Overview

Dataset statistics

Number of variables17
Number of observations33
Missing cells78
Missing cells (%)13.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 KiB
Average record size in memory152.0 B

Variable types

Text3
Numeric7
Categorical7

Dataset

Description부산광역시 영도구 관내 가로수에 관한 정보로 가로수 식재 위치, 식재 거리, 가로수 수종 등에 대한 항목을 제공합니다.
Author부산광역시 영도구
URLhttps://www.data.go.kr/data/15064294/fileData.do

Alerts

구군명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
위도 is highly overall correlated with 경도 and 4 other fieldsHigh correlation
경도 is highly overall correlated with 위도 and 5 other fieldsHigh correlation
식재거리(km) is highly overall correlated with 총합계 and 5 other fieldsHigh correlation
총합계 is highly overall correlated with 식재거리(km) and 6 other fieldsHigh correlation
왕벚나무 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
이팝나무 is highly overall correlated with 총합계High correlation
후박나무 is highly overall correlated with 경도 and 2 other fieldsHigh correlation
은행나무 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
느티나무 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
먼나무 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
은행나무 is highly imbalanced (66.6%)Imbalance
느티나무 is highly imbalanced (70.8%)Imbalance
먼나무 is highly imbalanced (66.6%)Imbalance
해송 is highly imbalanced (80.4%)Imbalance
가시나무 is highly imbalanced (80.4%)Imbalance
왕벚나무 has 26 (78.8%) missing valuesMissing
이팝나무 has 25 (75.8%) missing valuesMissing
후박나무 has 27 (81.8%) missing valuesMissing

Reproduction

Analysis started2023-12-23 07:44:45.052902
Analysis finished2023-12-23 07:45:12.653732
Duration27.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

위치명
Text

Distinct20
Distinct (%)60.6%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-23T07:45:12.917643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length13.454545
Min length13

Characters and Unicode

Total characters444
Distinct characters48
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

Unique15 ?
Unique (%)45.5%

Sample

1st row부산광역시 영도구 태종로
2nd row부산광역시 영도구 태종로
3rd row부산광역시 영도구 태종로
4th row부산광역시 영도구 태종로
5th row부산광역시 영도구 태종로
ValueCountFrequency (%)
부산광역시 33
33.3%
영도구 33
33.3%
태종로 7
 
7.1%
해양로 4
 
4.0%
절영로 3
 
3.0%
영선대로 2
 
2.0%
와치로 2
 
2.0%
꿈나무길 1
 
1.0%
하나길 1
 
1.0%
상리로 1
 
1.0%
Other values (12) 12
 
12.1%
2023-12-23T07:45:14.037727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
66
14.9%
영 39
8.8%
산 34
7.7%
시 34
7.7%
부 33
7.4%
광 33
7.4%
역 33
7.4%
도 33
7.4%
구 33
7.4%
로 27
 
6.1%
Other values (38) 79
17.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 376
84.7%
Space Separator 66
 
14.9%
Decimal Number 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 39
10.4%
산 34
9.0%
시 34
9.0%
부 33
8.8%
광 33
8.8%
역 33
8.8%
도 33
8.8%
구 33
8.8%
로 27
 
7.2%
종 7
 
1.9%
Other values (35) 70
18.6%
Decimal Number
ValueCountFrequency (%)
7 1
50.0%
2 1
50.0%
Space Separator
ValueCountFrequency (%)
66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 376
84.7%
Common 68
 
15.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 39
10.4%
산 34
9.0%
시 34
9.0%
부 33
8.8%
광 33
8.8%
역 33
8.8%
도 33
8.8%
구 33
8.8%
로 27
 
7.2%
종 7
 
1.9%
Other values (35) 70
18.6%
Common
ValueCountFrequency (%)
66
97.1%
7 1
 
1.5%
2 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 376
84.7%
ASCII 68
 
15.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
66
97.1%
7 1
 
1.5%
2 1
 
1.5%
Hangul
ValueCountFrequency (%)
영 39
10.4%
산 34
9.0%
시 34
9.0%
부 33
8.8%
광 33
8.8%
역 33
8.8%
도 33
8.8%
구 33
8.8%
로 27
 
7.2%
종 7
 
1.9%
Other values (35) 70
18.6%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.085985
Minimum35.064082
Maximum35.095856
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-23T07:45:15.038103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.064082
5-th percentile35.073375
Q135.081933
median35.08771
Q335.092116
95-th percentile35.09499
Maximum35.095856
Range0.031774
Interquartile range (IQR)0.010183

