4%
느 28
 
5.4%
티 28
 
5.4%
백 12
 
2.3%
홍 11
 
2.1%
목 11
 
2.1%
동 9
 
1.7%
종 9
 
1.7%
Other values (62) 164
31.8%

교목
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct51
Distinct (%)41.1%
Missing2
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean45.604839
Minimum0
Maximum570
Zeros38
Zeros (%)30.2%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-04T08:16:12.073590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q339.5
95-th percentile230.5
Maximum570
Range570
Interquartile range (IQR)39.5

Descriptive statistics

Standard deviation95.9306
Coefficient of variation (CV)2.103518
Kurtosis12.36704
Mean45.604839
Median Absolute Deviation (MAD)8
Skewness3.3735603
Sum5655
Variance9202.68
MonotonicityNot monotonic
2024-05-04T08:16:12.712353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38
30.2%
3 7
 
5.6%
1 6
 
4.8%
12 5
 
4.0%
22 5
 
4.0%
6 4
 
3.2%
5 4
 
3.2%
9 3
 
2.4%
58 2
 
1.6%
39 2
 
1.6%
Other values (41) 48
38.1%
ValueCountFrequency (%)
0 38
30.2%
1 6
 
4.8%
2 1
 
0.8%
3 7
 
5.6%
5 4
 
3.2%
6 4
 
3.2%
7 2
 
1.6%
9 3
 
2.4%
10 1
 
0.8%
11 1
 
0.8%
ValueCountFrequency (%)
570 1
0.8%
442 1
0.8%
440 1
0.8%
389 1
0.8%
365 1
0.8%
240 1
0.8%
238 1
0.8%
188 1
0.8%
178 1
0.8%
175 1
0.8%

관목
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct88
Distinct (%)71.0%
Missing2
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean1883.2903
Minimum0
Maximum42490
Zeros29
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-04T08:16:13.189689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q141.75
median400
Q31625.5
95-th percentile7353.7
Maximum42490
Range42490
Interquartile range (IQR)1583.75

Descriptive statistics

Standard deviation4837.3645
Coefficient of variation (CV)2.5685708
Kurtosis45.533249
Mean1883.2903
Median Absolute Deviation (MAD)400
Skewness6.1534106
Sum233528
Variance23400095
MonotonicityNot monotonic
2024-05-04T08:16:13.709557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29
 
23.0%
150 3
 
2.4%
400 3
 
2.4%
280 2
 
1.6%
184 2
 
1.6%
210 2
 
1.6%
200 2
 
1.6%
6808 1
 
0.8%
52 1
 
0.8%
300 1
 
0.8%
Other values (78) 78
61.9%
(Missing) 2
 
1.6%
ValueCountFrequency (%)
0 29
23.0%
10 1
 
0.8%
32 1
 
0.8%
45 1
 
0.8%
52 1
 
0.8%
60 1
 
0.8%
84 1
 
0.8%
86 1
 
0.8%
115 1
 
0.8%
131 1
 
0.8%
ValueCountFrequency (%)
42490 1
0.8%
26730 1
0.8%
10978 1
0.8%
9855 1
0.8%
8932 1
0.8%
7860 1
0.8%
7450 1
0.8%
6808 1
0.8%
6500 1
0.8%
5877 1
0.8%

편의시설(종)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)7.2%
Missing1
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean1.52
Minimum0
Maximum17
Zeros64
Zeros (%)50.8%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-04T08:16:14.259562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile4
Maximum17
Range17
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2201424
Coefficient of variation (CV)1.46062
Kurtosis18.102261
Mean1.52
Median Absolute Deviation (MAD)0
Skewness3.1634407
Sum190
Variance4.9290323
MonotonicityNot monotonic
2024-05-04T08:16:14.636267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 64
50.8%
3 21
 
16.7%
2 13
 
10.3%
4 11
 
8.7%
1 10
 
7.9%
7 2
 
1.6%
5 2
 
1.6%
6 1
 
0.8%
17 1
 
0.8%
(Missing) 1
 
0.8%
ValueCountFrequency (%)
0 64
50.8%
1 10
 
7.9%
2 13
 
10.3%
3 21
 
16.7%
4 11
 
8.7%
5 2
 
1.6%
6 1
 
0.8%
7 2
 
1.6%
17 1
 
0.8%
ValueCountFrequency (%)
17 1
 
0.8%
7 2
 
1.6%
6 1
 
0.8%
5 2
 
1.6%
4 11
 
8.7%
3 21
 
16.7%
2 13
 
10.3%
1 10
 
7.9%
0 64
50.8%

편의시설(점)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)16.8%
Missing1
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean4.432
Minimum0
Maximum86
Zeros64
Zeros (%)50.8%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-04T08:16:15.270849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile16
Maximum86
Range86
Interquartile range (IQR)7