Descriptive statistics

Standard deviation0.0079658913
Coefficient of variation (CV)0.00022703913
Kurtosis0.19728925
Mean35.085985
Median Absolute Deviation (MAD)0.005001
Skewness-0.86635335
Sum1157.8375
Variance6.3455424 × 10-5
MonotonicityNot monotonic
2023-12-23T07:45:15.763626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
35.091794 3
 
9.1%
35.085311 2
 
6.1%
35.088739 1
 
3.0%
35.081933 1
 
3.0%
35.093435 1
 
3.0%
35.08097017 1
 
3.0%
35.085175 1
 
3.0%
35.075558 1
 
3.0%
35.072022 1
 
3.0%
35.090399 1
 
3.0%
Other values (20) 20
60.6%
ValueCountFrequency (%)
35.064082 1
3.0%
35.072022 1
3.0%
35.074277 1
3.0%
35.075081 1
3.0%
35.075558 1
3.0%
35.075849 1
3.0%
35.078685 1
3.0%
35.08097017 1
3.0%
35.081933 1
3.0%
35.082709 1
3.0%
ValueCountFrequency (%)
35.095856 1
 
3.0%
35.09544 1
 
3.0%
35.09469 1
 
3.0%
35.094502 1
 
3.0%
35.093635 1
 
3.0%
35.093475 1
 
3.0%
35.093435 1
 
3.0%
35.09245 1
 
3.0%
35.092116 1
 
3.0%
35.091794 3
9.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.05538
Minimum129.03794
Maximum129.08098
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-23T07:45:17.155403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum129.03794
5-th percentile129.03966
Q1129.04128
median129.0455
Q3129.07029
95-th percentile129.07771
Maximum129.08098
Range0.043045
Interquartile range (IQR)0.029009

Descriptive statistics

Standard deviation0.015231975
Coefficient of variation (CV)0.00011802666
Kurtosis-1.6762569
Mean129.05538
Median Absolute Deviation (MAD)0.006852
Skewness0.35904568
Sum4258.8274
Variance0.00023201306
MonotonicityNot monotonic
2023-12-23T07:45:19.423316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
129.040685 3
 
9.1%
129.040333 2
 
6.1%
129.069015 1
 
3.0%
129.041788 1
 
3.0%
129.049299 1
 
3.0%
129.0445 1
 
3.0%
129.070707 1
 
3.0%
129.070363 1
 
3.0%
129.074207 1
 
3.0%
129.038646 1
 
3.0%
Other values (20) 20
60.6%
ValueCountFrequency (%)
129.037937 1
 
3.0%
129.038646 1
 
3.0%
129.040333 2
6.1%
129.040386 1
 
3.0%
129.040685 3
9.1%
129.041284 1
 
3.0%
129.041788 1
 
3.0%
129.04307 1
 
3.0%
129.043663 1
 
3.0%
129.0445 1
 
3.0%
ValueCountFrequency (%)
129.080982 1
3.0%
129.078442 1
3.0%
129.077222 1
3.0%
129.076356 1
3.0%
129.075385 1
3.0%
129.074207 1
3.0%
129.070707 1
3.0%
129.070363 1
3.0%
129.070293 1
3.0%
129.069015 1
3.0%

구간시점
Text

Distinct26
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-23T07:45:20.875167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.7272727
Min length4

Characters and Unicode

Total characters189
Distinct characters78
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

Unique21 ?
Unique (%)63.6%

Sample

1st row영도대교
2nd row소방서앞사거리
3rd row봉래교차로
4th row청학119
5th row영도구청
ValueCountFrequency (%)
봉래교차로 4
 
11.1%
대교사거리 2
 
5.6%
소방서앞사거리 2
 
5.6%
영선아래교차로 2
 
5.6%
항만119 2
 
5.6%
부산보건고 1
 
2.8%
봉래동 1
 
2.8%
영도대교 1
 
2.8%
강남의원 1
 
2.8%
신선중학교 1
 
2.8%
Other values (19) 19
52.8%
2023-12-23T07:45:22.211416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 13
 
6.9%
로 8
 
4.2%
차 7
 
3.7%
1 7
 
3.7%
래 7
 
3.7%
리 7
 
3.7%
영 6
 
3.2%
동 6
 
3.2%
대 6
 
3.2%
거 6
 
3.2%
Other values (68) 116
61.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 176
93.1%
Decimal Number 10
 
5.3%
Space Separator 3
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 13
 
7.4%
로 8
 
4.5%
차 7
 
4.0%
래 7
 
4.0%
리 7
 
4.0%
영 6
 
3.4%
동 6
 
3.4%
대 6
 
3.4%
거 6
 
3.4%
봉 5
 
2.8%
Other values (65) 105
59.7%
Decimal Number
ValueCountFrequency (%)
1 7
70.0%
9 3
30.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 176
93.1%
Common 13
 
6.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 13
 
7.4%
로 8
 
4.5%
차 7
 
4.0%
래 7
 
4.0%
리 7
 
4.0%
영 6
 
3.4%
동 6
 
3.4%
대 6
 
3.4%
거 6
 
3.4%
봉 5
 
2.8%
Other values (65) 105
59.7%
Common
ValueCountFrequency (%)
1 7
53.8%
9 3
23.1%
3
23.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 176
93.1%
ASCII 13
 
6.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 13
 
7.4%
로 8
 
4.5%
차 7
 
4.0%
래 7
 
4.0%
리 7
 
4.0%
영 6
 
3.4%
동 6
 
3.4%
대 6
 
3.4%
거 6
 
3.4%
봉 5
 
2.8%
Other values (65) 105
59.7%
ASCII
ValueCountFrequency (%)
1 7
53.8%
9 3
23.1%
3
23.1%

구간종점
Text

Distinct32
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-23T07:45:23.058232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length5.7575758
Min length4

Characters and Unicode

Total characters190
Distinct characters85
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

Unique31 ?
Unique (%)93.9%

Sample

1st row소방서앞사거리
2nd row봉래교차로
3rd row청학119
4th row영도구청
5th row항만119
ValueCountFrequency (%)
소방서앞사거리 2
 
5.7%
인제요양병원 1
 
2.9%
신선공영주차장 1
 
2.9%
부산보건고 1
 
2.9%
영도초교 1
 
2.9%
영도경로당 1
 
2.9%
신한기공사앞교차로 1
 
2.9%
부산대교 1
 
2.9%
대교사거리 1
 
2.9%
봉래교차로 1
 
2.9%
Other values (24) 24
68.6%
2023-12-23T07:45:24.364010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 14
 
7.4%
영 7
 
3.7%
대 7
 
3.7%
리 7
 
3.7%
도 6
 
3.2%
학 6
 
3.2%
로 5
 
2.6%
1 5
 
2.6%
공 4
 
2.1%
초 4
 
2.1%
Other values (75) 125
65.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 181
95.3%
Decimal Number 7
 
3.7%
Space Separator 2
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 14
 
7.7%
영 7
 
3.9%
대 7
 
3.9%
리 7
 
3.9%
도 6
 
3.3%
학 6
 
3.3%
로 5
 
2.8%
공 4
 
2.2%
초 4
 
2.2%
선 4
 
2.2%
Other values (72) 117
64.6%
Decimal Number
ValueCountFrequency (%)
1 5
71.4%
9 2
 
28.6%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 181
95.3%
Common 9
 
4.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 14
 
7.7%
영 7
 
3.9%
대 7
 
3.9%
리 7
 
3.9%
도 6
 
3.3%
학 6
 
3.3%
로 5
 
2.8%
공 4
 
2.2%
초 4
 
2.2%
선 4
 
2.2%
Other values (72) 117
64.6%
Common
ValueCountFrequency (%)
1 5
55.6%
2
 
22.2%
9 2
 
22.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 181
95.3%
ASCII 9
 
4.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 14
 
7.7%
영 7
 
3.9%
대 7
 
3.9%
리 7
 
3.9%
도 6
 
3.3%
학 6
 
3.3%
로 5
 
2.8%
공 4
 
2.2%
초 4
 
2.2%
선 4
 
2.2%
Other values (72) 117
64.6%
ASCII
ValueCountFrequency (%)
1 5
55.6%
2
 
22.2%
9 2
 
22.2%

식재거리(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean657.33333
Minimum80
Maximum2500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-23T07:45:24.851976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile157
Q1200
median500
Q3900
95-th percentile1640
Maximum2500
Range2420
Interquartile range (IQR)700