Descriptive statistics

Standard deviation9.3025629
Coefficient of variation (CV)2.0989537
Kurtosis48.193573
Mean4.432
Median Absolute Deviation (MAD)0
Skewness5.9499056
Sum554
Variance86.537677
MonotonicityNot monotonic
2024-05-04T08:16:15.736528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 64
50.8%
6 7
 
5.6%
2 7
 
5.6%
8 6
 
4.8%
4 5
 
4.0%
9 5
 
4.0%
7 4
 
3.2%
3 4
 
3.2%
1 4
 
3.2%
11 3
 
2.4%
Other values (11) 16
 
12.7%
ValueCountFrequency (%)
0 64
50.8%
1 4
 
3.2%
2 7
 
5.6%
3 4
 
3.2%
4 5
 
4.0%
5 2
 
1.6%
6 7
 
5.6%
7 4
 
3.2%
8 6
 
4.8%
9 5
 
4.0%
ValueCountFrequency (%)
86 1
 
0.8%
34 1
 
0.8%
23 1
 
0.8%
21 1
 
0.8%
17 1
 
0.8%
16 3
2.4%
14 1
 
0.8%
13 1
 
0.8%
12 1
 
0.8%
11 3
2.4%

조성년도
Real number (ℝ)

Distinct29
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.4206
Minimum1984
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-04T08:16:16.271929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1984
5-th percentile1996.25
Q12007
median2012
Q32015
95-th percentile2019
Maximum2023
Range39
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.1446239
Coefficient of variation (CV)0.0035537956
Kurtosis2.447656
Mean2010.4206
Median Absolute Deviation (MAD)3
Skewness-1.3698297
Sum253313
Variance51.045651
MonotonicityNot monotonic
2024-05-04T08:16:16.826051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2013 17
13.5%
2012 14
 
11.1%
2015 12
 
9.5%
2014 10
 
7.9%
2017 6
 
4.8%
2007 6
 
4.8%
2018 6
 
4.8%
2006 5
 
4.0%
2011 5
 
4.0%
2004 4
 
3.2%
Other values (19) 41
32.5%
ValueCountFrequency (%)
1984 2
1.6%
1989 1
 
0.8%
1993 2
1.6%
1996 2
1.6%
1997 2
1.6%
2000 1
 
0.8%
2001 2
1.6%
2002 4
3.2%
2003 2
1.6%
2004 4
3.2%
ValueCountFrequency (%)
2023 1
 
0.8%
2022 1
 
0.8%
2021 1
 
0.8%
2020 1
 
0.8%
2019 4
 
3.2%
2018 6
4.8%
2017 6
4.8%
2016 3
 
2.4%
2015 12
9.5%
2014 10
7.9%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-04-26
126 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-04-26
2nd row2024-04-26
3rd row2024-04-26
4th row2024-04-26
5th row2024-04-26

Common Values

ValueCountFrequency (%)
2024-04-26 126
100.0%

Length

2024-05-04T08:16:17.285906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T08:16:17.573206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-04-26 126
100.0%

Interactions

2024-05-04T08:15:58.421510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:41.339707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:44.105449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:46.697948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:48.887007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:50.983096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:53.012545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:55.772506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:58.752141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:41.672756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:44.459502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:46.987323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:49.199812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:51.230588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:53.278646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:56.257214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:59.003645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:41.948402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:44.872384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:47.249196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:49.489148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:51.478966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:53.675121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:56.557007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:59.292338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:42.295876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:45.148538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:47.519888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:49.724468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:51.716434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:53.962755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:56.849465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:59.618081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:42.548116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:45.456678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:47.780447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:50.013232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:51.972141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:54.231002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:57.083946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:59.882927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:42.901096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:45.735704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:48.032941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:50.270171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:52.206209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:54.510886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:57.311608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:16:00.157713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:43.428654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:45.997365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:48.378747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:50.546472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:52.474842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:54.931351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:57.724110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:16:00.455275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:43.781453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:46.429732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:48.600359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:50.778480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:52.739799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:55.378851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:15:58.084000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T08:16:17.733650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분면적(제곱미터)수목수종교목관목편의시설(종)편의시설(점)조성년도
연번1.0001.0000.0000.0000.8190.2800.1510.5230.3650.702
구분1.0001.0000.0980.0000.6530.1680.1400.8640.2830.516
면적(제곱미터)0.0000.0981.0000.9140.4250.7410.9020.7220.6920.717
수목0.0000.0000.9141.0000.8960.7020.9960.6240.8630.000
수종0.8190.6530.4250.8961.0000.8320.8190.2690.5950.000
교목0.2800.1680.7410.7020.8321.0000.7390.4270.5290.000
관목0.1510.1400.9020.9960.8190.7391.0000.6210.8670.000
편의시설(종)0.5230.8640.7220.6240.2690.4270.6211.0000.7390.399
편의시설(점)0.3650.2830.6920.8630.5950.5290.8670.7391.0000.147
조성년도0.7020.5160.7170.0000.0000.0000.0000.3990.1471.000
2024-05-04T08:16:18.091956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번면적(제곱미터)수목교목관목편의시설(종)편의시설(점)조성년도구분
연번1.000-0.0750.243-0.0890.216-0.706-0.7120.0560.951
면적(제곱미터)-0.0751.0000.5610.3890.3340.0970.1670.0270.063
수목0.2430.5611.0000.4210.756-0.155-0.1300.0290.000
교목-0.0890.3890.4211.0000.5890.0940.108-0.3530.121
관목0.2160.3340.7560.5891.000-0.0100.001-0.1340.168
편의시설(종)-0.7060.097-0.1550.094-0.0101.0000.9580.3230.662
편의시설(점)-0.7120.167-0.1300.1080.0010.9581.0000.3020.340
조성년도0.0560.0270.029-0.353-0.1340.3230.3021.0000.490
구분0.9510.0630.0000.1210.1680.6620.3400.4901.000