Descriptive statistics

Standard deviation570.43403
Coefficient of variation (CV)0.86780024
Kurtosis2.5164829
Mean657.33333
Median Absolute Deviation (MAD)300
Skewness1.5440838
Sum21692
Variance325394.98
MonotonicityNot monotonic
2023-12-23T07:45:25.365911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
200 8
24.2%
500 3
 
9.1%
800 2
 
6.1%
600 2
 
6.1%
1400 2
 
6.1%
700 2
 
6.1%
1000 2
 
6.1%
400 2
 
6.1%
195 1
 
3.0%
217 1
 
3.0%
Other values (8) 8
24.2%
ValueCountFrequency (%)
80 1
 
3.0%
100 1
 
3.0%
195 1
 
3.0%
200 8
24.2%
217 1
 
3.0%
300 1
 
3.0%
400 2
 
6.1%
500 3
 
9.1%
600 2
 
6.1%
700 2
 
6.1%
ValueCountFrequency (%)
2500 1
3.0%
2000 1
3.0%
1400 2
6.1%
1300 1
3.0%
1200 1
3.0%
1000 2
6.1%
900 1
3.0%
800 2
6.1%
700 2
6.1%
600 2
6.1%

총합계
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.242424
Minimum4
Maximum388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-23T07:45:25.959738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile9.6
Q140
median70
Q3129
95-th percentile283.6
Maximum388
Range384
Interquartile range (IQR)89

Descriptive statistics

Standard deviation89.918376
Coefficient of variation (CV)0.91527033
Kurtosis3.4741931
Mean98.242424
Median Absolute Deviation (MAD)39
Skewness1.7941546
Sum3242
Variance8085.3144
MonotonicityNot monotonic
2023-12-23T07:45:26.870708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
38 2
 
6.1%
42 2
 
6.1%
135 1
 
3.0%
86 1
 
3.0%
102 1
 
3.0%
70 1
 
3.0%
31 1
 
3.0%
188 1
 
3.0%
171 1
 
3.0%
10 1
 
3.0%
Other values (21) 21
63.6%
ValueCountFrequency (%)
4 1
3.0%
9 1
3.0%
10 1
3.0%
20 1
3.0%
21 1
3.0%
31 1
3.0%
38 2
6.1%
40 1
3.0%
42 2
6.1%
44 1
3.0%
ValueCountFrequency (%)
388 1
3.0%
346 1
3.0%
242 1
3.0%
188 1
3.0%
174 1
3.0%
171 1
3.0%
135 1
3.0%
131 1
3.0%
129 1
3.0%
120 1
3.0%

왕벚나무
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)100.0%
Missing26
Missing (%)78.8%
Infinite0
Infinite (%)0.0%
Mean97.142857
Minimum4
Maximum188
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-23T07:45:27.722004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile9.1
Q131.5
median120
Q3152.5
95-th percentile183.8
Maximum188
Range184
Interquartile range (IQR)121

Descriptive statistics

Standard deviation74.548482
Coefficient of variation (CV)0.76741085
Kurtosis-2.0343337
Mean97.142857
Median Absolute Deviation (MAD)68
Skewness-0.091476533
Sum680
Variance5557.4762
MonotonicityNot monotonic
2023-12-23T07:45:28.645192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
131 1
 
3.0%
120 1
 
3.0%
174 1
 
3.0%
42 1
 
3.0%
4 1
 
3.0%
21 1
 
3.0%
188 1
 
3.0%
(Missing) 26
78.8%
ValueCountFrequency (%)
4 1
3.0%
21 1
3.0%
42 1
3.0%
120 1
3.0%
131 1
3.0%
174 1
3.0%
188 1
3.0%
ValueCountFrequency (%)
188 1
3.0%
174 1
3.0%
131 1
3.0%
120 1
3.0%
42 1
3.0%
21 1
3.0%
4 1
3.0%

은행나무
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Memory size396.0 B
<NA>
29 
40
 
1
9
 
1
62
 
1
47
 
1

Length

Max length4
Median length4
Mean length3.7272727
Min length1

Unique

Unique4 ?
Unique (%)12.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 29
87.9%
40 1
 
3.0%
9 1
 
3.0%
62 1
 
3.0%
47 1
 
3.0%

Length

2023-12-23T07:45:29.362380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T07:45:29.911227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 29
87.9%
40 1
 
3.0%
9 1
 
3.0%
62 1
 
3.0%
47 1
 
3.0%

느티나무
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
<NA>
30 
86
 
1
70
 
1
102
 
1

Length

Max length4
Median length4
Mean length3.8484848
Min length2

Unique

Unique3 ?
Unique (%)9.1%

Sample

1st row<NA>
2nd row86
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 30
90.9%
86 1
 
3.0%
70 1
 
3.0%
102 1
 
3.0%

Length

2023-12-23T07:45:30.579075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T07:45:31.116368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 30
90.9%
86 1
 
3.0%
70 1
 
3.0%
102 1
 
3.0%

이팝나무
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)87.5%
Missing25
Missing (%)75.8%
Infinite0
Infinite (%)0.0%
Mean103.5
Minimum31
Maximum242
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-23T07:45:31.665658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile33.45
Q138
median86.5
Q3144
95-th percentile217.15
Maximum242
Range211
Interquartile range (IQR)106

Descriptive statistics

Standard deviation78.155523
Coefficient of variation (CV)0.75512582
Kurtosis-0.60776043
Mean103.5
Median Absolute Deviation (MAD)48.5
Skewness0.74147447
Sum828
Variance6108.2857
MonotonicityNot monotonic
2023-12-23T07:45:32.135112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
38 2
 
6.1%
135 1
 
3.0%
242 1
 
3.0%
129 1
 
3.0%
44 1
 
3.0%
171 1
 
3.0%
31 1
 
3.0%
(Missing) 25
75.8%
ValueCountFrequency (%)
31 1
3.0%
38 2
6.1%
44 1
3.0%
129 1
3.0%
135 1
3.0%
171 1
3.0%
242 1
3.0%
ValueCountFrequency (%)
242 1
3.0%
171 1
3.0%
135 1
3.0%
129 1
3.0%
44 1
3.0%
38 2
6.1%
31 1
3.0%

후박나무
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)100.0%
Missing27
Missing (%)81.8%
Infinite0
Infinite (%)0.0%
Mean100
Minimum10
Maximum346
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-23T07:45:32.769815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile12.5
Q128.75
median57.5
Q396.75
95-th percentile286.75
Maximum346
Range336
Interquartile range (IQR)68

Descriptive statistics

Standard deviation125.47669
Coefficient of variation (CV)1.2547669
Kurtosis4.4113323
Mean100
Median Absolute Deviation (MAD)42.5
Skewness2.0487719
Sum600
Variance15744.4
MonotonicityNot monotonic
2023-12-23T07:45:33.508146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
109 1
 
3.0%
346 1
 
3.0%
55 1
 
3.0%
20 1
 
3.0%
60 1
 
3.0%
10 1
 
3.0%
(Missing) 27
81.8%
ValueCountFrequency (%)
10 1
3.0%
20 1
3.0%
55 1
3.0%
60 1
3.0%
109 1
3.0%
346 1
3.0%
ValueCountFrequency (%)
346 1
3.0%
109 1
3.0%
60 1
3.0%
55 1
3.0%
20 1
3.0%
10 1
3.0%

먼나무
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Memory size396.0 B
<NA>
29 
110
 
1
95
 
1
388
 
1
2
 
1

Length

Max length4
Median length4
Mean length3.7878788
Min length1

Unique

Unique4 ?
Unique (%)12.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 29
87.9%
110 1
 
3.0%
95 1
 
3.0%
388 1
 
3.0%
2 1
 
3.0%

Length

2023-12-23T07:45:34.332360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T07:45:34.986082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 29
87.9%
110 1
 
3.0%
95 1
 
3.0%
388 1
 
3.0%
2 1
 
3.0%

해송
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
<NA>
32 
42
 
1

Length

Max length4
Median length4
Mean length3.9393939
Min length2

Unique

Unique1 ?
Unique (%)3.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 32
97.0%
42 1
 
3.0%

Length

2023-12-23T07:45:35.569000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T07:45:36.152498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 32
97.0%
42 1
 
3.0%

가시나무
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
<NA>
32 
81
 
1

Length

Max length4
Median length4
Mean length3.9393939
Min length2

Unique

Unique1 ?
Unique (%)3.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 32
97.0%
81 1
 
3.0%

Length

2023-12-23T07:45:36.631581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T07:45:37.370563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 32
97.0%
81 1
 
3.0%

구군명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
부산광역시 영도구
33 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 영도구
2nd row부산광역시 영도구
3rd row부산광역시 영도구
4th row부산광역시 영도구
5th row부산광역시 영도구