Missing values

2024-05-04T08:16:00.858042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T08:16:01.444184image/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.
2024-05-04T08:16:01.878006image/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

연번구분명칭위치(도로명 주소)면적(제곱미터)수목수종교목관목편의시설(종)편의시설(점)조성년도데이터기준일자
01쌈지공원능인사 입구 쌈지공원괘법동 228-13번지1601627느티나무 등6116243420092024-04-26
12쌈지공원모라 희망 쌈지공원모라1동 943-2번지(모라동원@ 108동 뒤 ~ 모라동 387-1 앞)31506503왕벚나무 등72650033420092024-04-26
23쌈지공원모덕역 쌈지공원덕포2동 257-11번지 앞(모덕역 1번출구 일원)701느티나무601320022024-04-26
34쌈지공원모라역 쌈지공원모라1동 1002-1 ~ 991-2번지(모라역 2번 ~ 4번출구)801느티나무601520022024-04-26
45쌈지공원덕포역 쌈지공원덕포2동 403-6번지(덕포역 3번 출구)266247후박나무 등122452420042024-04-26
56쌈지공원모라1동 공영주차장 쌈지공원모라1동 1371번지675272느티나무 등572720020022024-04-26
67쌈지공원감전역 쌈지공원감전동 174번지(감전역 1번,2번 출구)602느티나무1002620012024-04-26
78쌈지공원감전동 당산목 쌈지공원감전동 388번지 일원27196느티나무 등11951220132024-04-26
89쌈지공원학장교차로 쌈지공원학장동 395-5번지(학장동 486-3번지 앞)471402배롱나무 등124002919842024-04-26
910쌈지공원주례역 쌈지공원주례3동 1163-22번지(주례역 1번출구 앞)903느티나무503620112024-04-26
연번구분명칭위치(도로명 주소)면적(제곱미터)수목수종교목관목편의시설(종)편의시설(점)조성년도데이터기준일자
116117화단낙동제방 공원화 사업 (1차)부산광역시 사상구 낙동제방(감전교차로~북구경계)<NA>10990왕벚나무 등12109780020142024-04-26
117118화단사상공단 악취저감 수림대 조성사업부산광역시 사상구 학장제방(학장교차로~새벽로)<NA>27170태산목 등440267300020142024-04-26
118119화단백양로 가로변 녹지대 재정비사업부산광역시 사상구 모라동 동원아파트~덕포동 한일시멘트207410220느티나무 등36598550020142024-04-26
119120화단학장동 무학아파트 일원 가로화단 조성부산광역시 사상구 대동로 98번길(학장동 무학아파트 일원)1171190꽃댕강011900020142024-04-26
120121화단학장로 악취저감 가로화단 조성부산광역시 사상구 학장제방 보도(학장교차로 ~ 새벽로 입구)2717450꽃댕강 등074500020152024-04-26
121122화단주례로 계단길 화단 조성부산광역시 사상구 주례 럭키아파트 15동 뒤752859선주목 등928500020152024-04-26
122123화단덕포동 운산로 화단 정비부산광역시 사상구 덕포동 운산로 81 일원1001457홍가시 등014570020152024-04-26
123124화단목련빌라 옆 화단 조성부산광역시 사상구 목련빌라옆 화단330<NA>000020172024-04-26
124125화단덕포 청구아파트 뒤 철쭉 식재공사부산광역시 사상구 덕포 청구아파트 뒤1203600철쭉036000020172024-04-26
125126화단2017년 생활권 녹지마을(양지마을)조성사업부산광역시 사상구 주례동340번지7603139이팝나무 등38927500020172024-04-26