Common Values

ValueCountFrequency (%)
부산광역시 영도구 33
100.0%

Length

2023-12-23T07:45:38.274252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T07:45:38.645384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 33
50.0%
영도구 33
50.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-15
33 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-12-15
2nd row2023-12-15
3rd row2023-12-15
4th row2023-12-15
5th row2023-12-15

Common Values

ValueCountFrequency (%)
2023-12-15 33
100.0%

Length

2023-12-23T07:45:39.385380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T07:45:40.110073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-12-15 33
100.0%

Interactions

2023-12-23T07:45:06.794763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:47.821489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:51.186151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:54.283290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:57.552776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:00.249339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:03.280327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:07.482490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:48.215510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:51.542797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:54.688123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:57.979920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:00.724679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:04.065640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:08.029896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:48.780039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:51.934003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:55.112608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:58.334889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:01.194860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:04.692401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:08.614271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:49.205760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:52.292060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:55.385626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:58.716662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:01.491199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:05.073891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:09.148693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:49.625907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:52.610785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:55.788837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:59.100419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:02.015972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:05.397781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:09.604039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:50.170461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:53.434771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:56.344584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:59.492505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:02.450389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:05.835128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:09.929523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:50.765751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:53.902722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:57.130267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:44:59.805424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:02.877487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:06.385187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-23T07:45:40.584588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위치명위도경도구간시점구간종점식재거리(km)총합계왕벚나무은행나무느티나무이팝나무후박나무먼나무
위치명1.0000.0000.5020.8970.9890.0000.0001.0001.0001.0000.0000.5731.000
위도0.0001.0000.4230.8670.8520.0000.0000.8881.0001.0000.8140.6881.000
경도0.5020.4231.0000.9440.9620.6810.7740.0001.0001.0000.8140.5731.000
구간시점0.8970.8670.9441.0000.9770.5670.9431.0001.0001.0001.0000.5731.000
구간종점0.9890.8520.9620.9771.0001.0000.9551.0001.0001.0001.0001.0001.000
식재거리(km)0.0000.0000.6810.5671.0001.0000.9380.9281.0001.0001.0000.7591.000
총합계0.0000.0000.7740.9430.9550.9381.0001.0001.0001.0001.0001.0001.000
왕벚나무1.0000.8880.0001.0001.0000.9281.0001.000NaNNaNNaNNaNNaN
은행나무1.0001.0001.0001.0001.0001.0001.000NaN1.000NaNNaNNaNNaN
느티나무1.0001.0001.0001.0001.0001.0001.000NaNNaN1.000NaNNaNNaN
이팝나무0.0000.8140.8141.0001.0001.0001.000NaNNaNNaN1.000NaNNaN
후박나무0.5730.6880.5730.5731.0000.7591.000NaNNaNNaNNaN1.000NaN
먼나무1.0001.0001.0001.0001.0001.0001.000NaNNaNNaNNaNNaN1.000
2023-12-23T07:45:41.366505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
먼나무느티나무은행나무해송가시나무
먼나무1.000NaNNaNNaNNaN
느티나무NaN1.000NaNNaNNaN
은행나무NaNNaN1.000NaNNaN
해송NaNNaNNaN1.000NaN
가시나무NaNNaNNaNNaN1.000
2023-12-23T07:45:41.936109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도식재거리(km)총합계왕벚나무이팝나무후박나무은행나무느티나무먼나무해송가시나무
위도1.000-0.507-0.009-0.062-0.5000.4190.1431.0001.0001.000NaNNaN
경도-0.5071.0000.0420.2910.5360.2750.6001.0001.0001.000NaNNaN
식재거리(km)-0.0090.0421.0000.7820.9290.4550.6381.0001.0001.000NaNNaN
총합계-0.0620.2910.7821.0001.0001.0001.0001.0001.0001.000NaNNaN
왕벚나무-0.5000.5360.9291.0001.000NaNNaN0.0000.0000.0000.0000.000
이팝나무0.4190.2750.4551.000NaN1.000NaN0.0000.0000.0000.0000.000
후박나무0.1430.6000.6381.000NaNNaN1.0000.0000.000NaN0.0000.000
은행나무1.0001.0001.0001.0000.0000.0000.0001.0000.0000.0000.0000.000
느티나무1.0001.0001.0001.0000.0000.0000.0000.0001.0000.0000.0000.000
먼나무1.0001.0001.0001.0000.0000.000NaN0.0000.0001.0000.0000.000
해송NaNNaNNaNNaN0.0000.0000.0000.0000.0000.0001.0000.000
가시나무NaNNaNNaNNaN0.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-23T07:45:10.495943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-23T07:45:11.531352image/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.
2023-12-23T07:45:12.258299image/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

위치명위도경도구간시점구간종점식재거리(km)총합계왕벚나무은행나무느티나무이팝나무후박나무먼나무해송가시나무구군명데이터기준일자
0부산광역시 영도구 태종로35.088739129.069015영도대교소방서앞사거리800135<NA><NA><NA>135<NA><NA><NA><NA>부산광역시 영도구2023-12-15
1부산광역시 영도구 태종로35.082906129.076356소방서앞사거리봉래교차로20086<NA><NA>86<NA><NA><NA><NA><NA>부산광역시 영도구2023-12-15
2부산광역시 영도구 태종로35.064082129.080982봉래교차로청학1191400109<NA><NA><NA><NA>109<NA><NA><NA>부산광역시 영도구2023-12-15
3부산광역시 영도구 태종로35.095856129.053289청학119영도구청1400242<NA><NA><NA>242<NA><NA><NA><NA>부산광역시 영도구2023-12-15
4부산광역시 영도구 태종로35.091794129.040685영도구청항만119700131131<NA><NA><NA><NA><NA><NA><NA>부산광역시 영도구2023-12-15
5부산광역시 영도구 태종로35.091794129.040685항만119해경교차로1000110<NA><NA><NA><NA><NA>110<NA><NA>부산광역시 영도구2023-12-15
6부산광역시 영도구 태종로35.091794129.040685동삼동패총태종대입구70095<NA><NA><NA><NA><NA>95<NA><NA>부산광역시 영도구2023-12-15
7부산광역시 영도구 해양로35.083321129.077222해양대삼거리미창석유2000388<NA><NA><NA><NA><NA>388<NA><NA>부산광역시 영도구2023-12-15
8부산광역시 영도구 해양로35.09544129.065679미창석유유진선박의장2500346<NA><NA><NA><NA>346<NA><NA><NA>부산광역시 영도구2023-12-15
9부산광역시 영도구 해양로35.078685129.078442영도마린축구장국립해양박물관500129<NA><NA><NA>129<NA><NA><NA><NA>부산광역시 영도구2023-12-15
위치명위도경도구간시점구간종점식재거리(km)총합계왕벚나무은행나무느티나무이팝나무후박나무먼나무해송가시나무구군명데이터기준일자
23부산광역시 영도구 절영로35.093635129.040386서경세차장인제요양병원20047<NA>47<NA><NA><NA><NA><NA><NA>부산광역시 영도구2023-12-15
24부산광역시 영도구 절영로35.091592129.041284대교사거리대교사거리60010<NA><NA><NA><NA>10<NA><NA><NA>부산광역시 영도구2023-12-15
25부산광역시 영도구 절영로35.090399129.038646대교사거리영선윗로터리1000171<NA><NA><NA>171<NA><NA><NA><NA>부산광역시 영도구2023-12-15
26부산광역시 영도구 와치로35.072022129.074207동삼교회앞삼거리영도롯데캐슬20042<NA><NA><NA><NA><NA><NA>42<NA>부산광역시 영도구2023-12-15
27부산광역시 영도구 와치로35.075558129.070363한진로즈힐고신대학교900188188<NA><NA><NA><NA><NA><NA><NA>부산광역시 영도구2023-12-15
28부산광역시 영도구 영선대로35.085311129.040333영선아래교차로남항대교120031<NA><NA><NA>31<NA><NA><NA><NA>부산광역시 영도구2023-12-15
29부산광역시 영도구 영선대로35.085311129.040333영선아래교차로소방서앞사거리60070<NA><NA>70<NA><NA><NA><NA><NA>부산광역시 영도구2023-12-15
30부산광역시 영도구 상리로35.085175129.070707항만119상리초등학교500102<NA><NA>102<NA><NA><NA><NA><NA>부산광역시 영도구2023-12-15
31부산광역시 영도구 하나길35.08097129.0445신선중학교이송도삼거리21738<NA><NA><NA>38<NA><NA><NA><NA>부산광역시 영도구2023-12-15
32부산광역시 영도구 봉래언덕길35.093435129.049299봉래동 에일린의뜰하늘소공원19538<NA><NA><NA>38<NA><NA><NA><NA>부산광역시 영도구2023-12-15